我用OpenCV解决了你的问题 [分水岭](http://docs.opencv.org/3.1.0/d7/d1b/group\uu imgproc\uu misc.html\ga3267243e4d3f95165d55a618c65ac6e1 “分水岭”算法。你可以找到分水岭的理论和例子这里. 首先，我选择了几个点（标记）来指示对象I的位置 想保留，背景在哪里。此步骤是手动的，可以改变 从一张图片到另一张图片。而且，它需要一些重复，直到你得到正确的答案 期望的结果。我建议使用一个工具来获得像素坐标。然后我 创建了一个零的空整数数组，大小与汽车图像相同。以及 然后我将一些值（1:background，[255192128,64]：car\u parts）赋给 标记位置的像素。当我下载你的图片时，我不得不裁剪它以得到一个 那辆车。裁剪后，图像的大小为400x601。这可能不是什么 你拥有的图像的大小，所以标记将被关闭。 之后我使用了分水岭算法。第一个输入是图像，第二个输入是 输入是标记图像（除标记位置外，所有位置均为零）。这个 结果如下图所示。 我将值大于1的所有像素设置为255（汽车），其余的设置为255 （背景）归零。然后我用一个3x3的内核将得到的图像放大到 避免丢失有关汽车轮廓的信息。最后，我用了 放大图像作为原始图像的遮罩，使用cv2.bitwise和（）函数， 这是我的密码：
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()
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