update Extract_Line_Draft (#2021)

modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md

-Extract_Line_Draft
-类别 图像 - 图像分割
+# Extract_Line_Draft
 
-# 模型概述
-提取线稿（Extract_Line_Draft），该模型可自动根据彩色图生成线稿图。该PaddleHub Module支持API预测及命令行预测。
+|模型名称|Extract_Line_Draft|
+| :--- | :---: | 
+|类别|图像-图像分割|
+|网络|-|
+|数据集|-|
+|是否支持Fine-tuning|否|
+|模型大小|259MB|
+|指标|-|
+|最新更新日期|2021-02-26|
 
-# 选择模型版本进行安装
-$ hub install Extract_Line_Draft==1.0.0
 
-# 命令行预测示例
-$ hub run Extract_Line_Draft --image 1.png --use_gpu True
+## 一、模型基本信息
 
-# Module API说明
-## ExtractLine(self, image, use_gpu=False)
-提取线稿预测接口，预测输入一张图像，输出该图像的线稿
-### 参数
-- image(str): 待检测的图片路径
-- use_gpu (bool): 是否使用 GPU
+- ### 应用效果展示
 
+  - 样例结果示例：
+      <p align="center">
+      <img src="https://ai-studio-static-online.cdn.bcebos.com/1c30757e069541a18dc89b92f0750983b77ad762560849afa0170046672e57a3" width = "337" height = "505" hspace='10'/> <img src="https://ai-studio-static-online.cdn.bcebos.com/7ef00637e5974be2847317053f8abe97236cec75fba14f77be2c095529a1eeb3" width = "337" height = "505" hspace='10'/>
+      </p>
 
-# 代码示例
+- ### 模型介绍
 
-## API调用
-~~~
-import paddlehub as hub
+  - 提取线稿（Extract_Line_Draft），该模型可自动根据彩色图生成线稿图。该PaddleHub Module支持API预测及命令行预测。
 
-Extract_Line_Draft_test = hub.Module(name="Extract_Line_Draft")
 
-test_img = "testImage.png"
+## 二、安装
 
-# execute predict
-Extract_Line_Draft_test.ExtractLine(test_img, use_gpu=True)
-~~~
+- ### 1、环境依赖
 
-## 命令行调用
-~~~
-!hub run Extract_Line_Draft --input_path "testImage" --use_gpu True
-~~~
+  - paddlepaddle >= 2.0.0
 
-# 效果展示
+  - paddlehub >= 2.0.0  
 
-## 原图
-![](https://ai-studio-static-online.cdn.bcebos.com/1c30757e069541a18dc89b92f0750983b77ad762560849afa0170046672e57a3)
-![](https://ai-studio-static-online.cdn.bcebos.com/4a544c9ecd79461bbc1d1556d100b21d28b41b4f23db440ab776af78764292f2)
+- ### 2.安装
 
+    - ```shell
+      $ hub install Extract_Line_Draft
+      ```
+    -  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)
 
-## 线稿图
-![](https://ai-studio-static-online.cdn.bcebos.com/7ef00637e5974be2847317053f8abe97236cec75fba14f77be2c095529a1eeb3)
-![](https://ai-studio-static-online.cdn.bcebos.com/074ea02d89bc4b5c9004a077b61301fa49583c13af734bd6a49e81f59f9cd322)
+## 三、模型API预测
+  - ### 1、命令行预测
 
