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from PIL import Image
import numpy as np
import torch
def tensor_to_pil(img_tensor, batch_index=0):
# Convert tensor of shape [batch_size, channels, height, width] at the batch_index to PIL Image
img_tensor = img_tensor[batch_index].unsqueeze(0)
i = 255. * img_tensor.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
return img
def batch_tensor_to_pil(img_tensor):
# Convert tensor of shape [batch_size, channels, height, width] to a list of PIL Images
return [tensor_to_pil(img_tensor, i) for i in range(img_tensor.shape[0])]
def pil_to_tensor(image):
# Takes a PIL image and returns a tensor of shape [1, height, width, channels]
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image).unsqueeze(0)
if len(image.shape) == 3: # If the image is grayscale, add a channel dimension
image = image.unsqueeze(-1)
return image
def batched_pil_to_tensor(images):
# Takes a list of PIL images and returns a tensor of shape [batch_size, height, width, channels]
return torch.cat([pil_to_tensor(image) for image in images], dim=0)
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