mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-24 01:29:57 +08:00
huangjianhui
463ee0a088
* Add publish_task_example * Update CMakeLists.txt * Add gflags cmake * Update release task script * Delete windows related code in run.sh && add openvino option
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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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_dir",
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required=True,
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help="Path of PaddleDetection model directory")
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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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"--backend",
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nargs='?',
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type=str,
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default='default',
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help="Set inference backend, support one of ['default', 'ort', 'paddle', 'trt', 'openvino']."
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)
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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.backend == "ort":
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option.use_ort_backend()
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elif args.backend == "paddle":
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option.use_paddle_backend()
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elif args.backend == "trt":
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assert args.device.lower(
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) == "gpu", "Set trt backend must use gpu for inference"
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option.use_trt_backend()
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option.set_trt_input_shape("image", [1, 3, 640, 640])
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option.set_trt_input_shape("scale_factor", [1, 2])
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elif args.backend == 'openvino':
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assert args.device.lower(
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) == "cpu", "Set openvino backend must use cpu for inference"
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option.use_openvino_backend()
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elif args.backend == "default":
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pass
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else:
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raise Exception(
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"Don't support backend type: {}, please use one of ['default', 'ort', 'paddle', 'trt'].".
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format(args.backend))
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return option
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args = parse_arguments()
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model_file = os.path.join(args.model_dir, "model.pdmodel")
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params_file = os.path.join(args.model_dir, "model.pdiparams")
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config_file = os.path.join(args.model_dir, "infer_cfg.yml")
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.detection.PPYOLOE(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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# 预测结果可视化
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vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
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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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