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+OpenCV/tutoriel3/test.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cd59182 --- /dev/null +++ b/.gitignore @@ -0,0 +1,50 @@ +# ── Python ────────────────────────────────────────────────────────────────── +__pycache__/ +*.py[cod] +*.pyo +*.pyd +*.pyc + +# ── Environnements virtuels ────────────────────────────────────────────────── +venv/ +.venv/ +env/ +ENV/ +Pipfile.lock + +# ── Secrets ─────────────────────────────────────────────────────────────────── +.env +.env.* +secret.env +secrets.py +*.secret +credentials.json +token.json + +# ── Logs ───────────────────────────────────────────────────────────────────── +*.log +logs/ + +# ── IDE ─────────────────────────────────────────────────────────────────────── +.vscode/ +.idea/ +*.swp +.DS_Store +Thumbs.db + +# ── Tests / Coverage ───────────────────────────────────────────────────────── +.coverage +htmlcov/ +.pytest_cache/ + +# ── Build ───────────────────────────────────────────────────────────────────── +dist/ +build/ +*.egg-info/ + +# ── Modèles IA (trop lourds pour git) ─────────────────────────────────────── +*.gguf +*.bin +*.safetensors +models/ +weights/ diff --git a/Divers/decoupe_video/README.md b/Divers/decoupe_video/README.md new file mode 100644 index 0000000..f3dee1e --- /dev/null +++ b/Divers/decoupe_video/README.md @@ -0,0 +1,4 @@ +# Construction d'un dataset opensource + +Dataset actuel: https://drive.google.com/file/d/1PS40KnziMB7uVcmLHfOQZiD5uWNF-QiS/view?usp=sharing + diff --git a/Divers/decoupe_video/extract_image_from_video.py b/Divers/decoupe_video/extract_image_from_video.py new file mode 100644 index 0000000..48b34fc --- /dev/null +++ b/Divers/decoupe_video/extract_image_from_video.py @@ -0,0 +1,26 @@ +import cv2 +import os + +film='Le_chemin_du_passe.mp4' + +if not os.path.exists(film): + quit("Le film n'existe pas") + +nom_film=film.split('.')[0] + +cap=cv2.VideoCapture(film) + +if not os.path.isdir(nom_film): + os.mkdir(nom_film) + +id=0 +while True: + print("#", end="", flush=True) + for cpt in range(500): + ret, frame=cap.read() + if frame is None: + print("") + cap.release() + quit() + cv2.imwrite("{}/{}-{:d}.png".format(nom_film, nom_film, id), frame) + id+=1 diff --git a/Divers/descente_gradient/README.md b/Divers/descente_gradient/README.md new file mode 100644 index 0000000..aacf563 --- /dev/null +++ b/Divers/descente_gradient/README.md @@ -0,0 +1,6 @@ +# Algorithme d'apprentissage +## La descente de gradient + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=0MEyDJa2GTc + + diff --git a/Divers/descente_gradient/comparaison.py b/Divers/descente_gradient/comparaison.py new file mode 100644 index 0000000..0b28442 --- /dev/null +++ b/Divers/descente_gradient/comparaison.py @@ -0,0 +1,52 @@ +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.colors import LogNorm +import matplotlib.pyplot as plt +import numpy as np +import math + +def fonction(X, Y): + return X*np.exp(-X**2-Y**2)+(X**2+Y**2)/20 + +def gradient_fonction(X, Y): + g_x=np.exp(-X**2-Y**2)+X*-2*X*np.exp(-X**2-Y**2)+X/10 + g_y=-2*Y*X*np.exp(-X**2-Y**2)+Y/10 + return g_x, g_y + +fig=plt.figure() +fig.set_size_inches(9, 7, forward=True) +ax=Axes3D(fig, azim=-29, elev=49) +X=np.arange(-3, 3, 0.2) +Y=np.arange(-3, 3, 0.2) +X, Y=np.meshgrid(X, Y) +Z=fonction(X, Y) +ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1) +plt.xlabel("Paramètre 1 (x)") +plt.ylabel("Paramètre 2 (y)") + +x1=x2=np.random.random_integers(-2, 2)+np.random.rand(1)[0] +y1=y2=np.random.random_integers(-2, 2)+np.random.rand(1)[0] + +lr=0.2 +lr2=0.9 +correction_x1=0 +correction_y1=0 +i=0 +while True: + g_x1, g_y1=gradient_fonction(x1, y1) + g_x2, g_y2=gradient_fonction(x2, y2) + + correction_x1=lr2*correction_x1-lr*g_x1 + x1=x1+correction_x1 + correction_y1=lr2*correction_y1-lr*g_y1 + y1=y1+correction_y1 + + x2=x2-lr*g_x2 + y2=y2-lr*g_y2 + + ax.scatter(x1, y1, fonction(x1, y1), marker='o', s=10, color='#FF0000') + ax.scatter(x2, y2, fonction(x2, y2), marker='o', s=10, color='#00FF00') + plt.draw() + print("iteration= {} x1={:+7.5f} y1={:+7.5f} x2={:+7.5f} y2={:+7.5f}".format(i, x1, y1, x2, y2)) + plt.pause(0.05) + i+=1 diff --git a/Divers/descente_gradient/exemple_2d_1.py b/Divers/descente_gradient/exemple_2d_1.py new file mode 100644 index 0000000..3d68be2 --- /dev/null +++ b/Divers/descente_gradient/exemple_2d_1.py @@ -0,0 +1,23 @@ +import numpy as np +import matplotlib.pyplot as plt + +def fonction(x): + return x**2+3*x-2 + +def gradient_fonction(x): + return 2*x+3 + +xvals=np.arange(-5, 3, 0.1) +yvals=fonction(xvals) +plt.plot(xvals, yvals) + +x=np.random.random_integers(-4, 3)+np.random.rand(1)[0] +lr=0.2 +i=0 +while True: + plt.scatter(x, fonction(x), color='#FF0000') + plt.draw() + plt.pause(0.5) + x=x-lr*gradient_fonction(x) + print("itération {:3d} -> x={:+7.5f}".format(i, x)) + i+=1 diff --git a/Divers/descente_gradient/exemple_2d_2.py b/Divers/descente_gradient/exemple_2d_2.py new file mode 100644 index 0000000..03887fd --- /dev/null +++ b/Divers/descente_gradient/exemple_2d_2.py @@ -0,0 +1,24 @@ +import numpy as np +import matplotlib.pyplot as plt + +def fonction(x): + return 3*x**4-4*x**3-12*x**2-0*x-3 + +def gradient_fonction(x): + return 12*x**3-12*x**2-24*x + +xvals=np.arange(-3, 4, 0.1) +yvals=fonction(xvals) +plt.plot(xvals, yvals) + +x=np.random.random_integers(-3, 3)+np.random.rand(1)[0] +i=0 +print("itération: {} x={}".format(i, x)) +lr=0.015 +while True: + plt.scatter(x, fonction(x), color='#FF0000') + plt.draw() + plt.pause(0.5) + x=x-lr*gradient_fonction(x) + i+=1 + print("itération {:3d} -> x={}".format(i, x)) diff --git a/Divers/descente_gradient/exemple_2d_2_inertie.py b/Divers/descente_gradient/exemple_2d_2_inertie.py new file mode 100644 index 0000000..a04e510 --- /dev/null +++ b/Divers/descente_gradient/exemple_2d_2_inertie.py @@ -0,0 +1,27 @@ +import numpy as np +import matplotlib.pyplot as plt + +def fonction(x): + return 3*x**4-4*x**3-12*x**2-0*x-3 + +def gradient_fonction(x): + return 12*x**3-12*x**2-24*x + +xvals=np.arange(-3, 4, 0.1) +yvals=fonction(xvals) +plt.plot(xvals, yvals) + +x=np.random.random_integers(-3, 3)+np.random.rand(1)[0] +i=0 +print("itération: {} x={}".format(i, x)) +lr=0.015 +lr2=0.3 +correction=0 +while True: + plt.scatter(x, fonction(x), color='#FF0000') + plt.draw() + plt.pause(0.5) + correction=lr2*correction-lr*gradient_fonction(x) + x=x+correction + i+=1 + print("itération {:3d} -> x={}".format(i, x)) diff --git a/Divers/descente_gradient/exemple_3d.py b/Divers/descente_gradient/exemple_3d.py new file mode 100644 index 0000000..bea3446 --- /dev/null +++ b/Divers/descente_gradient/exemple_3d.py @@ -0,0 +1,42 @@ +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.colors import LogNorm +import matplotlib.pyplot as plt +import numpy as np +import math + +def fonction(X, Y): + return X*np.exp(-X**2-Y**2)+(X**2+Y**2)/20 + +def gradient_fonction(X, Y): + g_x=np.exp(-X**2-Y**2)+X*-2*X*np.exp(-X**2-Y**2)+X/10 + g_y=-2*Y*X*np.exp(-X**2-Y**2)+Y/10 + return g_x, g_y + +fig=plt.figure() +fig.set_size_inches(9, 7, forward=True) +ax=Axes3D(fig, azim=-29, elev=49) +X=np.arange(-3, 3, 0.2) +Y=np.arange(-3, 3, 0.2) +X, Y=np.meshgrid(X, Y) +Z=fonction(X, Y) +ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1) +plt.xlabel("Paramètre 1 (x)") +plt.ylabel("Paramètre 2 (y)") + +x=np.random.random_integers(-2, 2)+np.random.rand(1)[0] +y=np.random.random_integers(-2, 2)+np.random.rand(1)[0] + +lr=0.2 +correction_x=0 +correction_y=0 +i=0 +while True: + g_x, g_y=gradient_fonction(x, y) + x=x-lr*g_x + y=y-lr*g_y + ax.scatter(x, y, fonction(x, y), marker='o', s=10, color='#00FF00') + plt.draw() + print("itération {:3d} -> x={:+7.5f} y={:+7.5f}".format(i, x, y)) + plt.pause(0.05) + i+=1 diff --git a/Divers/descente_gradient/exemple_3d_inertie.py b/Divers/descente_gradient/exemple_3d_inertie.py new file mode 100644 index 0000000..83bc032 --- /dev/null +++ b/Divers/descente_gradient/exemple_3d_inertie.py @@ -0,0 +1,47 @@ +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.colors import LogNorm +import matplotlib.pyplot as plt +import numpy as np +import math + +def fonction(X, Y): + return X*np.exp(-X**2-Y**2)+(X**2+Y**2)/20 + +def gradient_fonction(X, Y): + g_x=np.exp(-X**2-Y**2)+X*-2*X*np.exp(-X**2-Y**2)+X/10 + g_y=-2*Y*X*np.exp(-X**2-Y**2)+Y/10 + return g_x, g_y + +fig=plt.figure() +fig.set_size_inches(9, 7, forward=True) +ax=Axes3D(fig, azim=-29, elev=49) +X=np.arange(-3, 3, 0.2) +Y=np.arange(-3, 3, 0.2) +X, Y=np.meshgrid(X, Y) +Z=fonction(X, Y) +ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1) +#ax.contour(X, Y, Z, 70, rstride=1, cstride=1, cmap='plasma') + +plt.xlabel("Paramètre 1 (x)") +plt.ylabel("Paramètre 2 (y)") + +x=np.random.random_integers(-2, 2)+np.random.rand(1)[0] +y=np.random.random_integers(-2, 2)+np.random.rand(1)[0] + +lr=0.2 +lr2=0.9 +correction_x=0 +correction_y=0 +i=0 +while True: + g_x, g_y=gradient_fonction(x, y) + correction_x=lr2*correction_x-lr*g_x + x=x+correction_x + correction_y=lr2*correction_y-lr*g_y + y=y+correction_y + ax.scatter(x, y, fonction(x, y), marker='o', s=10, color='#FF0000') + plt.draw() + print("itération {:3d} -> x={:+7.5f} y={:+7.5f}".format(i, x, y)) + plt.pause(0.05) + i+=1 diff --git a/Divers/descente_gradient/gradient.py b/Divers/descente_gradient/gradient.py new file mode 100644 index 0000000..69085f7 --- /dev/null +++ b/Divers/descente_gradient/gradient.py @@ -0,0 +1,35 @@ +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.colors import LogNorm +import matplotlib.pyplot as plt +import numpy as np +import math + +def fonction(X, Y): + return X*np.exp(-X**2-Y**2)+(X**2+Y**2)/20 + +def gradient_fonction(X, Y): + g_x=np.exp(-X**2-Y**2)+X*-2*X*np.exp(-X**2-Y**2)+X/10 + g_y=-2*Y*X*np.exp(-X**2-Y**2)+Y/10 + return g_x, g_y + +fig=plt.figure() +fig.set_size_inches(9, 7, forward=True) +ax=Axes3D(fig, azim=-29, elev=49) +X=np.arange(-3, 3, 0.2) +Y=np.arange(-3, 3, 0.2) +X, Y=np.meshgrid(X, Y) +Z=fonction(X, Y) +ax.plot_surface(X, Y, Z, rstride=1, cstride=1) +plt.xlabel("Paramètre 1 (x)") +plt.ylabel("Paramètre 2 (y)") + +x, y=np.meshgrid(np.arange(-3, 3, 0.2), + np.arange(-3, 3, 0.2)) +z=-1 + +u, v=gradient_fonction(x, y) +w=0 +ax.quiver(x, y, z, u, v, w, length=0.15, normalize=True, color='#333333') + +plt.show() diff --git a/Divers/jetson/README.md b/Divers/jetson/README.md new file mode 100644 index 0000000..626aee1 --- /dev/null +++ b/Divers/jetson/README.md @@ -0,0 +1,6 @@ +# Tutoriel 40 +## Inference sur Jetson Nano + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=xU9qlCy7j1c + + diff --git a/Divers/jetson/inference.py b/Divers/jetson/inference.py new file mode 100644 index 0000000..30e0ce4 --- /dev/null +++ b/Divers/jetson/inference.py @@ -0,0 +1,54 @@ +import pyrealsense2 as rs +import cv2 +import numpy as np +import jetson.inference +import jetson.utils +import time + +net=jetson.inference.detectNet("SSD-Inception-v2", threshold=0.5) +#net=jetson.inference.detectNet("SSD-MobileNet-v2", threshold=0.5) +display=jetson.utils.videoOutput("display://0") + +pipeline=rs.pipeline() +config=rs.config() + +config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15) +config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15) + +align_to = rs.stream.color +align = rs.align(align_to) + +pipeline.start(config) + +while True: + + frames=pipeline.wait_for_frames() + + aligned_frames = align.process(frames) + + depth_frame=aligned_frames.get_depth_frame() + color_frame=aligned_frames.get_color_frame() + + if not depth_frame or not color_frame: + continue + + depth_image=np.array(depth_frame.get_data()) + color_image=np.array(color_frame.get_data()) + + depth_colormap=cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) + + start=time.time() + cuda_image=jetson.utils.cudaFromNumpy(color_image) + detections=net.Detect(cuda_image, color_image.shape[1], color_image.shape[0]) + print("Temps", time.time()-start) + + display.Render(cuda_image) + cuda_image=jetson.utils.cudaToNumpy(cuda_image) + + cv2.imshow('RealSense1', depth_colormap) + #cv2.imshow('RealSense2', color_image) + cv2.imshow('cuda_image', cuda_image) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + pipeline.stop() + quit() diff --git a/Divers/jetson/inference_avec_distance.py b/Divers/jetson/inference_avec_distance.py new file mode 100644 index 0000000..05110f3 --- /dev/null +++ b/Divers/jetson/inference_avec_distance.py @@ -0,0 +1,72 @@ +import pyrealsense2 as rs +import cv2 +import numpy as np +import jetson.inference +import jetson.utils +import time + +net=jetson.inference.detectNet("SSD-Inception-v2", threshold=0.5) +#net=jetson.inference.detectNet("SSD-MobileNet-v2", threshold=0.5) +display=jetson.utils.videoOutput("display://0") + +pipeline=rs.pipeline() +config=rs.config() + +config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15) +config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15) + +align_to = rs.stream.color +align = rs.align(align_to) + +pipeline.start(config) + +while True: + + frames=pipeline.wait_for_frames() + + aligned_frames = align.process(frames) + + depth_frame=aligned_frames.get_depth_frame() + color_frame=aligned_frames.get_color_frame() + + if not depth_frame or not color_frame: + continue + + depth_image=np.array(depth_frame.get_data()) + color_image=np.array(color_frame.get_data()) + + depth_colormap=cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) + + start=time.time() + cuda_image=jetson.utils.cudaFromNumpy(color_image) + detections=net.Detect(cuda_image, color_image.shape[1], color_image.shape[0]) + + print("#######################################") + print("Temps", time.time()-start) + for detection in detections: + print(detection) + x, y=detection.Center + x1=int(x-detection.Width/2) + y1=int(y-detection.Height/2) + x2=int(x+detection.Width/2) + y2=int(y+detection.Height/2) + if detection.ClassID==1: + cv2.rectangle(color_image, (x1, y1), (x2, y2), (255, 0, 0), 2) + dist=depth_frame.get_distance(int(x), int(y)) + if dist<1: + msg="{:2.0f} cm".format(dist*100) + else: + msg="{:4.2f} m".format(dist) + cv2.putText(color_image, msg, (int(x), int(y)), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA) + + cv2.imshow('RealSense1', depth_colormap) + cv2.imshow('RealSense2', color_image) + + display.Render(cuda_image) + cuda_image=jetson.utils.cudaToNumpy(cuda_image) + #display.SetStatus("Object Detection | Network {:.0f} FPS".format(net.GetNetworkFPS())) + cv2.imshow('cuda_image', cuda_image) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + pipeline.stop() + quit() diff --git a/Divers/odrive/README.md b/Divers/odrive/README.md new file mode 100644 index 0000000..c6e650d --- /dev/null +++ b/Divers/odrive/README.md @@ -0,0 +1,5 @@ +# Parlons actuator, odrive, moteur ... +## Exemple de code avec la carte odrive robotics + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=Mg-KiG3Rq2Q + diff --git a/Divers/odrive/test0.py b/Divers/odrive/test0.py new file mode 100644 index 0000000..9ee5541 --- /dev/null +++ b/Divers/odrive/test0.py @@ -0,0 +1,49 @@ +import time +import odrive +from odrive.enums import * + +accel=30. +vel=4. +calibration=False + +odrv0=odrive.find_any(serial_number='205C3690424D') + +if calibration: + print("Calibration...", end='', flush=True) + odrv0.axis0.requested_state=4 + odrv0.axis1.requested_state=4 + + while odrv0.axis0.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + while odrv0.axis1.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + print("OK") + +odrv0.axis0.controller.config.input_mode=INPUT_MODE_TRAP_TRAJ +odrv0.axis0.trap_traj.config.vel_limit=vel +odrv0.axis0.trap_traj.config.accel_limit=accel +odrv0.axis0.trap_traj.config.decel_limit=accel +odrv0.axis0.controller.config.inertia=0 + +odrv0.axis1.controller.config.input_mode=INPUT_MODE_TRAP_TRAJ +odrv0.axis1.trap_traj.config.vel_limit=vel +odrv0.axis1.trap_traj.config.accel_limit=accel +odrv0.axis1.trap_traj.config.decel_limit=accel +odrv0.axis1.controller.config.inertia=0 + +odrv0.axis0.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL +odrv0.axis1.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL + +pos0=odrv0.axis0.encoder.pos_estimate +pos1=odrv0.axis1.encoder.pos_estimate +shift=1/5 + +while True: + pos0_finale=pos0+shift + pos1_finale=pos1+shift + odrv0.axis0.controller.input_pos=pos0_finale + odrv0.axis1.controller.input_pos=pos1_finale + while abs(odrv0.axis0.encoder.pos_estimate-pos0_finale)>0.02 or \ + abs(odrv0.axis1.encoder.pos_estimate-pos1_finale)>0.02: + time.sleep(0.1) + shift=-shift diff --git a/Divers/odrive/test1.py b/Divers/odrive/test1.py new file mode 100644 index 0000000..ad9c9bf --- /dev/null +++ b/Divers/odrive/test1.py @@ -0,0 +1,55 @@ +import time +import odrive +from odrive.enums import * + +accel=10. +vel=3. +calibration=False + +odrv0=odrive.find_any(serial_number='205C3690424D') + +if calibration: + print("Calibration...", end='', flush=True) + odrv0.axis0.requested_state=4 + odrv0.axis1.requested_state=4 + + while odrv0.axis0.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + while odrv0.axis1.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + + print("OK") + +odrv0.axis0.controller.config.control_mode = CONTROL_MODE_VELOCITY_CONTROL +odrv0.axis0.controller.config.input_mode = INPUT_MODE_VEL_RAMP +odrv0.axis1.controller.config.control_mode = CONTROL_MODE_VELOCITY_CONTROL +odrv0.axis1.controller.config.input_mode = INPUT_MODE_VEL_RAMP + +odrv0.axis0.controller.config.vel_ramp_rate=accel +odrv0.axis1.controller.config.vel_ramp_rate=accel + +odrv0.axis0.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL +odrv0.axis1.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL + +odrv0.axis0.controller.input_vel=vel +odrv0.axis1.controller.input_vel=0 + +evenement=time.time() +id=0 +while True: + if id==0: + torque=odrv0.axis0.motor.current_control.Iq_setpoint*odrv0.axis0.motor.config.torque_constant + else: + torque=odrv0.axis1.motor.current_control.Iq_setpoint*odrv0.axis1.motor.config.torque_constant + if abs(torque)>0.2 and (time.time()-evenement)>1: + if id==0: + odrv0.axis0.controller.input_vel=0 + odrv0.axis1.controller.input_vel=vel + id=1 + else: + odrv0.axis1.controller.input_vel=0 + odrv0.axis0.controller.input_vel=vel + id=0 + evenement0=time.time() + + diff --git a/Divers/odrive/test2.py b/Divers/odrive/test2.py new file mode 100644 index 0000000..ddcdb21 --- /dev/null +++ b/Divers/odrive/test2.py @@ -0,0 +1,40 @@ +import time +import odrive +from odrive.enums import * + +accel=20. +vel=5. +ratio=2 +calibration=False + +odrv0=odrive.find_any(serial_number='205C3690424D') + +if calibration: + print("Calibration...", end='', flush=True) + odrv0.axis0.requested_state=4 + odrv0.axis1.requested_state=4 + + while odrv0.axis0.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + while odrv0.axis1.current_state != AXIS_STATE_IDLE: + time.sleep(0.1) + + print("OK") + +odrv0.axis0.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL +odrv0.axis1.requested_state=AXIS_STATE_CLOSED_LOOP_CONTROL + +odrv0.axis0.controller.config.input_mode = INPUT_MODE_TRAP_TRAJ +odrv0.axis0.trap_traj.config.vel_limit=vel +odrv0.axis0.trap_traj.config.accel_limit=accel +odrv0.axis0.trap_traj.config.decel_limit=accel +odrv0.axis0.controller.config.inertia=0 + +pos0=odrv0.axis0.encoder.pos_estimate +pos1=odrv0.axis1.encoder.pos_estimate + +odrv0.axis1.requested_state=AXIS_STATE_IDLE + +while True: + delta1=odrv0.axis1.encoder.pos_estimate-pos1 + odrv0.axis0.controller.input_pos=pos0+ratio*delta1 diff --git a/Divers/renforcement1/README.md b/Divers/renforcement1/README.md new file mode 100644 index 0000000..e899571 --- /dev/null +++ b/Divers/renforcement1/README.md @@ -0,0 +1,5 @@ +# Apprentissage par renforcement +## Processus de décision markovien + +La vidéo de ce tutoriel est disponible à l'adresse suivante:
