visualization.py

import torch
from PIL import Image
import os

# def tensor_for_board(img_tensor):
#     # map into [0,1]
#     tensor = (img_tensor.clone()+1) * 0.5
#     tensor.cpu().clamp(0,1)

#     if tensor.size(1) == 1:
#         tensor = tensor.repeat(1,3,1,1)

#     return tensor

# def tensor_list_for_board(img_tensors_list):
#     grid_h = len(img_tensors_list)
#     grid_w = max(len(img_tensors)  for img_tensors in img_tensors_list)
    
#     batch_size, channel, height, width = tensor_for_board(img_tensors_list[0][0]).size()
#     canvas_h = grid_h * height
#     canvas_w = grid_w * width
#     canvas = torch.FloatTensor(batch_size, channel, canvas_h, canvas_w).fill_(0.5)
#     for i, img_tensors in enumerate(img_tensors_list):
#         for j, img_tensor in enumerate(img_tensors):
#             offset_h = i * height
#             offset_w = j * width
#             tensor = tensor_for_board(img_tensor)
#             canvas[:, :, offset_h : offset_h + height, offset_w : offset_w + width].copy_(tensor)

#     return canvas

def wandb_add_image(wandb_run, tag_name, img_tensor): # TODO wandb
    tensor = tensor_for_board(img_tensor)

    for i, img in enumerate(tensor):
        board.add_image('%s/%03d' % (tag_name, i), img, step_count)


def wandb_add_images(wandb_run, tag_name, img_tensors_list): # TODO wandb instrumentation
    tensor = tensor_list_for_board(img_tensors_list)

    for i, img in enumerate(tensor):
        board.add_image('%s/%03d' % (tag_name, i), img, step_count)

def save_images(img_tensors, img_names, save_dir):
    for img_tensor, img_name in zip(img_tensors, img_names):
        tensor = (img_tensor.clone()+1)*0.5 * 255
        tensor = tensor.cpu().clamp(0,255)

        array = tensor.numpy().astype('uint8')
        if array.shape[0] == 1:
            array = array.squeeze(0)
        elif array.shape[0] == 3:
            array = array.swapaxes(0, 1).swapaxes(1, 2)
            
        Image.fromarray(array).save(os.path.join(save_dir, img_name))