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ce4867d14e
* 测试 * delete test * add yolov7-face * fit vision.h * add yolov7-face test * fit: yolov7-face infer.cc * fit * fit Yolov7-face Cmakelist * fit yolov7Face.cc * add yolov7-face pybind * add yolov7-face python infer * feat yolov7-face pybind * feat yolov7-face format error * feat yolov7face_pybind error * feat add yolov7face-pybind to facedet-pybind * same as before * same sa before * feat __init__.py * add yolov7face.py * feat yolov7face.h ignore "," * feat .py * fit yolov7face.py * add yolov7face test teadme file * add test file * fit postprocess * delete remain annotation * fit preview * fit yolov7facepreprocessor * fomat code * fomat code * fomat code * fit format error and confthreshold and nmsthres * fit confthreshold and nmsthres * fit test-yolov7-face * fit test_yolov7face * fit review * fit ci error Co-authored-by: kongbohua <kongbh2022@stu.pku.edu.cn> Co-authored-by: CoolCola <49013063+kongbohua@users.noreply.github.com>
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
import fastdeploy as fd
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import cv2
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of yolov7face onnx model.")
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("images", [1, 3, 640, 640])
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return option
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args = parse_arguments()
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# Configure runtime and load the model
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runtime_option = build_option(args)
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model = fd.vision.facedet.YOLOv7Face(args.model, runtime_option=runtime_option)
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# Predict image detection results
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im = cv2.imread(args.image)
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result = model.predict(im)
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print(result)
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# Visualization of prediction Results
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vis_im = fd.vision.vis_face_detection(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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