add swin2sr_real_sr_x4 (#2085)

Author: jm12138

modules/image/Image_editing/super_resolution/swin2sr_real_sr_x4/README.md

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+# swin2sr_real_sr_x4
+
+|模型名称|swin2sr_real_sr_x4|
+| :--- | :---: |
+|类别|图像-图像编辑|
+|网络|Swin2SR|
+|数据集|DIV2K / Flickr2K|
+|是否支持Fine-tuning|否|
+|模型大小|68.4MB|
+|指标|-|
+|最新更新日期|2022-10-25|
+
+
+## 一、模型基本信息
+
+- ### 应用效果展示
+
+  - 网络结构：
+      <p align="center">
+      <img src="https://ai-studio-static-online.cdn.bcebos.com/884d4d4472b44bf1879606374ed64a7e8d2fec0bcf034285a5cecfc582e8cd65" hspace='10'/> <br />
+      </p>
+
+  - 样例结果示例：
+      <p align="center">
+      <img src="https://ai-studio-static-online.cdn.bcebos.com/c5517af6c3f944c4b281aedc417a4f8c02c0a969d0dd494c9106c4ff2709fc2f" hspace='10'/>
+      <img src="https://ai-studio-static-online.cdn.bcebos.com/183c5821029f45bbb78d1700ab8297baabba15f82ab4467e88414bbed056ccf0" hspace='10'/>
+      </p>
+
+- ### 模型介绍
+
+  - Swin2SR 是一个基于 Swin Transformer v2 的图像超分辨率模型。swin2sr_real_sr_x4 是基于 Swin2SR 的 4 倍现实图像超分辨率模型。
+
+
+
+## 二、安装
+
+- ### 1、环境依赖
+
+  - paddlepaddle >= 2.0.0
+
+  - paddlehub >= 2.0.0  
+
+- ### 2.安装
+
+    - ```shell
+      $ hub install swin2sr_real_sr_x4
+      ```
+    -  如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
+      | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
+
+## 三、模型API预测
+  - ### 1、命令行预测
+
+    ```shell
+    $ hub run swin2sr_real_sr_x4 \
+        --input_path "/PATH/TO/IMAGE" \
+        --output_dir "swin2sr_real_sr_x4_output"
+    ```
+
+  - ### 2、预测代码示例
+
+    ```python
+    import paddlehub as hub
+    import cv2
+
+    module = hub.Module(name="swin2sr_real_sr_x4")
+    result = module.real_sr(
+        image=cv2.imread('/PATH/TO/IMAGE'),
+        visualization=True,
+        output_dir='swin2sr_real_sr_x4_output'
+    )
+    ```
+
+  - ### 3、API
+
+    ```python
+    def real_sr(
+        image: Union[str, numpy.ndarray],
+        visualization: bool = True,
+        output_dir: str = "swin2sr_real_sr_x4_output"
+    ) -> numpy.ndarray
+    ```
+
+    - 超分辨率 API
+
+    - **参数**
+
+      * image (Union\[str, numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式；
+      * visualization (bool): 是否将识别结果保存为图片文件；
+      * output\_dir (str): 保存处理结果的文件目录。
+
+    - **返回**
+
+      * res (numpy.ndarray): 图像超分辨率结果 (BGR)；
+
+## 四、服务部署
+
+- PaddleHub Serving 可以部署一个图像超分辨率的在线服务。
+
+- ### 第一步：启动PaddleHub Serving
+
+  - 运行启动命令：
+
+    ```shell
+     $ hub serving start -m swin2sr_real_sr_x4
+    ```
+
+    - 这样就完成了一个图像超分辨率服务化API的部署，默认端口号为8866。
+
+- ### 第二步：发送预测请求
+
+  - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
+
+    ```python
+    import requests
+    import json
+    import base64
+
+    import cv2
+    import numpy as np
+
+    def cv2_to_base64(image):
+        data = cv2.imencode('.jpg', image)[1]
+        return base64.b64encode(data.tobytes()).decode('utf8')
+
+    def base64_to_cv2(b64str):
+        data = base64.b64decode(b64str.encode('utf8'))
+        data = np.frombuffer(data, np.uint8)
+        data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        return data
+
+    # 发送HTTP请求
+    org_im = cv2.imread('/PATH/TO/IMAGE')
+    data = {
+        'image': cv2_to_base64(org_im)
+    }
+    headers = {"Content-type": "application/json"}
+    url = "http://127.0.0.1:8866/predict/swin2sr_real_sr_x4"
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+    # 结果转换
+    results = r.json()['results']
+    results = base64_to_cv2(results)
+
+    # 保存结果
+    cv2.imwrite('output.jpg', results)
+    ```
+
+## 五、参考资料
+
+* 论文：[Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345)
+
+* 官方实现：[mv-lab/swin2sr](https://github.com/mv-lab/swin2sr/)
+
+## 六、更新历史
+
+* 1.0.0
+
+  初始发布
+
+  ```shell
+  $ hub install swin2sr_real_sr_x4==1.0.0
+  ```

modules/image/Image_editing/super_resolution/swin2sr_real_sr_x4/module.py

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+import argparse
+import base64
+import os
+import time
+from typing import Union
+
+import cv2
+import numpy as np
+import paddle
+import paddle.nn as nn
+
+from .swin2sr import Swin2SR
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import runnable
+from paddlehub.module.module import serving
+
+
+def cv2_to_base64(image):
+    data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(data.tobytes()).decode('utf8')