+https://www.youtube.com/watch?v=Rgxs8lfoG4I diff --git a/Divers/renforcement1/q_valeur.py b/Divers/renforcement1/q_valeur.py new file mode 100644 index 0000000..3536cfb --- /dev/null +++ b/Divers/renforcement1/q_valeur.py @@ -0,0 +1,35 @@ +import numpy as np +import time + +Q=[[0, 0], [0, 0], [0, 0]] + +T=[[[0.50, 0.00, 0.50], [0.00, 0.00, 1.00]], + [[0.70, 0.10, 0.20], [0.00, 0.95, 0.05]], + [[0.40, 0.00, 0.60], [0.30, 0.30, 0.40]]] + +R=[[[ 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00]], + [[+5.00, 0.00, 0.00], [ 0.00, 0.00, 0.00]], + [[ 0.00, 0.00, 0.00], [-1.00, 0.00, 0.00]]] + +gamma=0.95 + +for i in range(200): + time.sleep(0.05) + tab_somme_action=[] + for S in range(3): + for A in range(2): + somme=0 + for s in range(3): + somme+=T[S][A][s]*(R[S][A][s]+gamma*np.max(Q[s])) + Q[S][A]=somme + + print("---------------------------------") + print("Iteration:", i) + for S in range(3): + print() + for A in range(2): + text="Q[etat:{}, action:{}]={:+10.4f}".format(S, A, Q[S][A]) + if A==np.argmax(Q[S]): + text=text+" <-" + print(text) +print("---------------------------------") diff --git a/Divers/renforcement2/CartPole_common.py b/Divers/renforcement2/CartPole_common.py new file mode 100644 index 0000000..d6ee357 --- /dev/null +++ b/Divers/renforcement2/CartPole_common.py @@ -0,0 +1,12 @@ +import numpy as np + +# Valeurs hautes et basses des observations +low_values=np.array([-5, -5, -0.45, -5]) +high_values=np.array([5, 5, 0.45, 5]) + +division=[42, 42, 42, 42] +pas=(high_values-low_values)/division + +def discretise(state): + discrete_state=(state-low_values)/pas + return tuple(discrete_state.astype(np.int)) diff --git a/Divers/renforcement2/CartPole_predict.py b/Divers/renforcement2/CartPole_predict.py new file mode 100644 index 0000000..7275833 --- /dev/null +++ b/Divers/renforcement2/CartPole_predict.py @@ -0,0 +1,25 @@ +import gym +import numpy as np +import CartPole_common + +env=gym.make("CartPole-v0") +env._max_episode_steps=5000 + +q_table=np.load("CartPole_qtable.npy") + +for epoch in range(1000): + state = env.reset() + score = 0 + while True: + env.render() + discrete_state=CartPole_common.discretise(state) + action=np.argmax(q_table[discrete_state]) + #if not np.random.randint(5): + # action=np.random.randint(2) + state, reward, done, info=env.step(action) + score+=reward + if done: + print('Essai {:05d} Score: {:04d}'.format(epoch, int(score))) + break + +env.close() diff --git a/Divers/renforcement2/CartPole_train.py b/Divers/renforcement2/CartPole_train.py new file mode 100644 index 0000000..87cd7ca --- /dev/null +++ b/Divers/renforcement2/CartPole_train.py @@ -0,0 +1,82 @@ +import gym +import numpy as np +import cv2 +import CartPole_common + +env=gym.make("CartPole-v0") +env._max_episode_steps=500 + +alpha=0.05 +gamma=0.98 + +epoch=50000 +show_every=500 + +epsilon=1. +epsilon_min=0.05 +start_epsilon=1 +end_epsilon=epoch//2 +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +nbr_action=env.action_space.n +q_table=np.random.uniform(low=-1, high=1, size=(CartPole_common.division+[nbr_action])) + +result_done=0 +scores=[] +best_score=0 +for episode in range(epoch): + obs=env.reset() + discrete_state=CartPole_common.discretise(obs) + done=False + + if episode%show_every == 0: + render=True + mean_score=np.mean(scores) + print("Epoch {:06d}/{:06d} reussite:{:04d}/{:04d} epsilon={:06.4f} Mean score={:08.4f} alpha={:06.4f}".format(episode, epoch, result_done, show_every, epsilon, mean_score, alpha)) + scores=[] + result_done=0 + if mean_score>best_score: + print("Sauvegarde ...") + np.save("CartPole_qtable", q_table) + best_score=mean_score + alpha=alpha*0.99 + + else: + render=False + + score=1 + while not done: + + if np.random.random()>epsilon: + action=np.argmax(q_table[discrete_state]) + else: + action=np.random.randint(nbr_action) + + new_state, reward, done, info=env.step(action) + new_discrete_state=CartPole_common.discretise(new_state) + + if episode%show_every == 0: + env.render() + + #reward=2-np.abs(new_state[0]) + if done: + scores.append(score) + if score==env._max_episode_steps: + result_done+=1 + else: + reward=-10 + + max_future_q=np.max(q_table[new_discrete_state]) + current_q=q_table[discrete_state][action] + new_q=(1-alpha)*current_q+alpha*(reward+gamma*max_future_q) + q_table[discrete_state][action]=new_q + + score+=1 + discrete_state=new_discrete_state + + if end_epsilon>=episode>=start_epsilon: + epsilon-=epsilon_decay_value + if epsilon=env.goal_position else "raté ...")) + break +env.close() diff --git a/Divers/renforcement2/MountainCar_train.py b/Divers/renforcement2/MountainCar_train.py new file mode 100644 index 0000000..3e04b0c --- /dev/null +++ b/Divers/renforcement2/MountainCar_train.py @@ -0,0 +1,68 @@ +import gym +import numpy as np +import MountainCar_common + +env=gym.make("MountainCar-v0") + +# Coefficient d'apprentissage +alpha=0.1 +# Le "discount rate" +gamma=0.98 + +epoch=25000 +show_every=500 + +# Politique exploration/exploitation +epsilon=1. +epsilon_min=0.1 +start_epsilon=1 +end_epsilon=epoch//2 +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +nbr_action=env.action_space.n +q_table=np.random.uniform(low=-1, high=1, size=(MountainCar_common.division+[nbr_action])) + +OK=0 +for episode in range(epoch): + obs=env.reset() + discrete_state=MountainCar_common.discretise(obs) + done=False + + if episode%show_every == 0: + render=True + print("epoch {:06d}/{:06d} reussite:{:04d}/{:04d} epsilon={:08.6f}".format(episode, epoch, OK, show_every, epsilon)) + OK=0 + else: + render=False + + while not done: + + if np.random.random()>epsilon: + action=np.argmax(q_table[discrete_state]) + else: + action=np.random.randint(nbr_action) + + new_state, reward, done, info=env.step(action) + new_discrete_state=MountainCar_common.discretise(new_state) + if episode%show_every == 0: + env.render() + + if new_state[0]>=env.goal_position: + reward=1 + OK+=1 + + # Mise à jour de Q(s, a) avec la formule de Bellman + max_future_q=np.max(q_table[new_discrete_state]) + current_q=q_table[discrete_state][action] + new_q=(1-alpha)*current_q+alpha*(reward+gamma*max_future_q) + q_table[discrete_state][action]=new_q + + discrete_state=new_discrete_state + + if end_epsilon>=episode>=start_epsilon: + epsilon-=epsilon_decay_value + if epsilon +https://www.youtube.com/watch?v=4Ak6OyehqJc + diff --git a/Divers/renforcement3/README.md b/Divers/renforcement3/README.md new file mode 100644 index 0000000..4f1f653 --- /dev/null +++ b/Divers/renforcement3/README.md @@ -0,0 +1,7 @@ +# Apprentissage par renforcement +## Q learning "basique" avec un perceptron + +La vidéo de ce tutoriel est disponible à l'adresse suivante:
+https://www.youtube.com/watch?v=03U-3BOqfMs + + diff --git a/Divers/renforcement3/graph.py b/Divers/renforcement3/graph.py new file mode 100644 index 0000000..922a3c7 --- /dev/null +++ b/Divers/renforcement3/graph.py @@ -0,0 +1,25 @@ +import numpy as np +import matplotlib.pyplot as plot +import sys + +fenetre=50 +max_score=500 + +if len(sys.argv)!=2: + print("Usage:", sys.argv[0], "") + quit() + +tab_s=np.load(sys.argv[1]) + +tab_m=[] +for i in range(len(tab_s)-fenetre): + m=np.mean(tab_s[i:i+fenetre]) + tab_m.append(m) + +fig=plot.gcf() +fig.set_size_inches(12, 6) +plot.plot(tab_s) +plot.grid() +plot.ylim(0, max_score) +plot.plot(np.arange(fenetre, len(tab_s)), tab_m, color='#FF0000') +plot.show() diff --git a/Divers/renforcement3/predict.py b/Divers/renforcement3/predict.py new file mode 100644 index 0000000..1241cb4 --- /dev/null +++ b/Divers/renforcement3/predict.py @@ -0,0 +1,21 @@ +import gym +import tensorflow as tf +import numpy as np + +env=gym.make("CartPole-v0") +env._max_episode_steps=500 + +model=tf.keras.models.load_model("my_model") + +while True: + observations=env.reset() + score=0 + while True: + env.render() + valeurs_q=model(np.expand_dims(observations, axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + observations, reward, done, info=env.step(action) + if done: + print("SCORE", score) + break + score+=1 diff --git a/Divers/renforcement3/train.py b/Divers/renforcement3/train.py new file mode 100644 index 0000000..e25dc7b --- /dev/null +++ b/Divers/renforcement3/train.py @@ -0,0 +1,108 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np + +env = gym.make("CartPole-v0") +env._max_episode_steps=500 +nbr_action=2 + +gamma=tf.constant(0.98) +epoch=20000 +best_score=0 + +epsilon=1. +epsilon_min=0.10 +start_epsilon=1 +end_epsilon=epoch//2 +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(): + entree=layers.Input(shape=(4), dtype='float32') + result=layers.Dense(30, activation='relu')(entree) + result=layers.Dense(30, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def my_loss(target_q, predicted_q): + loss=tf.reduce_mean(tf.math.square(target_q-predicted_q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon, best_score, tab_score + for e in range(epoch): + print("EPOCH:", e, "epsilon", epsilon) + score=0 + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + observations=env.reset() + while True: + tab_observations.append(observations) + if np.random.random()>epsilon: + valeurs_q=model(np.expand_dims(observations, axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + observations, reward, done, info=env.step(action) + tab_actions.append(action) + tab_next_observations.append(observations) + tab_done.append(done) + if done: + tab_rewards.append(-10.) + print("FIN, score:", score) + tab_score.append(score) + score=0 + break + score+=1 + tab_rewards.append(reward) + + tab_rewards=np.array(tab_rewards, dtype=np.float32) + tab_actions=np.array(tab_actions, dtype=np.int32) + tab_observations=np.array(tab_observations, dtype=np.float32) + tab_next_observations=np.array(tab_next_observations, dtype=np.float32) + tab_done=np.array(tab_done, dtype=np.float32) + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + train_loss.reset_states() + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + if np.mean(tab_score[-20:])>best_score: + print("Sauvegarde du modele") + model.save("my_model") + best_score=np.mean(tab_score[-20:]) + if best_score==env._max_episode_steps-1: + return + +model=model() +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-4) +train_loss=tf.keras.metrics.Mean() +tab_s=[] + +tab_score=[] +train() + +np.save("tab_score", tab_score) + diff --git a/Divers/renforcement3/train_better.py b/Divers/renforcement3/train_better.py new file mode 100644 index 0000000..150f5ae --- /dev/null +++ b/Divers/renforcement3/train_better.py @@ -0,0 +1,108 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np + +env=gym.make("CartPole-v0") +env._max_episode_steps=200 +nbr_action=2 + +fichier_log=open("log_critic.csv", "a") + +gamma=0.98 +max_episode=600 +epsilon=1. +epsilon_min=0.10 +start_epsilon=10 +end_epsilon=max_episode +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(): + entree=layers.Input(shape=(4), dtype='float32') + result=layers.Dense(32, activation='relu')(entree) + result=layers.Dense(32, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def my_loss(target_q, predicted_q): + loss=tf.reduce_mean(tf.math.square(target_q-predicted_q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon + m_reward=0 + for episode in range(max_episode): + score=0 + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + observations=env.reset() + score=0 + while True: + tab_observations.append(observations) + if np.random.random()>epsilon: + valeurs_q=model(np.expand_dims(observations, axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + observations, reward, done, info=env.step(action) + score+=reward + tab_actions.append(action) + tab_next_observations.append(observations) + tab_done.append(done) + if done: + tab_rewards.append(-10.) + break + tab_rewards.append(reward) + + tab_rewards=np.array(tab_rewards, dtype=np.float32) + tab_actions=np.array(tab_actions, dtype=np.int32) + tab_observations=np.array(tab_observations, dtype=np.float32) + tab_next_observations=np.array(tab_next_observations, dtype=np.float32) + tab_done=np.array(tab_done, dtype=np.float32) + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + train_loss.reset_states() + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + + m_reward=0.05*score+(1-0.05)*m_reward + message="Episode {:04d} score:{:6.1f} moyenne lissée: {:6.1f} (epsilon={:5.3f})" + print(message.format(episode, score, m_reward, epsilon)) + + fichier_log.write("{:f}:{:f}\n".format(score, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +model=model() +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-2) +train_loss=tf.keras.metrics.Mean() +tab_s=[] + +train() + +fichier_log.close() diff --git a/Divers/renforcement4/README.md b/Divers/renforcement4/README.md new file mode 100644 index 0000000..b56a0ba --- /dev/null +++ b/Divers/renforcement4/README.md @@ -0,0 +1,7 @@ +# Apprentissage par renforcement +## Pacman en mode target ! + +La vidéo de ce tutoriel est disponible à l'adresse suivante:
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zkCu|Pq&u!5eV*OvG}=2B-uP?jh1JE0@A%dy8ZD=9n|$?j%sG|pK3ne3?tgA-wifsL z3;5x-h#;oPgQ8Kg%e1>_m#@P6=mEIeDsrbN(!Cu5x#8=DjN$0_LiB8t;Jx&CQlfkuu|D%>)f0Z%*M=R&wjryaM?|*^(Kb<@Z)$vCq-+F}X%2Ue?-O!7Cyf=Jt j8Iru8qW1q^|HQhajosIQ;voYc-v42NvN0_*cDwOENX=jR literal 0 HcmV?d00001 diff --git a/Divers/renforcement4/joue.py b/Divers/renforcement4/joue.py new file mode 100644 index 0000000..d1ee550 --- /dev/null +++ b/Divers/renforcement4/joue.py @@ -0,0 +1,72 @@ +import gym +import cv2 +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import time +import matplotlib.pyplot as plot +import os + +env=gym.make("MsPacman-v0") + +#model=tf.keras.models.load_model('my_model_v1') +model=tf.keras.models.load_model('my_model_target') + +decalage_debut=90 +taille_sequence=6 + +def transform_img(image): + result=np.expand_dims(image[:170, :, 0], axis=-1) + return result + +def joue(): + + ###### + observations=env.reset() + vie=3 + for i in range(decalage_debut-taille_sequence): + env.step(0) + tab_sequence=[] + for i in range(taille_sequence): + observation, reward, done, info=env.step(0) + img=transform_img(observation) + tab_sequence.append(img) + tab_sequence=np.array(tab_sequence, dtype=np.float32) + ###### + + tab_img=[] + score=0 + vie=3 + while True: + valeurs_q=model(np.expand_dims(np.concatenate(tab_sequence, axis=-1), axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + print(action+1, end=' ') + score+=min(reward, 10.) + if info['ale.lives']10: + print("MIAM", reward, end=" ") + + img=transform_img(observation) + tab_sequence[:-1]=tab_sequence[1:] + tab_sequence[taille_sequence-1]=img + tab_img.append(observation) + +score=0 +while score<1400: + start_time=time.time() + tab_img, score=joue() + print(time.time()-start_time) + +for i in range(len(tab_img)): + cv2.imshow("Pacman", tab_img[i]) + key=cv2.waitKey(20) + if key==ord('q'): + break + diff --git a/Divers/renforcement4/train_target.py b/Divers/renforcement4/train_target.py new file mode 100644 index 0000000..7d3e28e --- /dev/null +++ b/Divers/renforcement4/train_target.py @@ -0,0 +1,180 @@ +import gym +import cv2 +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import time +import matplotlib.pyplot as plot + +env = gym.make("MsPacman-v0") +print("Liste des actions", env.unwrapped.get_action_meanings()) +nbr_action=tf.constant(4) + +file_model='my_model_target' +file_stats='tab_score_target' + +gamma=tf.constant(0.999) +epoch=200 +decalage_debut=90 +taille_sequence=6 +nbr_jeu=300 +pourcentage_batch=0.20 +best_score=0 + +epsilon=1. +epsilon_min=0.10 +start_epsilon=1 +end_epsilon=epoch//4 +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(nbr_cc=8): + entree=layers.Input(shape=(170, 160, taille_sequence), dtype='float32') + result=layers.Conv2D( nbr_cc, 3, activation='relu', padding='same', strides=2)((entree/128)-1) + result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.BatchNormalization()(result) + result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.Conv2D(8*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.BatchNormalization()(result) + + result=layers.Flatten()(result) + + result=layers.Dense(512, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def transform_img(image): + result=np.expand_dims(image[:170, :, 0], axis=-1) + return result + +def simulation(epsilon, debug=False): + if debug: + start_time=time.time() + + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + ###### + observations=env.reset() + vie=3 + for i in range(decalage_debut-taille_sequence): + env.step(0) + tab_sequence=[] + for i in range(taille_sequence): + observation, reward, done, info=env.step(0) + img=transform_img(observation) + tab_sequence.append(img) + tab_sequence=np.array(tab_sequence, dtype=np.float32) + ###### + + score=0 + while True: + if np.random.random()>epsilon: + valeurs_q=model_primaire(np.expand_dims(np.concatenate(tab_sequence, axis=-1), axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + + h=np.random.randint(10) + if h==0: + tab_observations.append(np.concatenate(tab_sequence, axis=-1)) + tab_actions.append(action) + score+=reward + if info['ale.lives']10]=10. + tab_actions=np.array(tab_actions, dtype=np.int32) + if debug: + print(" Creation observations {:5.3f} seconde(s)".format(float(time.time()-start_time))) + print(" score:{:5d} batch:{:4d}".format(int(score), len(tab_done))) + return tab_observations,\ + tab_rewards,\ + tab_actions,\ + tab_next_observations,\ + tab_done + observation, reward, done, info=env.step(action+1) + img=transform_img(observation) + tab_sequence[:-1]=tab_sequence[1:] + tab_sequence[taille_sequence-1]=img + if h==0: + tab_next_observations.append(np.concatenate(tab_sequence, axis=-1)) + +def my_loss(y, q): + loss=tf.reduce_mean(tf.math.square(y-q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model_cible(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model_primaire(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model_primaire.trainable_variables) + optimizer.apply_gradients(zip(gradients, model_primaire.