How to detect a rectangle (whiteboard) in an image using opencv?

Question

I have the following image. I want to detect and perspective transform the rectangular whiteboard.

I want to detect these 4 boundaries/corners and apply a perspective transformation to it. Have a look at the below image:

I am not able to detect the boundaries of the rectangle. Here's what I have tried:

import cv2, os import numpy as np from google.colab.patches import cv2_imshow image = cv2.imread("img.jpg") orig1 = image.copy() # 1) Grayscale image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # cv2_imshow(gray) # 2) Erosion kernel = np.ones((5, 5), np.uint8) erosion = cv2.erode(gray, kernel, iterations = 1) # cv2_imshow(erosion) # 3) Thresholding (OTSU) blur = cv2.GaussianBlur(erosion, (5,5),0) ret3, thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # cv2_imshow(thresh) # 4) Contours copy = thresh; orig = image; cnts = cv2.findContours(copy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] area = -1; c1 = 0 for c in cnts: if area < cv2.contourArea(c): area = cv2.contourArea(c) c1 = c cv2.drawContours(orig,[c1], 0, (0,255,0), 3) epsilon = 0.09 * cv2.arcLength(c1,True) approx = cv2.approxPolyDP(c1,epsilon,True) if len(approx) != 4: # Then it will fail here. pass cood = [] for i in range(0, len(approx)): cood.append([approx[i][0][0], approx[i][0][1]]) # 5) Perspective Transformation def reorder(myPoints): myPoints = np.array(myPoints).reshape((4, 2)) myPointsNew = np.zeros((4, 1, 2), dtype=np.int32) add = myPoints.sum(1) myPointsNew[0] = myPoints[np.argmin(add)] myPointsNew[3] =myPoints[np.argmax(add)] diff = np.diff(myPoints, axis=1) myPointsNew[1] =myPoints[np.argmin(diff)] myPointsNew[2] = myPoints[np.argmax(diff)] return myPointsNew pts1 = np.float32(reorder(cood)) w = 1000; h = 1000; m1 = 1000; m2 = 1000 pts2 = np.float32([[0, 0], [w, 0], [0, h], [w, h]]) matrix = cv2.getPerspectiveTransform(pts1, pts2) result = cv2.warpPerspective(orig1, matrix, (m1, m2)) cv2_imshow(result)

I have also gone through Microsoft's research, but not sure how to implement it. I am not able to detect and perspective transform the board. It would be great if anyone of you can help me out. Also, do let me know if my question requires more details.

Did you tried color based approach? Is it always the same board? Than you know width, height ratio. Then with some edge detection/Contour detection it should work — PVerstraete
– PVerstraete, Commented Apr 7, 2021 at 12:47
I haven't tried the color-based approach yet. But, the board may not be the same. We can't fix the coordinates. Is there a way to generalize it? — Amish Sharma
– Amish Sharma, Commented Apr 7, 2021 at 13:01
The problem is the overexposed wall, so the wall is very white instead of his natural color gray. So you have to fix this first. I also saw that your image has a very poor quality. Sow texture based approach will be difficult. Maybe start looking for some model to detect the whiteboard in the image? — PVerstraete
– PVerstraete, Commented Apr 7, 2021 at 14:12

Amish Sharma · Accepted Answer · 2021-04-12 13:36:05Z

I manage to get the 4 coordinates of the whiteboard. I have used adaptive thresholding to detect the edges rather than canny-edge detection, not sure whether the methodology is correct or not, but it is giving the required results. Here's the code for the same.

import ... img = cv2.imread("path-to-image") gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 199, 5) cv2_imshow(thresh)

# finding contours and applying perspective try: copy = thresh.copy(); orig = img.copy() cnts = cv2.findContours(copy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] area = -1; c1 = 0 for c in cnts: epsilon = 0.01 * cv2.arcLength(c,True) approx = cv2.approxPolyDP(c,epsilon,True) if len(approx) == 4 and area < cv2.contourArea(c): area = cv2.contourArea(c) c1 = c; approx1 = approx warped = four_point_transform(orig, approx1.reshape(4, 2)) cv2_imshow(warped) except: print("Image cannot be transformed!!\n") # four point transform def order_points(pts): # https://www.pyimagesearch.com/2016/03/21/ordering-coordinates-clockwise-with-python-and-opencv/ xSorted = pts[np.argsort(pts[:, 0]), :] leftMost = xSorted[:2, :] rightMost = xSorted[2:, :] leftMost = leftMost[np.argsort(leftMost[:, 1]), :] (tl, bl) = leftMost D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0] (br, tr) = rightMost[np.argsort(D)[::-1], :] return np.array([tl, tr, br, bl], dtype="float32") def four_point_transform(image, pts): # https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/ rect = order_points(pts) (tl, tr, br, bl) = rect widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped

Here's the warped image:

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