def get_dst_folder_name(src_data_type, src_data_path): ''' Compute a output folder name based on data_type and data_path. The final output of this script looks like this: DST_FOLDER/folder_name/vidoe.avi DST_FOLDER/folder_name/skeletons/XXXXX.txt ''' assert (src_data_type in ["video", "folder", "webcam"]) if src_data_type == "video": # /root/data/video.avi --> video folder_name = os.path.basename(src_data_path).split(".")[-2] elif src_data_type == "folder": # /root/data/video/ --> video folder_name = src_data_path.rstrip("/").split("/")[-1] elif src_data_type == "webcam": # month-day-hour-minute-seconds, e.g.: 02-26-15-51-12 folder_name = lib_commons.get_time_string() return folder_name
import os sys.path.append("../") import utils.lib_images_io as lib_images_io import utils.lib_plot as lib_plot import utils.lib_commons as lib_commons from utils.lib_openpose import SkeletonDetector from utils.lib_tracker import Tracker from utils.lib_classifier import * import pickle SRC_DATA_TYPE = "webcam" SRC_MODEL_PATH = "../model/trained_classifier.pickle" DST_FOLDER_NAME = lib_commons.get_time_string() cfg_all = lib_commons.read_yaml("../config/config.yaml") cfg = cfg_all["s5_test.py"] CLASSES = np.array(cfg_all["classes"]) print("CLASSES ARE " + str(CLASSES)) SKELETON_FILENAME_FORMAT = cfg_all["skeleton_filename_format"] WINDOW_SIZE = int(cfg_all["features"]["window_size"]) SRC_WEBCAM_MAX_FPS = float(cfg["settings"]["source"]["webcam_max_framerate"]) OPENPOSE_MODEL = cfg["settings"]["openpose"]["model"] OPENPOSE_IMG_SIZE = cfg["settings"]["openpose"]["img_size"]