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