+    ```shell
+    $ hub run Extract_Line_Draft --input_path "testImage" --use_gpu True
+    ```
 
-# 贡献者
-彭兆帅、郑博培
+  - ### 2、预测代码示例
 
-# 依赖
-paddlepaddle >= 1.8.2
-paddlehub >= 1.8.0
+    ```python
+    import paddlehub as hub
+
+    Extract_Line_Draft_test = hub.Module(name="Extract_Line_Draft")
+
+    test_img = "testImage.png"
+
+    # execute predict
+    Extract_Line_Draft_test.ExtractLine(test_img, use_gpu=True)
+    ```
+  
+  - ### 3、API
+
+    ```python
+    def ExtractLine(image, use_gpu=False)
+    ```
+
+    - 预测API，用于图像分割得到人体解析。
+
+    - **参数**
+
+      * image(str): 待检测的图片路径
+      * use_gpu (bool): 是否使用 GPU
+
+
+## 四、更新历史
+
+* 1.0.0
+
+  初始发布
+
+* 1.1.0
+
+  移除 Fluid API
+
+  ```shell
+  $ hub install Extract_Line_Draft == 1.1.0
+  ```
\ No newline at end of file

modules/image/semantic_segmentation/Extract_Line_Draft/function.py

@@ -4,9 +4,9 @@ from scipy import ndimage
 
 
 def get_normal_map(img):
-    img = img.astype(np.float)
+    img = img.astype(np.float32)
     img = img / 255.0
-    img = -img + 1
+    img = - img + 1
     img[img < 0] = 0
     img[img > 1] = 1
     return img
@@ -14,7 +14,7 @@ def get_normal_map(img):
 
 def get_gray_map(img):
     gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
-    highPass = gray.astype(np.float)
+    highPass = gray.astype(np.float32)
     highPass = highPass / 255.0
     highPass = 1 - highPass
     highPass = highPass[None]
@@ -25,7 +25,7 @@ def get_light_map(img):
     gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
     blur = cv2.GaussianBlur(gray, (0, 0), 3)
     highPass = gray.astype(int) - blur.astype(int)
-    highPass = highPass.astype(np.float)
+    highPass = highPass.astype(np.float32)
     highPass = highPass / 128.0
     highPass = highPass[None]
     return highPass.transpose((1, 2, 0))
@@ -38,7 +38,7 @@ def get_light_map_single(img):
     blur = cv2.GaussianBlur(gray, (0, 0), 3)
     gray = gray.reshape((gray.shape[0], gray.shape[1]))
     highPass = gray.astype(int) - blur.astype(int)
-    highPass = highPass.astype(np.float)
+    highPass = highPass.astype(np.float32)
     highPass = highPass / 128.0
     return highPass
 
@@ -49,7 +49,7 @@ def get_light_map_drawer(img):
     highPass = gray.astype(int) - blur.astype(int) + 255
     highPass[highPass < 0] = 0
     highPass[highPass > 255] = 255
-    highPass = highPass.astype(np.float)
+    highPass = highPass.astype(np.float32)
     highPass = highPass / 255.0
     highPass = 1 - highPass
     highPass = highPass[None]
@@ -58,7 +58,7 @@ def get_light_map_drawer(img):
 
 def get_light_map_drawer2(img):
     ret = img.copy()
-    ret = ret.astype(np.float)
+    ret = ret.astype(np.float32)
     ret[:, :, 0] = get_light_map_drawer3(img[:, :, 0])
     ret[:, :, 1] = get_light_map_drawer3(img[:, :, 1])
     ret[:, :, 2] = get_light_map_drawer3(img[:, :, 2])
@@ -72,7 +72,7 @@ def get_light_map_drawer3(img):
     highPass = gray.astype(int) - blur.astype(int) + 255
     highPass[highPass < 0] = 0
     highPass[highPass > 255] = 255
-    highPass = highPass.astype(np.float)
+    highPass = highPass.astype(np.float32)
     highPass = highPass / 255.0
     highPass = 1 - highPass
     return highPass
@@ -91,7 +91,7 @@ def superlize_pic(img):
 
 def mask_pic(img, mask):
     mask_mat = mask
-    mask_mat = mask_mat.astype(np.float)
+    mask_mat = mask_mat.astype(np.float32)
     mask_mat = cv2.GaussianBlur(mask_mat, (0, 0), 1)
     mask_mat = mask_mat / np.max(mask_mat)
     mask_mat = mask_mat * 255
@@ -106,14 +106,14 @@ def mask_pic(img, mask):
 
 
 def resize_img_512(img):
-    zeros = np.zeros((512, 512, img.shape[2]), dtype=np.float)
+    zeros = np.zeros((512, 512, img.shape[2]), dtype=np.float32)
     zeros[:img.shape[0], :img.shape[1]] = img
     return zeros
 
 
 def resize_img_512_3d(img):
-    zeros = np.zeros((1, 3, 512, 512), dtype=np.float)
