Move third-party module

modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py → modules/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py

@@ -33,7 +33,8 @@ class Animegan_V1_Hayao_60(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py → modules/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Hayao_64(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py → modules/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Hayao_99(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py → modules/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Paprika_54(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py → modules/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Paprika_74(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py → modules/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Paprika_97(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py → modules/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Paprika_98(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py → modules/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Shinkai_33(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py → modules/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py

@@ -33,7 +33,8 @@ class Animegan_V2_Shinkai_53(Module):
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
+        processor = Processor(
+            images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/keypoint_detection/hand_pose_localization/README.md

-## 模型概述
-openpose 手部关键点检测模型
-
-模型详情请参考[openpose开源项目](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
-
-## 模型安装
-
-```shell
-$hub install hand_pose_localization
-```
-
-## API 说明
-
-```python
-def keypoint_detection(
-    self,
-    images=None,
-    paths=None,
-    batch_size=1,
-    output_dir='output',
-    visualization=False
-)
-```
-
-预测API，识别出人体手部关键点。
-
-![手部关键点](https://ai-studio-static-online.cdn.bcebos.com/97e1ae7c1e68477d85b37f53ee997fbc4ef0fc12c7634301bc08749bd003cac0)
-
-**参数**
-
-* images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\], 默认设为 None；
-* paths (list\[str\]): 图片的路径, 默认设为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
-* visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
-* output\_dir (str): 图片的保存路径，默认设为 output。
-
-**返回**
-
-* res (list[list[list[int]]]): 每张图片识别到的21个手部关键点组成的列表，每个关键点的格式为[x, y]，若有关键点未识别到则为None
-
-
-## 预测代码示例
-
-```python
-import cv2
-import paddlehub as hub
-
-# use_gpu：是否使用GPU进行预测
-model = hub.Module(name='hand_pose_localization', use_gpu=False)
-
-# 调用关键点检测API
-result = model.keypoint_detection(images=[cv2.imread('/PATH/TO/IMAGE')])
-
-# or
-# result = model.keypoint_detection(paths=['/PATH/TO/IMAGE'])
-
-# 打印预测结果
-print(result)
-```
-
-## 服务部署
-
-PaddleHub Serving可以部署一个在线人体手部关键点检测服务。
-
-## 第一步：启动PaddleHub Serving
-
-运行启动命令：
-```shell
-$ hub serving start -m hand_pose_localization
-```
-
-这样就完成了一个人体手部关键点检测的在线服务API的部署，默认端口号为8866。
-
-**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
-
-## 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 cv2
-import base64
-
-# 图片Base64编码函数
-def cv2_to_base64(image):
-    data = cv2.imencode('.jpg', image)[1]
-    return base64.b64encode(data.tostring()).decode('utf8')
-
-# 发送HTTP请求
-data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
-headers = {"Content-type": "application/json"}
-url = "http://127.0.0.1:8866/predict/hand_pose_localization"
-r = requests.post(url=url, headers=headers, data=json.dumps(data))
-
-# 打印预测结果
-print(r.json()["results"])
-```
-
-
-## 模型相关信息
-
-### 模型代码
-
-https://github.com/CMU-Perceptual-Computing-Lab/openpose
-
-### 依赖
-
-paddlepaddle >= 1.8.0
-
-paddlehub >= 1.8.0

modules/thirdparty/text/text_generation/reading_pictures_writing_poems/README.md

-reading_pictures_writing_poems
-类别 文本 - 文本生成
-
-# 模型概述
-看图写诗（reading_pictures_writing_poems），该模型可自动根据图像生成古诗词。该PaddleHub Module支持预测。
-
-# 选择模型版本进行安装
-$ hub install reading_pictures_writing_poems==1.0.0
-
-# 命令行预测示例
-$ hub run reading_pictures_writing_poems --input_image "scenery.jpg"
-
-![](https://ai-studio-static-online.cdn.bcebos.com/69a9d5a5472449678a08e1ee5066c81b5859827647d74eb8a674afabbc205ae5)
-<br>AI根据这张图片生成的古诗是： <br>
-- 蕾蕾海河海，岳峰岳麓蔓。
-- 不萌枝上春，自结心中线。
-
-<br>
-怎么样？还不错吧！
-# Module API说明
-## WritingPoem(self, image, use_gpu=False)
-看图写诗预测接口，预测输入一张图像，输出一首古诗词
-### 参数
-- image(str): 待检测的图片路径
-- use_gpu (bool): 是否使用 GPU
-### 返回
-- results (list[dict]): 识别结果的列表，列表中每一个元素为 dict，关键字有 image，Poetrys， 其中：
-image字段为原输入图片的路径
-Poetrys字段为输出的古诗词
-
-# 代码示例
-import paddlehub as hub
-
-readingPicturesWritingPoems = hub.Module(directory="./reading_pictures_writing_poems")
-readingPicturesWritingPoems.WritingPoem(image = "scenery.jpg", use_gpu=True)
-
-# 贡献者
-郑博培、彭兆帅
-
-# 依赖
-paddlepaddle >= 1.8.2
-paddlehub >= 1.8.0

modules/thirdparty/video/Video_editing/SkyAR/README.md → modules/video/Video_editing/SkyAR/README.md

@@ -125,4 +125,3 @@
 * 1.0.0
 
   初始发布
-