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57 lines
1.8 KiB
Markdown
57 lines
1.8 KiB
Markdown
English | [中文](../../../cn/quick_start/models/python.md)
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# Python Deployment
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Make sure that FastDeploy is installed in the development environment. Refer to [FastDeploy Installation](../../build_and_install/) to install the pre-built FastDeploy, or build and install according to your own needs.
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This document uses the PaddleDetection target detection model PPYOLOE as an example to show an inference example on the CPU.
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## 1. Get the Model and Test Image
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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. Load Model
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- More examples of models can be found in[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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model = fd.vision.detection.PPYOLOE(model_file, params_file, infer_cfg_file)
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```
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## 3. Get Prediction for Image Object Detection
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``` python
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import cv2
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im = cv2.imread("000000014439.jpg")
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result = model.predict(im)
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print(result)
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```
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## 4. Visualize image prediction results
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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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After the visualization is executed, open `vis_image.jpg` and the visualization effect is as follows：
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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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## other documents
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- [Switch model inference hardware and backend](../../faq/how_to_change_backend.md)
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