File size: 17,698 Bytes
0f90f73 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
#!/usr/bin/env python3
import cv2
import numpy as np
import sklearn
import torch
import os
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from joblib import Parallel, delayed
from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset, load_image
from saicinpainting.evaluation.losses.fid.inception import InceptionV3
from saicinpainting.evaluation.utils import load_yaml
from saicinpainting.training.visualizers.base import visualize_mask_and_images
def draw_score(img, score):
img = np.transpose(img, (1, 2, 0))
cv2.putText(img, f'{score:.2f}',
(40, 40),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 1, 0),
thickness=3)
img = np.transpose(img, (2, 0, 1))
return img
def save_global_samples(global_mask_fnames, mask2real_fname, mask2fake_fname, out_dir, real_scores_by_fname, fake_scores_by_fname):
for cur_mask_fname in global_mask_fnames:
cur_real_fname = mask2real_fname[cur_mask_fname]
orig_img = load_image(cur_real_fname, mode='RGB')
fake_img = load_image(mask2fake_fname[cur_mask_fname], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]
mask = load_image(cur_mask_fname, mode='L')[None, ...]
draw_score(orig_img, real_scores_by_fname.loc[cur_real_fname, 'real_score'])
draw_score(fake_img, fake_scores_by_fname.loc[cur_mask_fname, 'fake_score'])
cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=mask, fake=fake_img),
keys=['image', 'fake'],
last_without_mask=True)
cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8')
cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR)
cv2.imwrite(os.path.join(out_dir, os.path.splitext(os.path.basename(cur_mask_fname))[0] + '.jpg'),
cur_grid)
def save_samples_by_real(worst_best_by_real, mask2fake_fname, fake_info, out_dir):
for real_fname in worst_best_by_real.index:
worst_mask_path = worst_best_by_real.loc[real_fname, 'worst']
best_mask_path = worst_best_by_real.loc[real_fname, 'best']
orig_img = load_image(real_fname, mode='RGB')
worst_mask_img = load_image(worst_mask_path, mode='L')[None, ...]
worst_fake_img = load_image(mask2fake_fname[worst_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]
best_mask_img = load_image(best_mask_path, mode='L')[None, ...]
best_fake_img = load_image(mask2fake_fname[best_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]
draw_score(orig_img, worst_best_by_real.loc[real_fname, 'real_score'])
draw_score(worst_fake_img, worst_best_by_real.loc[real_fname, 'worst_score'])
draw_score(best_fake_img, worst_best_by_real.loc[real_fname, 'best_score'])
cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=np.zeros_like(worst_mask_img),
worst_mask=worst_mask_img, worst_img=worst_fake_img,
best_mask=best_mask_img, best_img=best_fake_img),
keys=['image', 'worst_mask', 'worst_img', 'best_mask', 'best_img'],
rescale_keys=['worst_mask', 'best_mask'],
last_without_mask=True)
cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8')
cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR)
cv2.imwrite(os.path.join(out_dir,
os.path.splitext(os.path.basename(real_fname))[0] + '.jpg'),
cur_grid)
fig, (ax1, ax2) = plt.subplots(1, 2)
cur_stat = fake_info[fake_info['real_fname'] == real_fname]
cur_stat['fake_score'].hist(ax=ax1)
cur_stat['real_score'].hist(ax=ax2)
fig.tight_layout()
fig.savefig(os.path.join(out_dir,
os.path.splitext(os.path.basename(real_fname))[0] + '_scores.png'))
plt.close(fig)
def extract_overlapping_masks(mask_fnames, cur_i, fake_scores_table, max_overlaps_n=2):
result_pairs = []
result_scores = []
mask_fname_a = mask_fnames[cur_i]
mask_a = load_image(mask_fname_a, mode='L')[None, ...] > 0.5
cur_score_a = fake_scores_table.loc[mask_fname_a, 'fake_score']
for mask_fname_b in mask_fnames[cur_i + 1:]:
mask_b = load_image(mask_fname_b, mode='L')[None, ...] > 0.5
if not np.any(mask_a & mask_b):
continue
cur_score_b = fake_scores_table.loc[mask_fname_b, 'fake_score']
result_pairs.append((mask_fname_a, mask_fname_b))
result_scores.append(cur_score_b - cur_score_a)
if len(result_pairs) >= max_overlaps_n:
break
return result_pairs, result_scores
def main(args):
config = load_yaml(args.config)
latents_dir = os.path.join(args.outpath, 'latents')
os.makedirs(latents_dir, exist_ok=True)
global_worst_dir = os.path.join(args.outpath, 'global_worst')
os.makedirs(global_worst_dir, exist_ok=True)
global_best_dir = os.path.join(args.outpath, 'global_best')
os.makedirs(global_best_dir, exist_ok=True)
worst_best_by_best_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_max')
os.makedirs(worst_best_by_best_worst_score_diff_max_dir, exist_ok=True)
worst_best_by_best_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_min')
os.makedirs(worst_best_by_best_worst_score_diff_min_dir, exist_ok=True)
worst_best_by_real_best_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_max')
