update humanseg_lite (#2181)

modules/image/semantic_segmentation/humanseg_lite/README.md

 # humanseg_lite
 
 |模型名称|humanseg_lite|
-| :--- | :---: | 
+| :--- | :---: |
 |类别|图像-图像分割|
 |网络|shufflenet|
 |数据集|百度自建数据集|
@@ -13,7 +13,7 @@
 ## 一、模型基本信息
 
 - ### 应用效果展示
-  
+
   - 样例结果示例：
     <p align="center">
     <img src="https://user-images.githubusercontent.com/35907364/130913092-312a5f37-842e-4fd0-8db4-5f853fd8419f.jpg" width = "337" height = "505" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130916087-7d537ad9-bbc8-4bce-9382-8eb132b35532.png" width = "337" height = "505" hspace='10'/>
@@ -37,7 +37,7 @@
     - ```shell
       $ hub install humanseg_lite
       ```
-      
+
     -  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)
 
@@ -226,6 +226,9 @@
         cv2.imwrite("segment_human_lite.png", rgba)
         ```
 
+- ### Gradio APP 支持
+
+    从 PaddleHub 2.3.1 开始支持使用链接 http://127.0.0.1:8866/gradio/humanseg_lite 在浏览器中访问 humanseg_lite 的 Gradio APP。
 
 ## 五、更新历史
 
@@ -234,7 +237,7 @@
     初始发布
 
 * 1.1.0
-    
+
     新增视频人像分割接口
 
     新增视频流人像分割接口
@@ -247,6 +250,10 @@
 
     移除 Fluid API
 
+* 1.3.0
+
+    添加 Gradio APP 支持
+
     ```shell
-    $ hub install humanseg_lite == 1.2.0
+    $ hub install humanseg_lite == 1.3.0
     ```

modules/image/semantic_segmentation/humanseg_lite/README_en.md

 # humanseg_lite
 
 |Module Name |humanseg_lite|
-| :--- | :---: | 
+| :--- | :---: |
 |Category |Image segmentation|
 |Network|shufflenet|
 |Dataset|Baidu self-built dataset|
@@ -10,10 +10,10 @@
 |Data indicators|-|
 |Latest update date|2021-02-26|
 
-## I. Basic Information 
+## I. Basic Information
 
 - ### Application Effect Display
-  
+
   - Sample results:
     <p align="center">
     <img src="https://user-images.githubusercontent.com/35907364/130913092-312a5f37-842e-4fd0-8db4-5f853fd8419f.jpg" width = "337" height = "505" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130916087-7d537ad9-bbc8-4bce-9382-8eb132b35532.png" width = "337" height = "505" hspace='10'/>
@@ -39,7 +39,7 @@
     - ```shell
       $ hub install humanseg_lite
       ```
-      
+
     - 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)  
 
@@ -49,7 +49,7 @@
 
     - ```
       hub run humanseg_lite --input_path "/PATH/TO/IMAGE"
-        
+
       ```
 
     - If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
@@ -122,9 +122,9 @@
 
         - **Return**
 
-            * res (list\[dict\]): The list of recognition results, where each element is dict and each field is: 
+            * res (list\[dict\]): The list of recognition results, where each element is dict and each field is:
                 * save\_path (str, optional): Save path of the result.
-                * data (numpy.ndarray): The result of portrait segmentation. 
+                * data (numpy.ndarray): The result of portrait segmentation.
 
     - ```python
       def video_stream_segment(self,
@@ -230,6 +230,8 @@
             cv2.imwrite("segment_human_lite.png", rgba)
             ```
 
+- ### Gradio APP support
+   Starting with PaddleHub 2.3.1, the Gradio APP for humanseg_lite is supported to be accessed in the browser using the link http://127.0.0.1:8866/gradio/humanseg_lite.
 
