在python中显示原始图像像素而不是蒙版 [英] Show original image pixels instead of mask in python

问题描述

我有一个深度学习模型，该模型会向我返回一个数组，该数组在绘制时像这样

I have a deep learning model which returns to me an array which when plotted like this

res = deeplab_model.predict(np.expand_dims(resized2,0))
labels = np.argmax(res.squeeze(),-1) #remove single dimension values, gives the indices of maximum values in the array  
plt.imshow(labels[:-pad_x])

(上面的最后一行只是在绘制之前清除了一些不清楚的线)

(the last line above just removes some unclear lines before plotting them)

看起来像这样

looks like this

原始图片是这样

当我做

print(labels[labels>0])
print(labels.shape)
print(len(labels))

我明白了

[12 12 12 ... 12 12 12]
(512, 512)
512

我想在出现遮罩的原始图像中显示彩色像素，然后将其他所有颜色都变为黑色(或者我会选择模糊或其他颜色)，该怎么办?

I want to show the colored pixels in the original image where mask appears and turn everything else to black (or blur or some other color I'll choose), how can I do that?

推荐答案

我能够扭转这种局面，实现我想要的目标

I was able to reverse this and achieve what I wanted to get

mask = labels[:-pad_x] == 0 resizedOrig = cv2.resize(frame, (512,384)) resizedOrig[mask] = 0

mask = labels[:-pad_x] == 0 resizedOrig = cv2.resize(frame, (512,384)) resizedOrig[mask] = 0

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