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon, best_score + for e in range(epoch): + for i in range(nbr_jeu): + print("Epoch {:04d}/{:05d} epsilon={:05.3f}".format(i, e, epsilon)) + tab_observations, tab_rewards, tab_actions, tab_next_observations, tab_done=simulation(epsilon, debug=True) + if debug: + start_time=time.time() + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + if debug: + print(" Entrainement {:5.3f} seconde(s)".format(float(time.time()-start_time))) + print(" loss: {:6.4f}".format(train_loss.result())) + train_loss.reset_states() + + print("Copie des poids primaire -> cible") + for a, b in zip(model_cible.variables, model_primaire.variables): + a.assign(b) + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + np.save(file_stats, tab_s) + if np.mean(tab_s[-200:])>best_score: + print("Sauvegarde du modele") + model_cible.save(file_model) + best_score=np.mean(tab_s[-200:]) + +model_primaire=model(16) +model_cible=tf.keras.models.clone_model(model_primaire) +for a, b in zip(model_cible.variables, model_primaire.variables): + a.assign(b) + +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-4) +train_loss=tf.keras.metrics.Mean() +tab_s=[] +train(debug=True) diff --git a/Divers/renforcement4/train_v1.py b/Divers/renforcement4/train_v1.py new file mode 100644 index 0000000..f37b49a --- /dev/null +++ b/Divers/renforcement4/train_v1.py @@ -0,0 +1,173 @@ +import gym +import cv2 +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import time +import matplotlib.pyplot as plot + +env = gym.make("MsPacman-v0") +print("Liste des actions", env.unwrapped.get_action_meanings()) +nbr_action=tf.constant(4) + +file_model='my_model_v1' +file_stats='tab_score_v1' + +gamma=tf.constant(0.999) +epoch=1500 +decalage_debut=90 +taille_sequence=6 +nbr_jeu=40 +pourcentage_batch=0.20 +best_score=0 + +epsilon=1. +epsilon_min=0.10 +start_epsilon=1 +end_epsilon=epoch//4 +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(nbr_cc=8): + entree=layers.Input(shape=(170, 160, taille_sequence), dtype='float32') + result=layers.Conv2D( nbr_cc, 3, activation='relu', padding='same', strides=2)((entree/128)-1) + result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.BatchNormalization()(result) + result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.Conv2D(8*nbr_cc, 3, activation='relu', padding='same', strides=2)(result) + result=layers.BatchNormalization()(result) + + result=layers.Flatten()(result) + + result=layers.Dense(512, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def transform_img(image): + result=np.expand_dims(image[:170, :, 0], axis=-1) + return result + +def simulation(epsilon, debug=False): + if debug: + start_time=time.time() + + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + ###### + observations=env.reset() + vie=3 + for i in range(decalage_debut-taille_sequence): + env.step(0) + tab_sequence=[] + for i in range(taille_sequence): + observation, reward, done, info=env.step(0) + img=transform_img(observation) + tab_sequence.append(img) + tab_sequence=np.array(tab_sequence, dtype=np.float32) + ###### + + score=0 + while True: + if np.random.random()>epsilon: + valeurs_q=model(np.expand_dims(np.concatenate(tab_sequence, axis=-1), axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + + h=np.random.randint(10) + if h==0: + tab_observations.append(np.concatenate(tab_sequence, axis=-1)) + tab_actions.append(action) + score+=reward + if info['ale.lives']10]=10. + tab_actions=np.array(tab_actions, dtype=np.int32) + if debug: + print(" Creation observations {:5.3f} seconde(s)".format(float(time.time()-start_time))) + print(" score:{:5d} batch:{:4d}".format(int(score), len(tab_done))) + return tab_observations,\ + tab_rewards,\ + tab_actions,\ + tab_next_observations,\ + tab_done + observation, reward, done, info=env.step(action+1) + img=transform_img(observation) + tab_sequence[:-1]=tab_sequence[1:] + tab_sequence[taille_sequence-1]=img + if h==0: + tab_next_observations.append(np.concatenate(tab_sequence, axis=-1)) + +def my_loss(y, q): + loss=tf.reduce_mean(tf.math.square(y-q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon, best_score + for e in range(epoch): + for i in range(nbr_jeu): + print("Epoch {:04d}/{:05d} epsilon={:05.3f}".format(i, e, epsilon)) + tab_observations, tab_rewards, tab_actions, tab_next_observations, tab_done=simulation(epsilon, debug=True) + if debug: + start_time=time.time() + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + if debug: + print(" Entrainement {:5.3f} seconde(s)".format(float(time.time()-start_time))) + print(" loss: {:6.4f}".format(train_loss.result())) + train_loss.reset_states() + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + np.save(file_stats, tab_s) + if np.mean(tab_s[-200:])>best_score: + print("Sauvegarde du modele") + model.save(file_model) + best_score=np.mean(tab_s[-200:]) + +model=model(16) + +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-4) +train_loss=tf.keras.metrics.Mean() +tab_s=[] +train(debug=True) diff --git a/Divers/renforcement5/README.md b/Divers/renforcement5/README.md new file mode 100644 index 0000000..05e335a --- /dev/null +++ b/Divers/renforcement5/README.md @@ -0,0 +1,11 @@ +# Apprentissage par renforcement +## Méthode 'acteur' + +La vidéo de ce tutoriel est disponible à l'adresse suivante:
+https://www.youtube.com/watch?v=LtRAgxRb5eQ + +Ci dessous, le graph de l'apprentissage sur l'environnement CartPole (https://gym.openai.com/envs/CartPole-v0/)
+En bleu: Méthode 'critique'
+En orange: Méthode 'acteur'
+ +![image](mini.png) diff --git a/Divers/renforcement5/cartpole_actor.py b/Divers/renforcement5/cartpole_actor.py new file mode 100644 index 0000000..53a7c72 --- /dev/null +++ b/Divers/renforcement5/cartpole_actor.py @@ -0,0 +1,83 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import os + +env=gym.make("CartPole-v0") +env._max_episode_steps=200 +nbr_actions=2 +gamma=0.99 +max_episode=600 + +prefix_log_file="log_actor" +id_file=0 +while os.path.exists(prefix_log_file+str(id_file)+".csv"): + id_file+=1 +fichier_log=open(prefix_log_file+str(id_file)+".csv", "w") +print("Création du fichier de log", prefix_log_file+str(id_file)+".csv") + +def model(nbr_inputs, nbr_hidden, nbr_actions): + entree=layers.Input(shape=(nbr_inputs), dtype='float32') + result=layers.Dense(32, activation='relu')(entree) + result=layers.Dense(32, activation='relu')(result) + sortie=layers.Dense(nbr_actions, activation='softmax')(result) + + my_model=models.Model(inputs=entree, outputs=sortie) + return my_model + +def calcul_discount_rate(rewards_history, gamma, normalize=False): + result=[] + discounted_sum=0 + for r in rewards_history[::-1]: + discounted_sum=r+gamma*discounted_sum + result.insert(0, discounted_sum) + + # Normalisation + if normalize is True: + result=np.array(result) + result=(result-np.mean(result))/(np.std(result)+1E-7) + result=list(result) + + return result + +def train(): + m_reward=0 + for episode in range(max_episode): + tab_rewards=[] + tab_prob_actions=[] + + observations=env.reset() + with tf.GradientTape() as tape: + while True: + action_probs=my_model(np.expand_dims(observations, axis=0)) + action=np.random.choice(nbr_actions, p=np.squeeze(action_probs)) + tab_prob_actions.append(action_probs[0, action]) + observations, reward, done, info=env.step(action) + tab_rewards.append(reward) + if done: + break + + discount_rate=calcul_discount_rate(tab_rewards, gamma, normalize=True) + + loss=-tf.math.log(tab_prob_actions)*discount_rate + gradients=tape.gradient(loss, my_model.trainable_variables) + optimizer.apply_gradients(zip(gradients, my_model.trainable_variables)) + + score=sum(tab_rewards) + m_reward=0.05*score+(1-0.05)*m_reward + message="Episode {:04d} score:{:6.1f} MPE: {:6.1f}" + print(message.format(episode, score, m_reward)) + + fichier_log.write("{:f}:{:f}\n".format(score, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +my_model=model(4, 32, nbr_actions) +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-2) + +train() + +fichier_log.close() diff --git a/Divers/renforcement5/cartpole_critic.py b/Divers/renforcement5/cartpole_critic.py new file mode 100644 index 0000000..c2f8ca9 --- /dev/null +++ b/Divers/renforcement5/cartpole_critic.py @@ -0,0 +1,114 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import os + +env=gym.make("CartPole-v0") +env._max_episode_steps=200 +nbr_action=2 + +prefix_log_file="log_critic_" +id_file=0 +while os.path.exists(prefix_log_file+str(id_file)+".csv"): + id_file+=1 +fichier_log=open(prefix_log_file+str(id_file)+".csv", "w") +print("Création du fichier de log", prefix_log_file+str(id_file)+".csv") + +gamma=0.98 +max_episode=600 +epsilon=1. +epsilon_min=0.10 +start_epsilon=10 +end_epsilon=max_episode +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(): + entree=layers.Input(shape=(4), dtype='float32') + result=layers.Dense(32, activation='relu')(entree) + result=layers.Dense(32, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def my_loss(target_q, predicted_q): + loss=tf.reduce_mean(tf.math.square(target_q-predicted_q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon + m_reward=0 + for episode in range(max_episode): + score=0 + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + observations=env.reset() + score=0 + while True: + tab_observations.append(observations) + if np.random.random()>epsilon: + valeurs_q=model(np.expand_dims(observations, axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + observations, reward, done, info=env.step(action) + score+=reward + tab_actions.append(action) + tab_next_observations.append(observations) + tab_done.append(done) + if done: + tab_rewards.append(-10.) + break + tab_rewards.append(reward) + + tab_rewards=np.array(tab_rewards, dtype=np.float32) + tab_actions=np.array(tab_actions, dtype=np.int32) + tab_observations=np.array(tab_observations, dtype=np.float32) + tab_next_observations=np.array(tab_next_observations, dtype=np.float32) + tab_done=np.array(tab_done, dtype=np.float32) + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + train_loss.reset_states() + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + + m_reward=0.05*score+(1-0.05)*m_reward + message="Episode {:04d} score:{:6.1f} MPE: {:6.1f} (epsilon={:5.3f})" + print(message.format(episode, score, m_reward, epsilon)) + + fichier_log.write("{:f}:{:f}\n".format(score, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +model=model() +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-2) +train_loss=tf.keras.metrics.Mean() +tab_s=[] + +train() + +fichier_log.close() diff --git a/Divers/renforcement5/mini.png b/Divers/renforcement5/mini.png new file mode 100644 index 0000000000000000000000000000000000000000..198bd47751b09af3911a5b13ba8d555a995e125e GIT binary patch literal 99933 zcmdSA<9B4;7cCsKgN|+6b~;JNPQ|uu+qOC#+qP}nw$;H~&+mSG|A0I0hZ|0vztn;BRvB3)EL7-hML*x}Zmzkq>S_1-_b!zH0z0@B0z%ve z&de6fr0G+2$SBJ7cT3OI@peWVE8y5~CWAB>l}N}R z1QhHCZebDc|M>}F9?+`_1_6Ui)H_RTp8~uI0mI}BqfP#Q2I7MOV3GeDDH`_w#4rPO zZL$B15fZWzwmL3Py%w1u;^b^8>L7~N#A;=-H&~2;4XDs077-EAY=`eJ4GDk;#}wa6 zM{X@gCt+f${r?Tjo$PoU!)s!?r9_ZFF69RPm;C?Ar8Na{A6vqf7UZ5kjRBmzKMZ!E 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+https://www.youtube.com/watch?v=1okjkEMP79c + +Ci dessous, le graph de l'apprentissage sur l'environnement CartPole (https://gym.openai.com/envs/CartPole-v0/)
+En bleu: Méthode 'critique'
+En orange: Méthode 'acteur'
+En vert: Méthode 'acteur/critique'
+ +![image](graph.png) diff --git a/Divers/renforcement6/cartpole_actor.py b/Divers/renforcement6/cartpole_actor.py new file mode 100644 index 0000000..53a7c72 --- /dev/null +++ b/Divers/renforcement6/cartpole_actor.py @@ -0,0 +1,83 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import os + +env=gym.make("CartPole-v0") +env._max_episode_steps=200 +nbr_actions=2 +gamma=0.99 +max_episode=600 + +prefix_log_file="log_actor" +id_file=0 +while os.path.exists(prefix_log_file+str(id_file)+".csv"): + id_file+=1 +fichier_log=open(prefix_log_file+str(id_file)+".csv", "w") +print("Création du fichier de log", prefix_log_file+str(id_file)+".csv") + +def model(nbr_inputs, nbr_hidden, nbr_actions): + entree=layers.Input(shape=(nbr_inputs), dtype='float32') + result=layers.Dense(32, activation='relu')(entree) + result=layers.Dense(32, activation='relu')(result) + sortie=layers.Dense(nbr_actions, activation='softmax')(result) + + my_model=models.Model(inputs=entree, outputs=sortie) + return my_model + +def calcul_discount_rate(rewards_history, gamma, normalize=False): + result=[] + discounted_sum=0 + for r in rewards_history[::-1]: + discounted_sum=r+gamma*discounted_sum + result.insert(0, discounted_sum) + + # Normalisation + if normalize is True: + result=np.array(result) + result=(result-np.mean(result))/(np.std(result)+1E-7) + result=list(result) + + return result + +def train(): + m_reward=0 + for episode in range(max_episode): + tab_rewards=[] + tab_prob_actions=[] + + observations=env.reset() + with tf.GradientTape() as tape: + while True: + action_probs=my_model(np.expand_dims(observations, axis=0)) + action=np.random.choice(nbr_actions, p=np.squeeze(action_probs)) + tab_prob_actions.append(action_probs[0, action]) + observations, reward, done, info=env.step(action) + tab_rewards.append(reward) + if done: + break + + discount_rate=calcul_discount_rate(tab_rewards, gamma, normalize=True) + + loss=-tf.math.log(tab_prob_actions)*discount_rate + gradients=tape.gradient(loss, my_model.trainable_variables) + optimizer.apply_gradients(zip(gradients, my_model.trainable_variables)) + + score=sum(tab_rewards) + m_reward=0.05*score+(1-0.05)*m_reward + message="Episode {:04d} score:{:6.1f} MPE: {:6.1f}" + print(message.format(episode, score, m_reward)) + + fichier_log.write("{:f}:{:f}\n".format(score, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +my_model=model(4, 32, nbr_actions) +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-2) + +train() + +fichier_log.close() diff --git a/Divers/renforcement6/cartpole_actor_critic.py b/Divers/renforcement6/cartpole_actor_critic.py new file mode 100644 index 0000000..981e474 --- /dev/null +++ b/Divers/renforcement6/cartpole_actor_critic.py @@ -0,0 +1,103 @@ +import gym +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +import os + +gamma=0.99 +max_steps_per_episode=10000 +env=gym.make("CartPole-v0") +env._max_episode_steps=200 + +prefix_log_file="log_actor_critic_dsum_" +id_file=0 +while os.path.exists(prefix_log_file+str(id_file)+".csv"): + id_file+=1 +fichier_log=open(prefix_log_file+str(id_file)+".csv", "w") +print("Création du fichier de log", prefix_log_file+str(id_file)+".csv") + +nbr_actions=2 +nbr_inputs=4 + +def calcul_discount_rate(rewards_history, gamma, normalize=False): + result=[] + discounted_sum=0 + for r in rewards_history[::-1]: + discounted_sum=r+gamma*discounted_sum + result.insert(0, discounted_sum) + + # Normalisation + if normalize is True: + result=np.array(result) + result=(result-np.mean(result))/(np.std(result)+1E-7) + result=list(result) + + return result + +def my_model(nbr_inputs, nbr_hidden, nbr_actions): + entree=layers.Input(shape=(nbr_inputs), dtype='float32') + + common=layers.Dense(nbr_hidden, activation="relu")(entree) + action=layers.Dense(nbr_actions, activation="softmax")(common) + critic=layers.Dense(1)(common) + + model=keras.Model(inputs=entree, outputs=[action, critic]) + return model + +model=my_model(nbr_inputs, 32, nbr_actions) + +optimizer=keras.optimizers.Adam(learning_rate=1E-2) +huber_loss=keras.losses.Huber() + +m_reward=0 +episode=0 + +while True: + action_probs_history=[] + critic_value_history=[] + rewards_history=[] + + state=env.reset() + episode_reward=0 + with tf.GradientTape() as tape: + + # Récupération de données + for timestep in range(1, max_steps_per_episode): + action_probs, critic_value=model(np.expand_dims(state, axis=0)) + critic_value_history.append(critic_value[0, 0]) + action=np.random.choice(nbr_actions, p=np.squeeze(action_probs)) + action_probs_history.append(action_probs[0, action]) + state, reward, done, infos=env.step(action) + rewards_history.append(reward) + episode_reward+=reward + if done: + break + + discount_rate=calcul_discount_rate(rewards_history, gamma, normalize=True) + + history=zip(action_probs_history, critic_value_history, discount_rate) + actor_losses=[] + critic_losses=[] + for action_prob, critic_value, discount_rate in history: + actor_losses.append(-tf.math.log(action_prob)*(discount_rate-critic_value)) + critic_losses.append(huber_loss([critic_value], [discount_rate])) + + loss_value=tf.reduce_mean(actor_losses+critic_losses) + grads=tape.gradient(loss_value, model.trainable_variables) + optimizer.apply_gradients(zip(grads, model.trainable_variables)) + + episode+=1 + m_reward=0.05*episode_reward+(1-0.05)*m_reward + + message="Episode {:04d} score:{:6.1f} MPE: {:6.1f}" + print(message.format(episode, episode_reward, m_reward)) + + fichier_log.write("{:f}:{:f}\n".format(episode_reward, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +fichier_log.close() +model.save("my_model") diff --git a/Divers/renforcement6/cartpole_critic.py b/Divers/renforcement6/cartpole_critic.py new file mode 100644 index 0000000..c2f8ca9 --- /dev/null +++ b/Divers/renforcement6/cartpole_critic.py @@ -0,0 +1,114 @@ +import gym +import tensorflow as tf +from tensorflow.keras import models, layers +import numpy as np +import os + +env=gym.make("CartPole-v0") +env._max_episode_steps=200 +nbr_action=2 + +prefix_log_file="log_critic_" +id_file=0 +while os.path.exists(prefix_log_file+str(id_file)+".csv"): + id_file+=1 +fichier_log=open(prefix_log_file+str(id_file)+".csv", "w") +print("Création du fichier de log", prefix_log_file+str(id_file)+".csv") + +gamma=0.98 +max_episode=600 +epsilon=1. +epsilon_min=0.10 +start_epsilon=10 +end_epsilon=max_episode +epsilon_decay_value=epsilon/(end_epsilon-start_epsilon) + +def model(): + entree=layers.Input(shape=(4), dtype='float32') + result=layers.Dense(32, activation='relu')(entree) + result=layers.Dense(32, activation='relu')(result) + sortie=layers.Dense(nbr_action)(result) + + model=models.Model(inputs=entree, outputs=sortie) + return model + +def my_loss(target_q, predicted_q): + loss=tf.reduce_mean(tf.math.square(target_q-predicted_q)) + return loss + +@tf.function +def train_step(reward, action, observation, next_observation, done): + next_Q_values=model(next_observation) + best_next_actions=tf.math.argmax(next_Q_values, axis=1) + next_mask=tf.one_hot(best_next_actions, nbr_action) + next_best_Q_values=tf.reduce_sum(next_Q_values*next_mask, axis=1) + target_Q_values=reward+(1-done)*gamma*next_best_Q_values + target_Q_values=tf.reshape(target_Q_values, (-1, 1)) + mask=tf.one_hot(action, nbr_action) + with tf.GradientTape() as tape: + all_Q_values=model(observation) + Q_values=tf.reduce_sum(all_Q_values*mask, axis=1, keepdims=True) + loss=my_loss(target_Q_values, Q_values) + gradients=tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + train_loss(loss) + +def train(debug=False): + global epsilon + m_reward=0 + for episode in range(max_episode): + score=0 + tab_observations=[] + tab_rewards=[] + tab_actions=[] + tab_next_observations=[] + tab_done=[] + + observations=env.reset() + score=0 + while True: + tab_observations.append(observations) + if np.random.random()>epsilon: + valeurs_q=model(np.expand_dims(observations, axis=0)) + action=int(tf.argmax(valeurs_q[0], axis=-1)) + else: + action=np.random.randint(0, nbr_action) + observations, reward, done, info=env.step(action) + score+=reward + tab_actions.append(action) + tab_next_observations.append(observations) + tab_done.append(done) + if done: + tab_rewards.append(-10.) + break + tab_rewards.append(reward) + + tab_rewards=np.array(tab_rewards, dtype=np.float32) + tab_actions=np.array(tab_actions, dtype=np.int32) + tab_observations=np.array(tab_observations, dtype=np.float32) + tab_next_observations=np.array(tab_next_observations, dtype=np.float32) + tab_done=np.array(tab_done, dtype=np.float32) + train_step(tab_rewards, tab_actions, tab_observations, tab_next_observations, tab_done) + train_loss.reset_states() + + epsilon-=epsilon_decay_value + epsilon=max(epsilon, epsilon_min) + + m_reward=0.05*score+(1-0.05)*m_reward + message="Episode {:04d} score:{:6.1f} MPE: {:6.1f} (epsilon={:5.3f})" + print(message.format(episode, score, m_reward, epsilon)) + + fichier_log.write("{:f}:{:f}\n".format(score, m_reward)) + + if m_reward>env._max_episode_steps-10: + print("Fin de l'apprentissage".format(episode)) + break + +model=model() +optimizer=tf.keras.optimizers.Adam(learning_rate=1E-2) +train_loss=tf.keras.metrics.Mean() +tab_s=[] + +train() + +fichier_log.close() diff --git a/Divers/renforcement6/graph.png b/Divers/renforcement6/graph.png new file mode 100644 index 0000000000000000000000000000000000000000..aa27865959fb7fd29e8ab2014b96bfd08eac6933 GIT binary patch literal 131143 zcmeFYWm{Wa)HNEQz>T|Gaa!D=NT6s-DNu^LySoJ_r8pFKFYfN{#Y=Gs?ht|o2yoKp zdC&O;=i~b!