101 lines
4.5 KiB
Python
101 lines
4.5 KiB
Python
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import cv2
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import numpy as np
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import tensorflow as tf
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import sudoku_solver as ss
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from time import sleep
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import operator
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marge=4
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case=28+2*marge
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taille_grille=9*case
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flag=0
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cap=cv2.VideoCapture(0)
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with tf.Session() as s:
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saver=tf.train.import_meta_graph('./mon_modele/modele.meta')
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saver.restore(s, tf.train.latest_checkpoint('./mon_modele/'))
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graph=tf.get_default_graph()
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images=graph.get_tensor_by_name("entree:0")
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sortie=graph.get_tensor_by_name("sortie:0")
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is_training=graph.get_tensor_by_name("is_training:0")
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maxArea=0
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while True:
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ret, frame=cap.read()
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if maxArea==0:
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cv2.imshow("frame", frame)
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gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray=cv2.GaussianBlur(gray, (5, 5), 0)
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thresh=cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2)
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contours, hierarchy=cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contour_grille=None
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maxArea=0
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for c in contours:
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area=cv2.contourArea(c)
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if area>25000:
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peri=cv2.arcLength(c, True)
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polygone=cv2.approxPolyDP(c, 0.02*peri, True)
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if area>maxArea and len(polygone)==4:
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contour_grille=polygone
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maxArea=area
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if contour_grille is not None:
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points=np.vstack(contour_grille).squeeze()
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points=sorted(points, key=operator.itemgetter(1))
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if points[0][0]<points[1][0]:
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if points[3][0]<points[2][0]:
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pts1=np.float32([points[0], points[1], points[3], points[2]])
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else:
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pts1=np.float32([points[0], points[1], points[2], points[3]])
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else:
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if points[3][0]<points[2][0]:
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pts1=np.float32([points[1], points[0], points[3], points[2]])
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else:
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pts1=np.float32([points[1], points[0], points[2], points[3]])
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pts2=np.float32([[0, 0], [taille_grille, 0], [0, taille_grille], [taille_grille, taille_grille]])
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M=cv2.getPerspectiveTransform(pts1, pts2)
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grille=cv2.warpPerspective(frame, M, (taille_grille, taille_grille))
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grille=cv2.cvtColor(grille, cv2.COLOR_BGR2GRAY)
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grille=cv2.adaptiveThreshold(grille, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2)
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cv2.imshow("grille", grille)
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if flag==0:
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grille=grille/255
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grille_txt=[]
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for y in range(9):
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ligne=""
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for x in range(9):
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y2min=y*case+marge
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y2max=(y+1)*case-marge
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x2min=x*case+marge
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x2max=(x+1)*case-marge
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prediction=s.run(sortie, feed_dict={images: [grille[y2min:y2max, x2min:x2max].reshape(28, 28, 1)], is_training: False})
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ligne+="{:d}".format(np.argmax(prediction[0]))
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grille_txt.append(ligne)
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result=ss.sudoku(grille_txt)
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print("Resultat:", result)
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#result=None
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if result is not None:
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flag=1
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fond=np.zeros(shape=(taille_grille, taille_grille, 3), dtype=np.float32)
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for y in range(len(result)):
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for x in range(len(result[y])):
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if grille_txt[y][x]=="0":
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cv2.putText(fond, "{:d}".format(result[y][x]), ((x)*case+marge+3, (y+1)*case-marge-3), cv2.FONT_HERSHEY_SCRIPT_COMPLEX, 0.9, (0, 0, 255), 1)
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M=cv2.getPerspectiveTransform(pts2, pts1)
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h, w, c=frame.shape
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fondP=cv2.warpPerspective(fond, M, (w, h))
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img2gray=cv2.cvtColor(fondP, cv2.COLOR_BGR2GRAY)
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ret, mask=cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
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mask=mask.astype('uint8')
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mask_inv=cv2.bitwise_not(mask)
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img1_bg=cv2.bitwise_and(frame, frame, mask=mask_inv)
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img2_fg=cv2.bitwise_and(fondP, fondP, mask=mask).astype('uint8')
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dst=cv2.add(img1_bg, img2_fg)
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cv2.imshow("frame", dst)
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else:
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cv2.imshow("frame", frame)
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else:
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flag=0
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key=cv2.waitKey(1)&0xFF
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if key==ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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