add en version readme for model 400

PaddlePaddle · Nov 25, 2021 · 4c07f02 · 4c07f02
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diff --git a/modules/image/Image_editing/colorization/deoldify/README_en.md b/modules/image/Image_editing/colorization/deoldify/README_en.md
@@ -2,7 +2,7 @@
 
 | Module Name |deoldify|
 | :--- | :---: | 
-|Category|image editing|
+|Category|Image editing|
 |Network |NoGAN|
 |Dataset|ILSVRC 2012|
 |Fine-tuning supported or not |No|
@@ -22,7 +22,7 @@
 
 - ### Module Introduction
 
-  - deoldify is a color rendering model for images and videos, which can restore color for black and white photos and videos.
+  - Deoldify is a color rendering model for images and videos, which can restore color for black and white photos and videos.
 
   - For more information, please refer to: [deoldify](https://github.com/jantic/DeOldify)
 
@@ -36,30 +36,30 @@
 
     - NOTE: This Module relies on ffmpeg, Please install ffmpeg before using this Module.
 
-      ```shell
-      $ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
-      ```
+        ```shell
+        $ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
+        ```
 
 
 - ### 2、Installation
     - ```shell
       $ hub install deoldify
       ```
 
-    - In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_ch/get_start/windows_quickstart.md)
-    | [Linux_Quickstart](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_ch/get_start/mac_quickstart.md)  
+    - In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
+    | [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)  
 
 
 ## III. Module API Prediction
 
   - ### 1、Prediction Code Example
 
-    ```python
-    import paddlehub as hub
+    - ```python
+      import paddlehub as hub
 
-    model = hub.Module(name='deoldify')
-    model.predict('/PATH/TO/IMAGE/OR/VIDEO')
-    ```
+      model = hub.Module(name='deoldify')
+      model.predict('/PATH/TO/IMAGE/OR/VIDEO')
+      ```
 
   - ### 2、API
 
@@ -69,32 +69,32 @@
 
         - Prediction API.
 
-    - **Parameter**
+        - **Parameter**
 
-        - input (str): image path.
+            - input (str): Image path.
 
-    - **Return**
+        - **Return**
 
-        - If input is image path, the output is：
-          - pred_img(np.ndarray): image data, ndarray.shape is in the format [H, W, C], BGR;
-          - out_path(str): save path of images.
+            - If input is image path, the output is：
+              - pred_img(np.ndarray): image data, ndarray.shape is in the format [H, W, C], BGR.
+              - out_path(str): save path of images.
 
-        - If input is video path, the output is ：
-          - frame_pattern_combined(str): save path of frames from output video;
-          - vid_out_path(str): save path of output video.
+            - If input is video path, the output is ：
+              - frame_pattern_combined(str): save path of frames from output video.
+              - vid_out_path(str): save path of output video.
 
     - ```python
       def run_image(self, img):
       ```
         - Prediction API for image.
 
-    - **Parameter**
+        - **Parameter**
 
-        - img (str｜np.ndarray): image data,  str or ndarray. ndarray.shape is in the format [H, W, C], BGR.
+            - img (str｜np.ndarray): Image data,  str or ndarray. ndarray.shape is in the format [H, W, C], BGR.
 
-    - **Return**
+        - **Return**
 
-        - pred_img(np.ndarray): ndarray.shape is in the format [H, W, C], BGR.
+            - pred_img(np.ndarray): Ndarray.shape is in the format [H, W, C], BGR.
 
     - ```python
       def run_video(self, video):
@@ -103,12 +103,12 @@
 
        - **Parameter**
 
-         - video(str): video path.
+         - video(str): Video path.
 
        - **Return**
 
-         - frame_pattern_combined(str): save path of frames from output video;
-         - vid_out_path(str): save path of output video.
+         - frame_pattern_combined(str): Save path of frames from output video.
+         - vid_out_path(str): Save path of output video.
 
 
 ## IV. Server Deployment
@@ -120,44 +120,44 @@
 
     - Run the startup command:
 
-        - ```shell
-          $ hub serving start -m deoldify
-          ```
+      - ```shell
+        $ hub serving start -m deoldify
+        ```
 
-        - The servitization API is now deployed and the default port number is 8866.
+    - The servitization API is now deployed and the default port number is 8866.
 
-        - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
+    - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
 
 - ### Step 2: Send a predictive request
 
     - With a configured server, use the following lines of code to send the prediction request and obtain the result.
 
-    - ```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.tostring()).decode('utf8')
-      def base64_to_cv2(b64str):
-          data = base64.b64decode(b64str.encode('utf8'))
-          data = np.fromstring(data, np.uint8)
-          data = cv2.imdecode(data, cv2.IMREAD_COLOR)
-          return data
-
-      # Send an HTTP request
-      org_im = cv2.imread('/PATH/TO/ORIGIN/IMAGE')
-      data = {'images':cv2_to_base64(org_im)}
-      headers = {"Content-type": "application/json"}
-      url = "http://127.0.0.1:8866/predict/deoldify"
-      r = requests.post(url=url, headers=headers, data=json.dumps(data))
-      img = base64_to_cv2(r.json()["results"])
-      cv2.imwrite('/PATH/TO/SAVE/IMAGE', img)
-      ```
+      - ```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.tostring()).decode('utf8')
+        def base64_to_cv2(b64str):
+            data = base64.b64decode(b64str.encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            return data
+
+        # Send an HTTP request
+        org_im = cv2.imread('/PATH/TO/ORIGIN/IMAGE')
+        data = {'images':cv2_to_base64(org_im)}
+        headers = {"Content-type": "application/json"}
+        url = "http://127.0.0.1:8866/predict/deoldify"
+        r = requests.post(url=url, headers=headers, data=json.dumps(data))
+        img = base64_to_cv2(r.json()["results"])
+        cv2.imwrite('/PATH/TO/SAVE/IMAGE', img)
+        ```
 
 
 ## V. Release Note

diff --git a/modules/image/Image_editing/colorization/photo_restoration/README_en.md b/modules/image/Image_editing/colorization/photo_restoration/README_en.md
@@ -2,7 +2,7 @@
 
 |Module Name|photo_restoration|
 | :--- | :---: | 
-|Category|image editing|
+|Category|Image editing|
 |Network|deoldify and realsr|
 |Fine-tuning supported or not|No|
 |Module Size |64MB+834MB|
@@ -47,16 +47,16 @@
       $ hub install photo_restoration
       ```
 
-    - In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_ch/get_start/windows_quickstart.md)
-    | [Linux_Quickstart](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_ch/get_start/mac_quickstart.md)  
+    - In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
+    | [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)  
 
 
 ## III. Module API Prediction
 
 - ### 1、Prediction Code Example
 
 
-    ```python
+  - ```python
     import cv2
     import paddlehub as hub
 