*Ou(8y|dQbQ^puGLiMu(4kjfg006-GpeXwV06-1`0FXBSLqnVqoL$>R 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cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color_infos, 1) + txt="{:s}:{:3.0%}".format(labels[classes[objet]], scores[objet]) + cv2.putText(frame, txt, (xmin, ymin-5), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2) + fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark) + cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2) + cv2.imshow('image', frame) + key=cv2.waitKey(1)&0xFF + if key==ord('a'): + for objet in range(500): + ret, frame=cap.read() + if key==ord('q'): + break +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel12/detection_tf2.py b/Divers/tutoriel12/detection_tf2.py new file mode 100644 index 0000000..040a1ca --- /dev/null +++ b/Divers/tutoriel12/detection_tf2.py @@ -0,0 +1,148 @@ +import numpy as np +import os +import tensorflow as tf +import cv2 + +labels={ + 1: "person", + 2: "bicycle", + 3: "car", + 4: "motorcycle", + 5: "airplane", + 6: "bus", + 7: "train", + 8: "truck", + 9: "boat", + 10: "traffic light", + 11: "fire hydrant", + 13: "stop sign", + 14: "parking meter", + 15: "bench", + 16: "bird", + 17: "cat", + 18: "dog", + 19: "horse", + 20: "sheep", + 21: "cow", + 22: "elephant", + 23: "bear", + 24: "zebra", + 25: "giraffe", + 27: "backpack", + 28: "umbrella", + 31: "handbag", + 32: "tie", + 33: "suitcase", + 34: "frisbee", + 35: "skis", + 36: "snowboard", + 37: "sports ball", + 38: "kite", + 39: "baseball bat", + 40: "baseball glove", + 41: "skateboard", + 42: "surfboard", + 43: "tennis racket", + 44: "bottle", + 46: "wine glass", + 47: "cup", + 48: "fork", + 49: "knife", + 50: "spoon", + 51: "bowl", + 52: "banana", + 53: "apple", + 54: "sandwich", + 55: "orange", + 56: "broccoli", + 57: "carrot", + 58: "hot dog", + 59: "pizza", + 60: "donut", + 61: "cake", + 62: "chair", + 63: "couch", + 64: "potted plant", + 65: "bed", + 67: "dining table", + 70: "toilet", + 72: "tv", + 73: "laptop", + 74: "mouse", + 75: "remote", + 76: "keyboard", + 77: "cell phone", + 78: "microwave", + 79: "oven", + 80: "toaster", + 81: "sink", + 82: "refrigerator", + 84: "book", + 85: "clock", + 86: "vase", + 87: "scissors", + 88: "teddy bear", + 89: "hair drier", + 90: "toothbrush" +} + +#MODEL_NAME='ssd_mobilenet_v2_coco_2018_03_29' +MODEL_NAME='faster_rcnn_inception_v2_coco_2018_01_28' +PATH_TO_FROZEN_GRAPH=MODEL_NAME+'/frozen_inference_graph.pb' +color_infos=(255, 255, 0) + +detection_graph=tf.compat.v1.Graph() +with detection_graph.as_default(): + od_graph_def=tf.compat.v1.GraphDef() + with tf.io.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid: + serialized_graph=fid.read() + od_graph_def.ParseFromString(serialized_graph) + tf.import_graph_def(od_graph_def, name='') + +with detection_graph.as_default(): + with tf.compat.v1.Session() as sess: + #cap=cv2.VideoCapture('../../En_cours/tuto8-suite/Plan_9_from_Outer_Space_1959_512kb.mp4') + cap=cv2.VideoCapture(0) + ops=tf.compat.v1.get_default_graph().get_operations() + all_tensor_names={output.name for op in ops for output in op.outputs} + tensor_dict={} + for key in [ + 'num_detections', 'detection_boxes', 'detection_scores', + 'detection_classes', 'detection_masks']: + tensor_name=key+':0' + if tensor_name in all_tensor_names: + tensor_dict[key]=tf.compat.v1.get_default_graph().get_tensor_by_name(tensor_name) + if 'detection_masks' in tensor_dict: + quit("Masque non géré") + image_tensor=tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:0') + + while True: + ret, frame=cap.read() + tickmark=cv2.getTickCount() + output_dict=sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(frame, 0)}) + nbr_object=int(output_dict['num_detections']) + classes=output_dict['detection_classes'][0].astype(np.uint8) + boxes=output_dict['detection_boxes'][0] + scores=output_dict['detection_scores'][0] + for objet in range(nbr_object): + ymin, xmin, ymax, xmax=boxes[objet] + if scores[objet]>0.30: + height, width=frame.shape[:2] + xmin=int(xmin*width) + xmax=int(xmax*width) + ymin=int(ymin*height) + ymax=int(ymax*height) + cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color_infos, 1) + txt="{:s}:{:3.0%}".format(labels[classes[objet]], scores[objet]) + cv2.putText(frame, txt, (xmin, ymin-5), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2) + fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark) + cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, color_infos, 2) + cv2.imshow('image', frame) + key=cv2.waitKey(1)&0xFF + if key==ord('a'): + for objet in range(500): + ret, frame=cap.read() + if key==ord('q'): + break +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel18-1/README.md b/Divers/tutoriel18-1/README.md new file mode 100644 index 0000000..6af9f8b --- /dev/null +++ b/Divers/tutoriel18-1/README.md @@ -0,0 +1,5 @@ +# Tutoriel 18 1artie 1 +## Sudoku + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=WwPHs1SJrec + diff --git a/Divers/tutoriel18-1/perspective.py b/Divers/tutoriel18-1/perspective.py new file mode 100644 index 0000000..cb19308 --- /dev/null +++ b/Divers/tutoriel18-1/perspective.py @@ -0,0 +1,69 @@ +import cv2 +import numpy as np +import operator + +marge=4 +case=28+2*marge +taille_grille=9*case + +methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C +v1=9 + +cap=cv2.VideoCapture(0) +while True: + ret, frame=cap.read() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.GaussianBlur(gray, (5, 5), 0) + thresh=cv2.adaptiveThreshold(gray, 255, methode, cv2.THRESH_BINARY_INV, v1, 2) + #cv2.imshow("thresh", thresh) + contours, hierarchy=cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + contour_grille=None + maxArea=0 + for c in contours: + area=cv2.contourArea(c) + if area>25000: + peri=cv2.arcLength(c, True) + polygone=cv2.approxPolyDP(c, 0.01*peri, True) + if area>maxArea and len(polygone)==4: + contour_grille=polygone + maxArea=area + if contour_grille is not None: + cv2.drawContours(frame, [contour_grille], 0, (0, 255, 0), 2) + points=np.vstack(contour_grille).squeeze() + points=sorted(points, key=operator.itemgetter(1)) + if points[0][0]25000: + peri=cv2.arcLength(c, True) + polygone=cv2.approxPolyDP(c, 0.01*peri, True) + if area>maxArea and len(polygone)==4: + contour_grille=polygone + maxArea=area + if contour_grille is not None: + cv2.drawContours(frame, [contour_grille], 0, (0, 255, 0), 2) + txt="ADAPTIVE_THRESH_MEAN_C" if methode==cv2.ADAPTIVE_THRESH_MEAN_C else "ADAPTIVE_THRESH_GAUSSIAN_C" + cv2.putText(frame, "[p|m]v1: {:2d} [o]methode: {}".format(v1, txt), (10, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.9, (0, 0, 255), 1) + + cv2.imshow("frame", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + if key==ord('p'): + v1=min(21, v1+2) + if key==ord('m'): + v1=max(3, v1-2) + print(v1) + if key==ord('o'): + if methode==cv2.ADAPTIVE_THRESH_GAUSSIAN_C: + methode=cv2.ADAPTIVE_THRESH_MEAN_C + else: + methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel18-1/threshold.py b/Divers/tutoriel18-1/threshold.py new file mode 100644 index 0000000..3c76fcd --- /dev/null +++ b/Divers/tutoriel18-1/threshold.py @@ -0,0 +1,31 @@ +import cv2 +import numpy as np +import operator + +methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C +v1=9 + +cap=cv2.VideoCapture(0) +while True: + ret, frame=cap.read() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.GaussianBlur(gray, (5, 5), 0) + thresh=cv2.adaptiveThreshold(gray, 255, methode, cv2.THRESH_BINARY_INV, v1, 2) + cv2.imshow("thresh", thresh) + txt="ADAPTIVE_THRESH_MEAN_C" if methode==cv2.ADAPTIVE_THRESH_MEAN_C else "ADAPTIVE_THRESH_GAUSSIAN_C" + cv2.putText(frame, "[p|m]v1: {:2d} [o]methode: {}".format(v1, txt), (10, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.9, (0, 0, 255), 1) + cv2.imshow("frame", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + if key==ord('p'): + v1=min(21, v1+2) + if key==ord('m'): + v1=max(3, v1-2) + if key==ord('o'): + if methode==cv2.ADAPTIVE_THRESH_GAUSSIAN_C: + methode=cv2.ADAPTIVE_THRESH_MEAN_C + else: + methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel18-2/README.md b/Divers/tutoriel18-2/README.md new file mode 100644 index 0000000..f3984ae --- /dev/null +++ b/Divers/tutoriel18-2/README.md @@ -0,0 +1,5 @@ +# Tutoriel 18 partie 2 +## Sudoku + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=XFNg8lXe-Tk + diff --git a/Divers/tutoriel18-2/din1451altG.ttf b/Divers/tutoriel18-2/din1451altG.ttf new file mode 100644 index 0000000000000000000000000000000000000000..c7e9034aa6ab8575ae554c4b8048f22064fc2fba GIT binary patch literal 65292 zcmeEvcbpu>m49`Z?wRSHoYU@v-PxS8c2}#gimS9rDCY}eJiJWsXSY#}~ zU?CeDOt1lA3*!SsF!(SWPUpLmjcufz`F&sY^lFje>^uAO`ThR*wN%yBVXCWMy;SeL zs$~LW%#BQDY|_jbWsR^hSQhc&X{m9u9wX2ojhmb%&l)RrmVv| zA1q(L^vt`?dSEkSscPK2X!+K&lB^PMvWjwK;;J)OuUF>W+R9kj0o1W&^}6k=>fG=D 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zFMJ|=EW81Y>_5P||F7_h@Vamm^y83lr*N(CnDBofbG#(HiZyspcv$!eG`gP&e*kEo zgXP>LJR-a)>=S-2>=phCIa+_j0t>?LS*-9fmX`i~0Bvu-@EnU1t_Hn%hsB$AY+O@P zQd(XxbIr!p%a?6;k6=HtZtKXJZJ-_l%QkK+92fz~a4%T1YSqYA_rS=C(QRjYa@&rP zjjQs{^2&p2Mz)^qxij&6XM65~U1xu8>za+*VmIS`H?3STvT^$esHi$Y%`=S~mcfL- w@ho3`gS&af==QUH#NLq|8z;`iGfZCq%=wfBphuL~Z`!zeo1u4Zcefz?f406*5dZ)H literal 0 HcmV?d00001 diff --git a/Divers/tutoriel18-2/font.py b/Divers/tutoriel18-2/font.py new file mode 100644 index 0000000..173d9c0 --- /dev/null +++ b/Divers/tutoriel18-2/font.py @@ -0,0 +1,18 @@ +from PIL import ImageFont, ImageDraw, Image +import cv2 +import numpy as np + +for i in range(1, 10): + image=Image.new("L", (28, 28)) + draw=ImageDraw.Draw(image) + font=ImageFont.truetype("din1451altG.ttf", 27) + text="{:d}".format(i) + draw.text((10, 0), text, font=font, fill=(255)) + image=np.array(image).reshape(28, 28, 1) + cv2.imshow("image", image) + key=cv2.waitKey() + if key&0xFF==ord('q'): + quit() + + + diff --git a/Divers/tutoriel18-2/sudoku.py b/Divers/tutoriel18-2/sudoku.py new file mode 100644 index 0000000..cf03b70 --- /dev/null +++ b/Divers/tutoriel18-2/sudoku.py @@ -0,0 +1,100 @@ +import cv2 +import numpy as np +import tensorflow as tf +import sudoku_solver as ss +from time import sleep +import operator + +marge=4 +case=28+2*marge +taille_grille=9*case +flag=0 +cap=cv2.VideoCapture(0) +with tf.Session() as s: + saver=tf.train.import_meta_graph('./mon_modele/modele.meta') + saver.restore(s, tf.train.latest_checkpoint('./mon_modele/')) + graph=tf.get_default_graph() + images=graph.get_tensor_by_name("entree:0") + sortie=graph.get_tensor_by_name("sortie:0") + is_training=graph.get_tensor_by_name("is_training:0") + maxArea=0 + while True: + ret, frame=cap.read() + if maxArea==0: + cv2.imshow("frame", frame) + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.GaussianBlur(gray, (5, 5), 0) + thresh=cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2) + contours, hierarchy=cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + contour_grille=None + maxArea=0 + for c in contours: + area=cv2.contourArea(c) + if area>25000: + peri=cv2.arcLength(c, True) + polygone=cv2.approxPolyDP(c, 0.02*peri, True) + if area>maxArea and len(polygone)==4: + contour_grille=polygone + maxArea=area + if contour_grille is not None: + points=np.vstack(contour_grille).squeeze() + points=sorted(points, key=operator.itemgetter(1)) + if points[0][0]seuil]=255 + a[b<-seuil]=0 + return a + +def convolution(input, taille_noyau, nbr_noyau, stride, b_norm=False, f_activation=None, training=False): + w_filtre=tf.Variable(tf.random.truncated_normal(shape=(taille_noyau, taille_noyau, int(input.get_shape()[-1]), nbr_noyau))) + b_filtre=np.zeros(nbr_noyau) + result=tf.nn.conv2d(input, w_filtre, strides=[1, stride, stride, 1], padding='SAME')+b_filtre + if b_norm is True: + result=tf.layers.batch_normalization(result, training=training) + if f_activation is not None: + result=f_activation(result) + return result + +def fc(input, nbr_neurone, b_norm=False, f_activation=None, training=False): + w=tf.Variable(tf.random.truncated_normal(shape=(int(input.get_shape()[-1]), nbr_neurone), dtype=tf.float32)) + b=tf.Variable(np.zeros(shape=(nbr_neurone)), dtype=tf.float32) + result=tf.matmul(input, w)+b + if b_norm is True: + result=tf.layers.batch_normalization(result, training=training) + if f_activation is not None: + result=f_activation(result) + return result + +def ia(nbr_classes, size, couche, learning_rate=1E-3): + ph_images=tf.placeholder(shape=(None, size, size, couche), dtype=tf.float32, name='entree') + ph_labels=tf.placeholder(shape=(None, nbr_classes), dtype=tf.float32) + ph_is_training=tf.placeholder_with_default(False, (), name='is_training') + + result=convolution(ph_images, 3, 64, 1, True, tf.nn.relu, ph_is_training) + result=tf.layers.dropout(result, 0.3, training=ph_is_training) + result=convolution(result, 3, 128, 1, True, tf.nn.relu, ph_is_training) + result=tf.layers.dropout(result, 0.4, training=ph_is_training) + result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') + + result=tf.contrib.layers.flatten(result) + + result=fc(result, 128, True, tf.nn.relu, ph_is_training) + result=tf.layers.dropout(result, 0.5, training=ph_is_training) + result=fc(result, nbr_classes) + socs=tf.nn.softmax(result, name="sortie") + + loss=tf.nn.softmax_cross_entropy_with_logits_v2(labels=ph_labels, logits=result) + extra_update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(extra_update_ops): + train=tf.train.AdamOptimizer(learning_rate).minimize(loss) + accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(socs, 1), tf.argmax(ph_labels, 1)), tf.float32)) + + return ph_images, ph_labels, ph_is_training, socs, train, accuracy, tf.train.Saver() + +tab_images=[] +tab_labels=[] + +for dir in ["/usr/share/fonts/truetype/ubuntu-font-family/", "/usr/share/fonts/truetype/freefont/"]: + for root, dirs, files in os.walk(dir): + for file in files: + if file.endswith("ttf"): + print(root+"/"+file) + for i in range(1, 10): + for cpt in range(nbr): + image=Image.new("L", (28, 28)) + draw=ImageDraw.Draw(image) + font=ImageFont.truetype(root+"/"+file, np.random.randint(26, 32)) + text="{:d}".format(i) + draw.text((np.random.randint(1, 10), np.random.randint(-4, 0)), text, font=font, fill=(255)) + image=np.array(image).reshape(28, 28, 1) + tab_images.append(image) + tab_labels.append(np.eye(10)[i]) + image_m=modif_image(image, 1.05+np.random.rand()) + tab_images.append(image_m) + tab_labels.append(np.eye(10)[i]) + image=np.zeros((28, 28, 1)) + for cpt in range(3*nbr): + image_m=modif_image(image, 1.05+np.random.rand()) + tab_images.append(image_m) + tab_labels.append(np.eye(10)[0]) + +tab_images=np.array(tab_images) +tab_labels=np.array(tab_labels) + +tab_images=tab_images/255 + +tab_images, tab_labels=shuffle(tab_images, tab_labels) + +if False: # Changer en True si vous voulez voir les images générées + for i in range(len(tab_images)): + cv2.imshow('chiffre', tab_images[i].reshape(28, 28, 1)) + print(tab_labels[i], np.argmax(tab_labels[i])) + if cv2.waitKey()&0xFF==ord('q'): + break + +print("Nbr:", len(tab_images)) + +train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10) + +images, labels, is_training, sortie, train, accuracy, saver=ia(10, 28, 1) + +with tf.Session() as s: + s.run(tf.global_variables_initializer()) + tab_train=[] + tab_test=[] + for id_entrainement in np.arange(nbr_entrainement): + print("> Entrainement", id_entrainement) + for batch in np.arange(0, len(train_images), taille_batch): + s.run(train, feed_dict={ + images: train_images[batch:batch+taille_batch], + labels: train_labels[batch:batch+taille_batch], + is_training: True + }) + print(" entrainement OK") + tab_accuracy_train=[] + for batch in np.arange(0, len(train_images), taille_batch): + p=s.run(accuracy, feed_dict={ + images: train_images[batch:batch+taille_batch], + labels: train_labels[batch:batch+taille_batch], + is_training: True + }) + tab_accuracy_train.append(p) + print(" train:", np.mean(tab_accuracy_train)) + tab_accuracy_test=[] + for batch in np.arange(0, len(test_images), taille_batch): + p=s.run(accuracy, feed_dict={ + images: test_images[batch:batch+taille_batch], + labels: test_labels[batch:batch+taille_batch], + is_training: True + }) + tab_accuracy_test.append(p) + print(" test :", np.mean(tab_accuracy_test)) + tab_train.append(1-np.mean(tab_accuracy_train)) + tab_test.append(1-np.mean(tab_accuracy_test)) + saver.save(s, './mon_modele/modele') diff --git a/Divers/tutoriel20/README.md b/Divers/tutoriel20/README.md new file mode 100644 index 0000000..979ff31 --- /dev/null +++ b/Divers/tutoriel20/README.md @@ -0,0 +1,7 @@ +# Tutoriel 20 +## Dlib: Detection et évaluation de la position de la tête + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=ibuEFfpVWlU + + +![image](https://github.com/L42Project/Tutoriels/blob/master/Divers/tutoriel20/dlib-landmark-mean.png) diff --git a/Divers/tutoriel20/cube.py b/Divers/tutoriel20/cube.py new file mode 100644 index 0000000..fd6c6bc --- /dev/null +++ b/Divers/tutoriel20/cube.py @@ -0,0 +1,113 @@ +import cv2 +import numpy as np +import dlib +import math + +cap=cv2.VideoCapture(0) +#cap=cv2.VideoCapture("debat.webm") + +detector=dlib.get_frontal_face_detector() +predictor=dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") + +def tr(c, o, coeff): + return(int((c-o)*coeff)+o) + +def cube(image, pt1, pt2, a1, a2, a3): + color=(0, 255, 0) + epaisseur=2 + offset=1.6 + offset2=2 + + d_eyes=math.sqrt(math.pow(landmarks.part(36).x-landmarks.part(45).x, 2)+math.pow(landmarks.part(36).y-landmarks.part(45).y, 2)) + + ox1=int((-(pt2.y-pt1.y)+pt2.x-pt1.x)/2)+pt1.x + oy1=int(((pt2.x-pt1.x+pt2.y)-pt1.y)/2)+pt1.y + + cv2.line(image, + (tr(pt1.x, ox1, offset), tr(pt1.y, oy1, offset)), + (tr(pt2.x, ox1, offset), tr(pt2.y, oy1, offset)), + color, epaisseur) + cv2.line(image, + (tr(pt2.x, ox1, offset), tr(pt2.y, oy1, offset)), + (tr(-(pt2.y-pt1.y)+pt2.x, ox1, offset), tr(pt2.x-pt1.x+pt2.y, oy1, offset)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt2.x, ox1, offset), tr(pt2.x-pt1.x+pt2.y, oy1, offset)), + (tr(-(pt2.y-pt1.y)+pt1.x, ox1, offset), tr(pt2.x-pt1.x+pt1.y, oy1, offset)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt1.x, ox1, offset), tr(pt2.x-pt1.x+pt1.y, oy1, offset)), + (tr(pt1.x, ox1, offset), tr(pt1.y, oy1, offset)), + color, epaisseur) + + ox2=int((-(pt2.y-pt1.y)+pt2.x-pt1.x)/2)+pt1.x+int(a2) + oy2=int(((pt2.x-pt1.x+pt2.y)-pt1.y)/2)+pt1.y+int(a3) + + cv2.line(image, + (tr(pt1.x+a2, ox2, offset2), tr(pt1.y+a3, oy2, offset2)), + (tr(pt2.x+a2, ox2, offset2), tr(pt2.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(pt2.x+a2, ox2, offset2), tr(pt2.y+a3, oy2, offset2)), + (tr(-(pt2.y-pt1.y)+pt2.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt2.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt2.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt2.y+a3, oy2, offset2)), + (tr(-(pt2.y-pt1.y)+pt1.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt1.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt1.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt1.y+a3, oy2, offset2)), + (tr(pt1.x+a2, ox2, offset2), tr(pt1.y+a3, oy2, offset2)), + color, epaisseur) + + cv2.line(image, + (tr(pt1.x, ox1, offset), tr(pt1.y, oy1, offset)), + (tr(pt1.x+a2, ox2, offset2), tr(pt1.