[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)

* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
2025-10-23 00:29:35 +08:00 · 2023-03-17 11:22:46 +08:00
parent 3b1343c726
commit 5b143219ce
177 changed files with 1019 additions and 815 deletions
--- a/examples/vision/segmentation/paddleseg/matting/cpu-gpu/python/README.md
+++ b/examples/vision/segmentation/paddleseg/matting/cpu-gpu/python/README.md
@@ -0,0 +1,63 @@
+[English](README.md) | 简体中文
+# PP-Matting CPU-GPU Python部署示例
+
+本目录下提供`infer.py`快速完成PP-Matting在CPU/GPU、昆仑芯、华为昇腾，以及GPU上通过Paddle-TensorRT加速部署的示例。执行如下脚本即可完成
+
+## 1. 说明  
+PaddleSeg支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署Matting模型
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考文档[FastDeploy预编译库安装](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install)，**注意** 只有CPU、GPU提供预编译库，华为昇腾以及昆仑芯需要参考以上文档自行编译部署环境。
+
+## 3. 部署模型准备
+在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)。
+
+## 4. 运行部署示例  
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/matting/cpp-gpu/python
+# # 如果您希望从PaddleSeg下载示例代码，请运行
+# git clone https://github.com/PaddlePaddle/PaddleSeg.git
+# # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+# # git checkout develop
+# cd PaddleSeg/deploy/fastdeploy/matting/cpp-gpu/python
+
+# 下载PP-Matting模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz
+tar -xvf PP-Matting-512.tgz
+wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
+wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
+
+# CPU推理
+python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu
+# GPU推理
+python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
+# 昆仑芯XPU推理
+python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
+```
+**注意** 以上示例未提供华为昇腾的示例，在编译好昇腾部署环境后，只需改造一行代码，将示例文件中的`option.use_kunlunxin()`为`option.use_ascend()`就可以完成在华为昇腾上的推理部署
+
+运行完成可视化结果如下图所示
+<div width="840">
+<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
+<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
+<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
+<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
+</div>
+
+## 5. 更多指南
+- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
+- [FastDeploy部署PaddleSeg模型概览](..)
+- [PaddleSeg C++部署](../cpp)
+
+## 6. 常见问题
+- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
+- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
--- a/examples/vision/segmentation/paddleseg/matting/cpu-gpu/python/infer.py
+++ b/examples/vision/segmentation/paddleseg/matting/cpu-gpu/python/infer.py
@@ -0,0 +1,75 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleSeg model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--bg",
+        type=str,
+        required=True,
+        default=None,
+        help="Path of test background image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+        option.use_paddle_infer_backend()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        # If use original Tensorrt, not Paddle-TensorRT,
+        # comment the following two lines
+        option.enable_paddle_to_trt()
+        option.enable_paddle_trt_collect_shape()
+        option.set_trt_input_shape("img", [1, 3, 512, 512])
+
+    if args.device.lower() == "kunlunxin":
+        option.use_kunlunxin()
+    return option
+
+
+args = parse_arguments()
+
+# setup runtime
+runtime_option = build_option(args)
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+config_file = os.path.join(args.model, "deploy.yaml")
+model = fd.vision.matting.PPMatting(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# predict
+im = cv2.imread(args.image)
+bg = cv2.imread(args.bg)
+result = model.predict(im)
+print(result)
+
+# visualize
+vis_im = fd.vision.vis_matting(im, result)
+vis_im_with_bg = fd.vision.swap_background(im, bg, result)
+cv2.imwrite("visualized_result_fg.png", vis_im)
+cv2.imwrite("visualized_result_replaced_bg.jpg", vis_im_with_bg)
+print(
+    "Visualized result save in ./visualized_result_replaced_bg.jpg and ./visualized_result_fg.png"
+)