def __init__(self, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.img = []
self.img_black = []
self.show_img = show_img
def __init__(self, path=config.video_path, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer()
self.object_tracker = ObjectTracker()
self.video_path = path
self.cap = cv2.VideoCapture(self.video_path)
self.img = []
self.img_black = []
self.show_img = show_img
self.locator = Locator([1, 2])
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=opt.yolo_cfg, weight=opt.yolo_weight)
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.show_img = show_img
self.CNN_model = CNNInference()
def __init__(self, video_path):
# self.BBV = BBoxVisualizer()
self.cap = cv2.VideoCapture(video_path)
self.height, self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] +
"_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=yolo_cfg, weight=yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=pose_cfg, pose_weight=pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.kps = {}
self.kps_score = {}
self.show_img = show_img
def __init__(self, resize_size, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=config.yolo_cfg, weight=config.yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=config.pose_cfg, pose_weight=config.pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer()
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.frame = np.array([])
self.id2bbox = {}
self.CNN_model = CNNInference()
self.kps = {}
self.kps_score = {}
self.show_img = show_img
self.resize_size = resize_size
def __init__(self, model_name, video_path, label_path):
model_name, video_path, label_path = model_name.replace(
"\\", "/"), video_path.replace("\\",
"/"), label_path.replace("\\", "/")
self.tester = self.__get_tester(model_name)
self.video_name = video_path.split("/")[-1]
self.cap = cv2.VideoCapture(video_path)
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.kps_dict = defaultdict(list)
self.KPSP = KPSProcessor(self.height, self.width)
self.label, self.test_id = self.__get_label(label_path)
self.coord = []
self.pred = defaultdict(str)
self.pred_dict = defaultdict(list)
self.res = defaultdict(bool)
self.label_dict = defaultdict(bool)
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
if write_video:
res_video = "/".join(
video_path.split("/")[:-1]) + "_" + model_name.split(
"/")[-1][:-4] + "/" + self.video_name
self.out = cv2.VideoWriter(res_video,
cv2.VideoWriter_fourcc(*'XVID'), 10,
store_size)