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(pt2.x, ox1, offset), tr(pt2.y, oy1, offset)), + (tr(pt2.x+a2, ox2, offset2), tr(pt2.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt2.x, ox1, offset), tr(pt2.x-pt1.x+pt2.y, oy1, offset)), + (tr(-(pt2.y-pt1.y)+pt2.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt2.y+a3, oy2, offset2)), + color, epaisseur) + cv2.line(image, + (tr(-(pt2.y-pt1.y)+pt1.x, ox1, offset), tr(pt2.x-pt1.x+pt1.y, oy1, offset)), + (tr(-(pt2.y-pt1.y)+pt1.x+a2, ox2, offset2), tr(pt2.x-pt1.x+pt1.y+a3, oy2, offset2)), + color, epaisseur) + +while True: + ret, frame=cap.read() + tickmark=cv2.getTickCount() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + faces=detector(gray) + for face in faces: + x1=face.left() + y1=face.top() + x2=face.right() + y2=face.bottom() + + landmarks=predictor(gray, face) + + d_eyes=math.sqrt(math.pow(landmarks.part(36).x-landmarks.part(45).x, 2)+math.pow(landmarks.part(36).y-landmarks.part(45).y, 2)) + d1=math.sqrt(math.pow(landmarks.part(36).x-landmarks.part(30).x, 2)+math.pow(landmarks.part(36).y-landmarks.part(30).y, 2)) + d2=math.sqrt(math.pow(landmarks.part(45).x-landmarks.part(30).x, 2)+math.pow(landmarks.part(45).y-landmarks.part(30).y, 2)) + coeff=d1+d2 + + a1=int(250*(landmarks.part(36).y-landmarks.part(45).y)/coeff) + a2=int(250*(d1-d2)/coeff) + 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incline la tete a gauche " + if a1>40: + txt+="et incline la tete a droite " + cv2.putText(frame, txt, (10, 30), cv2.FONT_HERSHEY_PLAIN, 1.2, (255, 255, 255), 2) + cv2.imshow("Frame", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel20/visage_point.py b/Divers/tutoriel20/visage_point.py new file mode 100644 index 0000000..ff75b54 --- /dev/null +++ b/Divers/tutoriel20/visage_point.py @@ -0,0 +1,35 @@ +import cv2 +import numpy as np +import dlib +import math + +cap=cv2.VideoCapture(0) +detector=dlib.get_frontal_face_detector() +predictor=dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") + +while True: + ret, frame=cap.read() + tickmark=cv2.getTickCount() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces=detector(gray) + for face in faces: + landmarks=predictor(gray, face) + i=np.zeros(shape=(frame.shape), dtype=np.uint8) + for n in range(0, 68): + x=landmarks.part(n).x + y=landmarks.part(n).y + cv2.circle(frame, (x, y), 3, (255, 0, 0), -1) + if n==30 or n==36 or n==45: + cv2.circle(i, (x, y), 3, (255, 255, 0), -1) + else: + cv2.circle(i, (x, y), 3, (255, 0, 0), -1) + cv2.imshow("i", i) + fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark) + cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) + cv2.imshow("Frame", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel20/visage_rectangle.py b/Divers/tutoriel20/visage_rectangle.py new file mode 100644 index 0000000..801720a --- /dev/null +++ b/Divers/tutoriel20/visage_rectangle.py @@ -0,0 +1,27 @@ +import cv2 +import numpy as np +import dlib + +cap=cv2.VideoCapture(0) +detector=dlib.get_frontal_face_detector() + +while True: + ret, frame=cap.read() + tickmark=cv2.getTickCount() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces=detector(gray) + for face in faces: + x1=face.left() + y1=face.top() + x2=face.right() + y2=face.bottom() + cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2) + fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark) + cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) + cv2.imshow("Frame", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel25-2/README.md b/Divers/tutoriel25-2/README.md new file mode 100644 index 0000000..9988744 --- /dev/null +++ b/Divers/tutoriel25-2/README.md @@ -0,0 +1,8 @@ +# Tutoriel 25 +## Lecture des panneaux de limitation de vitesse + +Les vidéos de ce tutoriel sont disponibles aux adresses suivantes:
+partie 1: https://www.youtube.com/watch?v=PvD5POjXw8Q
+partie 2: https://www.youtube.com/watch?v=TYDi0SNCUr0
+partie 3: https://www.youtube.com/watch?v=fBysd-Y17Tw + diff --git a/Divers/tutoriel25-2/common.py b/Divers/tutoriel25-2/common.py new file mode 100644 index 0000000..743435f --- /dev/null +++ b/Divers/tutoriel25-2/common.py @@ -0,0 +1,89 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import os +import cv2 + +size=42 +dir_images_panneaux="images_panneaux" +dir_images_autres_panneaux="images_autres_panneaux" +dir_images_sans_panneaux="images_sans_panneaux" + +def panneau_model(nbr_classes): + model=tf.keras.Sequential() + + model.add(layers.Input(shape=(size, size, 3), dtype='float32')) + + model.add(layers.Conv2D(128, 3, strides=1)) + model.add(layers.Dropout(0.2)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.Conv2D(128, 3, strides=1)) + model.add(layers.Dropout(0.2)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Conv2D(256, 3, strides=1)) + model.add(layers.Dropout(0.3)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.Conv2D(256, 3, strides=1)) + model.add(layers.Dropout(0.4)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Flatten()) + model.add(layers.Dense(512, activation='relu')) + model.add(layers.Dropout(0.5)) + model.add(layers.BatchNormalization()) + model.add(layers.Dense(nbr_classes, activation='softmax')) + + return model + +def is_panneau_model(): + model=tf.keras.Sequential() + + model.add(layers.Input(shape=(size, size, 3), dtype='float32')) + + model.add(layers.Conv2D(64, 3, strides=1)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Conv2D(128, 3, strides=1)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Flatten()) + model.add(layers.Dense(256, activation='relu')) + model.add(layers.BatchNormalization()) + model.add(layers.Dense(1, activation='sigmoid')) + + return model + +def lire_images_panneaux(dir_images_panneaux, size=None): + tab_panneau=[] + tab_image_panneau=[] + + if not os.path.exists(dir_images_panneaux): + quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux)) + + files=os.listdir(dir_images_panneaux) + if files is None: + quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux)) + + for file in sorted(files): + if file.endswith("png"): + tab_panneau.append(file.split(".")[0]) + image=cv2.imread(dir_images_panneaux+"/"+file) + if size is not None: + image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4) + tab_image_panneau.append(image) + + return tab_panneau, tab_image_panneau diff --git a/Divers/tutoriel25-2/dataset.py b/Divers/tutoriel25-2/dataset.py new file mode 100644 index 0000000..a339036 --- /dev/null +++ b/Divers/tutoriel25-2/dataset.py @@ -0,0 +1,82 @@ +import numpy as np +import cv2 +from multiprocessing import Pool +import multiprocessing +import random + +def bruit(image_orig): + h, w, c=image_orig.shape + n=np.random.randn(h, w, c)*random.randint(5, 30) + return np.clip(image_orig+n, 0, 255).astype(np.uint8) + +def change_gamma(image, alpha=1.0, beta=0.0): + return np.clip(alpha*image+beta, 0, 255).astype(np.uint8) + +def modif_img(img): + h, w, c=img.shape + + r_color=[np.random.randint(255), np.random.randint(255), np.random.randint(255)] + img=np.where(img==[142, 142, 142], r_color, img).astype(np.uint8) + + if np.random.randint(3): + k_max=3 + kernel_blur=np.random.randint(k_max)*2+1 + img=cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0) + + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), random.randint(-10, 10), 1) + img=cv2.warpAffine(img, M, (w, h)) + + if np.random.randint(2): + a=int(max(w, h)/5)+1 + pts1=np.float32([[0, 0], [w, 0], [0, h], [w, h]]) + pts2=np.float32([[0+random.randint(-a, a), 0+random.randint(-a, a)], [w-random.randint(-a, a), 0+random.randint(-a, a)], [0+random.randint(-a, a), h-random.randint(-a, a)], [w-random.randint(-a, a), h-random.randint(-a, a)]]) + M=cv2.getPerspectiveTransform(pts1,pts2) + img=cv2.warpPerspective(img, M, (w, h)) + + if np.random.randint(2): + r=random.randint(0, 5) + h2=int(h*0.9) + w2=int(w*0.9) + if r==0: + img=img[0:w2, 0:h2] + elif r==1: + img=img[w-w2:w, 0:h2] + elif r==2: + img=img[0:w2, h-h2:h] + elif r==3: + img=img[w-w2:w, h-h2:h] + img=cv2.resize(img, (h, w)) + + if np.random.randint(2): + r=random.randint(1, int(max(w, h)*0.15)) + img=img[r:w-r, r:h-r] + img=cv2.resize(img, (h, w)) + + if not np.random.randint(4): + t=np.empty((h, w, c) , dtype=np.float32) + for i in range(h): + for j in range(w): + for k in range(c): + t[i][j][k]=(i/h) + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), np.random.randint(4)*90, 1) + t=cv2.warpAffine(t, M, (w, h)) + img=(cv2.multiply((img/255).astype(np.float32), t)*255).astype(np.uint8) + + img=change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50)) + + if not np.random.randint(4): + p=(15+np.random.randint(10))/100 + img=(img*p+50*(1-p)).astype(np.uint8)+np.random.randint(100) + + img=bruit(img) + + return img + +def create_lot_img(image, nbr, nbr_thread=None): + if nbr_thread is None: + nbr_thread=multiprocessing.cpu_count() + lot_original=np.repeat([image], nbr, axis=0) + with Pool(nbr_thread) as p: + lot_result=p.map(modif_img, lot_original) + p.close() + return lot_result diff --git a/Divers/tutoriel25-2/genere_fond.py b/Divers/tutoriel25-2/genere_fond.py new file mode 100644 index 0000000..957eb4b --- /dev/null +++ b/Divers/tutoriel25-2/genere_fond.py @@ -0,0 +1,40 @@ +import cv2 +import numpy as np +import random +import os +import common + +video="videos/France_Motorway.mp4" + +if not os.path.isdir(common.dir_images_sans_panneaux): + os.mkdir(common.dir_images_sans_panneaux) + +if not os.path.exists(video): + print("Vidéo non présente:", video) + quit() + +cap=cv2.VideoCapture(video) + +id=0 +nbr_image=100000 + +nbr_image_par_frame=int(100000/cap.get(cv2.CAP_PROP_FRAME_COUNT))+1 + +while True: + ret, frame=cap.read() + if ret is False: + quit() + h, w, c=frame.shape + + for cpt in range(nbr_image_par_frame): + x=random.randint(0, w-common.size) + y=random.randint(0, h-common.size) + img=frame[y:y+common.size, x:x+common.size] + cv2.imwrite(common.dir_images_sans_panneaux+"/{:d}.png".format(id), img) + id+=1 + if id==nbr_image: + quit() + + + + diff --git a/Divers/tutoriel25-2/genere_panneaux.py b/Divers/tutoriel25-2/genere_panneaux.py new file mode 100644 index 0000000..d5a9e41 --- /dev/null +++ b/Divers/tutoriel25-2/genere_panneaux.py @@ -0,0 +1,29 @@ +import numpy as np +from sklearn.utils import shuffle +import cv2 +import common +import dataset + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) +tab_labels=[] + +id=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 1000) + tab_images=np.concatenate([tab_images, lot]) + tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)]) + id+=1 + +tab_panneau=np.array(tab_panneau) +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels).reshape([-1, 1]) + +tab_images, tab_labels=shuffle(tab_images, tab_labels) + +for i in range(len(tab_images)): + cv2.imshow("panneau", tab_images[i]) + print("label", tab_labels[i], "panneau", tab_panneau[int(tab_labels[i])]) + if cv2.waitKey()&0xFF==ord('q'): + quit() diff --git a/Divers/tutoriel25-2/houghcircles.py b/Divers/tutoriel25-2/houghcircles.py new file mode 100644 index 0000000..cedf264 --- /dev/null +++ b/Divers/tutoriel25-2/houghcircles.py @@ -0,0 +1,36 @@ +import cv2 +import numpy as np + +param1=30 +param2=55 +dp=1.0 + +cap=cv2.VideoCapture(0) + +while True: + ret, frame=cap.read() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp, 20, param1=param1, param2=param2, minRadius=10, maxRadius=50) + if circles is not None: + circles=np.around(circles).astype(np.int32) + for i in circles[0, :]: + if i[2]!=0: + cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 4) + cv2.putText(frame, "[i|k]dp: {:4.2f} [o|l]param1: {:d} [p|m]param2: {:d}".format(dp, param1, param2), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 1) + cv2.imshow("Video", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() + if key==ord('i'): + dp=min(10, dp+0.1) + if key==ord('k'): + dp=max(0.1, dp-0.1) + if key==ord('o'): + param1=min(255, param1+1) + if key==ord('l'): + param1=max(1, param1-1) + if key==ord('p'): + param2=min(255, param2+1) + if key==ord('m'): + param2=max(1, param2-1) +cv2.destroyAllWindows() diff --git a/Divers/tutoriel25-2/lire_panneau.py b/Divers/tutoriel25-2/lire_panneau.py new file mode 100644 index 0000000..741d42a --- /dev/null +++ b/Divers/tutoriel25-2/lire_panneau.py @@ -0,0 +1,70 @@ +import tensorflow as tf +import cv2 +import os +import numpy as np +import random +import common + +th1=30 +th2=55 + +video_dir="dashcam Cedric" + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux) + +model_is_panneau=common.is_panneau_model() +checkpoint=tf.train.Checkpoint(model_is_panneau=model_is_panneau) +checkpoint.restore(tf.train.latest_checkpoint("./training_is_panneau/")) + +model_panneau=common.panneau_model(len(tab_panneau)) +checkpoint=tf.train.Checkpoint(model_panneau=model_panneau) +checkpoint.restore(tf.train.latest_checkpoint("./training_panneau/")) + +l=os.listdir(video_dir) +random.shuffle(l) + +for video in l: + if not video.endswith("mp4"): + continue + cap=cv2.VideoCapture(video_dir+"/"+video) + + print("video:", video) + id_panneau=-1 + while True: + ret, frame=cap.read() + if ret is False: + break + f_w, f_h, f_c=frame.shape + frame=cv2.resize(frame, (int(f_h/1.5), int(f_w/1.5))) + + image=frame[200:400, 700:1000] + cv2.rectangle(frame, (700, 200), (1000, 400), (255, 255, 255), 1) + gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=th1, param2=th2, minRadius=5, maxRadius=45) + if circles is not None: + circles=np.int16(np.around(circles)) + for i in circles[0,:]: + if i[2]!=0: + panneau=cv2.resize(image[max(0, i[1]-i[2]):i[1]+i[2], max(0, i[0]-i[2]):i[0]+i[2]], (common.size, common.size))/255 + cv2.imshow("panneau", panneau) + prediction=model_is_panneau(np.array([panneau]), training=False) + print("prediction", prediction) + if prediction[0][0]>0.9: + prediction=model_panneau(np.array([panneau]), training=False) + id_panneau=np.argmax(prediction[0]) + print("panneau", prediction, id_panneau, tab_panneau[id_panneau]) + w, h, c=tab_image_panneau[id_panneau].shape + if id_panneau!=-1: + frame[0:h, 0:w, :]=tab_image_panneau[id_panneau] + cv2.putText(frame, "fichier:"+video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA) + cv2.imshow("Video", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() + if key==ord('a'): + for cpt in range(100): + ret, frame=cap.read() + if key==ord('f'): + break + +cv2.destroyAllWindows() diff --git a/Divers/tutoriel25-2/train_is_panneau.py b/Divers/tutoriel25-2/train_is_panneau.py new file mode 100644 index 0000000..8eea4ec --- /dev/null +++ b/Divers/tutoriel25-2/train_is_panneau.py @@ -0,0 +1,135 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import numpy as np +from sklearn.utils import shuffle +from sklearn.model_selection import train_test_split +import cv2 +import os +import common +import time +import dataset + +batch_size=64 +nbr_entrainement=20 + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +if not os.path.exists(common.dir_images_autres_panneaux): + quit("Le repertoire d'image n'existe pas: {}".format(common.dir_images_autres_panneaux)) + +if not os.path.exists(common.dir_images_sans_panneaux): + quit("Le repertoire d'image n'existe pas:".format(common.dir_images_sans_panneaux)) + +nbr=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 12000) + tab_images=np.concatenate([tab_images, lot]) + nbr+=len(lot) + +tab_labels=np.full(nbr, 1) + +print("Image panneaux:", nbr) + +files=os.listdir(common.dir_images_autres_panneaux) +if files is None: + quit("Le repertoire d'image est vide:".format(common.dir_images_autres_panneaux)) + +nbr=0 +for file in files: + if file.endswith("png"): + path=os.path.join(common.dir_images_autres_panneaux, file) + image=cv2.resize(cv2.imread(path), (common.size, common.size), cv2.INTER_LANCZOS4) + lot=dataset.create_lot_img(image, 700) + tab_images=np.concatenate([tab_images, lot]) + nbr+=len(lot) + +tab_labels=np.concatenate([tab_labels, np.full(nbr, 0)]) + +print("Image autres panneaux:", nbr) + +nbr_np=int(len(tab_images)/2) +print("nbr_np", nbr_np) + +id=1 +nbr=0 +tab=[] +for cpt in range(nbr_np): + file=common.dir_images_sans_panneaux+"/{:d}.png".format(id) + if not os.path.isfile(file): + break + image=cv2.resize(cv2.imread(file), (common.size, common.size)) + tab.append(image) + id+=1 + nbr+=1 + +tab_images=np.concatenate([tab_images, tab]) +tab_labels=np.concatenate([tab_labels, np.full(nbr, 0)]) +print("Image sans panneaux:", nbr) + +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels, dtype=np.float32).reshape([-1, 1]) + +tab_images, tab_labels=shuffle(tab_images, tab_labels) +train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10) + +train_ds=tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size) +test_ds=tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size) + +print("train_images", len(train_images)) +print("test_images", len(test_images)) +print("nbr panneau", len(np.where(train_labels==0.)[1]), train_labels.shape) + +@tf.function +def train_step(images, labels): + with tf.GradientTape() as tape: + predictions=model_is_panneau(images) + loss=loss_object(labels, predictions) + gradients=tape.gradient(loss, model_is_panneau.trainable_variables) + optimizer.apply_gradients(zip(gradients, model_is_panneau.trainable_variables)) + train_loss(loss) + train_accuracy(labels, predictions) + +def train(train_ds, nbr_entrainement): + for entrainement in range(nbr_entrainement): + start=time.time() + for images, labels in train_ds: + train_step(images, labels) + message='Entrainement {:04d}, loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(entrainement+1, + train_loss.result(), + train_accuracy.result()*100, + time.time()-start)) + train_loss.reset_states() + train_accuracy.reset_states() + test(test_ds) + +def test(test_ds): + start=time.time() + for test_images, test_labels in test_ds: + predictions=model_is_panneau(test_images) + t_loss=loss_object(test_labels, predictions) + test_loss(t_loss) + test_accuracy(test_labels, predictions) + message=' >>> Test: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(test_loss.result(), + test_accuracy.result()*100, + time.time()-start)) + test_loss.reset_states() + test_accuracy.reset_states() + +optimizer=tf.keras.optimizers.Adam() +loss_object=tf.keras.losses.BinaryCrossentropy() +train_loss=tf.keras.metrics.Mean() +train_accuracy=tf.keras.metrics.BinaryAccuracy() +test_loss=tf.keras.metrics.Mean() +test_accuracy=tf.keras.metrics.BinaryAccuracy() +model_is_panneau=common.is_panneau_model() +checkpoint=tf.train.Checkpoint(model_is_panneau=model_is_panneau) + +print("Entrainement") +train(train_ds, nbr_entrainement) +test(test_ds) + +checkpoint.save(file_prefix="./training_is_panneau/is_panneau") diff --git a/Divers/tutoriel25-2/train_panneau.py b/Divers/tutoriel25-2/train_panneau.py new file mode 100644 index 0000000..55bed31 --- /dev/null +++ b/Divers/tutoriel25-2/train_panneau.py @@ -0,0 +1,95 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import numpy as np +from sklearn.utils import shuffle +from sklearn.model_selection import train_test_split +import cv2 +import os +import time +import common +import dataset + +batch_size=128 +nbr_entrainement=20 + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) +tab_labels=[] + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +id=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 12000) + tab_images=np.concatenate((tab_images, lot)) + tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)]) + id+=1 + +tab_panneau=np.array(tab_panneau) +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels, dtype=np.float32).reshape([-1, 1]) + +train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10) + +train_ds=tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size) +test_ds=tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size) + +print("train_images", len(train_images)) +print("test_images", len(test_images)) + +@tf.function +def train_step(images, labels): + with tf.GradientTape() as tape: + predictions=model_panneau(images) + loss=loss_object(labels, predictions) + gradients=tape.gradient(loss, model_panneau.trainable_variables) + optimizer.apply_gradients(zip(gradients, model_panneau.trainable_variables)) + train_loss(loss) + train_accuracy(labels, predictions) + +def train(train_ds, nbr_entrainement): + for entrainement in range(nbr_entrainement): + start=time.time() + for images, labels in train_ds: + train_step(images, labels) + message='Entrainement {:04d}: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(entrainement+1, + train_loss.result(), + train_accuracy.result()*100, + time.time()-start)) + train_loss.reset_states() + train_accuracy.reset_states() + test(test_ds) + +def test(test_ds): + start=time.time() + for test_images, test_labels in test_ds: + predictions=model_panneau(test_images) + t_loss=loss_object(test_labels, predictions) + test_loss(t_loss) + test_accuracy(test_labels, predictions) + message=' >>> Test: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(test_loss.result(), + test_accuracy.result()*100, + time.time()-start)) + test_loss.reset_states() + test_accuracy.reset_states() + +optimizer=tf.keras.optimizers.Adam() +loss_object=tf.keras.losses.SparseCategoricalCrossentropy() +train_loss=tf.keras.metrics.Mean() +train_accuracy=tf.keras.metrics.SparseCategoricalAccuracy() +test_loss=tf.keras.metrics.Mean() +test_accuracy=tf.keras.metrics.SparseCategoricalAccuracy() +model_panneau=common.panneau_model(len(tab_panneau)) +checkpoint=tf.train.Checkpoint(model_panneau=model_panneau) + +print("Entrainement") +train(train_ds, nbr_entrainement) +checkpoint.save(file_prefix="./training_panneau/panneau") + +for i in range(len(test_images)): + prediction=model_panneau(np.array([test_images[i]])) + print("prediction", prediction, tab_panneau[np.argmax(prediction[0])]) + cv2.imshow("image", test_images[i]) + if cv2.waitKey()&0xFF==ord('q'): + break diff --git a/Divers/tutoriel25-3/README.md b/Divers/tutoriel25-3/README.md new file mode 100644 index 0000000..9988744 --- /dev/null +++ b/Divers/tutoriel25-3/README.md @@ -0,0 +1,8 @@ +# Tutoriel 25 +## Lecture des panneaux de limitation de vitesse + +Les vidéos de ce tutoriel sont disponibles aux adresses suivantes:
