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HoughLinesP未检测到预期的行

opencv python

Chris · 技术社区 · 7 年前

我正在试用OpenCV做一些图像处理。诚然,我对这件事很在行,但我觉得我的大脑好像在某种程度上被它包围着。我用遮罩来检测图像中较亮的区域,然后运行canny检测器,最后是HoughLinesP检测。代码如下。我得到的结果是:

我的期望(和愿望)如下(注意结果上的红线):

值得一提的是,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,建议将不胜感激。

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)

1 回复 | 直到 7 年前

Christoffer 7 年前

我建议大家从不同的角度看问题 Morphological Transformations 可以将其应用于canny边缘检测,以改进hough线变换。

这并不完美,但可以让你开始:

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)

另一种方法可能是调查 Corner Detection 然后在检测到的角点之间画一条线(我没有尝试过这种方法,但这只是为了灵感:)。