mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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58d63f3e90
* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs
59 lines
1.6 KiB
Python
Executable File
59 lines
1.6 KiB
Python
Executable File
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 'kunlunxin', 'cpu' or 'gpu'.")
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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() == "kunlunxin":
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option.use_kunlunxin()
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if args.device.lower() == "ascend":
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option.use_ascend()
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if args.device.lower() == "gpu":
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option.use_gpu()
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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.SSD(model_file,
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params_file,
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config_file,
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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)
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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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