+partie 1: https://www.youtube.com/watch?v=PvD5POjXw8Q
+partie 2: https://www.youtube.com/watch?v=TYDi0SNCUr0
+partie 3: https://www.youtube.com/watch?v=fBysd-Y17Tw + diff --git a/Divers/tutoriel25-3/common.py b/Divers/tutoriel25-3/common.py new file mode 100644 index 0000000..bbfb542 --- /dev/null +++ b/Divers/tutoriel25-3/common.py @@ -0,0 +1,67 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import os +import cv2 + +size=42 +dir_images_panneaux="images_panneaux" +dir_images_autres_panneaux="images_autres_panneaux" +dir_images_sans_panneaux="images_sans_panneaux" + +def panneau_model(nbr_classes): + model=tf.keras.Sequential() + + model.add(layers.Input(shape=(size, size, 3), dtype='float32')) + + model.add(layers.Conv2D(128, 3, strides=1)) + model.add(layers.Dropout(0.2)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.Conv2D(128, 3, strides=1)) + model.add(layers.Dropout(0.2)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Conv2D(256, 3, strides=1)) + model.add(layers.Dropout(0.3)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.Conv2D(256, 3, strides=1)) + model.add(layers.Dropout(0.4)) + model.add(layers.BatchNormalization()) + model.add(layers.Activation('relu')) + + model.add(layers.MaxPool2D(pool_size=2, strides=2)) + + model.add(layers.Flatten()) + model.add(layers.Dense(512, activation='relu')) + model.add(layers.Dropout(0.5)) + model.add(layers.BatchNormalization()) + model.add(layers.Dense(nbr_classes, activation='sigmoid')) + + return model + +def lire_images_panneaux(dir_images_panneaux, size=None): + tab_panneau=[] + tab_image_panneau=[] + + if not os.path.exists(dir_images_panneaux): + quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux)) + + files=os.listdir(dir_images_panneaux) + if files is None: + quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux)) + + for file in sorted(files): + if file.endswith("png"): + tab_panneau.append(file.split(".")[0]) + image=cv2.imread(dir_images_panneaux+"/"+file) + if size is not None: + image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4) + tab_image_panneau.append(image) + + return tab_panneau, tab_image_panneau diff --git a/Divers/tutoriel25-3/dataset.py b/Divers/tutoriel25-3/dataset.py new file mode 100644 index 0000000..a339036 --- /dev/null +++ b/Divers/tutoriel25-3/dataset.py @@ -0,0 +1,82 @@ +import numpy as np +import cv2 +from multiprocessing import Pool +import multiprocessing +import random + +def bruit(image_orig): + h, w, c=image_orig.shape + n=np.random.randn(h, w, c)*random.randint(5, 30) + return np.clip(image_orig+n, 0, 255).astype(np.uint8) + +def change_gamma(image, alpha=1.0, beta=0.0): + return np.clip(alpha*image+beta, 0, 255).astype(np.uint8) + +def modif_img(img): + h, w, c=img.shape + + r_color=[np.random.randint(255), np.random.randint(255), np.random.randint(255)] + img=np.where(img==[142, 142, 142], r_color, img).astype(np.uint8) + + if np.random.randint(3): + k_max=3 + kernel_blur=np.random.randint(k_max)*2+1 + img=cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0) + + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), random.randint(-10, 10), 1) + img=cv2.warpAffine(img, M, (w, h)) + + if np.random.randint(2): + a=int(max(w, h)/5)+1 + pts1=np.float32([[0, 0], [w, 0], [0, h], [w, h]]) + pts2=np.float32([[0+random.randint(-a, a), 0+random.randint(-a, a)], [w-random.randint(-a, a), 0+random.randint(-a, a)], [0+random.randint(-a, a), h-random.randint(-a, a)], [w-random.randint(-a, a), h-random.randint(-a, a)]]) + M=cv2.getPerspectiveTransform(pts1,pts2) + img=cv2.warpPerspective(img, M, (w, h)) + + if np.random.randint(2): + r=random.randint(0, 5) + h2=int(h*0.9) + w2=int(w*0.9) + if r==0: + img=img[0:w2, 0:h2] + elif r==1: + img=img[w-w2:w, 0:h2] + elif r==2: + img=img[0:w2, h-h2:h] + elif r==3: + img=img[w-w2:w, h-h2:h] + img=cv2.resize(img, (h, w)) + + if np.random.randint(2): + r=random.randint(1, int(max(w, h)*0.15)) + img=img[r:w-r, r:h-r] + img=cv2.resize(img, (h, w)) + + if not np.random.randint(4): + t=np.empty((h, w, c) , dtype=np.float32) + for i in range(h): + for j in range(w): + for k in range(c): + t[i][j][k]=(i/h) + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), np.random.randint(4)*90, 1) + t=cv2.warpAffine(t, M, (w, h)) + img=(cv2.multiply((img/255).astype(np.float32), t)*255).astype(np.uint8) + + img=change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50)) + + if not np.random.randint(4): + p=(15+np.random.randint(10))/100 + img=(img*p+50*(1-p)).astype(np.uint8)+np.random.randint(100) + + img=bruit(img) + + return img + +def create_lot_img(image, nbr, nbr_thread=None): + if nbr_thread is None: + nbr_thread=multiprocessing.cpu_count() + lot_original=np.repeat([image], nbr, axis=0) + with Pool(nbr_thread) as p: + lot_result=p.map(modif_img, lot_original) + p.close() + return lot_result diff --git a/Divers/tutoriel25-3/genere_fond.py b/Divers/tutoriel25-3/genere_fond.py new file mode 100644 index 0000000..957eb4b --- /dev/null +++ b/Divers/tutoriel25-3/genere_fond.py @@ -0,0 +1,40 @@ +import cv2 +import numpy as np +import random +import os +import common + +video="videos/France_Motorway.mp4" + +if not os.path.isdir(common.dir_images_sans_panneaux): + os.mkdir(common.dir_images_sans_panneaux) + +if not os.path.exists(video): + print("Vidéo non présente:", video) + quit() + +cap=cv2.VideoCapture(video) + +id=0 +nbr_image=100000 + +nbr_image_par_frame=int(100000/cap.get(cv2.CAP_PROP_FRAME_COUNT))+1 + +while True: + ret, frame=cap.read() + if ret is False: + quit() + h, w, c=frame.shape + + for cpt in range(nbr_image_par_frame): + x=random.randint(0, w-common.size) + y=random.randint(0, h-common.size) + img=frame[y:y+common.size, x:x+common.size] + cv2.imwrite(common.dir_images_sans_panneaux+"/{:d}.png".format(id), img) + id+=1 + if id==nbr_image: + quit() + + + + diff --git a/Divers/tutoriel25-3/genere_panneaux.py b/Divers/tutoriel25-3/genere_panneaux.py new file mode 100644 index 0000000..d5a9e41 --- /dev/null +++ b/Divers/tutoriel25-3/genere_panneaux.py @@ -0,0 +1,29 @@ +import numpy as np +from sklearn.utils import shuffle +import cv2 +import common +import dataset + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) +tab_labels=[] + +id=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 1000) + tab_images=np.concatenate([tab_images, lot]) + tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)]) + id+=1 + +tab_panneau=np.array(tab_panneau) +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels).reshape([-1, 1]) + +tab_images, tab_labels=shuffle(tab_images, tab_labels) + +for i in range(len(tab_images)): + cv2.imshow("panneau", tab_images[i]) + print("label", tab_labels[i], "panneau", tab_panneau[int(tab_labels[i])]) + if cv2.waitKey()&0xFF==ord('q'): + quit() diff --git a/Divers/tutoriel25-3/lire_panneau.py b/Divers/tutoriel25-3/lire_panneau.py new file mode 100644 index 0000000..56fb05c --- /dev/null +++ b/Divers/tutoriel25-3/lire_panneau.py @@ -0,0 +1,69 @@ +import tensorflow as tf +import cv2 +import os +import numpy as np +import random +import common + +th1=30 +th2=55 + +video_dir="d:/dashcam Cedric" + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux) + +model_panneau=common.panneau_model(len(tab_panneau)) +checkpoint=tf.train.Checkpoint(model_panneau=model_panneau) +checkpoint.restore(tf.train.latest_checkpoint("./training_panneau/")) + +l=os.listdir(video_dir) +random.shuffle(l) + +for video in l: + if not video.endswith("mp4"): + continue + cap=cv2.VideoCapture(video_dir+"/"+video) + + print("video:", video) + id_panneau=-1 + while True: + ret, frame=cap.read() + if ret is False: + break + f_w, f_h, f_c=frame.shape + frame=cv2.resize(frame, (int(f_h/1.5), int(f_w/1.5))) + + image=frame[200:400, 700:1000] + cv2.rectangle(frame, (700, 200), (1000, 400), (255, 255, 255), 1) + + gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=th1, param2=th2, minRadius=5, maxRadius=45) + if circles is not None: + circles=np.int16(np.around(circles)) + for i in circles[0,:]: + if i[2]!=0: + panneau=cv2.resize(image[max(0, i[1]-i[2]):i[1]+i[2], max(0, i[0]-i[2]):i[0]+i[2]], (common.size, common.size))/255 + cv2.imshow("panneau", panneau) + prediction=model_panneau(np.array([panneau]), training=False) + print("Prediction:", prediction) + if np.any(np.greater(prediction[0], 0.6)): + id_panneau=np.argmax(prediction[0]) + print(" -> C'est un panneau:", tab_panneau[id_panneau], "KM/H") + w, h, c=tab_image_panneau[id_panneau].shape + else: + print(" -> Ce n'est pas un panneau") + if id_panneau!=-1: + frame[0:h, 0:w, :]=tab_image_panneau[id_panneau] + cv2.putText(frame, "fichier:"+video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA) + cv2.imshow("Video", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() + if key==ord('a'): + for cpt in range(100): + ret, frame=cap.read() + if key==ord('f'): + break + +cv2.destroyAllWindows() diff --git a/Divers/tutoriel25-3/train_panneau.py b/Divers/tutoriel25-3/train_panneau.py new file mode 100644 index 0000000..07bd5e5 --- /dev/null +++ b/Divers/tutoriel25-3/train_panneau.py @@ -0,0 +1,140 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import numpy as np +from sklearn.utils import shuffle +from sklearn.model_selection import train_test_split +import cv2 +import os +import time +import common +import dataset + +batch_size=128 +nbr_entrainement=20 + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) +tab_labels=np.array([]).reshape(0, len(tab_image_panneau)) + +id=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 12000) + tab_images=np.concatenate((tab_images, lot)) + tab_labels=np.concatenate([tab_labels, np.repeat([np.eye(len(tab_image_panneau))[id]], len(lot), axis=0)]) + id+=1 + + +files=os.listdir(common.dir_images_autres_panneaux) +if files is None: + quit("Le repertoire d'image est vide:".format(common.dir_images_autres_panneaux)) + +nbr=0 +for file in files: + if file.endswith("png"): + path=os.path.join(common.dir_images_autres_panneaux, file) + image=cv2.resize(cv2.imread(path), (common.size, common.size), cv2.INTER_LANCZOS4) + lot=dataset.create_lot_img(image, 700) + tab_images=np.concatenate([tab_images, lot]) + nbr+=len(lot) + +tab_labels=np.concatenate([tab_labels, np.repeat([np.full(len(tab_image_panneau), 0)], nbr, axis=0)]) + +nbr_np=int(len(tab_images)/2) + +id=1 +nbr=0 +tab=[] +for cpt in range(nbr_np): + file=common.dir_images_sans_panneaux+"/{:d}.png".format(id) + if not os.path.isfile(file): + break + image=cv2.resize(cv2.imread(file), (common.size, common.size)) + tab.append(image) + id+=1 + nbr+=1 + +tab_images=np.concatenate([tab_images, tab]) +tab_labels=np.concatenate([tab_labels, np.repeat([np.full(len(tab_image_panneau), 0)], nbr, axis=0)]) + +tab_panneau=np.array(tab_panneau) +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels, dtype=np.float32) #.reshape([-1, 1]) + +train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10) + +train_ds=tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size) +test_ds=tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size) + +print("train_images", len(train_images)) +print("test_images", len(test_images)) + +@tf.function +def train_step(images, labels): + with tf.GradientTape() as tape: + predictions=model_panneau(images) + loss=my_loss(labels, predictions) + gradients=tape.gradient(loss, model_panneau.trainable_variables) + optimizer.apply_gradients(zip(gradients, model_panneau.trainable_variables)) + train_loss(loss) + train_accuracy(labels, predictions) + +def train(train_ds, nbr_entrainement): + for entrainement in range(nbr_entrainement): + start=time.time() + for images, labels in train_ds: + train_step(images, labels) + message='Entrainement {:04d}: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(entrainement+1, + train_loss.result(), + train_accuracy.result()*100, + time.time()-start)) + train_loss.reset_states() + train_accuracy.reset_states() + test(test_ds) + +def my_loss(labels, preds): + labels_reshape=tf.reshape(labels, (-1, 1)) + preds_reshape=tf.reshape(preds, (-1, 1)) + result=loss_object(labels_reshape, preds_reshape) + return result + +def test(test_ds): + start=time.time() + for test_images, test_labels in test_ds: + predictions=model_panneau(test_images) + t_loss=my_loss(test_labels, predictions) + test_loss(t_loss) + test_accuracy(test_labels, predictions) + message=' >>> Test: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}' + print(message.format(test_loss.result(), + test_accuracy.result()*100, + time.time()-start)) + test_loss.reset_states() + test_accuracy.reset_states() + +optimizer=tf.keras.optimizers.Adam() +loss_object=tf.keras.losses.BinaryCrossentropy() +train_loss=tf.keras.metrics.Mean() +train_accuracy=tf.keras.metrics.BinaryAccuracy() +test_loss=tf.keras.metrics.Mean() +test_accuracy=tf.keras.metrics.BinaryAccuracy() +model_panneau=common.panneau_model(len(tab_panneau)) +checkpoint=tf.train.Checkpoint(model_panneau=model_panneau) + +print("Entrainement") +train(train_ds, nbr_entrainement) +checkpoint.save(file_prefix="./training_panneau/panneau") + +quit() + +for i in range(len(test_images)): + prediction=model_panneau(np.array([test_images[i]])) + print("prediction", prediction[0]) + if np.sum(prediction[0])<0.6: + print("Ce n'est pas un panneau") + else: + print("C'est un panneau:", tab_panneau[np.argmax(prediction[0])]) + cv2.imshow("image", test_images[i]) + if cv2.waitKey()&0xFF==ord('q'): + break diff --git a/Divers/tutoriel25/README.md b/Divers/tutoriel25/README.md new file mode 100644 index 0000000..9988744 --- /dev/null +++ b/Divers/tutoriel25/README.md @@ -0,0 +1,8 @@ +# Tutoriel 25 +## Lecture des panneaux de limitation de vitesse + +Les vidéos de ce tutoriel sont disponibles aux adresses suivantes:
+partie 1: https://www.youtube.com/watch?v=PvD5POjXw8Q
+partie 2: https://www.youtube.com/watch?v=TYDi0SNCUr0
