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| import cv2 | |
| import numpy as np | |
| def order_points(pts): | |
| # initialize a list of coordinates that will be ordered | |
| # such that the first entry in the list is the top-left, | |
| # the second entry is the top-right, the third is the | |
| # bottom-right, and the fourth is the bottom-left | |
| rect = np.zeros((4, 2), dtype="float32") | |
| # the top-left point will have the smallest sum, whereas | |
| # the bottom-right point will have the largest sum | |
| s = pts.sum(axis=1) | |
| rect[0] = pts[np.argmin(s)] | |
| rect[2] = pts[np.argmax(s)] | |
| # now, compute the difference between the points, the | |
| # top-right point will have the smallest difference, | |
| # whereas the bottom-left will have the largest difference | |
| diff = np.diff(pts, axis=1) | |
| rect[1] = pts[np.argmin(diff)] | |
| rect[3] = pts[np.argmax(diff)] | |
| # return the ordered coordinates | |
| return rect | |
| def four_point_transform(image, pts): | |
| # obtain a consistent order of the points and unpack them | |
| # individually | |
| rect = order_points(pts) | |
| (tl, tr, br, bl) = rect | |
| # compute the width of the new image, which will be the | |
| # maximum distance between bottom-right and bottom-left | |
| # x-coordiates or the top-right and top-left x-coordinates | |
| 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)) | |
| # compute the height of the new image, which will be the | |
| # maximum distance between the top-right and bottom-right | |
| # y-coordinates or the top-left and bottom-left y-coordinates | |
| 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)) | |
| # now that we have the dimensions of the new image, construct | |
| # the set of destination points to obtain a "birds eye view", | |
| # (i.e. top-down view) of the image, again specifying points | |
| # in the top-left, top-right, bottom-right, and bottom-left | |
| # order | |
| dst = np.array( | |
| [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], | |
| dtype="float32", | |
| ) | |
| # compute the perspective transform matrix and then apply it | |
| M = cv2.getPerspectiveTransform(rect, dst) | |
| warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) | |
| # return the warped image | |
| return warped | |