def unary_feature_extraction(anno_dir, photo_dir, output_dir, label_dict_file=None):
"""
features extraction to train unary potential
"""
# prepare gabor kernels
gabor_kernels = feat.prepare_gabor_kernels(gabor_freqs=[0.2])
# prepare face detectors
face_detectors = feat.prepare_face_detectors()
# label dictionary
label_dict = util.get_label_dictionary(label_dict_file)
# get all csv annotation files
csv_dirs = os.listdir(anno_dir)
X, y = np.array([]), np.array([])
for i, csv_file in enumerate(csv_dirs):
print "%i from %i" % (i, len(csv_dirs))
csv_path = os.path.join(anno_dir, csv_file)
image_path = os.path.join(photo_dir, csv_file.split(".")[0] + ".jpg")
if os.path.isfile(csv_path):
image = io.imread(image_path)
sps = feat.compute_superpixels(image, 300)
pixel_class = feat.load_pixel_annotations(csv_path)
unary_features = feat.compute_unary_features(image, sps, gabor_kernels, face_detectors, pixel_class)
if unary_features is not None:
# generate labels
labels = feat.generate_labels(pixel_class, sps, label_dict)
X = np.vstack([X, unary_features]) if X.size else unary_features
y = np.append(y, labels) if y.size else labels
# save the features
part_idx = anno_dir.split("/")[-2]
np.save(os.path.join(output_dir, 'X_unary_part_' + part_idx + '.npy'), X)
np.save(os.path.join(output_dir, 'y_unary_part_' + part_idx + '.npy'), y)
def crf_feature_extraction(anno_dir,
photo_dir,
unary_scaler,
unary_clf,
output_dir,
label_dict_file=None):
"""
features extraction to train unary potential
"""
# prepare gabor kernels
gabor_kernels = feat.prepare_gabor_kernels(gabor_freqs=[0.2])
# prepare face detectors
face_detectors = feat.prepare_face_detectors()
# label dictionary
label_dict = util.get_label_dictionary(label_dict_file)
X, y = [], []
csv_dirs = os.listdir(anno_dir)
for i, csv_file in enumerate(csv_dirs):
print "%i from %i" % (i, len(csv_dirs))
csv_path = os.path.join(anno_dir, csv_file)
image_path = os.path.join(photo_dir, csv_file.split(".")[0] + ".jpg")
if os.path.isfile(csv_path):
image = io.imread(image_path)
sps = feat.compute_superpixels(image, 300)
pixel_class = feat.load_pixel_annotations(csv_path)
unary_features = feat.compute_unary_features(
image, sps, gabor_kernels, face_detectors, pixel_class)
if unary_features is not None:
# compute crf node features from unary potentials
scaled_unary_features = unary_scaler.transform(unary_features)
node_features = unary_clf.predict_proba(scaled_unary_features)
# generate edges
# vertices, edges = feat.generate_edges(sps)
edges = np.array(graph.rag_mean_color(image, sps).edges())
# extract edge features
edge_features = feat.compute_crf_edge_features(
unary_features, edges)
# generate labels
labels = feat.generate_labels(pixel_class, sps, label_dict)
X.append((node_features, edges, edge_features))
y.append(labels)
# save the features
part_idx = anno_dir.split("/")[-2]
np.save(os.path.join(output_dir, 'X_crf_part_' + part_idx + '.npy'), X)
np.save(os.path.join(output_dir, 'y_crf_part_' + part_idx + '.npy'), y)
# prepare gabor kernels
gabor_kernels = feat.prepare_gabor_kernels(gabor_freqs=[0.2])
# prepare face detectors
face_detectors = feat.prepare_face_detectors()
# load label dictionary
label_dict = util.get_label_dictionary(label_dict_file=LABEL_DICT_FILE,
fileformat='str')
cat_id = int([key for key in label_dict if label_dict[key] == args.cat][0])
# load image
image = io.imread(args.image_path)
# extract superpixels
sps = feat.compute_superpixels(image, 300)
# extract unary features from test image
unary_features = feat.compute_unary_features(image, sps, gabor_kernels,
face_detectors)
# compute mask
if unary_features is None:
active_mask = feat.get_default_mask(image)
else:
active_mask = feat.get_semantic_segmentation_mask(
image, args.unary_model_path, sps, unary_features,
args.crf_model_path, cat_id)
# feature extraction
features = feat.compute_sim_features(image, active_mask)