+partie 3: https://www.youtube.com/watch?v=fBysd-Y17Tw + diff --git a/Divers/tutoriel25/common.py b/Divers/tutoriel25/common.py new file mode 100644 index 0000000..ff84059 --- /dev/null +++ b/Divers/tutoriel25/common.py @@ -0,0 +1,30 @@ +import tensorflow as tf +from tensorflow.keras import layers, models +import os +import cv2 + +size=42 +dir_images_panneaux="images_panneaux" +dir_images_autres_panneaux="images_autres_panneaux" +dir_images_sans_panneaux="images_sans_panneaux" + +def lire_images_panneaux(dir_images_panneaux, size=None): + tab_panneau=[] + tab_image_panneau=[] + + if not os.path.exists(dir_images_panneaux): + quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux)) + + files=os.listdir(dir_images_panneaux) + if files is None: + quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux)) + + for file in sorted(files): + if file.endswith("png"): + tab_panneau.append(file.split(".")[0]) + image=cv2.imread(dir_images_panneaux+"/"+file) + if size is not None: + image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4) + tab_image_panneau.append(image) + + return tab_panneau, tab_image_panneau diff --git a/Divers/tutoriel25/dataset.py b/Divers/tutoriel25/dataset.py new file mode 100644 index 0000000..a339036 --- /dev/null +++ b/Divers/tutoriel25/dataset.py @@ -0,0 +1,82 @@ +import numpy as np +import cv2 +from multiprocessing import Pool +import multiprocessing +import random + +def bruit(image_orig): + h, w, c=image_orig.shape + n=np.random.randn(h, w, c)*random.randint(5, 30) + return np.clip(image_orig+n, 0, 255).astype(np.uint8) + +def change_gamma(image, alpha=1.0, beta=0.0): + return np.clip(alpha*image+beta, 0, 255).astype(np.uint8) + +def modif_img(img): + h, w, c=img.shape + + r_color=[np.random.randint(255), np.random.randint(255), np.random.randint(255)] + img=np.where(img==[142, 142, 142], r_color, img).astype(np.uint8) + + if np.random.randint(3): + k_max=3 + kernel_blur=np.random.randint(k_max)*2+1 + img=cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0) + + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), random.randint(-10, 10), 1) + img=cv2.warpAffine(img, M, (w, h)) + + if np.random.randint(2): + a=int(max(w, h)/5)+1 + pts1=np.float32([[0, 0], [w, 0], [0, h], [w, h]]) + pts2=np.float32([[0+random.randint(-a, a), 0+random.randint(-a, a)], [w-random.randint(-a, a), 0+random.randint(-a, a)], [0+random.randint(-a, a), h-random.randint(-a, a)], [w-random.randint(-a, a), h-random.randint(-a, a)]]) + M=cv2.getPerspectiveTransform(pts1,pts2) + img=cv2.warpPerspective(img, M, (w, h)) + + if np.random.randint(2): + r=random.randint(0, 5) + h2=int(h*0.9) + w2=int(w*0.9) + if r==0: + img=img[0:w2, 0:h2] + elif r==1: + img=img[w-w2:w, 0:h2] + elif r==2: + img=img[0:w2, h-h2:h] + elif r==3: + img=img[w-w2:w, h-h2:h] + img=cv2.resize(img, (h, w)) + + if np.random.randint(2): + r=random.randint(1, int(max(w, h)*0.15)) + img=img[r:w-r, r:h-r] + img=cv2.resize(img, (h, w)) + + if not np.random.randint(4): + t=np.empty((h, w, c) , dtype=np.float32) + for i in range(h): + for j in range(w): + for k in range(c): + t[i][j][k]=(i/h) + M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), np.random.randint(4)*90, 1) + t=cv2.warpAffine(t, M, (w, h)) + img=(cv2.multiply((img/255).astype(np.float32), t)*255).astype(np.uint8) + + img=change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50)) + + if not np.random.randint(4): + p=(15+np.random.randint(10))/100 + img=(img*p+50*(1-p)).astype(np.uint8)+np.random.randint(100) + + img=bruit(img) + + return img + +def create_lot_img(image, nbr, nbr_thread=None): + if nbr_thread is None: + nbr_thread=multiprocessing.cpu_count() + lot_original=np.repeat([image], nbr, axis=0) + with Pool(nbr_thread) as p: + lot_result=p.map(modif_img, lot_original) + p.close() + return lot_result diff --git a/Divers/tutoriel25/extract_panneau.py b/Divers/tutoriel25/extract_panneau.py new file mode 100644 index 0000000..0feebff --- /dev/null +++ b/Divers/tutoriel25/extract_panneau.py @@ -0,0 +1,46 @@ +import cv2 +import os +import numpy as np +import random +import common + +video_dir="D:\dashcam Cedric" + +l=os.listdir(video_dir) + +for video in l: + if not video.endswith("mp4"): + continue + cap=cv2.VideoCapture(video_dir+"/"+video) + + print("video:", video) + while True: + ret, frame=cap.read() + if ret is False: + break + f_w, f_h, f_c=frame.shape + frame=cv2.resize(frame, (int(f_h/1.5), int(f_w/1.5))) + + image=frame[200:400, 700:1000] + cv2.rectangle(frame, (700, 200), (1000, 400), (255, 255, 255), 1) + + gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=30, param2=60, minRadius=5, maxRadius=45) + if circles is not None: + circles=np.int16(np.around(circles)) + for i in circles[0,:]: + if i[2]!=0: + panneau=cv2.resize(image[max(0, i[1]-i[2]):i[1]+i[2], max(0, i[0]-i[2]):i[0]+i[2]], (common.size, common.size))/255 + cv2.imshow("panneau", panneau) + cv2.putText(frame, "fichier:"+video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA) + cv2.imshow("Video", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() + if key==ord('a'): + for cpt in range(100): + ret, frame=cap.read() + if key==ord('f'): + break + +cv2.destroyAllWindows() diff --git a/Divers/tutoriel25/genere_panneaux.py b/Divers/tutoriel25/genere_panneaux.py new file mode 100644 index 0000000..d5a9e41 --- /dev/null +++ b/Divers/tutoriel25/genere_panneaux.py @@ -0,0 +1,29 @@ +import numpy as np +from sklearn.utils import shuffle +import cv2 +import common +import dataset + +tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size) + +tab_images=np.array([]).reshape(0, common.size, common.size, 3) +tab_labels=[] + +id=0 +for image in tab_image_panneau: + lot=dataset.create_lot_img(image, 1000) + tab_images=np.concatenate([tab_images, lot]) + tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)]) + id+=1 + +tab_panneau=np.array(tab_panneau) +tab_images=np.array(tab_images, dtype=np.float32)/255 +tab_labels=np.array(tab_labels).reshape([-1, 1]) + +tab_images, tab_labels=shuffle(tab_images, tab_labels) + +for i in range(len(tab_images)): + cv2.imshow("panneau", tab_images[i]) + print("label", tab_labels[i], "panneau", tab_panneau[int(tab_labels[i])]) + if cv2.waitKey()&0xFF==ord('q'): + quit() diff --git a/Divers/tutoriel25/houghcircles.py b/Divers/tutoriel25/houghcircles.py new file mode 100644 index 0000000..cedf264 --- /dev/null +++ b/Divers/tutoriel25/houghcircles.py @@ -0,0 +1,36 @@ +import cv2 +import numpy as np + +param1=30 +param2=55 +dp=1.0 + +cap=cv2.VideoCapture(0) + +while True: + ret, frame=cap.read() + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp, 20, param1=param1, param2=param2, minRadius=10, maxRadius=50) + if circles is not None: + circles=np.around(circles).astype(np.int32) + for i in circles[0, :]: + if i[2]!=0: + cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 4) + cv2.putText(frame, "[i|k]dp: {:4.2f} [o|l]param1: {:d} [p|m]param2: {:d}".format(dp, param1, param2), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 1) + cv2.imshow("Video", frame) + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() + if key==ord('i'): + dp=min(10, dp+0.1) + if key==ord('k'): + dp=max(0.1, dp-0.1) + if key==ord('o'): + param1=min(255, param1+1) + if key==ord('l'): + param1=max(1, param1-1) + if key==ord('p'): + param2=min(255, param2+1) + if key==ord('m'): + param2=max(1, param2-1) +cv2.destroyAllWindows() diff --git a/Divers/tutoriel29/README.md b/Divers/tutoriel29/README.md new file mode 100644 index 0000000..648d8a7 --- /dev/null +++ b/Divers/tutoriel29/README.md @@ -0,0 +1,7 @@ +# Tutoriel 29 +## Segmentation d'image avec l'algorithme des K-moyennes + +La vidéo de ce tutoriel est disponible l'adresse suivante:
+https://www.youtube.com/watch?v=ytii3XvapRY + + diff --git a/Divers/tutoriel29/ishihara.py b/Divers/tutoriel29/ishihara.py new file mode 100644 index 0000000..e44f607 --- /dev/null +++ b/Divers/tutoriel29/ishihara.py @@ -0,0 +1,46 @@ +import numpy as np +from matplotlib import pyplot as plt +from sklearn.cluster import KMeans +import cv2 +import glob + +k=2 +ESPACE="HSV" +CH=[0, 2] +size=200 + +for image in glob.glob('.\images\*.png'): + print("Image:", image) + + # Lecture et affichage de l'image + img=cv2.imread(image) + img=cv2.resize(img, (size, size)) + cv2.imshow("image", img) + + # Changement d'espace colorimétrique + img=cv2.cvtColor(img, eval("cv2.COLOR_BGR2"+ESPACE)) + X=img[:, :, CH].reshape(img.shape[0]*img.shape[1], len(CH)) + + # Graph 2D des couches A et B + if len(CH)==2: + plt.scatter(X[:,0], X[:,1], s=3)#, marker='+') + plt.show() + + # Algorithme K moyennes + kmeans=KMeans(n_clusters=k) + #kmeans.fit(X) + pred=kmeans.fit_predict(X) + + # Graph 2D des couches A et B après utilisation de l'algorithme K moyennes + if len(CH)==2: + plt.scatter(X[:,0], X[:,1], c=pred, s=3) #10, marker='+') + plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=50, c='red') + plt.show() + + # Affichage du résultat + pred=pred.reshape(img.shape[0], img.shape[1]) + pred=pred/(k-1) + cv2.imshow("kmeans", pred) + + if cv2.waitKey()&0xFF==ord('q'): + break diff --git a/Divers/tutoriel29/ishihara2.py b/Divers/tutoriel29/ishihara2.py new file mode 100644 index 0000000..f04cbc2 --- /dev/null +++ b/Divers/tutoriel29/ishihara2.py @@ -0,0 +1,35 @@ +import numpy as np +from matplotlib import pyplot as plt +from sklearn.cluster import KMeans +import cv2 +import sys +import glob + +min_clusters=2 +max_clusters=4 +ESPACES=["YCrCb", "HSV", "LAB"] +COUCHES=[[1], [0, 2], [1, 2]] +size=200 + +for image in glob.glob('.\images\*.png'): + print("Image: {} ".format(image), end='') + tab=np.zeros([(len(ESPACES))*size, (max_clusters-min_clusters+1)*size], dtype=np.float32) + img=cv2.imread(image) + cv2.imshow("image", cv2.resize(img, (2*size, 2*size))) + img=cv2.resize(img, (size, size)) + + for index in range(len(ESPACES)): + img2=cv2.cvtColor(img, eval("cv2.COLOR_BGR2"+ESPACES[index])) + X=img2[:, :, COUCHES[index]].reshape(img2.shape[0]*img2.shape[1], len(COUCHES[index])) + for k in range(min_clusters, max_clusters+1): + sys.stdout.write('.') + sys.stdout.flush() + kmeans=KMeans(n_clusters=k) + pred=kmeans.fit_predict(X) + pred=pred.reshape(img2.shape[0], img2.shape[1]) + pred=pred/(k-1) + tab[index*size:(index+1)*size, (k-min_clusters)*size:(k-min_clusters)*size+size]=pred + sys.stdout.write('\n') + cv2.imshow("kmeans", tab) + if cv2.waitKey()&0xFF==ord('q'): + break diff --git a/Divers/tutoriel31/README.md b/Divers/tutoriel31/README.md new file mode 100644 index 0000000..56daf60 --- /dev/null +++ b/Divers/tutoriel31/README.md @@ -0,0 +1,5 @@ +# Tutoriel 31 +## K-moyennes: coefficient silhouette + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=H_AW_lwvdDk + diff --git a/Divers/tutoriel31/silouhette.py b/Divers/tutoriel31/silouhette.py new file mode 100644 index 0000000..1544e50 --- /dev/null +++ b/Divers/tutoriel31/silouhette.py @@ -0,0 +1,42 @@ +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.figure import Figure +from matplotlib.backends.backend_agg import FigureCanvas +from sklearn.cluster import KMeans +from sklearn.datasets.samples_generator import make_blobs +import cv2 + +cluster_std=1.30 +n_samples=300 +X, y=make_blobs(n_samples=n_samples, centers=5, cluster_std=cluster_std) + +fig, (ax1, ax2)=plt.subplots(1, 2) +canvas=FigureCanvas(fig) +fig.set_size_inches(11, 6) + +k=2 +while 1: + ax1.cla() + ax1.scatter(X[:,0], X[:,1], marker='+', c="#FF0000") + + kmeans=KMeans(n_clusters=k) + pred_y=kmeans.fit_predict(X) + + ax2.cla() + ax2.scatter(X[:,0], X[:,1], c=pred_y, marker='+') + ax2.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=50, c='#0000FF') + canvas.draw() + + img=np.array(canvas.renderer.buffer_rgba()) + cv2.putText(img, "Nbr cluster={:02d} [p|m] nbr clusters [r] reset [q] quit".format(k), (250, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2) + + cv2.imshow("plot", img) + key=cv2.waitKey()&0xFF + if key==ord('p'): + k=min(99, k+1) + if key==ord('m'): + k=max(2, k-1) + if key==ord('r'): + X, y=make_blobs(n_samples=n_samples, centers=5, cluster_std=cluster_std) + if key==ord('q'): + quit() diff --git a/Divers/tutoriel31/silouhette2.py b/Divers/tutoriel31/silouhette2.py new file mode 100644 index 0000000..381403b --- /dev/null +++ b/Divers/tutoriel31/silouhette2.py @@ -0,0 +1,43 @@ +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.figure import Figure +from matplotlib.backends.backend_agg import FigureCanvas +from sklearn.cluster import KMeans +from sklearn.datasets.samples_generator import make_blobs +import cv2 +import glob + +k=5 +cluster_std=1.30 +n_samples=300 +X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std) + +fig, (ax1, ax2)=plt.subplots(1, 2) +canvas=FigureCanvas(fig) +fig.set_size_inches(10, 6) + +while 1: + ax1.cla() + ax1.scatter(X[:,0], X[:,1], marker='+', c="#FF0000") + ax1.set_title('Données') + + wcss=[] + for i in range(1, 11): + kmeans=KMeans(n_clusters=i) + kmeans.fit(X) + wcss.append(kmeans.inertia_) + + ax2.cla() + ax2.plot(range(1, 11), wcss, c="#FF0000") + ax2.set_title('WCSS pour "elbow method"') + + canvas.draw() + img=np.array(canvas.renderer.buffer_rgba()) + cv2.putText(img, "[r] reset [q] quit".format(k), (450, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2) + + cv2.imshow("plot", img) + key=cv2.waitKey()&0xFF + if key==ord('r'): + X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std) + if key==ord('q'): + quit() diff --git a/Divers/tutoriel31/silouhette3.py b/Divers/tutoriel31/silouhette3.py new file mode 100644 index 0000000..5c61464 --- /dev/null +++ b/Divers/tutoriel31/silouhette3.py @@ -0,0 +1,57 @@ +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.figure import Figure +from matplotlib.backends.backend_agg import FigureCanvas +from sklearn.cluster import KMeans +from sklearn.datasets.samples_generator import make_blobs +from sklearn.metrics import silhouette_score +import cv2 +import glob + +k=5 +cluster_std=1.30 +n_samples=300 +fig, ((ax1, ax2), (ax3, ax4))=plt.subplots(2, 2) +canvas=FigureCanvas(fig) +fig.set_size_inches(12, 8) + +X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std) + +while 1: + ax1.cla() + ax1.plot(X[:,0], X[:,1], "+", c="#FF0000") + ax1.set_title('Données') + + wcss=[] + tab_silhouette=[] + for i in range(2, 11): + kmeans=KMeans(n_clusters=i) + cluster_labels=kmeans.fit_predict(X) + wcss.append(kmeans.inertia_) + tab_silhouette.append(silhouette_score(X, cluster_labels)) + + ax2.cla() + ax2.plot(range(2, 11), wcss, c="#FF0000") + ax2.set_title('WCSS pour "elbow method"') + + ax3.cla() + ax3.plot(range(2, 11), tab_silhouette, c="#FF0000") + ax3.set_title('Coefficient silhouette') + + kmeans=KMeans(n_clusters=np.argmax(tab_silhouette)+2) + pred_y=kmeans.fit_predict(X) + ax4.cla() + ax4.scatter(X[:,0], X[:,1], c=pred_y, marker='+') + ax4.set_title('Données + centre clusters') + ax4.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c="#0000FF") + + canvas.draw() + img=np.array(canvas.renderer.buffer_rgba()) + cv2.putText(img, "[r] reset [q] quit".format(k), (450, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2) + + cv2.imshow("plot", img) + key=cv2.waitKey()&0xFF + if key==ord('r'): + X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std) + if key==ord('q'): + quit() diff --git a/Divers/tutoriel36-2/KalmanFilter.py b/Divers/tutoriel36-2/KalmanFilter.py new file mode 100644 index 0000000..2786c11 --- /dev/null +++ b/Divers/tutoriel36-2/KalmanFilter.py @@ -0,0 +1,115 @@ +import numpy as np + +class KalmanFilter(object): + def __init__(self, dt, point, box): + self.dt=dt + + # Vecteur d'etat initial + self.E=np.matrix([[point[0]], [point[1]], [0], [0], [box[0]], [box[1]]]) + + # Matrice de transition + self.A=np.matrix([[1, 0, self.dt, 0, 0, 0], + [0, 1, 0, self.dt, 0, 0], + [0, 0, 1, 0, 0, 0], + [0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 1]]) + + # Matrice d'observation, on observe que x et y + self.H=np.matrix([[1, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 1]]) + + v=1E-5 + #v=1 + self.Q=np.matrix([[v, 0, 0, 0, 0, 0], + [0, v, 0, 0, 0, 0], + [0, 0, v, 0, 0, 0], + [0, 0, 0, v, 0, 0], + [0, 0, 0, 0, v, 0], + [0, 0, 0, 0, 0, v]]) + + v=1E-5 + #v=1 + self.R=np.matrix([[v, 0, 0, 0], + [0, v, 0, 0], + [0, 0, v, 0], + [0, 0, 0, v]]) + + self.P=np.eye(self.A.shape[1]) + + def predict(self): + self.E=np.dot(self.A, self.E) + # Calcul de la covariance de l'erreur + self.P=np.dot(np.dot(self.A, self.P), self.A.T)+self.Q + return self.E + + def update(self, z): + # Calcul du gain de Kalman + S=np.dot(self.H, np.dot(self.P, self.H.T))+self.R + K=np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S)) + + # Correction / innovation + self.E=np.round(self.E+np.dot(K, (z-np.dot(self.H, self.E)))) + I=np.eye(self.H.shape[1]) + self.P=(I-(K*self.H))*self.P + + return self.E + +class KalmanFilter_old(object): + def __init__(self, dt, point, box): + self.dt=dt + + # Vecteur d'etat initial + self.E=np.matrix([[point[0]], [point[1]], [0], [0], [box[0]], [box[1]], [0], [0]]) + + # Matrice de transition + self.A=np.matrix([[1, 0, self.dt, 0, 0, 0, 0, 0], + [0, 1, 0, self.dt, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, self.dt, 0], + [0, 0, 0, 0, 0, 1, 0, self.dt], + [0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 0, 1]]) + + # Matrice d'observation, on observe que x et y + self.H=np.matrix([[1, 0, 0, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0]]) + + self.Q=np.matrix([[1, 0, 0, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0, 0, 1]]) + + self.R=np.matrix([[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + self.P=np.eye(self.A.shape[1]) + + def predict(self): + self.E=np.dot(self.A, self.E) + # Calcul de la covariance de l'erreur + self.P=np.dot(np.dot(self.A, self.P), self.A.T)+self.Q + return self.E + + def update(self, z): + # Calcul du gain de Kalman + S=np.dot(self.H, np.dot(self.P, self.H.T))+self.R + K=np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S)) + + # Correction / innovation + self.E=np.round(self.E+np.dot(K, (z-np.dot(self.H, self.E)))) + I=np.eye(self.H.shape[1]) + self.P=(I-(K*self.H))*self.P + + return self.E diff --git a/Divers/tutoriel36-2/README.md b/Divers/tutoriel36-2/README.md new file mode 100644 index 0000000..b56f42e --- /dev/null +++ b/Divers/tutoriel36-2/README.md @@ -0,0 +1,4 @@ +# Tutoriel 36 +## Filtre de Kalman partie 2 + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=pR0TAFWnDdU diff --git a/Divers/tutoriel36-2/affiche_video_label.py b/Divers/tutoriel36-2/affiche_video_label.py new file mode 100644 index 0000000..fc0c3fb --- /dev/null +++ b/Divers/tutoriel36-2/affiche_video_label.py @@ -0,0 +1,33 @@ +import cv2 +import numpy as np +from numpy import genfromtxt +import os +import glob + +datasets="2DMOT2015Labels/train/" +dataset="PETS09-S2L1" + +dir_images=datasets+"/"+dataset+"/img1/" +fichier_label=datasets+"/"+dataset+"/gt/gt.txt" + +if not os.path.exists(fichier_label): + print("Le fichier de label n'existe pas ...", fichier) + quit() + +data=genfromtxt(fichier_label, delimiter=',') +id_frame=0 +id_objet=0 + +for image in glob.glob(dir_images+"*.jpg"): + frame=cv2.imread(image) + + mask=data[:, 0]==id_frame + for d in data[mask, :]: + cv2.rectangle(frame, (int(d[2]), int(d[3])), (int(d[2]+d[4]), int(d[3]+d[5])), (0, 255, 0), 2) + + cv2.imshow("frame", frame) + + key=cv2.waitKey(70)&0xFF + if key==ord('q'): + quit() + id_frame+=1 diff --git a/Divers/tutoriel36-2/suivi_multiple.py b/Divers/tutoriel36-2/suivi_multiple.py new file mode 100644 index 0000000..a642653 --- /dev/null +++ b/Divers/tutoriel36-2/suivi_multiple.py @@ -0,0 +1,180 @@ +import cv2 +import numpy as np +from numpy import genfromtxt +from KalmanFilter import KalmanFilter +import math +import os +import glob + +datasets="2DMOT2015Labels/train" +dataset="PETS09-S2L1" + +distance_mini=500 +rectangle=0 +trace=0 + +dir_images=datasets+"/"+dataset+"/img1/" +fichier_label=datasets+"/"+dataset+"/gt/gt.txt" + +if not os.path.exists(dir_images): + print("Le repertoire n'existe pas ...", dir_images) + quit() + +if not os.path.exists(fichier_label): + print("Le fichier de label n'existe pas ...", fichier) + quit() + +objets_points=[] +objets_id=[] +objets_KF=[] +objets_historique=[] + +def distance(point, liste_points): + distances=[] + for p in liste_points: + distances.append(np.sum(np.power(p-np.expand_dims(point, axis=-1), 2))) + return distances + +def trace_historique(tab_points, longueur, couleur=(0, 255, 255)): + historique=np.array(tab_points) + nbr_point=len(historique) + longueur=min(nbr_point, longueur) + for i in range(nbr_point-1, nbr_point-longueur, -1): + cv2.line(frame, + (historique[i-1, 0], historique[i-1, 1]), + (historique[i, 0], historique[i, 1]), + couleur, + 2) + +data=genfromtxt(fichier_label, delimiter=',') +id_frame=0 +id_objet=0 + +start=0 +for image in glob.glob(dir_images+"*.jpg"): + frame=cv2.imread(image) + + # Prediction de l'ensemble des objets + affichage + for id_obj in range(len(objets_points)): + etat=objets_KF[id_obj].predict() + etat=np.array(etat, dtype=np.int32) + objets_points[id_obj]=np.array([etat[0], etat[1], etat[4], etat[5]]) + objets_historique[id_obj].append([etat[0], etat[1]]) + cv2.circle(frame, (etat[0], etat[1]), 5, (0, 0, 255), 2) + if rectangle: + cv2.rectangle(frame, + (int(etat[0]-etat[4]/2), int(etat[1]-etat[5]/2)), + (int(etat[0]+etat[4]/2), int(etat[1]+etat[5]/2)), + (0, 0, 255), + 2) + cv2.arrowedLine(frame, + (etat[0], etat[1]), + (etat[0]+3*etat[2], etat[1]+3*etat[3]), + color=(0, 0, 255), + thickness=2, + tipLength=0.2) + cv2.putText(frame, + "ID{:d}".format(objets_id[id_obj]), + (int(etat[0]-etat[4]/2), int(etat[1]-etat[5]/2)), + cv2.FONT_HERSHEY_PLAIN, + 1.5, + (255, 0, 0), + 2) + + if trace: + trace_historique(objets_historique[id_obj], 42) + + # Permet de suivre l'ID 0 avec une flèche + if objets_id[id_obj]==0: + cv2.arrowedLine(frame, + (etat[0], int(etat[1]-etat[5]/2-80)), + (etat[0], int(etat[1]-etat[5]/2-30)), + color=(0, 0, 255), + thickness=5, + tipLength=0.2) + + # Récupération des objets de la frame concernée + mask=data[:, 0]==id_frame + + # Affichage des données (rectangle) du detecteur + points=[] + for d in data[mask, :]: + #if np.random.randint(2): + if rectangle: + cv2.rectangle(frame, (int(d[2]), int(d[3])), (int(d[2]+d[4]), int(d[3]+d[5])), (0, 255, 0), 2) + xm=int(d[2]+d[4]/2) + ym=int(d[3]+d[5]/2) + cv2.circle(frame, (xm, ym), 2, (0, 255, 0), 