prosoli.blogg.se

approxPolyDP ( cnt, epsilon, True ) if len ( approx ) = 4 : target = approx cv2. contourArea, reverse = True ) for cnt in contours : epsilon = 0.051 * cv2. CHAIN_APPROX_NONE ) contours = sorted ( contours, key = cv2. Canny ( blurred, 0, 20 ) _, contours, _ = cv2. GaussianBlur ( gray, ( 5, 5 ), 0 ) edged = cv2. warpPerspective ( image, M, ( maxWidth, maxHeight )) # return the warped imageĭef detect_edge ( self, image, enabled_transform = False ): dst = None orig = image. getPerspectiveTransform ( rect, dst ) warped = cv2. array (,, , ], dtype = "float32" ) # compute the perspective transform matrix and then apply it

# in the top-left, top-right, bottom-right, and bottom-leftĭst = np. top-down view) of the image, again specifying points # the set of destination points to obtain a "birds eye view", sqrt ((( tl - bl ) ** 2 ) + (( tl - bl ) ** 2 )) maxHeight = max ( int ( heightA ), int ( heightB )) # now that we have the dimensions of the new image, construct # y-coordinates or the top-left and bottom-left y-coordinates # maximum distance between the top-right and bottom-right sqrt ((( tr - tl ) ** 2 ) + (( tr - tl ) ** 2 )) maxWidth = max ( int ( widthA ), int ( widthB )) # compute the height of the new image, which will be the # x-coordiates or the top-right and top-left x-coordinates # maximum distance between bottom-right and bottom-left

( tl, tr, br, bl ) = rect # compute the width of the new image, which will be the Import cv2 import rect import numpy as np class Scanner ( object ): # ĭef four_point_transform ( self, image, rect ): # obtain a consistent order of the points and unpack them