<p>我用OpenCV的<a href="http://docs.opencv.org/3.1.0/d7/d1b/group__imgproc__misc.html#ga3267243e4d3f95165d55a618c65ac6e1" rel="noreferrer" title="watershed">watershed</a>算法解决了你的问题。你可以找到分水岭的理论和例子。</p>
<p>首先,我选择了几个点(标记)来指定我要保留的对象的位置,以及背景的位置。这个步骤是手动的,并且可以在不同的图像之间变化很大。而且,它需要一些重复,直到你得到想要的结果。我建议使用一个工具来获取像素坐标。
然后我创建了一个由零组成的空整数数组,其中包含汽车图像的大小。然后我给标记位置的像素分配了一些值(1:背景,[255192128,64]:汽车部件)。</p>
<p><strong>注意:</strong>当我下载您的图像时,我必须将其裁剪以获得带有汽车的图像。裁剪后的图像大小为400x601。这可能不是图像的大小,所以标记将关闭。</p>
<p>之后我使用分水岭算法。第一个输入是您的图像,第二个输入是标记图像(除标记位置外,其他位置均为零)。结果如下图所示。
<a href="https://i.stack.imgur.com/QWmzF.png" rel="noreferrer"><img src="https://i.stack.imgur.com/QWmzF.png" alt="after watershed"/></a></p>
<p>我将所有像素的值设置为1到255(汽车),其余的(背景)为零。然后用3x3核对得到的图像进行放大,以避免丢失汽车轮廓的信息。最后,我使用cv2.bitwise_and()函数将放大的图像用作原始图像的遮罩,结果如下:
<a href="https://i.stack.imgur.com/GpFFg.png" rel="noreferrer"><img src="https://i.stack.imgur.com/GpFFg.png" alt="final cropped image"/></a></p>
<p>这是我的代码:</p>
<pre><code>import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load the image
img = cv2.imread("/path/to/image.png", 3)
# Create a blank image of zeros (same dimension as img)
# It should be grayscale (1 color channel)
marker = np.zeros_like(img[:,:,0]).astype(np.int32)
# This step is manual. The goal is to find the points
# which create the result we want. I suggest using a
# tool to get the pixel coordinates.
# Dictate the background and set the markers to 1
marker[204][95] = 1
marker[240][137] = 1
marker[245][444] = 1
marker[260][427] = 1
marker[257][378] = 1
marker[217][466] = 1
# Dictate the area of interest
# I used different values for each part of the car (for visibility)
marker[235][370] = 255 # car body
marker[135][294] = 64 # rooftop
marker[190][454] = 64 # rear light
marker[167][458] = 64 # rear wing
marker[205][103] = 128 # front bumper
# rear bumper
marker[225][456] = 128
marker[224][461] = 128
marker[216][461] = 128
# front wheel
marker[225][189] = 192
marker[240][147] = 192
# rear wheel
marker[258][409] = 192
marker[257][391] = 192
marker[254][421] = 192
# Now we have set the markers, we use the watershed
# algorithm to generate a marked image
marked = cv2.watershed(img, marker)
# Plot this one. If it does what we want, proceed;
# otherwise edit your markers and repeat
plt.imshow(marked, cmap='gray')
plt.show()
# Make the background black, and what we want to keep white
marked[marked == 1] = 0
marked[marked > 1] = 255
# Use a kernel to dilate the image, to not lose any detail on the outline
# I used a kernel of 3x3 pixels
kernel = np.ones((3,3),np.uint8)
dilation = cv2.dilate(marked.astype(np.float32), kernel, iterations = 1)
# Plot again to check whether the dilation is according to our needs
# If not, repeat by using a smaller/bigger kernel, or more/less iterations
plt.imshow(dilation, cmap='gray')
plt.show()
# Now apply the mask we created on the initial image
final_img = cv2.bitwise_and(img, img, mask=dilation.astype(np.uint8))
# cv2.imread reads the image as BGR, but matplotlib uses RGB
# BGR to RGB so we can plot the image with accurate colors
b, g, r = cv2.split(final_img)
final_img = cv2.merge([r, g, b])
# Plot the final result
plt.imshow(final_img)
plt.show()
</code></pre>
<p>如果你有很多图像,你可能需要创建一个工具来以图形方式注释标记,甚至需要一个算法来自动查找标记。</p>