mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +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
57 lines
1.8 KiB
Markdown
57 lines
1.8 KiB
Markdown
English | [中文](../../../cn/quick_start/models/python.md)
|
||
|
||
# Python Deployment
|
||
|
||
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.
|
||
|
||
This document uses the PaddleDetection target detection model PPYOLOE as an example to show an inference example on the CPU.
|
||
|
||
## 1. Get the Model and Test Image
|
||
|
||
``` 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. Load Model
|
||
|
||
- More examples of models can be found in[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"
|
||
model = fd.vision.detection.PPYOLOE(model_file, params_file, infer_cfg_file)
|
||
```
|
||
|
||
## 3. Get Prediction for Image Object Detection
|
||
|
||
``` python
|
||
import cv2
|
||
im = cv2.imread("000000014439.jpg")
|
||
|
||
result = model.predict(im)
|
||
print(result)
|
||
```
|
||
|
||
## 4. Visualize image prediction results
|
||
|
||
``` python
|
||
vis_im = fd.vision.visualize.vis_detection(im, result, score_threshold=0.5)
|
||
cv2.imwrite("vis_image.jpg", vis_im)
|
||
```
|
||
|
||
After the visualization is executed, open `vis_image.jpg` and the visualization effect is as follows：
|
||
|
||
<div align="center">
|
||
<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
|
||
</div>
|
||
|
||
## other documents
|
||
|
||
- [Switch model inference hardware and backend](../../faq/how_to_change_backend.md)
|