exclude some far away targets

fawkes/utils.py

@@ -153,7 +153,6 @@ class Faces(object):
                     base[0:img.shape[0], 0:img.shape[1], :] = img
                 cur_faces_square.append(base)
 
-
             cur_index = align_img[1]
             cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
             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')
         path2emb = pickle.load(fp)
         fp.close()
-
         dictionaries.append(path2emb)
 
     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)
 
     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]
     embs = [p[1] for p in items]
     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])
 
     target_data_id = paths[int(max_id)]
+
     image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id))
     # if not os.path.exists(image_dir):
     os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True)