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Divers/tutoriel41/README.md

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+# 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

Divers/tutoriel41/identification.py

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+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)<self.tolerance:
+                best_match_index=np.argmin(distances)
+                name=self.known_face_names[best_match_index]
+            else:
+                name="Inconnu"
+            self.face_names.append(name)
+            self.face_distances.append(np.min(distances))
+        
+    def render(self):
+        self.frame_render=self.frame
+        for (top, right, bottom, left), name, distance in zip(self.face_locations, self.face_names, self.face_distances):
+            top   =int(top    /self.ratio)
+            right =int(right  /self.ratio)
+            bottom=int(bottom /self.ratio)
+            left  =int(left   /self.ratio)
+            cv2.rectangle(self.frame_render, (left, top), (right, bottom), self.color, 2)
+            msg="[{:4.2f}] {}".format(distance, name)
+            cv2.putText(self.frame_render, msg, (left, int(bottom+(bottom-top)*0.2)), self.font, 0.8, self.color, 1)
+        
+#toto=identify("realsense", "face_encodings.npy", "face_names.npy")
+toto=identify("https://www.youtube.com/watch?v=m_xWEofOqHI", "face_encodings.npy", "face_names.npy")
+#toto=identify("csi", "face_encodings.npy", "face_names.npy")
+#toto=identify("file:photo.jpg", "face_encodings.npy", "face_names.npy")
+
+while True:
+    toto.read()
+    toto.analyse()
+    print("#############")
+    for name, distance in zip(toto.face_names, toto.face_distances):
+        print("   ", name, distance)
+    toto.render()
+    cv2.imshow("Frame render", toto.frame_render)
+
+    key=cv2.waitKey(1)&0xFF
+    if key == ord('q'):
+        break
+    if key==ord('a'):
+        for i in range(100):
+            toto.read()
+    if key==ord('z'):
+        for i in range(2000):
+            toto.read()
+
+cv2.destroyAllWindows()

Divers/tutoriel41/prepare_embedding.py

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+import face_recognition
+import numpy as np
+import os
+import glob
+
+dir_identites="Identites_1photo/"
+
+face_encodings=[]
+face_names=[]
+
+id=1
+for dir_identite in os.listdir(dir_identites):
+    print("ID", id)
+    id+=1
+
+    fichiers=[]
+    for ext in ["*.jpg", "*.jpeg", "*.png"]:
+        fichiers.extend(glob.glob(dir_identites+"/"+dir_identite+"/"+ext))
+    if len(fichiers)==0:
+        print("Repertoire vide", dir_identite)
+        continue
+    for fichier in fichiers:
+        image=face_recognition.load_image_file(fichier)
+        embedding=face_recognition.face_encodings(image)[0]
+        face_encodings.append(embedding)
+        face_names.append(dir_identite.replace('_', ' '))
+            
+np.save("face_encodings", np.array(face_encodings))
+np.save("face_names", np.array(face_names))
+
+
+