> > - tuple(frame_path(str), out_path(str)): frame_path is the save path of each frame of the video after super resolution, and out_path is the save path of the video after super resolution.
> Build the instance of RealSR. EDVR is a model designed for video super resolution. For more details, see the paper, EDVR: Video Restoration with Enhanced Deformable Convolutional Networks (https://arxiv.org/abs/1905.02716). The interface imposes 2x super resolution on the input video. The recommended video format is mp4.
> Build the instance of PPMSVSR. PPMSVSR is a multi-stage VSR deep architecture. For more details, see the paper, PP-MSVSR: Multi-Stage Video Super-Resolution (https://arxiv.org/pdf/2112.02828.pdf). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> *Note: The interface is only available in static graph, add the following codes to enable static graph before using it：
> **Parameter**
>
> ```
> import paddle
> paddle.enable_static() #enable static graph
> paddle.disable_static() #disable static graph
> from ppgan.apps import PPMSVSRPredictor
> sr = PPMSVSRPredictor()
> # test a video file
> sr.run("docs/imgs/test.mp4")
> ```
> **参数**
>
> > - output (str): path of the output image, default: output. Note that the path should be set as output/EDVR.
> > - weight_path (str): path of the model, default: None，pre-trained integral model will then be automatically downloaded.
> > - num_frames (int): the number of input frames of the PPMSVSR model, the default value: 10. Note that the larger the num_frames, the better the effect of the video after super resolution.
```python
run(video_path)
```
> The execution interface after building the instance.
> **Parameter**
>
> > - video_path (str): path of the video files.
>
> **Return Value**
>
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
> Build the instance of PPMSVSRLarge. PPMSVSRLarge is a Large PPMSVSR model. For more details, see the paper, PP-MSVSR: Multi-Stage Video Super-Resolution (https://arxiv.org/pdf/2112.02828.pdf). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> **Parameter**
>
> ```
> from ppgan.apps import PPMSVSRLargePredictor
> sr = PPMSVSRLargePredictor()
> # test a video file
> sr.run("docs/imgs/test.mp4")
> ```
> **参数**
>
> > - output (str): path of the output image, default: output. Note that the path should be set as output/EDVR.
> > - weight_path (str): path of the model, default: None，pre-trained integral model will then be automatically downloaded.
> > - num_frames (int): the number of input frames of the PPMSVSR model, the default value: 10. Note that the larger the num_frames, the better the effect of the video after super resolution.
```python
run(video_path)
```
> The execution interface after building the instance.
> **Parameter**
>
> > - video_path (str): path of the video files.
>
> **Return Value**
>
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
> Build the instance of EDVR. EDVR is a model designed for video super resolution. For more details, see the paper, EDVR: Video Restoration with Enhanced Deformable Convolutional Networks (https://arxiv.org/abs/1905.02716). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> **Parameter**
>
...
...
@@ -247,6 +314,111 @@ run(video_path)
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
> Build the instance of BasicVSR. BasicVSR is a model designed for video super resolution. For more details, see the paper, BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond (https://arxiv.org/pdf/2012.02181.pdf). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> **Parameter**
>
> ```
> from ppgan.apps import BasicVSRPredictor
> sr = BasicVSRPredictor()
> # test a video file
> sr.run("docs/imgs/test.mp4")
> ```
> **参数**
>
> > - output (str): path of the output image, default: output. Note that the path should be set as output/EDVR.
> > - weight_path (str): path of the model, default: None，pre-trained integral model will then be automatically downloaded.
> > - num_frames (int): the number of input frames of the PPMSVSR model, the default value: 10. Note that the larger the num_frames, the better the effect of the video after super resolution.
```python
run(video_path)
```
> The execution interface after building the instance.
> **Parameter**
>
> > - video_path (str): path of the video files.
>
> **Return Value**
>
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
> Build the instance of IconVSR. IconVSR is a VSR model expanded by BasicVSR. For more details, see the paper, BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond (https://arxiv.org/pdf/2012.02181.pdf). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> **Parameter**
>
> ```
> from ppgan.apps import IconVSRPredictor
> sr = IconVSRPredictor()
> # test a video file
> sr.run("docs/imgs/test.mp4")
> ```
> **参数**
>
> > - output (str): path of the output image, default: output. Note that the path should be set as output/EDVR.
> > - weight_path (str): path of the model, default: None，pre-trained integral model will then be automatically downloaded.
> > - num_frames (int): the number of input frames of the PPMSVSR model, the default value: 10. Note that the larger the num_frames, the better the effect of the video after super resolution.
```python
run(video_path)
```
> The execution interface after building the instance.
> **Parameter**
>
> > - video_path (str): path of the video files.
>
> **Return Value**
>
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
> Build the instance of BasiVSRPlusPlus. BasiVSRPlusPlus is a model designed for video super resolution. For more details, see the paper, BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment (https://arxiv.org/pdf/2104.13371v1.pdf). The interface imposes 4x super resolution on the input video. The recommended video format is mp4.
>
> **Parameter**
>
> ```
> from ppgan.apps import BasiVSRPlusPlusPredictor
> sr = BasiVSRPlusPlusPredictor()
> # test a video file
> sr.run("docs/imgs/test.mp4")
> ```
> **参数**
>
> > - output (str): path of the output image, default: output. Note that the path should be set as output/EDVR.
> > - weight_path (str): path of the model, default: None，pre-trained integral model will then be automatically downloaded.
> > - num_frames (int): the number of input frames of the PPMSVSR model, the default value: 10. Note that the larger the num_frames, the better the effect of the video after super resolution.
```python
run(video_path)
```
> The execution interface after building the instance.
> **Parameter**
>
> > - video_path (str): path of the video files.
>
> **Return Value**
>
> > - tuple(str, str): the former is the save path of each frame of the video after super resolution, the latter is the save path of the video after super resolution.