@@ -68,7 +68,7 @@
 - ### 2、API
 
 
-    ```python
+  - ```python
     def run_image(self,
                   input,
                   model_select= ['Colorization', 'SuperResolution'],
@@ -79,16 +79,16 @@
 
     - **Parameter**
 
-        - input (numpy.ndarray｜str): image data，numpy.ndarray or str. ndarray.shape is in the format [H, W, C], BGR;
+        - input (numpy.ndarray｜str): Image data，numpy.ndarray or str. ndarray.shape is in the format [H, W, C], BGR.
 
         - model_select (list\[str\]): Mode selection，\['Colorization'\] only colorize the input image， \['SuperResolution'\] only increase the image resolution；
         default is \['Colorization', 'SuperResolution'\]。
 
-        - save_path (str): save path, default is 'photo_restoration'.
+        - save_path (str): Save path, default is 'photo_restoration'.
 
      - **Return**
 
-        - output (numpy.ndarray): restoration result，ndarray.shape is in the format [H, W, C], BGR.
+        - output (numpy.ndarray): Restoration result，ndarray.shape is in the format [H, W, C], BGR.
 
 
 ## IV. Server Deployment
@@ -103,15 +103,15 @@
           $ hub serving start -m photo_restoration
           ```
 
-        - The servitization API is now deployed and the default port number is 8866.
+    - The servitization API is now deployed and the default port number is 8866.
 
-        - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
+    - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
 
 - ### Step 2: Send a predictive request
 
     - With a configured server, use the following lines of code to send the prediction request and obtain the result
 
-        ```python
+      - ```python
         import requests
         import json
         import base64