def extract_features(path, filenames): hog_length = len(extract_hog(imread(join(path, filenames[0])))) data = zeros((len(filenames), hog_length)) for i in range(0, len(filenames)): filename = join(path, filenames[i]) data[i, :] = extract_hog(imread(filename)) return data
def extract_features(path, filenames): hog_length = len( extract_hog(imread(path + '/' + filenames[0], plugin='matplotlib'))) data = zeros((len(filenames), hog_length)) for i in range(0, len(filenames)): filename = path + '/' + filenames[i] data[i, :] = extract_hog(imread(filename, plugin='matplotlib')) return data
def dump_features(path, filenames): hog_length = len(extract_hog(imread(join(path, filenames[0])))) data = zeros((len(filenames), hog_length)) with open(HOG_FILENAME, mode='w') as hog_file: hog_writer = csv.writer(hog_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) hog_writer.writerow(['filename', 'hog_vector']) for i in range(0, len(filenames)): filename = join(path, filenames[i]) data[i, :] = extract_hog(imread(filename)) hog_writer.writerow([filename, ','.join(np.asarray(np.round(data[i], 3), dtype=str))]) if i % 100 == 0: print('{} done'.format(i))
def extract_features(path, filenames): hog_length = len(extract_hog(imread(join(path, filenames[0])))) data = zeros((len(filenames), hog_length)) hog_data = pd.read_csv(HOG_FILENAME) hog_data = hog_data.set_index('filename') for i in range(0, len(filenames)): filename = join(path, filenames[i]) data[i, :] = np.asarray(hog_data.loc[filename].hog_vector.split(','), dtype=float) if i % 5000 == 0: print('{} done'.format(i)) train_data, test_data = extract_data(seed) train_data['filenames'] = 'public_tests/00_test_img_input/train/' + train_data['filenames'] test_data['filenames'] = 'public_tests/00_test_img_input/train/' + test_data['filenames'] train_data = train_data.merge(hog_data, how='inner', left_on='filenames', right_on='filename') test_data = test_data.merge(hog_data, how='inner', left_on='filenames', right_on='filename') return train_data, test_data