python-3.x - 如何使用opencv python在各种彩色背景中找到文档边缘？ 【各种背景下的文档扫描】

Question

我目前有一个需要智能扫描的文档。

为此，我需要在任何背景中找到文档的适当轮廓，以便我可以对该图像进行扭曲透视投影和检测。

这样做时面临的主要问题是文档边缘检测任何类型的背景。

到目前为止，我一直尝试使用函数 HoughLineP 并尝试在通过精明边缘检测的灰度模糊图像上找到轮廓。

            MORPH = 9
            CANNY = 84
            HOUGH = 25

            IM_HEIGHT, IM_WIDTH, _ = rescaled_image.shape

            # convert the image to grayscale and blur it slightly
            gray = cv2.cvtColor(rescaled_image, cv2.COLOR_BGR2GRAY)
            gray = cv2.GaussianBlur(gray, (7,7), 0)

            #dilate helps to remove potential holes between edge segments
            kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(MORPH,MORPH))
            dilated = cv2.dilate(gray, kernel)

            # find edges and mark them in the output map using the Canny algorithm
            edged = cv2.Canny(dilated, 0, CANNY)
            test_corners = self.get_corners(edged)

            approx_contours = []

    (_, cnts, hierarchy) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

            # loop over the contours
            for c in cnts:
                # approximate the contour
                approx = cv2.approxPolyDP(c, 80, True)
                if self.is_valid_contour(approx, IM_WIDTH, IM_HEIGHT):
                    approx_contours.append(approx)
                    break

如何通过 OpenCV 代码在文档周围找到合适的边界框。
任何帮助都感激不尽。
(文档是从相机以任何角度和任何彩色背景拍摄的。)

Answer 1

以下代码可能会帮助您检测/分割图像中的页面...

import cv2
import matplotlib.pyplot as plt
import numpy as np
image = cv2.imread('test_p.jpg')
image = cv2.imread('test_p.jpg')
print(image.shape)
ori = image.copy()
image = cv2.resize(image, (image.shape[1]//10,image.shape[0]//10))

调整图像大小以使操作更快，以便我们可以实时工作..

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (11,11), 0)
edged = cv2.Canny(gray, 75, 200)
print("STEP 1: Edge Detection")
plt.imshow(edged)
plt.show()
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts[1], key = cv2.contourArea, reverse = True)[:5]

在这里，我们将仅考虑基于面积的排序列表中的前 5 个轮廓
这里高斯模糊的大小有点敏感，所以根据图像大小相应地选择它。
以上操作后的图像可能看起来像..

for c in cnts:
    ### Approximating the contour
    #Calculates a contour perimeter or a curve length
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.01 * peri, True)
    # if our approximated contour has four points, then we
    # can assume that we have found our screen
    screenCnt = approx
    if len(approx) == 4:
        screenCnt = approx
        break
    # show the contour (outline) 
    print("STEP 2: Finding Boundary")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
image_e = cv2.resize(image,(image.shape[1],image.shape[0]))
cv2.imwrite('image_edge.jpg',image_e)
plt.imshow(image_e)
plt.show()

最终图像可能看起来像...
剩下的事情可能会在得到最终图像后处理......
代码引用:- Git Repository
我想这个答案会有所帮助...