import cv2 import numpy as np from PIL import Image import matplotlib.pyplot as plt import random # 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]] # asign random colors to more 200 classes random.seed(0) for i in range(200): colors.append([random.randint(0,255),random.randint(0,255),random.randint(0,255)]) 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')