def hunmen_detect_in_img(origin_img, img_name): detector = joblib.load(params.model_path) img = np.copy(gamma_normalize.gamma_normalize(origin_img)) pos_windows_scaled_coordinates = [] pos_feature_vector_in_img = [] pos_windows_scale = [] v, h = img.shape scale = 0 # to record the resizing times while params.window_height <= v and params.window_width <= h: print img.shape pos_windows_in_single_scale, pos_feature_vector_in_scale = classifier_trainer.search_a_scale(detector, img) for i in xrange(pos_feature_vector_in_scale.__len__()): pos_windows_scale.append(scale) pos_feature_vector_in_img += pos_feature_vector_in_scale pos_windows_scaled_coordinates += pos_windows_in_single_scale img = cv2.resize(img, None, fx=params.scale_gap, fy=params.scale_gap) v, h = img.shape scale += 1 mark(pos_windows_scaled_coordinates, pos_windows_scale, origin_img, img_name) return pos_windows_scaled_coordinates, pos_feature_vector_in_img