+
+
+def base64_to_cv2(b64str):
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.frombuffer(data, np.uint8)
+    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    return data
+
+
+@moduleinfo(
+    name='swin2sr_real_sr_x4',
+    version='1.0.0',
+    type="CV/image_editing",
+    author="",
+    author_email="",
+    summary="SwinV2 Transformer for Compressed Image Super-Resolution and Restoration.",
+)
+class SwinIRMRealSR(nn.Layer):
+
+    def __init__(self):
+        super(SwinIRMRealSR, self).__init__()
+        self.default_pretrained_model_path = os.path.join(self.directory,
+                                                          'Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pdparams')
+        self.swin2sr = Swin2SR(upscale=4,
+                               in_chans=3,
+                               img_size=64,
+                               window_size=8,
+                               img_range=1.,
+                               depths=[6, 6, 6, 6, 6, 6],
+                               embed_dim=180,
+                               num_heads=[6, 6, 6, 6, 6, 6],
+                               mlp_ratio=2,
+                               upsampler='nearest+conv',
+                               resi_connection='1conv')
+        state_dict = paddle.load(self.default_pretrained_model_path)
+        self.swin2sr.set_state_dict(state_dict)
+        self.swin2sr.eval()
+
+    def preprocess(self, img: np.ndarray) -> np.ndarray:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = img.transpose((2, 0, 1))
+        img = img / 255.0
+        return img.astype(np.float32)
+
+    def postprocess(self, img: np.ndarray) -> np.ndarray:
+        img = img.clip(0, 1)
+        img = img * 255.0
+        img = img.transpose((1, 2, 0))
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        return img.astype(np.uint8)
+
+    def real_sr(self,
+                image: Union[str, np.ndarray],
+                visualization: bool = True,
+                output_dir: str = "swin2sr_real_sr_x4_output") -> np.ndarray:
+        if isinstance(image, str):
+            _, file_name = os.path.split(image)
+            save_name, _ = os.path.splitext(file_name)
+            save_name = save_name + '_' + str(int(time.time())) + '.jpg'
+            image = cv2.imdecode(np.fromfile(image, dtype=np.uint8), cv2.IMREAD_COLOR)
+        elif isinstance(image, np.ndarray):
+            save_name = str(int(time.time())) + '.jpg'
+            image = image
+        else:
+            raise Exception("image should be a str / np.ndarray")
+
+        with paddle.no_grad():
+            img_input = self.preprocess(image)
+            img_input = paddle.to_tensor(img_input[None, ...], dtype=paddle.float32)
+
+            img_output = self.swin2sr(img_input)
+            img_output = img_output.numpy()[0]
+            img_output = self.postprocess(img_output)
+
+        if visualization:
+            if not os.path.isdir(output_dir):
+                os.makedirs(output_dir)
+            save_path = os.path.join(output_dir, save_name)
+            cv2.imwrite(save_path, img_output)
+
+        return img_output
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        Run as a command.
+        """
+        self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
+                                              prog='hub run {}'.format(self.name),
+                                              usage='%(prog)s',
+                                              add_help=True)
+        self.parser.add_argument('--input_path', type=str, help="Path to image.")
+        self.parser.add_argument('--output_dir',
+                                 type=str,
+                                 default='swin2sr_real_sr_x4_output',
+                                 help="The directory to save output images.")
+        args = self.parser.parse_args(argvs)
+        self.real_sr(image=args.input_path, visualization=True, output_dir=args.output_dir)
+        return 'Results are saved in %s' % args.output_dir
+
+    @serving
+    def serving_method(self, image, **kwargs):
+        """
+        Run as a service.
+        """
+        image = base64_to_cv2(image)
+        img_output = self.real_sr(image=image, **kwargs)
+
+        return cv2_to_base64(img_output)