os.makedirs(worst_best_by_real_best_score_diff_max_dir, exist_ok=True)
worst_best_by_real_best_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_min')
os.makedirs(worst_best_by_real_best_score_diff_min_dir, exist_ok=True)
worst_best_by_real_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_max')
os.makedirs(worst_best_by_real_worst_score_diff_max_dir, exist_ok=True)
worst_best_by_real_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_min')
os.makedirs(worst_best_by_real_worst_score_diff_min_dir, exist_ok=True)
if not args.only_report:
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
inception_model = InceptionV3([block_idx]).eval().cuda()
dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
real2vector_cache = {}
real_features = []
fake_features = []
orig_fnames = []
mask_fnames = []
mask2real_fname = {}
mask2fake_fname = {}
for batch_i, batch in enumerate(dataset):
orig_img_fname = dataset.img_filenames[batch_i]
mask_fname = dataset.mask_filenames[batch_i]
fake_fname = dataset.pred_filenames[batch_i]
mask2real_fname[mask_fname] = orig_img_fname
mask2fake_fname[mask_fname] = fake_fname
cur_real_vector = real2vector_cache.get(orig_img_fname, None)
if cur_real_vector is None:
with torch.no_grad():
in_img = torch.from_numpy(batch['image'][None, ...]).cuda()
cur_real_vector = inception_model(in_img)[0].squeeze(-1).squeeze(-1).cpu().numpy()
real2vector_cache[orig_img_fname] = cur_real_vector
pred_img = torch.from_numpy(batch['inpainted'][None, ...]).cuda()
cur_fake_vector = inception_model(pred_img)[0].squeeze(-1).squeeze(-1).cpu().numpy()
real_features.append(cur_real_vector)
fake_features.append(cur_fake_vector)
orig_fnames.append(orig_img_fname)
mask_fnames.append(mask_fname)
ids_features = np.concatenate(real_features + fake_features, axis=0)
ids_labels = np.array(([1] * len(real_features)) + ([0] * len(fake_features)))
with open(os.path.join(latents_dir, 'featues.pkl'), 'wb') as f:
pickle.dump(ids_features, f, protocol=3)
with open(os.path.join(latents_dir, 'labels.pkl'), 'wb') as f:
pickle.dump(ids_labels, f, protocol=3)
with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'wb') as f:
pickle.dump(orig_fnames, f, protocol=3)
with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'wb') as f:
pickle.dump(mask_fnames, f, protocol=3)
with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'wb') as f:
pickle.dump(mask2real_fname, f, protocol=3)
with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'wb') as f:
pickle.dump(mask2fake_fname, f, protocol=3)
svm = sklearn.svm.LinearSVC(dual=False)
svm.fit(ids_features, ids_labels)
pred_scores = svm.decision_function(ids_features)
real_scores = pred_scores[:len(real_features)]
fake_scores = pred_scores[len(real_features):]
with open(os.path.join(latents_dir, 'pred_scores.pkl'), 'wb') as f:
pickle.dump(pred_scores, f, protocol=3)
with open(os.path.join(latents_dir, 'real_scores.pkl'), 'wb') as f:
pickle.dump(real_scores, f, protocol=3)
with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'wb') as f:
pickle.dump(fake_scores, f, protocol=3)
else:
with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'rb') as f:
orig_fnames = pickle.load(f)
with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'rb') as f:
mask_fnames = pickle.load(f)
with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'rb') as f:
mask2real_fname = pickle.load(f)
with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'rb') as f:
mask2fake_fname = pickle.load(f)
with open(os.path.join(latents_dir, 'real_scores.pkl'), 'rb') as f:
real_scores = pickle.load(f)
with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'rb') as f:
fake_scores = pickle.load(f)
real_info = pd.DataFrame(data=[dict(real_fname=fname,
real_score=score)
for fname, score
in zip(orig_fnames, real_scores)])
real_info.set_index('real_fname', drop=True, inplace=True)
fake_info = pd.DataFrame(data=[dict(mask_fname=fname,
fake_fname=mask2fake_fname[fname],
real_fname=mask2real_fname[fname],
fake_score=score)
for fname, score
in zip(mask_fnames, fake_scores)])
fake_info = fake_info.join(real_info, on='real_fname', how='left')
fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True)
fake_stats_by_real = fake_info.groupby('real_fname')['fake_score'].describe()[['mean', 'std']].rename(
{'mean': 'mean_fake_by_real', 'std': 'std_fake_by_real'}, axis=1)
fake_info = fake_info.join(fake_stats_by_real, on='real_fname', rsuffix='stat_by_real')
fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True)
fake_info.to_csv(os.path.join(latents_dir, 'join_scores_table.csv'), sep='\t', index=False)
fake_scores_table = fake_info.set_index('mask_fname')['fake_score'].to_frame()
real_scores_table = fake_info.set_index('real_fname')['real_score'].drop_duplicates().to_frame()
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(fake_scores)
ax2.hist(real_scores)
fig.tight_layout()
