ResearchPapers/fawkes
ResearchPapers/fawkes
2
0
Fork 0
mirror of https://github.com/Shawn-Shan/fawkes.git synced 2024-12-22 07:09:33 +05:30
Code

exclude some far away targets

Browse Source
This commit is contained in:
Shawn-Shan 2020-07-24 16:34:26 -05:00
parent 0180c98e20
commit 2b2d054118
1 changed files with 27 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
fawkes/utils.py
View File

@ -153,7 +153,6 @@ class Faces(object):
base[0:img.shape[0], 0:img.shape[1], :] = img base[0:img.shape[0], 0:img.shape[1], :] = img
cur_faces_square.append(base) cur_faces_square.append(base)
cur_index = align_img[1] cur_index = align_img[1]
cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square] cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
self.cropped_faces_shape.extend(cur_shapes) self.cropped_faces_shape.extend(cur_shapes)
@ -425,7 +424,6 @@ def load_embeddings(feature_extractors_names):
fp = gzip.open(os.path.join(model_dir, "{}_emb.p.gz".format(extractor_name)), 'rb') fp = gzip.open(os.path.join(model_dir, "{}_emb.p.gz".format(extractor_name)), 'rb')
path2emb = pickle.load(fp) path2emb = pickle.load(fp)
fp.close() fp.close()
dictionaries.append(path2emb) dictionaries.append(path2emb)
merge_dict = {} merge_dict = {}
@ -481,7 +479,32 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs) original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs)
path2emb = load_embeddings(feature_extractors_names) path2emb = load_embeddings(feature_extractors_names)
items = list(path2emb.items()) exclude_list = [1691, 19236, 20552, 9231, 18221, 8250, 18785, 6989, 17170,
1704, 19394, 6058, 3327, 11885, 20375, 19150, 676, 11663,
17261, 3527, 3956, 1973, 1197, 4859, 590, 13873, 928,
14397, 4288, 3393, 6975, 16988, 1269, 323, 6409, 588,
19738, 1845, 12123, 2714, 5318, 15325, 19268, 4650, 4714,
3953, 6715, 6015, 12668, 13933, 14306, 2768, 20597, 4578,
1278, 17549, 19355, 8882, 3276, 9148, 14517, 14915, 18209,
3162, 8615, 18647, 749, 19259, 11490, 16046, 13259, 4429,
10705, 12258, 13699, 4323, 15112, 14170, 3520, 17180, 5195,
728, 2680, 13117, 20241, 15320, 8079, 2894, 11533, 10083,
9628, 14944, 13124, 13316, 8006, 15353, 15261, 8865, 1213,
1469, 20777, 9868, 10972, 9058, 18890, 13178, 13772, 15675,
10572, 8771, 14211, 18781, 16347, 17985, 11456, 5849, 15709,
20856, 2590, 15964, 8377, 5465, 16928, 13063, 19766, 19643,
8651, 8517, 5985, 14817, 18926, 3791, 1864, 20061, 7697,
13449, 19525, 13131, 421, 7629, 14689, 17521, 4509, 19374,
17584, 11055, 11929, 17117, 7492, 14182, 409, 14294, 15033,
10074, 9081, 7682, 19306, 3674, 945, 13211, 10933, 17953,
12729, 8087, 20723, 5396, 14015, 20110, 15186, 6939, 239,
2393, 17326, 13712, 9921, 7997, 6215, 14582, 864, 18906,
9351, 9178, 3600, 18567, 8614, 19429, 286, 10042, 13030,
7076, 3370, 15285, 7925, 10851, 5155, 14732, 12051, 11334,
17035, 15476]
exclude_list = set(exclude_list)
items = list([(k, v) for k, v in path2emb.items() if k not in exclude_list])
paths = [p[0] for p in items] paths = [p[0] for p in items]
embs = [p[1] for p in items] embs = [p[1] for p in items]
embs = np.array(embs) embs = np.array(embs)
@ -494,6 +517,7 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
max_id = random.choice(max_id_ls[:20]) max_id = random.choice(max_id_ls[:20])
target_data_id = paths[int(max_id)] target_data_id = paths[int(max_id)]
image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id)) image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id))
# if not os.path.exists(image_dir): # if not os.path.exists(image_dir):
os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True) os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True)