CODEWITHSUNDEEP
imagenumpyimage-processingcomputer-visionpytorch
sayeedk06
sayeedk06

Reputation: 103

Does changing image channel break the image?

so I have images with the format(width,height,channel). My original channel is in rgb. So I Load the images in grayscale

for r, d, file in tqdm(os.walk(path)):
  for i in tqdm(file):
    if i[0:2]=="01":
      dist_one.append(cv2.imread(os.path.join(path,i),cv2.IMREAD_GRAYSCALE))
    else:
      dist_two.append(cv2.imread(os.path.join(path,i),cv2.IMREAD_GRAYSCALE))

Suppose the images have the shape (187, 187). So I add a channel using the code

g = np.expand_dims(dist_one[0], axis=0)

But this breaks the images when I try to plot the image.

TypeError Traceback (most recent call last) in () 1 import matplotlib.pyplot as plt 2 ----> 3 plt.imshow(dist_one[0],cmap='gray') 4 plt.show() 5 frames/usr/local/lib/python3.6/dist-packages/matplotlib/image.py in set_data(self, A) 688 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]): 689 raise TypeError("Invalid shape {} for image data" --> 690
.format(self._A.shape)) 691 692 if self._A.ndim == 3: TypeError: Invalid shape (1, 187, 187) for image data

But it works when the channel is put last. g = np.expand_dims(dist_one[0], axis=-1). Whats the reason for this? I need the channel at the first for pytorch. Or am I suppose to train the model with broken images?

Upvotes: 0

Views: 189

Answers (1)

Sai Krishnan
Sai Krishnan

Reputation: 116

Pytorch requires [C, H, W], whereas numpy and matplotlib (which is where the error is being thrown) require the image to be [H, W, C]. To fix this issue, I'd suggest plotting g obtained by doing this g = np.expand_dims(dist_one[0], axis=-1). Before sending it to PyTorch, you can do one of two things. You can use 'g = torch.tensor(g).permute(2, 0, 1)` which will results in a [C, H, W] tensor or you can use the PyTorch Dataset and Dataloader setup, which handles other things like batching for you.

Upvotes: 0

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