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DetectChessCorners.py

+import numpy as np
+import cv2 as cv
+import glob
+import os
+
+heightImg = 800
+widthImg  = 1000
+
+FieldSize = 100
+
+BASE = os.path.dirname(__file__)
+pathImage = "SchachBrett2.jpg"
+# termination criteria
+criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
+objp = np.zeros((7*7,3), np.float32)
+
+objp[:,:2] = np.mgrid[0:7,0:7].T.reshape(-1,2)
+# Arrays to store object points and image points from all the images.
+objpoints = [] # 3d point in real world space
+imgpoints = [] # 2d points in image plane.
+img = cv.imread(BASE+ "/"+ pathImage) #glob.glob('*.jpg')
+img = cv.resize(img, (widthImg, heightImg))
+
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+# Find the chess board corners
+ret, corners = cv.findChessboardCorners(gray, (7,7), None)
+ # If found, add object points, image points (after refining them)
+if ret == True:
+    #get Corner Position of the 1. Rank
+    #print(corners)
+    leftupper = corners[6][0]
+    leftlower = corners[48][0]
+    rightupper = corners[42][0]
+    rightlower = corners[0][0]
+
+    #Transform Image with the founded Corner Position
+    pts1 = np.float32([rightupper, rightlower, leftupper, leftlower])  # PREPARE POINTS FOR WARP
+    print(pts1)
+    pts2 = np.float32([[FieldSize, FieldSize*7], [FieldSize*7, FieldSize*7], [FieldSize*7, FieldSize], [FieldSize, FieldSize]])  # PREPARE POINTS FOR WARP
+    matrix = cv.getPerspectiveTransform(pts1, pts2)
+    imgWarpColored = cv.warpPerspective(img, matrix, (FieldSize*8, FieldSize*8))
+
+    #Draw founded Chess Corners
+    imgContours = img.copy()
+    objpoints.append(objp)
+    corners2 = cv.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
+    imgpoints.append(corners)
+    # Draw and display the corners
+    cv.drawChessboardCorners(img, (7,7), corners2, ret)
+
+else:
+    ret, corners = cv.findChessboardCorners(gray, (5, 5), None)
+
+#cv.imshow('img', img)
+cv.imshow('img', imgWarpColored)
+
+cv.waitKey(500000)

DocumentScannerMain.py

+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