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| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| # Log images | |
| def log_input_image(x, opts): | |
| if opts.label_nc == 0: | |
| return tensor2im(x) | |
| elif opts.label_nc == 1: | |
| return tensor2sketch(x) | |
| else: | |
| return tensor2map(x) | |
| def tensor2im(var): | |
| var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() | |
| var = ((var + 1) / 2) | |
| var[var < 0] = 0 | |
| var[var > 1] = 1 | |
| var = var * 255 | |
| return Image.fromarray(var.astype('uint8')) | |
| def tensor2map(var): | |
| mask = np.argmax(var.data.cpu().numpy(), axis=0) | |
| colors = get_colors() | |
| mask_image = np.ones(shape=(mask.shape[0], mask.shape[1], 3)) | |
| for class_idx in np.unique(mask): | |
| mask_image[mask == class_idx] = colors[class_idx] | |
| mask_image = mask_image.astype('uint8') | |
| return Image.fromarray(mask_image) | |
| def tensor2sketch(var): | |
| im = var[0].cpu().detach().numpy() | |
| im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) | |
| im = (im * 255).astype(np.uint8) | |
| return Image.fromarray(im) | |
| # Visualization utils | |
| def get_colors(): | |
| # currently support up to 19 classes (for the celebs-hq-mask dataset) | |
| colors = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], | |
| [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], | |
| [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] | |
| return colors | |
| def vis_faces(log_hooks): | |
| display_count = len(log_hooks) | |
| fig = plt.figure(figsize=(8, 4 * display_count)) | |
| gs = fig.add_gridspec(display_count, 3) | |
| for i in range(display_count): | |
| hooks_dict = log_hooks[i] | |
| fig.add_subplot(gs[i, 0]) | |
| if 'diff_input' in hooks_dict: | |
| vis_faces_with_id(hooks_dict, fig, gs, i) | |
| else: | |
| vis_faces_no_id(hooks_dict, fig, gs, i) | |
| plt.tight_layout() | |
| return fig | |
| def vis_faces_with_id(hooks_dict, fig, gs, i): | |
| plt.imshow(hooks_dict['input_face']) | |
| plt.title('Input\nOut Sim={:.2f}'.format(float(hooks_dict['diff_input']))) | |
| fig.add_subplot(gs[i, 1]) | |
| plt.imshow(hooks_dict['target_face']) | |
| plt.title('Target\nIn={:.2f}, Out={:.2f}'.format(float(hooks_dict['diff_views']), | |
| float(hooks_dict['diff_target']))) | |
| fig.add_subplot(gs[i, 2]) | |
| plt.imshow(hooks_dict['output_face']) | |
| plt.title('Output\n Target Sim={:.2f}'.format(float(hooks_dict['diff_target']))) | |
| def vis_faces_no_id(hooks_dict, fig, gs, i): | |
| plt.imshow(hooks_dict['input_face'], cmap="gray") | |
| plt.title('Input') | |
| fig.add_subplot(gs[i, 1]) | |
| plt.imshow(hooks_dict['target_face']) | |
| plt.title('Target') | |
| fig.add_subplot(gs[i, 2]) | |
| plt.imshow(hooks_dict['output_face']) | |
| plt.title('Output') | |