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- import cv2
- import numpy as np
- import utlis
- import os
-
- ########################################################################
- webCamFeed = True
- pathImage = "SchachBrett2.jpg"
- cap = cv2.VideoCapture()
- #cap = cv2.VideoCapture(1)
- cap.set(10,160)
- heightImg = 640
- widthImg = 480
-
- BASE = os.path.dirname(__file__)
- ########################################################################
-
- utlis.initializeTrackbars()
- count=0
-
- while True:
- img = cv2.imread(BASE+ "/"+ pathImage)
- img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
- imgBlank = np.zeros((heightImg,widthImg, 3), np.uint8) # CREATE A BLANK IMAGE FOR TESTING DEBUGING IF REQUIRED
- imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CONVERT IMAGE TO GRAY SCALE
- imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # ADD GAUSSIAN BLUR
- thres=utlis.valTrackbars() # GET TRACK BAR VALUES FOR THRESHOLDS
- imgThreshold = cv2.Canny(imgBlur,thres[0],thres[1]) # APPLY CANNY BLUR
- kernel = np.ones((5, 5))
- imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) # APPLY DILATION
- imgThreshold = cv2.erode(imgDial, kernel, iterations=1) # APPLY EROSION
-
- ## FIND ALL COUNTOURS
- imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
- imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
- #contours =cv2.findChessboardCorners(img, (8,8),corners, cv2.CALIB_CB_ADAPTIVE_THRESH )
- contours, hierarchy = cv2.findContours(imgThreshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # FIND ALL CONTOURS
- cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS
-
-
- # FIND THE BIGGEST COUNTOUR
- biggest, maxArea = utlis.biggestContour(contours) # FIND THE BIGGEST CONTOUR
- if biggest.size != 0:
- biggest=utlis.reorder(biggest)
- cv2.drawContours(imgBigContour, biggest, -1, (0, 255, 0), 20) # DRAW THE BIGGEST CONTOUR
- imgBigContour = utlis.drawRectangle(imgBigContour,biggest,2)
- pts1 = np.float32(biggest) # PREPARE POINTS FOR WARP
- pts2 = np.float32([[0, 0],[widthImg, 0], [0, heightImg],[widthImg, heightImg]]) # PREPARE POINTS FOR WARP
- matrix = cv2.getPerspectiveTransform(pts1, pts2)
- imgWarpColored = cv2.warpPerspective(img, matrix, (widthImg, heightImg))
-
- #REMOVE 20 PIXELS FORM EACH SIDE
- imgWarpColored=imgWarpColored[20:imgWarpColored.shape[0] - 20, 20:imgWarpColored.shape[1] - 20]
- imgWarpColored = cv2.resize(imgWarpColored,(widthImg,heightImg))
-
- # APPLY ADAPTIVE THRESHOLD
- imgWarpGray = cv2.cvtColor(imgWarpColored,cv2.COLOR_BGR2GRAY)
- imgAdaptiveThre= cv2.adaptiveThreshold(imgWarpGray, 255, 1, 1, 7, 2)
- imgAdaptiveThre = cv2.bitwise_not(imgAdaptiveThre)
- imgAdaptiveThre=cv2.medianBlur(imgAdaptiveThre,3)
-
- # Image Array for Display
- imageArray = ([img,imgGray,imgThreshold,imgContours],
- [imgBigContour,imgWarpColored, imgWarpGray,imgAdaptiveThre])
-
- else:
- imageArray = ([img,imgGray,imgThreshold,imgContours],
- [imgBlank, imgBlank, imgBlank, imgBlank])
-
- # LABELS FOR DISPLAY
- lables = [["Original","Gray","Threshold","Contours"],
- ["Biggest Contour","Warp Prespective","Warp Gray","Adaptive Threshold"]]
-
- stackedImage = utlis.stackImages(imageArray,0.75,lables)
- cv2.imshow("Result",stackedImage)
-
- # SAVE IMAGE WHEN 's' key is pressed
- if cv2.waitKey(1) & 0xFF == ord('s'):
- cv2.imwrite("Scanned/myImage"+str(count)+".jpg",imgWarpColored)
- cv2.rectangle(stackedImage, ((int(stackedImage.shape[1] / 2) - 230), int(stackedImage.shape[0] / 2) + 50),
- (1100, 350), (0, 255, 0), cv2.FILLED)
- cv2.putText(stackedImage, "Scan Saved", (int(stackedImage.shape[1] / 2) - 200, int(stackedImage.shape[0] / 2)),
- cv2.FONT_HERSHEY_DUPLEX, 3, (0, 0, 255), 5, cv2.LINE_AA)
- cv2.imshow('Result', stackedImage)
- cv2.waitKey(300)
- count += 1
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