2) + points.append([xm, ym, int(d[4]), int(d[5])]) + + # calcul des distances + nouveaux_objets=np.ones((len(points))) + tab_distances=[] + if len(objets_points): + for point_id in range(len(points)): + distances=distance(points[point_id], objets_points) + tab_distances.append(distances) + + tab_distances=np.array(tab_distances) + sorted_distances=np.sort(tab_distances, axis=None) + + for d in sorted_distances: + if d>distance_mini: + break + id1, id2=np.where(tab_distances==d) + if not len(id1) or not len(id2): + continue + tab_distances[id1, :]=distance_mini+1 + tab_distances[:, id2]=distance_mini+1 + objets_KF[id2[0]].update(np.expand_dims(points[id1[0]], axis=-1)) + nouveaux_objets[id1]=0 + + # Création du filtre de Kalman pour les nouveaux objets + for point_id in range(len(points)): + if nouveaux_objets[point_id]: + print("NOUVEAU", points[point_id]) + objets_points.append(points[point_id]) + objets_KF.append(KalmanFilter(0.5, [points[point_id][0], points[point_id][1]], [points[point_id][2], points[point_id][3]])) + objets_id.append(id_objet) + objets_historique.append([]) + id_objet+=1 + + # Nettoyage ... + tab_id=[] + for id_point in range(len(objets_points)): + if int(objets_points[id_point][0])<-100 or \ + int(objets_points[id_point][1])<-100 or \ + objets_points[id_point][0]>frame.shape[1]+100 or \ + objets_points[id_point][1]>frame.shape[0]+100: + print("SUPPRESSION", objets_points[id_point]) + tab_id.append(id_point) + + for index in sorted(tab_id, reverse=True): + del objets_points[index] + del objets_KF[index] + del objets_id[index] + del objets_historique[index] + + cv2.rectangle(frame, (0, 0), (frame.shape[1], 30), (100, 100, 100), cv2.FILLED) + message="Frame: {:03d} Nbr personne: {:d} nbr filtre: {:d} [r]Rectangle: {:3} [t]Trace: {:3}".format(id_frame, + len(points), + len(objets_points), + "ON" if rectangle else "OFF", + "ON" if trace else "OFF") + cv2.putText(frame, + message, + (20, 20), + cv2.FONT_HERSHEY_PLAIN, + 1, + (255, 255, 255), + 1) + + cv2.imshow("frame", frame) + key=cv2.waitKey(70)&0xFF + if key==ord('r'): + rectangle=not rectangle + if key==ord('t'): + trace=not trace + if key==ord('q'): + quit() + id_frame+=1 diff --git a/Divers/tutoriel36/Detector.py b/Divers/tutoriel36/Detector.py new file mode 100644 index 0000000..7ecb589 --- /dev/null +++ b/Divers/tutoriel36/Detector.py @@ -0,0 +1,33 @@ +import numpy as np +import cv2 + +lo=np.array([80, 50, 50]) +hi=np.array([100, 255, 255]) + +def detect_inrange(image, surface): + points=[] + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + image=cv2.blur(image, (5, 5)) + mask=cv2.inRange(image, lo, hi) + mask=cv2.erode(mask, None, iterations=2) + mask=cv2.dilate(mask, None, iterations=2) + elements=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] + elements=sorted(elements, key=lambda x:cv2.contourArea(x), reverse=True) + for element in elements: + if cv2.contourArea(element)>surface: + ((x, y), rayon)=cv2.minEnclosingCircle(element) + points.append(np.array([int(x), int(y)])) + else: + break + + return points, mask + +def detect_visage(image): + face_cascade=cv2.CascadeClassifier("./haarcascade_frontalface_alt2.xml") + points=[] + gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + face=face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3) + for x, y, w, h in face: + points.append(np.array([int(x+w/2), int(y+h/2)])) + + return points, None diff --git a/Divers/tutoriel36/KalmanFilter.py b/Divers/tutoriel36/KalmanFilter.py new file mode 100644 index 0000000..b227bdc --- /dev/null +++ b/Divers/tutoriel36/KalmanFilter.py @@ -0,0 +1,46 @@ +import numpy as np + +class KalmanFilter(object): + def __init__(self, dt, point): + self.dt=dt + + # Vecteur d'etat initial + self.E=np.matrix([[point[0]], [point[1]], [0], [0]]) + + # Matrice de transition + self.A=np.matrix([[1, 0, self.dt, 0], + [0, 1, 0, self.dt], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + # Matrice d'observation, on observe que x et y + self.H=np.matrix([[1, 0, 0, 0], + [0, 1, 0, 0]]) + + self.Q=np.matrix([[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + self.R=np.matrix([[1, 0], + [0, 1]]) + + self.P=np.eye(self.A.shape[1]) + + def predict(self): + self.E=np.dot(self.A, self.E) + # Calcul de la covariance de l'erreur + self.P=np.dot(np.dot(self.A, self.P), self.A.T)+self.Q + return self.E + + def update(self, z): + # Calcul du gain de Kalman + S=np.dot(self.H, np.dot(self.P, self.H.T))+self.R + K=np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S)) + + # Correction / innovation + self.E=np.round(self.E+np.dot(K, (z-np.dot(self.H, self.E)))) + I=np.eye(self.H.shape[1]) + self.P=(I-(K*self.H))*self.P + + return self.E diff --git a/Divers/tutoriel36/README.md b/Divers/tutoriel36/README.md new file mode 100644 index 0000000..3cf1e2c --- /dev/null +++ b/Divers/tutoriel36/README.md @@ -0,0 +1,5 @@ +# Tutoriel 36 +## Filtre de Kalman + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=IT4i_ooQDDM + diff --git a/Divers/tutoriel36/haarcascade_frontalface_alt2.xml b/Divers/tutoriel36/haarcascade_frontalface_alt2.xml new file mode 100644 index 0000000..b49cf5d --- /dev/null +++ b/Divers/tutoriel36/haarcascade_frontalface_alt2.xml @@ -0,0 +1,20719 @@ + + + +BOOST + HAAR + 20 + 20 + + 109 + + 0 + 20 + + <_> + 3 + 3.5069230198860168e-01 + + <_> + + 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02 + + 3.8381900638341904e-02 8.9652568101882935e-01 + 2.6293140649795532e-01 + <_> + + 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03 + + 1.0216630250215530e-01 1.2384019792079926e-01 + 6.9103831052780151e-01 + <_> + + 1 0 4 -9.2708261217921972e-04 -1 -2 5 3.3989109215326607e-04 + + 1.9536970555782318e-01 2.1014410257339478e-01 + 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new file mode 100644 index 0000000..cf6bd0f --- /dev/null +++ b/Divers/tutoriel36/selectionne_couleur.py @@ -0,0 +1,65 @@ +import cv2 +import numpy as np + +def souris(event, x, y, flags, param): + global lo, hi, color + if event==cv2.EVENT_LBUTTONDBLCLK: + color=image[y, x][0] + if event==cv2.EVENT_MOUSEWHEEL: + if flags<0: + if color>5: + color-=1 + else: + if color<250: + color+=1 + lo[0]=color-15 + hi[0]=color+15 + +color=90 +S=50 +V=50 +lo=np.array([color-5, S, V]) +hi=np.array([color+5, 255,255]) +color_info=(0, 255, 0) +cap=cv2.VideoCapture(0) +cv2.namedWindow('Camera') +cv2.setMouseCallback('Camera', souris) +while True: + ret, frame=cap.read() + image=cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + image=cv2.blur(image, (5, 5)) + mask=cv2.inRange(image, lo, hi) + mask=cv2.erode(mask, None, iterations=2) + mask=cv2.dilate(mask, None, iterations=8) + elements=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] + if len(elements) > 0: + c=max(elements, key=cv2.contourArea) + ((x, y), radius)=cv2.minEnclosingCircle(c) + if radius>10: + cv2.circle(frame, (int(x), int(y)), 5, color_info, 10) + cv2.line(frame, (int(x), int(y)), (int(x)+150, int(y)), color_info, 2) + cv2.putText(frame, "Objet !!!", (int(x)+10, int(y) -10), cv2.FONT_HERSHEY_DUPLEX, 1, color_info, 1, cv2.LINE_AA) + + cv2.rectangle(frame, (0, 0), (frame.shape[1], 30), (100, 100, 100), cv2.FILLED) + cv2.putText(frame, "[Souris]Couleur: {:d} [o|l] S:{:d} [p|m] V{:d}".format(color, S, V), (5, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1, cv2.LINE_AA) + cv2.imshow('Camera', frame) + cv2.imshow('Mask', mask) + + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + break + if key==ord('p'): + V=min(255, V+1) + lo=np.array([color-5, S, V]) + if key==ord('m'): + V=max(1, V-1) + lo=np.array([color-5, S, V]) + if key==ord('o'): + S=min(255, S+1) + lo=np.array([color-5, S, V]) + if key==ord('l'): + S=max(1, S-1) + lo=np.array([color-5, S, V]) + +cap.release() +cv2.destroyAllWindows() diff --git a/Divers/tutoriel36/suivi.py b/Divers/tutoriel36/suivi.py new file mode 100644 index 0000000..a85c811 --- /dev/null +++ b/Divers/tutoriel36/suivi.py @@ -0,0 +1,35 @@ +import cv2 +from Detector import detect_inrange, detect_visage +from KalmanFilter import KalmanFilter +import numpy as np + +VideoCap=cv2.VideoCapture(0) + +KF=KalmanFilter(0.1, [0, 0]) + +while(True): + ret, frame=VideoCap.read() + + points, mask=detect_inrange(frame, 800) + #points, mask=detect_visage(frame) + + etat=KF.predict().astype(np.int32) + + cv2.circle(frame, (int(etat[0]), int(etat[1])), 2, (0, 255, 0), 5) + cv2.arrowedLine(frame, + (etat[0], etat[1]), (etat[0]+etat[2], etat[1]+etat[3]), + color=(0, 255, 0), + thickness=3, + tipLength=0.2) + if (len(points)>0): + cv2.circle(frame, (points[0][0], points[0][1]), 10, (0, 0, 255), 2) + KF.update(np.expand_dims(points[0], axis=-1)) + + cv2.imshow('image', frame) + if mask is not None: + cv2.imshow('mask', mask) + + if cv2.waitKey(1)&0xFF==ord('q'): + VideoCap.release() + cv2.destroyAllWindows() + break diff --git a/Divers/tutoriel37/README.md b/Divers/tutoriel37/README.md new file mode 100644 index 0000000..4f6d343 --- /dev/null +++ b/Divers/tutoriel37/README.md @@ -0,0 +1,6 @@ +# Tutoriel 37 +## Local Binary Pattern + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=zUdEaE8Kpac + + diff --git a/Divers/tutoriel37/camera.py b/Divers/tutoriel37/camera.py new file mode 100644 index 0000000..969ee37 --- /dev/null +++ b/Divers/tutoriel37/camera.py @@ -0,0 +1,82 @@ +from skimage import feature +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +method_distance=[cv2.HISTCMP_CORREL, + cv2.HISTCMP_CHISQR, + cv2.HISTCMP_INTERSECT, + cv2.HISTCMP_BHATTACHARYYA, + cv2.HISTCMP_HELLINGER, + cv2.HISTCMP_CHISQR_ALT, + cv2.HISTCMP_KL_DIV] + +method_lbp=['default', + 'ror', + 'uniform', + 'var'] + +cap=cv2.VideoCapture(0) +numPoints=24 +radius=3 +image_ref=None + +width=320 +height=240 + +windowsize_r=30 +windowsize_c=30 + +rows=int(height/windowsize_r) +cols=int(width/windowsize_c) + +tab_score=np.empty((rows, cols), dtype=np.float32) +id_method_distance=6 +id_method_lbp=2 +seuil=0.3 + +while True: + ret, frame=cap.read() + image=cv2.resize(frame, (width, height)) + + if image_ref is not None: + gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + for r in range(rows): + for c in range(cols): + window=gray[r*windowsize_r:(r+1)*windowsize_r, c*windowsize_c:(c+1)*windowsize_c] + lbp=feature.local_binary_pattern(window, numPoints, radius, method=method_lbp[id_method_lbp]) + hist, _=np.histogram(lbp, normed=True, bins=numPoints, range=(0, numPoints)) + score=cv2.compareHist(hist.astype(np.float32), hist_ref.astype(np.float32), method_distance[id_method_distance]) + tab_score[r, c]=score + + if image_ref is not None: + tab_score2=np.zeros_like(tab_score) + tab_score2[tab_score>>", "{}.jpg".format(texture)) + image=cv2.imread("{}.jpg".format(texture), 0) + if image is None: + quit("Probleme image...") + lbp=feature.local_binary_pattern(image, numPoints, radius, method=method_lbp[id_method_lbp]) + hist_ref, _=np.histogram(lbp, normed=True, density=True, bins=2**numPoints, range=(0, 2**numPoints)) + tab_images.append(image) + tab_hist.append(hist_ref) + +for fichier in glob.glob("textures/*.jpg"): + print("Lecture de", fichier) + frame=cv2.imread(fichier) + + if frame is None: + print("Probleme image ...") + continue + + cv2.imshow("Image", frame) + + gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + tab_distance=[] + for id in range(len(tab_hist)): + h=tab_hist[id] + lbp=feature.local_binary_pattern(gray, numPoints, radius, method=method_lbp[id_method_lbp]) + hist, _=np.histogram(lbp, normed=True, density=True, bins=2**numPoints, range=(0, 2**numPoints)) + score=cv2.compareHist(hist.astype(np.float32), h.astype(np.float32), method_distance[id_method_distance]) + tab_distance.append(score) + print(" score {:10}: {:2.6f}".format(textures[id], score)) + + tab_distance=np.array(tab_distance) + print(" -> texture:", textures[np.argmin(tab_distance)]) + + key=cv2.waitKey()&0xFF + if key==ord('q'): + quit() diff --git a/Divers/tutoriel37/lbp.py b/Divers/tutoriel37/lbp.py new file mode 100644 index 0000000..49d5fa0 --- /dev/null +++ b/Divers/tutoriel37/lbp.py @@ -0,0 +1,41 @@ +from skimage import feature +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +method_lbp=['default', + 'ror', + 'uniform', + 'var'] + +cap=cv2.VideoCapture(0) +numPoints=24 +radius=3 +id_method_lbp=0 + +while True: + ret, frame=cap.read() + + image=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + lbp=feature.local_binary_pattern(image, numPoints, radius, method=method_lbp[id_method_lbp]) + + cv2.rectangle(frame, (0, 0), (frame.shape[1], 30), (100, 100, 100), cv2.FILLED) + txt="[q] Quit [o|l]numPoints:{:d} [i|k]rayon:{:d} [m] Methode: {}".format(numPoints, radius, method_lbp[id_method_lbp]) + cv2.putText(frame, txt, (20, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) + + cv2.imshow("Image", frame) + cv2.imshow("LBP", lbp/np.max(lbp)) + + key=cv2.waitKey(1)&0xFF + if key==ord('m'): + id_method_lbp=(id_method_lbp+1)%len(method_lbp) + if key==ord('i'): + radius=radius+1 + if key==ord('k'): + radius=max(3, radius-1) + if key==ord('o'): + numPoints=numPoints+1 + if key==ord('l'): + numPoints=max(3, numPoints-1) + if key==ord('q'): + quit() diff --git a/Divers/tutoriel37/lbp_hist.py b/Divers/tutoriel37/lbp_hist.py new file mode 100644 index 0000000..4b1a6ef --- /dev/null +++ b/Divers/tutoriel37/lbp_hist.py @@ -0,0 +1,27 @@ +from skimage import feature +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import glob + +numPoints=8 +radius=3 + +textures=['gazon', 'gravier', 'bois'] + +for texture in textures: + print(">>>", "{}.jpg".format(texture)) + image=cv2.imread("{}.jpg".format(texture), 0) + if image is None: + quit("Probleme image...") + lbp=feature.local_binary_pattern(image, numPoints, radius, method='default') + hist_ref, _=np.histogram(lbp, bins=2**numPoints, range=(0, 2**numPoints)) + + cv2.imshow("Image", image) + cv2.imshow("LBP", lbp) + plt.plot(hist_ref) + plt.show() + + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + quit() diff --git a/Divers/tutoriel39/README.md b/Divers/tutoriel39/README.md new file mode 100644 index 0000000..ab933e7 --- /dev/null +++ b/Divers/tutoriel39/README.md @@ -0,0 +1,7 @@ +# Tutoriel 39 +## RealSense sur Jetson Nano + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=RPZB2qE8Yp4 + + + diff --git a/Divers/tutoriel39/realsense.py b/Divers/tutoriel39/realsense.py new file mode 100644 index 0000000..8ff371f --- /dev/null +++ b/Divers/tutoriel39/realsense.py @@ -0,0 +1,66 @@ +import pyrealsense2 as rs +import numpy as np +import cv2 + +lo=np.array([95, 100, 50]) +hi=np.array([105, 255, 255]) +color_infos=(0, 255, 255) + +pipeline=rs.pipeline() +config=rs.config() + +config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15) +config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15) + +align_to = rs.stream.color +align = rs.align(align_to) + +pipeline.start(config) + +while True: + + frames=pipeline.wait_for_frames() + + aligned_frames = align.process(frames) + + depth_frame=aligned_frames.get_depth_frame() + color_frame=aligned_frames.get_color_frame() + + if not depth_frame or not color_frame: + continue + + depth_image=np.array(depth_frame.get_data()) + color_image=np.array(color_frame.get_data()) + + depth_colormap=cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) + + cv2.imshow('RealSense1', depth_colormap) + + frame=color_image + image=cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + mask=cv2.inRange(image, lo, hi) + image=cv2.blur(image, (7, 7)) + mask=cv2.erode(mask, None, iterations=4) + mask=cv2.dilate(mask, None, iterations=4) + image2=cv2.bitwise_and(frame, frame, mask=mask) + elements=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] + if len(elements) > 0: + c=max(elements, key=cv2.contourArea) + ((x, y), rayon)=cv2.minEnclosingCircle(c) + if rayon>30: + cv2.circle(image2, (int(x), int(y)), int(rayon), color_infos, 2) + cv2.circle(frame, (int(x), int(y)), 5, color_infos, 10) + cv2.line(frame, (int(x), int(y)), (int(x)+150, int(y)), color_infos, 2) + print(">>>", depth_colormap[int(y), int(x)]) + dist=depth_frame.get_distance(int(x), int(y)) + if dist<1: + msg="{:2.0f} cm".format(dist*100) + else: + msg="{:4.2f} m".format(dist) + cv2.putText(frame, msg, (int(x)+10, int(y) -10), cv2.FONT_HERSHEY_DUPLEX, 1, color_infos, 1, cv2.LINE_AA) + cv2.imshow('Camera', frame) + + key=cv2.waitKey(1)&0xFF + if key==ord('q'): + pipeline.stop() + quit() diff --git a/Divers/tutoriel41/README.md b/Divers/tutoriel41/README.md new file mode 100644 index 0000000..8f9fee7 --- /dev/null +++ b/Divers/tutoriel41/README.md @@ -0,0 +1,4 @@ +# Tutoriel 41 +## Identification avec face_recognition + +La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=HHv_V2PkZGQ diff --git a/Divers/tutoriel41/identification.py b/Divers/tutoriel41/identification.py new file mode 100644 index 0000000..c7a6d32 --- /dev/null +++ b/Divers/tutoriel41/identification.py @@ -0,0 +1,120 @@ +import face_recognition +import cv2 +import numpy as np +import os +import time + +class identify: + font=cv2.FONT_HERSHEY_DUPLEX + color=(255, 255, 255) + + def __init__(self, entree, embedding_file, name_file, width_max=320, tolerance=0.5): + self.entree=entree + self.width_max=width_max + self.tolerance=tolerance + + if not os.path.exists(embedding_file): + print("Fichier", embedding_file, "non trouvé") + quit() + if not os.path.exists(name_file): + print("Fichier", name_file, "non trouvé") + quit() + self.known_face_encodings=np.load(embedding_file) + self.known_face_names=np.load(name_file) + + if entree.split(':')[0]=="https": + import pafy + video=pafy.new(entree) + video_mp4=video.getbest(preftype="mp4") + self.video_capture=cv2.VideoCapture(video_mp4.url) + elif entree=="csi": + self.video_capture=cv2.VideoCapture("nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)640, height=(int)480, format=(string)NV12, framerate=(fraction)15/1 ! nvvidconv flip-method=2 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"); + elif entree=="realsense": + import pyrealsense2 as rs + self.pipeline=rs.pipeline() + config=rs.config() + config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15) + self.pipeline.start(config) + self.video_capture=None + elif entree.split(':')[0]=="file": + fichier=entree.split(':')[1] + if not os.path.exists(fichier): + print("Fichier", fichier, "non trouvé") + quit() + self.video_capture=cv2.VideoCapture(fichier) + else: + print("Entree inconnue") + quit() + + def read(self): + if self.video_capture is not None: + ret, self.frame=self.video_capture.read() + else: + while True: + frames=self.pipeline.wait_for_frames() + color_frame=frames.get_color_frame() + if not color_frame: + continue + break + self.frame=np.array(color_frame.get_data()) + + def analyse(self): + frame=self.frame + if frame.shape[1]>self.width_max: + self.ratio=self.width_max/frame.shape[1] + frame_to_analyse=cv2.resize(frame, (0, 0), fx=self.ratio, fy=self.ratio) + else: + self.ratio=1 + frame_to_analyse=frame + + self.face_locations=face_recognition.face_locations(frame_to_analyse) + face_encodings=face_recognition.face_encodings(frame_to_analyse, self.face_locations) + + self.face_names=[] + self.face_distances=[] + for face_encoding in face_encodings: + distances=face_recognition.face_distance(self.known_face_encodings, face_encoding) + if np.min(distances)aFUAA13Tn_Ln%rE zD8k4vTX0w|S9RR(+oLaU?eA4+j#1w#&MzeD85vNusDaUO!BkxXV``k95lt7lVXuvR zq2tW3+o5GZwc2Zgvr#V)10tdW51Q+m z&`fAl|0w^6r1)TSS|W0tsH>|>jSMyq3r?iQ9|}Ou45=~kNk}K;(=RT_TvwZhbV5F; 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