Spaces:
Runtime error
Runtime error
| #!/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()) | |