# 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))
