def __init__(self):
CONFIG = get_config("SSD_1")
self.CONFIDENCE_THRESHOLD = CONFIG["confidence_threshold"]
self.num_predictions = CONFIG["num_predictions"]
self.show_class_label = CONFIG["show_class_label"]
weights = CONFIG["weights_path"]
self.class_names = list(CONFIG["labels"].keys())
self.THRESHS = CONFIG["labels"]
args = {
'config': CONFIG["config"],
'weights': weights,
'label_maps': self.class_names,
'confidence_threshold': self.CONFIDENCE_THRESHOLD,
'num_predictions': self.num_predictions,
'show_class_label': self.show_class_label
}
with open(CONFIG["config"], "r") as config_file:
config = json.load(config_file)
self.input_size = config["model"]["input_size"]
model, process_input_fn = inference_utils.inference_ssd_vgg16(
config, args)
model.load_weights(args["weights"])
self.model = model
self.process_input_fn = process_input_fn
def __init__(self):
CONFIG = get_config("yolov4")
self.CONFIDENCE_THRESHOLD = CONFIG["confidence_threshold"]
self.NMS_THRESHOLD = CONFIG["nms_threshold"]
self.class_names = list(CONFIG["labels"].keys())
self.THRESHS = CONFIG["labels"]
weights = CONFIG["weights_path"]
model_config = CONFIG["cfg_path"]
input_width = CONFIG["input_width"]
input_height = CONFIG["input_height"]
net = cv2.dnn.readNet(weights, model_config)
model = cv2.dnn_DetectionModel(net)
model.setInputParams(size=(input_width, input_height),
scale=1 / 255,
swapRB=True)
self.model = model