I want to do some planar rectification, to convert from left to right: 
我有进行校正的代码，但我需要 4 个角坐标。
我使用以下代码来查找它们：
import cv2
image = cv2.imread('input.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 120, 255, 1)
corners = cv2.goodFeaturesToTrack(canny,4,0.5,50)
for corner in corners:
x,y = corner.ravel()
cv2.circle(image,(x,y),5,(36,255,12),-1)
cv2.imshow("result", image)
cv2.waitKey()
It reads the image, and transforms it to grayscale + canny
But the resultant corners (found by cv2.goodFeaturesToTrack) aren't the desired ones:
我需要卡片的外角，有什么线索可以实现吗？
Thanks
这是输入.png：
更新：添加了四点透视变换。
我跳过了透视变换，因为问题是关于找到正确的角点。
您可以通过获取轮廓来跳过循环maximum area然后对其进行处理。一些模糊可能会进一步帮助它。按Esc按钮获取下一张图像输出。
另一个有用的方法，如何在opencv中找到图像中形状的角点？ https://stackoverflow.com/questions/50984205/how-to-find-corners-points-of-a-shape-in-an-image-in-opencv/50991492#50991492
"""
Task: Detect card corners and fix perspective
"""
import cv2
import numpy as np
img = cv2.imread('resources/KSuVq.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,127,255,0)
cv2.imshow('Thresholded original',thresh)
cv2.waitKey(0)
## Get contours
contours,h = cv2.findContours(thresh,cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
## only draw contour that have big areas
imx = img.shape[0]
imy = img.shape[1]
lp_area = (imx * imy) / 10
#################################################################
# Four point perspective transform
# https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
#################################################################
def order_points(pts):
# initialzie 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
#################################################################
## Get only rectangles given exceeding area
for cnt in contours:
approx = cv2.approxPolyDP(cnt,0.01 * cv2.arcLength(cnt, True), True)
## calculate number of vertices
#print(len(approx))
if len(approx) == 4 and cv2.contourArea(cnt) > lp_area:
print("rectangle")
tmp_img = img.copy()
cv2.drawContours(tmp_img, [cnt], 0, (0, 255, 255), 6)
cv2.imshow('Contour Borders', tmp_img)
cv2.waitKey(0)
tmp_img = img.copy()
cv2.drawContours(tmp_img, [cnt], 0, (255, 0, 255), -1)
cv2.imshow('Contour Filled', tmp_img)
cv2.waitKey(0)
# Make a hull arround the contour and draw it on the original image
tmp_img = img.copy()
mask = np.zeros((img.shape[:2]), np.uint8)
hull = cv2.convexHull(cnt)
cv2.drawContours(mask, [hull], 0, (255, 255, 255), -1)
cv2.imshow('Convex Hull Mask', mask)
cv2.waitKey(0)
# Draw minimum area rectangle
tmp_img = img.copy()
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(tmp_img, [box], 0, (0, 0, 255), 2)
cv2.imshow('Minimum Area Rectangle', tmp_img)
cv2.waitKey(0)
# Draw bounding rectangle
tmp_img = img.copy()
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(tmp_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('Bounding Rectangle', tmp_img)
cv2.waitKey(0)
# Bounding Rectangle and Minimum Area Rectangle
tmp_img = img.copy()
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(tmp_img, [box], 0, (0, 0, 255), 2)
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(tmp_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('Bounding Rectangle', tmp_img)
cv2.waitKey(0)
# determine the most extreme points along the contour
# https://www.pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/
tmp_img = img.copy()
extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0])
extRight = tuple(cnt[cnt[:, :, 0].argmax()][0])
extTop = tuple(cnt[cnt[:, :, 1].argmin()][0])
extBot = tuple(cnt[cnt[:, :, 1].argmax()][0])
cv2.drawContours(tmp_img, [cnt], -1, (0, 255, 255), 2)
cv2.circle(tmp_img, extLeft, 8, (0, 0, 255), -1)
cv2.circle(tmp_img, extRight, 8, (0, 255, 0), -1)
cv2.circle(tmp_img, extTop, 8, (255, 0, 0), -1)
cv2.circle(tmp_img, extBot, 8, (255, 255, 0), -1)
print("Corner Points: ", extLeft, extRight, extTop, extBot)
cv2.imshow('img contour drawn', tmp_img)
cv2.waitKey(0)
#cv2.destroyAllWindows()
## Perspective Transform
tmp_img = img.copy()
pts = np.array([extLeft, extRight, extTop, extBot])
warped = four_point_transform(tmp_img, pts)
cv2.imshow("Warped", warped)
cv2.waitKey(0)
cv2.destroyAllWindows()
https://docs.opencv.org/4.5.0/dd/d49/tutorial_py_contour_features.html https://docs.opencv.org/4.5.0/dd/d49/tutorial_py_contour_features.html
https://www.pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/ https://www.pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/
https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/ https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/