def select_images_loader(src_data_type, src_data_path):
if src_data_type == "video":
images_loader = lib_images_io.ReadFromVideo(
src_data_path, sample_interval=SRC_VIDEO_SAMPLE_INTERVAL)
elif src_data_type == "folder":
images_loader = lib_images_io.ReadFromFolder(folder_path=src_data_path)
elif src_data_type == "webcam":
if src_data_path == "":
webcam_idx = 0
elif src_data_path.isdigit():
webcam_idx = int(src_data_path)
else:
webcam_idx = src_data_path
images_loader = lib_images_io.ReadFromWebcam(SRC_WEBCAM_MAX_FPS,
webcam_idx)
return images_loader
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"]
# Display settings
img_disp_desired_rows = int(cfg["settings"]["display"]["desired_rows"])
loaded_model = pickle.load(open(SRC_MODEL_PATH, "rb"))
print(loaded_model)
images_loader = lib_images_io.ReadFromWebcam(SRC_WEBCAM_MAX_FPS, 0)
from s5_test import MultiPersonClassifier, remove_skeletons_with_few_joints, draw_result_img
skeleton_detector = SkeletonDetector(OPENPOSE_MODEL, OPENPOSE_IMG_SIZE)
multiperson_tracker = Tracker()
multiperson_classifier = MultiPersonClassifier(SRC_MODEL_PATH, CLASSES)
images_displayer = lib_images_io.ImageDisplayer()
if __name__ == "__main__":
ith_img = -1
while images_loader.has_image():
img = images_loader.read_image()
ith_img += 1