<p>您可以使用<code>Harris corner detector</code>算法检测上述图像中的静脉连接。与以前的技术相比,<code>Harris corner detector</code>直接考虑了角点得分相对于方向的差异,而不是每45度角使用移动面片,并且已被证明在区分边和角方面更准确(来源:<a href="https://en.wikipedia.org/wiki/Harris_Corner_Detector" rel="nofollow noreferrer">wikipedia</a>)。你知道吗</p>
<p>代码:</p>
<pre><code>img = cv2.imread('wings-bee.png')
# convert image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
'''
args:
img - Input image, it should be grayscale and float32 type.
blockSize - It is the size of neighbourhood considered for corner detection
ksize - Aperture parameter of Sobel derivative used.
k - Harris detector free parameter in the equation.
'''
dst = cv2.cornerHarris(gray, 9, 5, 0.04)
# result is dilated for marking the corners
dst = cv2.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img_thresh = cv2.threshold(dst, 0.32*dst.max(), 255, 0)[1]
img_thresh = np.uint8(img_thresh)
# get the matrix with the x and y locations of each centroid
centroids = cv2.connectedComponentsWithStats(img_thresh)[3]
stop_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# refine corner coordinates to subpixel accuracy
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5,5), (-1,-1), stop_criteria)
for i in range(1, len(corners)):
#print(corners[i])
cv2.circle(img, (int(corners[i,0]), int(corners[i,1])), 5, (0,255,0), 2)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
</code></pre>
<p>输出:</p>
<p><a href="https://i.stack.imgur.com/sHaxi.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sHaxi.png" alt="enter image description here"/></a></p>
<p>您可以从<a href="https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.html" rel="nofollow noreferrer">here</a>检查<code>Harris Corner detector</code>算法背后的理论。你知道吗</p>