Initial commit

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]<points[1][0]:
+                if points[3][0]<points[2][0]:
+                    pts1=np.float32([points[0], points[1], points[3], points[2]])
+                else:
+                    pts1=np.float32([points[0], points[1], points[2], points[3]])
+            else:
+                if points[3][0]<points[2][0]:
+                    pts1=np.float32([points[1], points[0], points[3], points[2]])
+                else:
+                    pts1=np.float32([points[1], points[0], points[2], points[3]])
+            pts2=np.float32([[0, 0], [taille_grille, 0], [0, taille_grille], [taille_grille, taille_grille]])
+            M=cv2.getPerspectiveTransform(pts1, pts2)
+            grille=cv2.warpPerspective(frame, M, (taille_grille, taille_grille))
+            grille=cv2.cvtColor(grille, cv2.COLOR_BGR2GRAY)
+            grille=cv2.adaptiveThreshold(grille, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2)
+            cv2.imshow("grille", grille)
+            if flag==0:
+                grille=grille/255
+                grille_txt=[]
+                for y in range(9):
+                    ligne=""
+                    for x in range(9):
+                        y2min=y*case+marge
+                        y2max=(y+1)*case-marge
+                        x2min=x*case+marge
+                        x2max=(x+1)*case-marge
+                        prediction=s.run(sortie, feed_dict={images: [grille[y2min:y2max, x2min:x2max].reshape(28, 28, 1)], is_training: False})
+                        ligne+="{:d}".format(np.argmax(prediction[0]))
+                    grille_txt.append(ligne)
+                result=ss.sudoku(grille_txt)
+                print("Resultat:", result)
+            #result=None
+            if result is not None:
+                flag=1
+                fond=np.zeros(shape=(taille_grille, taille_grille, 3), dtype=np.float32)
+                for y in range(len(result)):
+                    for x in range(len(result[y])):
+                            if grille_txt[y][x]=="0":
+                                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)
+                M=cv2.getPerspectiveTransform(pts2, pts1)
+                h, w, c=frame.shape
+                fondP=cv2.warpPerspective(fond, M, (w, h))
+                img2gray=cv2.cvtColor(fondP, cv2.COLOR_BGR2GRAY)
+                ret, mask=cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
+                mask=mask.astype('uint8')
+                mask_inv=cv2.bitwise_not(mask)
+                img1_bg=cv2.bitwise_and(frame, frame, mask=mask_inv)
+                img2_fg=cv2.bitwise_and(fondP, fondP, mask=mask).astype('uint8')
+                dst=cv2.add(img1_bg, img2_fg)
+                cv2.imshow("frame", dst)
+            else:
+                cv2.imshow("frame", frame)
+        else:
+            flag=0
+        key=cv2.waitKey(1)&0xFF
+        if key==ord('q'):
+            break
+cap.release()
+cv2.destroyAllWindows()