 ## V. Release Note
 
@@ -238,7 +240,7 @@
     First release
 
 - 1.1.0
-    
+
     Added video portrait segmentation interface
 
     Added video stream portrait segmentation interface
@@ -251,6 +253,10 @@
 
     Remove Fluid API
 
+* 1.3.0
+
+    Add Gradio APP support.
+
     ```shell
-    $ hub install humanseg_lite == 1.2.0
+    $ hub install humanseg_lite == 1.3.0
     ```

modules/image/semantic_segmentation/humanseg_lite/module.py

@@ -12,32 +12,39 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import argparse
 import ast
 import os
 import os.path as osp
-import argparse
 
 import cv2
 import numpy as np
-import paddle
 import paddle.jit
 import paddle.static
-from paddle.inference import Config, create_predictor
-from paddlehub.module.module import moduleinfo, runnable, serving
-
-from .processor import postprocess, base64_to_cv2, cv2_to_base64, check_dir
-from .data_feed import reader, preprocess_v
-from .optimal import postprocess_v, threshold_mask
-
-
-@moduleinfo(
-    name="humanseg_lite",
-    type="CV/semantic_segmentation",
-    author="paddlepaddle",
-    author_email="",
-    summary="humanseg_lite is a semantic segmentation model.",
-    version="1.2.0")
+from paddle.inference import Config
+from paddle.inference import create_predictor
+
+from .data_feed import preprocess_v
+from .data_feed import reader
+from .optimal import postprocess_v
+from .optimal import threshold_mask
+from .processor import base64_to_cv2
+from .processor import check_dir
+from .processor import cv2_to_base64
+from .processor import postprocess
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import runnable
+from paddlehub.module.module import serving
+
+
+@moduleinfo(name="humanseg_lite",
+            type="CV/semantic_segmentation",
+            author="paddlepaddle",
+            author_email="",
+            summary="humanseg_lite is a semantic segmentation model.",
+            version="1.3.0")
 class ShufflenetHumanSeg:
+
     def __init__(self):
         self.default_pretrained_model_path = os.path.join(self.directory, "humanseg_lite_inference", "model")
         self._set_config()
@@ -46,8 +53,8 @@ class ShufflenetHumanSeg:
         """
         predictor config setting
         """
-        model = self.default_pretrained_model_path+'.pdmodel'
-        params = self.default_pretrained_model_path+'.pdiparams'
+        model = self.default_pretrained_model_path + '.pdmodel'
+        params = self.default_pretrained_model_path + '.pdiparams'
         cpu_config = Config(model, params)
         cpu_config.disable_glog_info()
         cpu_config.disable_gpu()
@@ -64,7 +71,7 @@ class ShufflenetHumanSeg:
             gpu_config = Config(model, params)
             gpu_config.disable_glog_info()
             gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)
-            
+
             if paddle.get_cudnn_version() == 8004:
                 gpu_config.delete_pass('conv_elementwise_add_act_fuse_pass')
                 gpu_config.delete_pass('conv_elementwise_add2_act_fuse_pass')
@@ -134,13 +141,12 @@ class ShufflenetHumanSeg:
             output = np.expand_dims(output[:, 1, :, :], axis=1)
             # postprocess one by one
             for i in range(len(batch_data)):
-                out = postprocess(
-                    data_out=output[i],
-                    org_im=batch_data[i]['org_im'],
-                    org_im_shape=batch_data[i]['org_im_shape'],
-                    org_im_path=batch_data[i]['org_im_path'],
-                    output_dir=output_dir,
-                    visualization=visualization)
+                out = postprocess(data_out=output[i],
+                                  org_im=batch_data[i]['org_im'],
+                                  org_im_shape=batch_data[i]['org_im_shape'],
+                                  org_im_path=batch_data[i]['org_im_path'],
+                                  output_dir=output_dir,
+                                  visualization=visualization)
                 res.append(out)
         return res
 
@@ -327,11 +333,10 @@ class ShufflenetHumanSeg:
         """
         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 = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
+                                              prog='hub run {}'.format(self.name),
+                                              usage='%(prog)s',
+                                              add_help=True)
 
         self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
         self.arg_config_group = self.parser.add_argument_group(
@@ -339,12 +344,11 @@ class ShufflenetHumanSeg:
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.segment(
-            paths=[args.input_path],
-            batch_size=args.batch_size,
-            use_gpu=args.use_gpu,
-            output_dir=args.output_dir,
-            visualization=args.visualization)
+        results = self.segment(paths=[args.input_path],
+                               batch_size=args.batch_size,
+                               use_gpu=args.use_gpu,
+                               output_dir=args.output_dir,
+                               visualization=args.visualization)
         if args.save_dir is not None:
             check_dir(args.save_dir)
             self.save_inference_model(args.save_dir)
@@ -355,14 +359,22 @@ class ShufflenetHumanSeg:
         """
         Add the command config options.
         """
-        self.arg_config_group.add_argument(
-            '--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not")
-        self.arg_config_group.add_argument(
-            '--output_dir', type=str, default='humanseg_lite_output', help="The directory to save output images.")
