getMetrics_market.py
4.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
import os
from inception_score import get_inception_score
from skimage.io import imread, imsave
from skimage.measure import compare_ssim
import numpy as np
import pandas as pd
from tqdm import tqdm
import re
def l1_score(generated_images, reference_images):
score_list = []
for reference_image, generated_image in zip(reference_images, generated_images):
score = np.abs(2 * (reference_image/255.0 - 0.5) - 2 * (generated_image/255.0 - 0.5)).mean()
score_list.append(score)
return np.mean(score_list)
def ssim_score(generated_images, reference_images):
ssim_score_list = []
for reference_image, generated_image in zip(reference_images, generated_images):
ssim = compare_ssim(reference_image, generated_image, gaussian_weights=True, sigma=1.5,
use_sample_covariance=False, multichannel=True,
data_range=generated_image.max() - generated_image.min())
ssim_score_list.append(ssim)
return np.mean(ssim_score_list)
def save_images(input_images, target_images, generated_images, names, output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for images in zip(input_images, target_images, generated_images, names):
res_name = str('_'.join(images[-1])) + '.png'
imsave(os.path.join(output_folder, res_name), np.concatenate(images[:-1], axis=1))
def create_masked_image(names, images, annotation_file):
import pose_utils
masked_images = []
df = pd.read_csv(annotation_file, sep=':')
for name, image in zip(names, images):
to = name[1]
ano_to = df[df['name'] == to].iloc[0]
kp_to = pose_utils.load_pose_cords_from_strings(ano_to['keypoints_y'], ano_to['keypoints_x'])
mask = pose_utils.produce_ma_mask(kp_to, image.shape[:2])
masked_images.append(image * mask[..., np.newaxis])
return masked_images
def load_generated_images(images_folder):
input_images = []
target_images = []
generated_images = []
names = []
for img_name in os.listdir(images_folder):
img = imread(os.path.join(images_folder, img_name))
w = int(img.shape[1] / 5) #h, w ,c
input_images.append(img[:, :w])
target_images.append(img[:, 2*w:3*w])
generated_images.append(img[:, 4*w:5*w])
# assert img_name.endswith('_vis.png'), 'unexpected img name: should end with _vis.png'
assert img_name.endswith('_vis.png') or img_name.endswith('_vis.jpg'), 'unexpected img name: should end with _vis.png'
img_name = img_name[:-8]
img_name = img_name.split('___')
assert len(img_name) == 2, 'unexpected img split: length 2 expect!'
fr = img_name[0]
to = img_name[1]
# m = re.match(r'([A-Za-z0-9_]*.jpg)_([A-Za-z0-9_]*.jpg)', img_name)
# m = re.match(r'([A-Za-z0-9_]*.jpg)_([A-Za-z0-9_]*.jpg)_vis.png', img_name)
# fr = m.groups()[0]
# to = m.groups()[1]
names.append([fr, to])
return input_images, target_images, generated_images, names
def test(generated_images_dir, annotations_file_test):
print(generated_images_dir, annotations_file_test)
print ("Loading images...")
input_images, target_images, generated_images, names = load_generated_images(generated_images_dir)
print ("Compute inception score...")
inception_score = get_inception_score(generated_images)
print ("Inception score %s" % inception_score[0])
print ("Compute structured similarity score (SSIM)...")
structured_score = ssim_score(generated_images, target_images)
print ("SSIM score %s" % structured_score)
print ("Compute l1 score...")
norm_score = l1_score(generated_images, target_images)
print ("L1 score %s" % norm_score)
print ("Compute masked inception score...")
generated_images_masked = create_masked_image(names, generated_images, annotations_file_test)
reference_images_masked = create_masked_image(names, target_images, annotations_file_test)
inception_score_masked = get_inception_score(generated_images_masked)
print ("Inception score masked %s" % inception_score_masked[0])
print ("Compute masked SSIM...")
structured_score_masked = ssim_score(generated_images_masked, reference_images_masked)
print ("SSIM score masked %s" % structured_score_masked)
print ("Inception score = %s, masked = %s; SSIM score = %s, masked = %s; l1 score = %s" %
(inception_score, inception_score_masked, structured_score, structured_score_masked, norm_score))
if __name__ == "__main__":
generated_images_dir = 'results/market_PATN/test_latest/images'
annotations_file_test = 'market_data/market-annotation-test.csv'
test(generated_images_dir, annotations_file_test)