mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
02eab973ce
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md
73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
[English](../../../en/quick_start/models/python.md) | 中文
|
||
|
||
# PPYOLOE Python部署
|
||
|
||
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。
|
||
|
||
本文档以PaddleDetection目标检测模型PPYOLOE为例展示CPU上的推理示例
|
||
|
||
## 1. 获取模型和测试图像
|
||
|
||
``` python
|
||
import fastdeploy as fd
|
||
|
||
model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz"
|
||
image_url = "https://bj.bcebos.com/fastdeploy/tests/test_det.jpg"
|
||
fd.download_and_decompress(model_url, path=".")
|
||
fd.download(image_url, path=".")
|
||
```
|
||
|
||
## 2. 加载模型
|
||
|
||
- 更多模型的示例可参考[FastDeploy/examples](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples)
|
||
|
||
``` python
|
||
model_file = "ppyoloe_crn_l_300e_coco/model.pdmodel"
|
||
params_file = "ppyoloe_crn_l_300e_coco/model.pdiparams"
|
||
infer_cfg_file = "ppyoloe_crn_l_300e_coco/infer_cfg.yml"
|
||
|
||
# 模型推理的配置信息
|
||
option = fd.RuntimeOption()
|
||
model = fd.vision.detection.PPYOLOE(model_file, params_file, infer_cfg_file, option)
|
||
```
|
||
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
|
||
```
|
||
[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OPENVINO in device Device::CPU.
|
||
```
|
||
|
||
## 3. 预测图片检测结果
|
||
|
||
``` python
|
||
import cv2
|
||
im = cv2.imread("test_det.jpg")
|
||
|
||
result = model.predict(im)
|
||
print(result)
|
||
```
|
||
预测完，输出预测结果如下
|
||
```
|
||
DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
|
||
415.047180,89.311569, 506.009613, 283.863098, 0.950423, 0
|
||
163.665710,81.914932, 198.585342, 166.760895, 0.896433, 0
|
||
581.788635,113.027618, 612.623474, 198.521713, 0.842596, 0
|
||
267.217224,89.777306, 298.796051, 169.361526, 0.837951, 0
|
||
104.465584,45.482422, 127.688850, 93.533867, 0.773348, 0
|
||
...
|
||
...
|
||
```
|
||
|
||
## 4. 可视化图片预测结果
|
||
|
||
``` python
|
||
vis_im = fd.vision.visualize.vis_detection(im, result, score_threshold=0.5)
|
||
cv2.imwrite("vis_image.jpg", vis_im)
|
||
```
|
||
可视化执行完，打开`vis_image.jpg`可视化效果如下
|
||
<div align="center">
|
||
<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
|
||
</div>
|
||
|
||
## 其它文档
|
||
|
||
- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)
|