-        self.arg_config_group.add_argument(
-            '--save_dir', type=str, default='humanseg_lite_model', help="The directory to save model.")
-        self.arg_config_group.add_argument(
-            '--visualization', type=ast.literal_eval, default=False, help="whether to save output as images.")
+        self.arg_config_group.add_argument('--use_gpu',
+                                           type=ast.literal_eval,
+                                           default=False,
+                                           help="whether use GPU or not")
+        self.arg_config_group.add_argument('--output_dir',
+                                           type=str,
+                                           default='humanseg_lite_output',
+                                           help="The directory to save output images.")
+        self.arg_config_group.add_argument('--save_dir',
+                                           type=str,
+                                           default='humanseg_lite_model',
+                                           help="The directory to save model.")
+        self.arg_config_group.add_argument('--visualization',
+                                           type=ast.literal_eval,
+                                           default=False,
+                                           help="whether to save output as images.")
         self.arg_config_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.")
 
     def add_module_input_arg(self):
@@ -371,33 +383,20 @@ class ShufflenetHumanSeg:
         """
         self.arg_input_group.add_argument('--input_path', type=str, help="path to image.")
 
-
-if __name__ == "__main__":
-    m = ShufflenetHumanSeg()
-    #shuffle.video_segment()
-    img = cv2.imread('photo.jpg')
-    # res = m.segment(images=[img], visualization=True)
-    # print(res[0]['data'])
-    # m.video_segment('')
-    cap_video = cv2.VideoCapture('video_test.mp4')
-    fps = cap_video.get(cv2.CAP_PROP_FPS)
-    save_path = 'result_frame.avi'
-    width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    cap_out = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (width, height))
-    prev_gray = None
-    prev_cfd = None
-    while cap_video.isOpened():
-        ret, frame_org = cap_video.read()
-        if ret:
-            [img_matting, prev_gray, prev_cfd] = m.video_stream_segment(
-                frame_org=frame_org, frame_id=cap_video.get(1), prev_gray=prev_gray, prev_cfd=prev_cfd)
-            img_matting = np.repeat(img_matting[:, :, np.newaxis], 3, axis=2)
-            bg_im = np.ones_like(img_matting) * 255
-            comb = (img_matting * frame_org + (1 - img_matting) * bg_im).astype(np.uint8)
-            cap_out.write(comb)
-        else:
-            break
-
-    cap_video.release()
-    cap_out.release()
+    def create_gradio_app(self):
+        import gradio as gr
+        import tempfile
+        import os
+        from PIL import Image
+
+        def inference(image, use_gpu=False):
+            with tempfile.TemporaryDirectory() as temp_dir:
+                self.segment(paths=[image], use_gpu=use_gpu, visualization=True, output_dir=temp_dir)
+                return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0]))
+
+        interface = gr.Interface(
+            inference,
+            [gr.inputs.Image(type="filepath"), gr.Checkbox(label='use_gpu')],
+            gr.outputs.Image(type="ndarray"),
+            title='humanseg_lite')
+        return interface

modules/image/semantic_segmentation/humanseg_lite/optimal.py

@@ -32,8 +32,8 @@ def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
     # 超出边界不跟踪
     not_track = (cur_x < 0) + (cur_x >= w) + (cur_y < 0) + (cur_y >= h)
     flow_bw[~not_track] = flow_bw[cur_y[~not_track], cur_x[~not_track]]
-    not_track += (
-        np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) + np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])) >= check_thres
+    not_track += (np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) +
+                  np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])) >= check_thres
     track_cfd[cur_y[~not_track], cur_x[~not_track]] = prev_cfd[~not_track]
 
     is_track[cur_y[~not_track], cur_x[~not_track]] = 1

modules/image/semantic_segmentation/humanseg_lite/processor.py

 # -*- coding:utf-8 -*-
+import base64
 import os
 import time
-import base64
 
 import cv2
 import numpy as np

modules/image/semantic_segmentation/humanseg_lite/test.py

@@ -3,15 +3,16 @@ import shutil
 import unittest
 
 import cv2
-import requests
 import numpy as np
-import paddlehub as hub
+import requests
 
+import paddlehub as hub
 
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
 
 class TestHubModule(unittest.TestCase):
+
     @classmethod
     def setUpClass(cls) -> None:
         img_url = 'https://unsplash.com/photos/pg_WCHWSdT8/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjYyNDM2ODI4&force=true&w=640'
@@ -23,8 +24,7 @@ class TestHubModule(unittest.TestCase):
             f.write(response.content)
         fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
         img = cv2.imread('tests/test.jpg')
-        video = cv2.VideoWriter('tests/test.avi', fourcc,
-                                20.0, tuple(img.shape[:2]))
+        video = cv2.VideoWriter('tests/test.avi', fourcc, 20.0, tuple(img.shape[:2]))
         for i in range(40):
             video.write(img)
         video.release()
@@ -38,101 +38,65 @@ class TestHubModule(unittest.TestCase):
         shutil.rmtree('humanseg_lite_video_result')
 
     def test_segment1(self):
