def __init__(self, threshold=0.5):
args = utils.get_arguments()
self.weight_path = args.weight_path
self.cfg_path = args.config_path
self.labels = utils.get_labels(args.classes_path)
self.threshold = threshold
# Load model
self.model = cv2.dnn.readNet(model=self.weight_path,
config=self.cfg_path)
def __init__(self, threshold=0.5):
args = utils.get_arguments()
self.weight_path = args.weight_path
self.cfg_path = args.config_path
self.labels = utils.get_labels(args.classes_path)
self.threshold = threshold
# Tải mô hình YOLOv3 Tiny cho phát hiện vùng biển số
self.model = cv2.dnn.readNet(model=self.weight_path,
config=self.cfg_path)
import data_utils as utils
from imutils.video import FileVideoStream, VideoStream
from imutils.video import FPS
from mtcnn import MTCNN
import time
import cv2
import model
import tensorflow as tf
if __name__ == '__main__':
arguments = utils.get_arguments()
image_path = None
video_path = None
session = tf.compat.v1.Session()
# load model multitask learning
multitask_model = model.Model(session=session, trainable=False, prediction=True)
# load model detect faces
detect_model = MTCNN()
if arguments.image_path is not None:
image_path = arguments.image_path
else:
video_path = arguments.video_path
if image_path is not None: # case 1: detect image
img = cv2.imread(image_path)
# detect faces
result = detect_model.detect_faces(img)