#find all your connected components (white blobs in your image)
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(img, connectivity=8)
#connectedComponentswithStats yields every seperated component with information on each of them, such as size
#the following part is just taking out the background which is also considered a component, but most of the time we don't want that.
sizes = stats[1:, -1]; nb_components = nb_components - 1
# minimum size of particles we want to keep (number of pixels)
#here, it's a fixed value, but you can set it as you want, eg the mean of the sizes or whatever
min_size = 150
#your answer image
img2 = np.zeros((output.shape))
#for every component in the image, you keep it only if it's above min_size
for i in range(0, nb_components):
if sizes[i] >= min_size:
img2[output == i + 1] = 255
用
connectedComponentsWithStats
怎么样:输出:
为了自动删除对象,您需要在图像中找到它们。 从你提供的图片上看,我看不出有什么区别于其他7个突出显示的项目。 你必须告诉你的电脑如何识别你不想要的东西。如果他们看起来一样,这是不可能的。
如果您有多个图像,其中的对象总是看起来像,您可以使用模板匹配技术。
另外,关闭操作对我来说也没什么意义。
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