Python 没有足够的背景过滤

我试图过滤呈现电缆的图像的背景。我试着做到以下几点：

从颜色转换为灰色

应用

cv2.Laplacian

或2次

cv2.Sobel

查找两个方向的边

应用阈值化

cv2.THRESH\u BINARY（\u INV）

，

cv2.THRESH\u OTSU

最后，我尝试使用

cv2.Canny

和

cv2.HoughLinesP

总的来说，结果一点也不令人满意。我将给出两幅图像的示例：

以及我的脚本的输出：

我还使用了

config

中的值，但结果差别不大

以下是我设法完成的小脚本：

import cv2
import matplotlib.pyplot as plt
import numpy as np

def img_show(images, cmap=None):
  fig = plt.figure(figsize=(17, 10))
  root = 3 # len(images) ** 0.5
  for i, img in enumerate(images):
    ax = fig.add_subplot(root, root, i + 1)
    ax.imshow(img, cmap=cmap[i])
  plt.show()


class Config:
  scale = 0.4
  min_threshold = 120
  max_threshold = 200
  canny_min_threshold = 100
  canny_max_threshold = 200


config = Config()


def find_lines(img, rgb_img):
  dst = cv2.Canny(img, config.canny_min_threshold, config.canny_max_threshold)

  cdstP = np.copy(rgb_img)

  lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 150, None, 0, 0)

  lines1 = lines[:, 0, :]
  for x1, y1, x2, y2 in lines1[:]:
    cv2.line(cdstP, (x1, y1), (x2, y2), (255, 0, 0), 5)
  return cdstP


if __name__ == "__main__":
    bgr_img = cv2.imread('DJI_0009.JPG')

    bgr_img = cv2.resize(bgr_img, (0, 0), bgr_img, config.scale, config.scale)

    rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
    gray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)

    # _, threshold = cv2.threshold(gray_img, config.min_threshold, config.max_threshold, cv2.THRESH_BINARY)

    # laplacian = cv2.Laplacian(rgb_img, cv2.CV_8UC1)
    sobelx = cv2.Sobel(gray_img, cv2.CV_8UC1, 1, 0)
    sobely = cv2.Sobel(gray_img, cv2.CV_8UC1, 0, 1)
    blended = cv2.addWeighted(src1=sobelx, alpha=0.5, src2=sobely, beta=0.5, gamma=0)

    _, threshold = cv2.threshold(blended, config.min_threshold, config.max_threshold,
                                 cv2.THRESH_BINARY | cv2.THRESH_OTSU)

    p1 = find_lines(threshold, rgb_img)
    p2 = find_lines(blended, rgb_img)
    p3 = find_lines(gray_img, rgb_img)

    plots = [rgb_img, p1, p2, p3]
    cmaps = [None] + ['gray'] * (len(plots) - 1)
    img_show(plots, cmaps)

我想我需要做更好的过滤。然而，我也尝试了图像分割，但结果一点也不乐观。 关于如何改进这一点有什么想法吗？

---

谢谢

在Python/OpenCV中有一种方法可以做到这一点。I阈值，然后可选地使用形态学进行清理。然后得到轮廓，并为每个轮廓计算其旋转矩形。然后获得旋转矩形的尺寸，并计算纵横比（最大尺寸/最小尺寸）和面积（可选）。然后，我在纵横比（和可选面积）上设置阈值，并仅保留通过的轮廓）

输入：

阈值图像：

形态学清理图像：

结果图像：

---

我建议您使用适当的阈值，然后计算轮廓并根据面积和某些长度度量进行过滤，或者使用连接的组件根据纵横比和面积进行过滤。您是否详细说明关于面积和长度度量的过滤

，或者使用连接的组件根据纵横比和面积进行过滤

。我应该寻找什么样的过滤？

import cv2
import numpy as np

image = cv2.imread("DCIM-100-MEDIA-DJI-0009-JPG.jpg")
hh, ww = image.shape[:2]

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

# create a binary thresholded image
thresh = cv2.threshold(gray, 64, 255, cv2.THRESH_BINARY)[1]

# invert so line is white on black background
thresh = 255 - thresh

# apply morphology
kernel = np.ones((11,11), np.uint8)
clean = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# get external contours
contours = cv2.findContours(clean, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

area_thresh = ww / 2
aspect_thresh = ww / 30
print(area_thresh,aspect_thresh)
print('')
result = image.copy()
for c in contours:
    
    # get rotated rectangle from contour
    # get its dimensions
    rotrect = cv2.minAreaRect(c)
    (center), (dim1,dim2), angle = rotrect
    maxdim = max(dim1,dim2)
    mindim = min(dim1,dim2)
    area = dim1 * dim2
    if mindim != 0:
        aspect = maxdim / mindim
    #print(area, aspect)

    #if area > area_thresh and aspect > aspect_thresh:
    if aspect > aspect_thresh:
        # draw contour on input
        cv2.drawContours(result,[c],0,(0,0,255),3)
        print(area, aspect)

# save result
cv2.imwrite("DCIM-100-MEDIA-DJI-0009-JPG_thresh.jpg",thresh)
cv2.imwrite("DCIM-100-MEDIA-DJI-0009-JPG_clean.jpg",clean)
cv2.imwrite("DCIM-100-MEDIA-DJI-0009-JPG_result.jpg",result)

# display result
cv2.imshow("thresh", thresh)
cv2.imshow("clean", clean)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()