# imgThreshold = cv2.dilate(imgThreshold, kernel, iterations=3) ## FIND ALL COUNTOURS imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES _, contours, _ = cv2.findContours( imgThreshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # FIND ALL CONTOURS cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS resized = cv2.resize(imgContours, (heightImg // 2, widthImg // 2)) cv2.imshow("contoured image", resized) cv2.waitKey(0) # FIND THE BIGGEST COUNTOUR biggest, maxArea = utlis.biggestContour( contours) # FIND THE BIGGEST CONTOUR if biggest.size != 0: # print("biggest",biggest) biggest, flag = utlis.reorder(biggest) if flag == 1: for contour in contours: (x, y, w, h) = cv2.boundingRect(contour) cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 10) print("error!!") img = cv2.resize(img, (heightImg // 2, widthImg // 2)) cv2.imshow("image for try", img) cv2.waitKey(0) break # out = img[ topx:bottomx+1,topy:bottomy+1]
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) thres = utlis.valTrackbars() imgThreshold = cv2.Canny(imgBlur, thres[0], thres[1]) kernel = np.ones((5, 5)) imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) #elimina blancos imgThreshold = cv2.erode(imgDial, kernel, iterations=1) # engruesa la imagen imgContours = img.copy() imgBigContour = img.copy() contours, hierarchy = cv2.findContours(imgThreshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) biggest, maxArea = utlis.biggestContour(contours) if biggest.size != 0: biggest = utlis.reorder(biggest) cv2.drawContours(imgBigContour, biggest, -1, (0, 255, 0), 20) imgBigContour = utlis.drawRectangle(imgBigContour, biggest, 2) pts1 = np.float32(biggest) pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]]) matrix = cv2.getPerspectiveTransform(pts1, pts2) imgWarpColored = cv2.warpPerspective(img, matrix, (widthImg, heightImg))