-        results = self.module.segment(
-            paths=['tests/test.jpg'],
-            use_gpu=False,
-            visualization=False
-        )
+        results = self.module.segment(paths=['tests/test.jpg'], use_gpu=False, visualization=False)
         self.assertIsInstance(results[0]['data'], np.ndarray)
 
     def test_segment2(self):
-        results = self.module.segment(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=False,
-            visualization=False
-        )
+        results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=False)
         self.assertIsInstance(results[0]['data'], np.ndarray)
 
     def test_segment3(self):
-        results = self.module.segment(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=False,
-            visualization=True
-        )
+        results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=True)
         self.assertIsInstance(results[0]['data'], np.ndarray)
 
     def test_segment4(self):
-        results = self.module.segment(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=True,
-            visualization=False
-        )
+        results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=True, visualization=False)
         self.assertIsInstance(results[0]['data'], np.ndarray)
 
     def test_segment5(self):
-        self.assertRaises(
-            AssertionError,
-            self.module.segment,
-            paths=['no.jpg']
-        )
+        self.assertRaises(AssertionError, self.module.segment, paths=['no.jpg'])
 
     def test_segment6(self):
-        self.assertRaises(
-            AttributeError,
-            self.module.segment,
-            images=['test.jpg']
-        )
+        self.assertRaises(AttributeError, self.module.segment, images=['test.jpg'])
 
     def test_video_stream_segment1(self):
-        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
-            frame_org=cv2.imread('tests/test.jpg'),
-            frame_id=1,
-            prev_gray=None,
-            prev_cfd=None,
-            use_gpu=False
-        )
+        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+                                                                              frame_id=1,
+                                                                              prev_gray=None,
+                                                                              prev_cfd=None,
+                                                                              use_gpu=False)
         self.assertIsInstance(img_matting, np.ndarray)
         self.assertIsInstance(cur_gray, np.ndarray)
         self.assertIsInstance(optflow_map, np.ndarray)
-        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
-            frame_org=cv2.imread('tests/test.jpg'),
-            frame_id=2,
-            prev_gray=cur_gray,
-            prev_cfd=optflow_map,
-            use_gpu=False
-        )
+        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+                                                                              frame_id=2,
+                                                                              prev_gray=cur_gray,
+                                                                              prev_cfd=optflow_map,
+                                                                              use_gpu=False)
         self.assertIsInstance(img_matting, np.ndarray)
         self.assertIsInstance(cur_gray, np.ndarray)
         self.assertIsInstance(optflow_map, np.ndarray)
 
     def test_video_stream_segment2(self):
-        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
-            frame_org=cv2.imread('tests/test.jpg'),
-            frame_id=1,
-            prev_gray=None,
-            prev_cfd=None,
-            use_gpu=True
-        )
+        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+                                                                              frame_id=1,
+                                                                              prev_gray=None,
+                                                                              prev_cfd=None,
+                                                                              use_gpu=True)
         self.assertIsInstance(img_matting, np.ndarray)
         self.assertIsInstance(cur_gray, np.ndarray)
         self.assertIsInstance(optflow_map, np.ndarray)
-        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
-            frame_org=cv2.imread('tests/test.jpg'),
-            frame_id=2,
-            prev_gray=cur_gray,
-            prev_cfd=optflow_map,
-            use_gpu=True
-        )
+        img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+                                                                              frame_id=2,
+                                                                              prev_gray=cur_gray,
+                                                                              prev_cfd=optflow_map,
+                                                                              use_gpu=True)
         self.assertIsInstance(img_matting, np.ndarray)
         self.assertIsInstance(cur_gray, np.ndarray)
         self.assertIsInstance(optflow_map, np.ndarray)
 
     def test_video_segment1(self):
-        self.module.video_segment(
-            video_path="tests/test.avi",
-            use_gpu=False,
-            save_dir='humanseg_lite_video_result'
-        )
+        self.module.video_segment(video_path="tests/test.avi", use_gpu=False, save_dir='humanseg_lite_video_result')
 
     def test_save_inference_model(self):
         self.module.save_inference_model('./inference/model')