-    zeros[0, 0:img.shape[0], 0:img.shape[1], 0:img.shape[2]] = img
+    zeros = np.zeros((1, 3, 512, 512), dtype=np.float32)
+    zeros[0, 0: img.shape[0], 0: img.shape[1], 0: img.shape[2]] = img
     return zeros.transpose((1, 2, 3, 0))
 
 
@@ -122,8 +122,8 @@ def denoise_mat(img, i):
 
 
 def show_active_img_and_save_denoise(img, path):
-    mat = img.astype(np.float)
-    mat = -mat + 1
+    mat = img.astype(np.float32)
+    mat = - mat + 1
     mat = mat * 255.0
     mat[mat < 0] = 0
     mat[mat > 255] = 255
@@ -134,8 +134,8 @@ def show_active_img_and_save_denoise(img, path):
 
 
 def show_active_img(name, img):
-    mat = img.astype(np.float)
-    mat = -mat + 1
+    mat = img.astype(np.float32)
+    mat = - mat + 1
     mat = mat * 255.0
     mat[mat < 0] = 0
     mat[mat > 255] = 255
@@ -145,8 +145,8 @@ def show_active_img(name, img):
 
 
 def get_active_img(img):
-    mat = img.astype(np.float)
-    mat = -mat + 1
+    mat = img.astype(np.float32)
+    mat = - mat + 1
     mat = mat * 255.0
     mat[mat < 0] = 0
     mat[mat > 255] = 255
@@ -155,9 +155,9 @@ def get_active_img(img):
 
 
 def get_active_img_fil(img):
-    mat = img.astype(np.float)
+    mat = img.astype(np.float32)
     mat[mat < 0.18] = 0
-    mat = -mat + 1
+    mat = - mat + 1
     mat = mat * 255.0
     mat[mat < 0] = 0
     mat[mat > 255] = 255
@@ -166,7 +166,7 @@ def get_active_img_fil(img):
 
 
 def show_double_active_img(name, img):
-    mat = img.astype(np.float)
+    mat = img.astype(np.float32)
     mat = mat * 128.0
     mat = mat + 127.0
     mat[mat < 0] = 0

modules/image/semantic_segmentation/Extract_Line_Draft/module.py

 import argparse
 import ast
 import os
-import math
-import six
-import time
+import cv2
 from pathlib import Path
 
-from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
-from paddlehub.module.module import runnable, serving, moduleinfo
-from paddlehub.io.parser import txt_parser
+from paddle.inference import Config, create_predictor
+from paddlehub.module.module import runnable, moduleinfo
 import numpy as np
-import paddle.fluid as fluid
-import paddlehub as hub
-from Extract_Line_Draft.function import *
+from .function import get_light_map_single, normalize_pic, resize_img_512_3d, show_active_img_and_save_denoise
 
 
 @moduleinfo(
     name="Extract_Line_Draft",
-    version="1.0.0",
+    version="1.1.0",
     type="cv/segmentation",
     summary="Import the color picture and generate the line draft of the picture",
     author="彭兆帅，郑博培",
     author_email="1084667371@qq.com，2733821739@qq.com")
-class ExtractLineDraft(hub.Module):
-    def _initialize(self):
+class ExtractLineDraft:
+    def __init__(self):
         """
         Initialize with the necessary elements
         """
         # 加载模型路径
-        self.default_pretrained_model_path = os.path.join(self.directory, "assets", "infer_model")
+        self.default_pretrained_model_path = os.path.join(
+            self.directory, "assets", "infer_model", "model")
         self._set_config()
 
     def _set_config(self):
@@ -36,7 +32,9 @@ class ExtractLineDraft(hub.Module):
         predictor config setting
         """
         self.model_file_path = self.default_pretrained_model_path
-        cpu_config = AnalysisConfig(self.model_file_path)
+        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.switch_ir_optim(True)
         cpu_config.enable_memory_optim()
@@ -44,7 +42,7 @@ class ExtractLineDraft(hub.Module):
         cpu_config.switch_specify_input_names(True)
         cpu_config.disable_glog_info()
         cpu_config.disable_gpu()
-        self.cpu_predictor = create_paddle_predictor(cpu_config)
+        self.cpu_predictor = create_predictor(cpu_config)
 
         try:
             _places = os.environ["CUDA_VISIBLE_DEVICES"]
@@ -53,7 +51,7 @@ class ExtractLineDraft(hub.Module):
