fig, m_axs = plt.subplots(20, 2, figsize = (10, 40))
[c_ax.axis('off') for c_ax in m_axs.flatten()]
for (ax1, ax2), c_img_name in zip(m_axs, test_paths):
c_path = os.path.join(test_image_dir, c_img_name)
c_img = imread(c_path)
c_img = cv2.resize(c_img,(256,256))
first_img = np.expand_dims(c_img, 0)/255.0
first_seg = seg_model.predict(first_img)
first_img[0][:,:,0] = (first_img[0][:,:,0]*0.7 + 0.5*first_seg[0, :, :, 0])
result = np.array(np.clip(first_img[0]*255.,0,255),dtype=np.int32)
ax1.imshow(result)
ax1.set_title('Image')
ax2.imshow(first_seg[0, :, :, 0], vmin = 0, vmax = 1, cmap='gray')
ax2.set_title('Prediction')
fig.savefig('test_predictions.png')
[[1]: https://i.stack.imgur.com/kJi3k.png][1]
полужирный как я могу решить эту проблему