fig.savefig(os.path.join(args.outpath, 'global_scores_hist.png'))
plt.close(fig)
global_worst_masks = fake_info.sort_values('fake_score', ascending=True)['mask_fname'].iloc[:config.take_global_top].to_list()
global_best_masks = fake_info.sort_values('fake_score', ascending=False)['mask_fname'].iloc[:config.take_global_top].to_list()
save_global_samples(global_worst_masks, mask2real_fname, mask2fake_fname, global_worst_dir, real_scores_table, fake_scores_table)
save_global_samples(global_best_masks, mask2real_fname, mask2fake_fname, global_best_dir, real_scores_table, fake_scores_table)
# grouped by real
worst_samples_by_real = fake_info.groupby('real_fname').apply(
lambda d: d.set_index('mask_fname')['fake_score'].idxmin()).to_frame().rename({0: 'worst'}, axis=1)
best_samples_by_real = fake_info.groupby('real_fname').apply(
lambda d: d.set_index('mask_fname')['fake_score'].idxmax()).to_frame().rename({0: 'best'}, axis=1)
worst_best_by_real = pd.concat([worst_samples_by_real, best_samples_by_real], axis=1)
worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'worst_score'}, axis=1),
on='worst')
worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'best_score'}, axis=1),
on='best')
worst_best_by_real = worst_best_by_real.join(real_scores_table)
worst_best_by_real['best_worst_score_diff'] = worst_best_by_real['best_score'] - worst_best_by_real['worst_score']
worst_best_by_real['real_best_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['best_score']
worst_best_by_real['real_worst_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['worst_score']
worst_best_by_best_worst_score_diff_min = worst_best_by_real.sort_values('best_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top]
worst_best_by_best_worst_score_diff_max = worst_best_by_real.sort_values('best_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top]
save_samples_by_real(worst_best_by_best_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_min_dir)
save_samples_by_real(worst_best_by_best_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_max_dir)
worst_best_by_real_best_score_diff_min = worst_best_by_real.sort_values('real_best_score_diff', ascending=True).iloc[:config.take_worst_best_top]
worst_best_by_real_best_score_diff_max = worst_best_by_real.sort_values('real_best_score_diff', ascending=False).iloc[:config.take_worst_best_top]
save_samples_by_real(worst_best_by_real_best_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_min_dir)
save_samples_by_real(worst_best_by_real_best_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_max_dir)
worst_best_by_real_worst_score_diff_min = worst_best_by_real.sort_values('real_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top]
worst_best_by_real_worst_score_diff_max = worst_best_by_real.sort_values('real_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top]
save_samples_by_real(worst_best_by_real_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_min_dir)
save_samples_by_real(worst_best_by_real_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_max_dir)
# analyze what change of mask causes bigger change of score
overlapping_mask_fname_pairs = []
overlapping_mask_fname_score_diffs = []
for cur_real_fname in orig_fnames:
cur_fakes_info = fake_info[fake_info['real_fname'] == cur_real_fname]
cur_mask_fnames = sorted(cur_fakes_info['mask_fname'].unique())
cur_mask_pairs_and_scores = Parallel(args.n_jobs)(
delayed(extract_overlapping_masks)(cur_mask_fnames, i, fake_scores_table)
for i in range(len(cur_mask_fnames) - 1)
)
for cur_pairs, cur_scores in cur_mask_pairs_and_scores:
overlapping_mask_fname_pairs.extend(cur_pairs)
overlapping_mask_fname_score_diffs.extend(cur_scores)
overlapping_mask_fname_pairs = np.asarray(overlapping_mask_fname_pairs)
overlapping_mask_fname_score_diffs = np.asarray(overlapping_mask_fname_score_diffs)
overlapping_sort_idx = np.argsort(overlapping_mask_fname_score_diffs)
overlapping_mask_fname_pairs = overlapping_mask_fname_pairs[overlapping_sort_idx]
overlapping_mask_fname_score_diffs = overlapping_mask_fname_score_diffs[overlapping_sort_idx]
if __name__ == '__main__':
import argparse
aparser = argparse.ArgumentParser()
aparser.add_argument('config', type=str, help='Path to config for dataset generation')
aparser.add_argument('datadir', type=str,
help='Path to folder with images and masks (output of gen_mask_dataset.py)')
aparser.add_argument('predictdir', type=str,
help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
aparser.add_argument('outpath', type=str, help='Where to put results')
aparser.add_argument('--only-report', action='store_true',
help='Whether to skip prediction and feature extraction, '
'load all the possible latents and proceed with report only')
aparser.add_argument('--n-jobs', type=int, default=8, help='how many processes to use for pair mask mining')
main(aparser.parse_args())
|