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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')
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