HoughLinesP 未检测到预期的线条

Question

我正在尝试使用 OpenCV 进行一些图像处理。诚然，我是这个东西的菜鸟，但我觉得我的大脑有点绕着它。我使用蒙版来检测图像的较亮区域，然后运行精明检测器，最后运行 HoughLinesP 检测。代码如下。我得到的结果是：

我的预期（和渴望）更像是下面（注意结果上的红线）：

不管怎样，我的最终目的是自动旋转图像，使收据笔直。如果我完全走错了路，我们将不胜感激。

import cv2
import numpy as np
from matplotlib import pyplot


def detect_lines(img):

    temp = cv2.cvtColor(img,cv2.COLOR_BGR2HLS)
    lower = np.uint8([0, 160, 0])
    upper = np.uint8([255, 255, 255])
    white_mask = cv2.inRange(temp, lower, upper)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.blur(gray, (3, 3))

    canny_low = 100
    edges = cv2.Canny(white_mask, canny_low, canny_low * 3, apertureSize=5)
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 10, 2, 80)

    result = img.copy()
    if lines is not None:
        for x in range(0, len(lines)):
            for x1, y1, x2, y2 in lines[x]:
                print(x1, y1, x2, y2)
                cv2.line(result, (x1, y1), (x2, y2), (255, 0, 0), 2)

    pyplot.subplot(141), pyplot.imshow(img, cmap='gray')
    pyplot.title('Original Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(142), pyplot.imshow(white_mask, cmap='gray')
    pyplot.title('Gray Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(143), pyplot.imshow(edges, cmap='gray')
    pyplot.title('Edge Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(144), pyplot.imshow(result, cmap='gray')
    pyplot.title('Result Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.show()
    return img


if __name__ == '__main__':

    image = cv2.imread('receipt.jpg')
    image = detect_lines(image)
    cv2.imwrite('output.jpg', image)

Answer 1

我建议开始查看不同的Morphological Transformations，您可以将其应用于您的精明边缘检测，以改进霍夫线变换。

这并不完美，但可以帮助您入门：

import cv2
import numpy as np
from matplotlib import pyplot


def detect_lines(img):

    temp = cv2.cvtColor(img,cv2.COLOR_BGR2HLS)
    kernel = np.ones((5, 5), np.uint8) # < --- Added a kernel you can differ
    lower = np.uint8([0, 160, 0])
    upper = np.uint8([255, 255, 255])
    white_mask = cv2.inRange(temp, lower, upper)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.blur(gray, (3, 3))

    canny_low = 100
    edges = cv2.Canny(white_mask, canny_low, canny_low * 3, apertureSize=3)
    dilate = cv2.dilate(edges, kernel, iterations=2) # < --- Added a dilate, check link I provided
    ero = cv2.erode(dilate, kernel, iterations=1) # < --- Added an erosion, check link I provided
    lines = cv2.HoughLinesP(dilate, 1, np.pi/180, 10, 2, 80)

    result = img.copy()
    if lines is not None:
        for x in range(0, len(lines)):
            for x1, y1, x2, y2 in lines[x]:
                print(x1, y1, x2, y2)
                cv2.line(result, (x1, y1), (x2, y2), (255, 0, 0), 2)

    pyplot.subplot(151), pyplot.imshow(img, cmap='gray')
    pyplot.title('Original Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(152), pyplot.imshow(white_mask, cmap='gray')
    pyplot.title('Mask Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(153), pyplot.imshow(edges, cmap='gray')
    pyplot.title('Edge Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.subplot(154), pyplot.imshow(ero, cmap='gray')
    pyplot.title('Dilate/Erosion Image'), pyplot.xticks([]), pyplot.yticks([]) # <--- Added a display

    pyplot.subplot(155), pyplot.imshow(result, cmap='gray')
    pyplot.title('Result Image'), pyplot.xticks([]), pyplot.yticks([])

    pyplot.show()
    return result # <--- You want to return the result right?


if __name__ == '__main__':

    image = cv2.imread('receipt.jpg')
    image = detect_lines(image)
    cv2.imwrite('output.jpg', image)

另一种方法可能是查看Corner Detection，然后在检测到的角之间画一条线（我没有尝试过这种方法，但它只是为了灵感:)）。