[英]Remove bounding box outline
您可以在 skimage.filters.sobel 中使用掩碼:
import skimage
img = skimage.io.imread('N35nj.png', as_gray=True)
mask = img > skimage.filters.threshold_otsu(img)
edges = skimage.filters.sobel(img, mask=mask)
讓我們繪制結果:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,5))
ax[0].imshow(img, cmap='gray')
ax[0].set_title('Original image')
ax[1].imshow(edges, cmap='magma')
ax[1].set_title('Sobel edges')
for a in ax.ravel():
a.axis('off')
plt.tight_layout()
plt.show()
這是 Python/OpenCV 中的一種方式。 只需從原始灰度圖像中獲取輪廓即可。 然后在紅色輪廓圖像上以黑色繪制 3 像素厚(Sobel 邊緣厚度)。 我注意到您的兩個圖像大小不同,並且輪廓相對於灰色框偏移。 這是為什么?
灰色原件:
索貝爾紅邊:
import cv2
import numpy as np
# read original image as grayscale
img = cv2.imread('gray_rectangle.png', cv2.IMREAD_GRAYSCALE)
hi, wi = img.shape[:2]
# read edge image
edges = cv2.imread('red_edges.png')
# edges image is larger than original and shifted, so crop it to same size
edges2 = edges[3:hi+3, 3:wi+3]
# threshold img
thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY)[1]
# get contours and draw them as black on edges image
result = edges2.copy()
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
cv2.drawContours(result, contours, -1, (0,0,0), 3)
# write result to disk
cv2.imwrite("red_edges_removed.png", result)
# display it
cv2.imshow("ORIG", img)
cv2.imshow("EDGES", edges)
cv2.imshow("THRESH", thresh)
cv2.imshow("RESULT", result)
cv2.waitKey(0)
結果:
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