当纸张本身上有打印的角/线时，如何找到纸张的角？

Question

我正在Python中使用openCV来查找一张纸的角以使其变形。

img = cv2.imread(images[i])

        corners = cv2.goodFeaturesToTrack(cv2.cvtColor(img,cv2.COLOR_BGR2GRAY),4,.01,1000,useHarrisDetector=True,k=.04)
        corners = np.float32(corners)
        print(corners)
        ratio = 1.6
        cardH = math.sqrt((corners[2][0][0] - corners[1][0][0]) * (corners[2][0][0] - corners[1][0][0]) + (corners[2][0][1] - corners[1][0][1]) * (
                    corners[2][0][1] - corners[1][0][1]))
        cardW = ratio * cardH;
        pts2 = np.float32(
            [[corners[0][0][0], corners[0][0][1]], [corners[0][0][0] + cardW, corners[0][0][1]], [corners[0][0][0] + cardW, corners[0][0][1] + cardH],
             [corners[0][0][0], corners[0][0][1] + cardH]])

        M = cv2.getPerspectiveTransform(corners, pts2)

        offsetSize = 500
        transformed = np.zeros((int(cardW + offsetSize), int(cardH + offsetSize)), dtype=np.uint8);
        dst = cv2.warpPerspective(img, M, transformed.shape)

之前：https://imgur.com/a/H7HjFro

之后：https://imgur.com/a/OA6Iscq

您可以从这些图像中看到，它们检测的是纸张本身内部的边缘，而不是纸张的角落。我应该考虑完全使用其他算法吗？我很迷路。

我已经尝试将最小欧几里德距离增加到1000，但这实际上没有任何作用。

请注意，这是一个人的真实信息，这是在Kaggle上发现的虚假数据集。

kaggle数据集可以找到https://www.kaggle.com/mcvishnu1/fake-w2-us-tax-form-dataset

Answer 1

这里是在Python / OpenCV中获得成功的一种方法。我将把视角转换留给您。 请注意，找到的角是从右上角起逆时针列出的。

• 读取输入
• 转换为灰色
• 高斯模糊
• 大津阈值
• 形态打开/关闭以清理阈值
• 获得最大轮廓
• 从轮廓近似多边形
• 获取角落
• 在输入上绘制多边形
• 保存结果

输入：

import cv2
import numpy as np

# read image
img = cv2.imread("efile.jpg")

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# blur image
blur = cv2.GaussianBlur(gray, (3,3), 0)

# do otsu threshold on gray image
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# apply morphology
kernel = np.ones((7,7), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
area_thresh = 0
for c in contours:
    area = cv2.contourArea(c)
    if area > area_thresh:
        area_thresh = area
        big_contour = c

# draw white filled largest contour on black just as a check to see it got the correct region
page = np.zeros_like(img)
cv2.drawContours(page, [big_contour], 0, (255,255,255), -1)

# get perimeter and approximate a polygon
peri = cv2.arcLength(big_contour, True)
corners = cv2.approxPolyDP(big_contour, 0.04 * peri, True)

# draw polygon on input image from detected corners
result = img.copy()
cv2.polylines(result, [corners], True, (0,0,255), 1, cv2.LINE_AA)
# Alternate: cv2.drawContours(page,[corners],0,(0,0,255),1)

# print the number of found corners and the corner coordinates
# They seem to be listed counter-clockwise from the top right corner
print(len(corners))
print(corners)


# write results
cv2.imwrite("efile_thresh.jpg", thresh)
cv2.imwrite("efile_morph.jpg", morph)
cv2.imwrite("efile_polygon.jpg", result)

# display it
cv2.imshow("efile_thresh", thresh)
cv2.imshow("efile_morph", morph)
cv2.imshow("efile_page", page)
cv2.imshow("efile_polygon", result)
cv2.waitKey(0)

阈值图像：

形态清洗图像：

在输入上绘制的结果多边形：

提取角（从右上角逆时针方向）

4

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