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73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
[English](../../../en/quick_start/models/python.md) | 中文
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# PPYOLOE Python部署
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确认开发环境已安装FastDeploy，参考[FastDeploy安装](../../build_and_install/)安装预编译的FastDeploy，或根据自己需求进行编译安装。
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本文档以PaddleDetection目标检测模型PPYOLOE为例展示CPU上的推理示例
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## 1. 获取模型和测试图像
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``` python
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import fastdeploy as fd
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz"
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image_url = "https://bj.bcebos.com/fastdeploy/tests/test_det.jpg"
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fd.download_and_decompress(model_url, path=".")
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fd.download(image_url, path=".")
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```
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## 2. 加载模型
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- 更多模型的示例可参考[FastDeploy/examples](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples)
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``` python
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model_file = "ppyoloe_crn_l_300e_coco/model.pdmodel"
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params_file = "ppyoloe_crn_l_300e_coco/model.pdiparams"
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infer_cfg_file = "ppyoloe_crn_l_300e_coco/infer_cfg.yml"
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# 模型推理的配置信息
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option = fd.RuntimeOption()
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model = fd.vision.detection.PPYOLOE(model_file, params_file, infer_cfg_file, option)
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```
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加载模型完后，会输出提示如下，说明模型初始化的后端，以及运行的硬件设备
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OPENVINO in device Device::CPU.
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```
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## 3. 预测图片检测结果
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``` python
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import cv2
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im = cv2.imread("test_det.jpg")
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result = model.predict(im)
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print(result)
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```
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预测完，输出预测结果如下
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```
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DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
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415.047180,89.311569, 506.009613, 283.863098, 0.950423, 0
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163.665710,81.914932, 198.585342, 166.760895, 0.896433, 0
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581.788635,113.027618, 612.623474, 198.521713, 0.842596, 0
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267.217224,89.777306, 298.796051, 169.361526, 0.837951, 0
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104.465584,45.482422, 127.688850, 93.533867, 0.773348, 0
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...
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...
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```
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## 4. 可视化图片预测结果
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``` python
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vis_im = fd.vision.visualize.vis_detection(im, result, score_threshold=0.5)
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cv2.imwrite("vis_image.jpg", vis_im)
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```
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可视化执行完，打开`vis_image.jpg`可视化效果如下
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<div align="center">
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<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
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</div>
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## 其它文档
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- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)
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