         except:
             use_gpu = False
         if use_gpu:
-            gpu_config = AnalysisConfig(self.model_file_path)
+            gpu_config = Config(model, params)
             gpu_config.disable_glog_info()
             gpu_config.switch_ir_optim(True)
             gpu_config.enable_memory_optim()
@@ -61,7 +59,7 @@ class ExtractLineDraft(hub.Module):
             gpu_config.switch_specify_input_names(True)
             gpu_config.disable_glog_info()
             gpu_config.enable_use_gpu(100, 0)
-            self.gpu_predictor = create_paddle_predictor(gpu_config)
+            self.gpu_predictor = create_predictor(gpu_config)
 
     # 模型预测函数
     def predict(self, input_datas):
@@ -69,9 +67,9 @@ class ExtractLineDraft(hub.Module):
         # 遍历输入数据进行预测
         for input_data in input_datas:
             inputs = input_data.copy()
-            self.input_tensor.copy_from_cpu(inputs)
-            self.predictor.zero_copy_run()
-            output = self.output_tensor.copy_to_cpu()
+            self.input_handle.copy_from_cpu(inputs)
+            self.predictor.run()
+            output = self.output_handle.copy_to_cpu()
             outputs.append(output)
 
         # 预测结果合并
@@ -85,7 +83,7 @@ class ExtractLineDraft(hub.Module):
         Get the input and program of the infer model
 
         Args:
-             image (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
+             image (str): image path
              use_gpu(bool): Weather to use gpu
         """
         if use_gpu:
@@ -103,16 +101,18 @@ class ExtractLineDraft(hub.Module):
         new_width = 0
         new_height = 0
         if (width > height):
-            from_mat = cv2.resize(from_mat, (512, int(512 / width * height)), interpolation=cv2.INTER_AREA)
+            from_mat = cv2.resize(
+                from_mat, (512, int(512 / width * height)), interpolation=cv2.INTER_AREA)
             new_width = 512
             new_height = int(512 / width * height)
         else:
-            from_mat = cv2.resize(from_mat, (int(512 / height * width), 512), interpolation=cv2.INTER_AREA)
+            from_mat = cv2.resize(
+                from_mat, (int(512 / height * width), 512), interpolation=cv2.INTER_AREA)
             new_width = int(512 / height * width)
             new_height = 512
 
         from_mat = from_mat.transpose((2, 0, 1))
-        light_map = np.zeros(from_mat.shape, dtype=np.float)
+        light_map = np.zeros(from_mat.shape, dtype=np.float32)
         for channel in range(3):
             light_map[channel] = get_light_map_single(from_mat[channel])
         light_map = normalize_pic(light_map)
@@ -127,9 +127,12 @@ class ExtractLineDraft(hub.Module):
 
         self.input_names = self.predictor.get_input_names()
         self.output_names = self.predictor.get_output_names()
-        self.input_tensor = self.predictor.get_input_tensor(self.input_names[0])
-        self.output_tensor = self.predictor.get_output_tensor(self.output_names[0])
-        line_mat = self.predict(np.expand_dims(light_map, axis=0).astype('float32'))
+        self.input_handle = self.predictor.get_input_handle(
+            self.input_names[0])
+        self.output_handle = self.predictor.get_output_handle(
+            self.output_names[0])
+        line_mat = self.predict(np.expand_dims(
+            light_map, axis=0).astype('float32'))
         # 去除 batch 维度 (512, 512, 3)
         line_mat = line_mat.transpose((3, 1, 2, 0))[0]
         # 裁剪 (512, 384, 3)
@@ -137,10 +140,12 @@ class ExtractLineDraft(hub.Module):
         line_mat = np.amax(line_mat, 2)
         # 保存图片
         if Path('./output/').exists():
-            show_active_img_and_save_denoise(line_mat, './output/' + 'output.png')
+            show_active_img_and_save_denoise(
+                line_mat, './output/' + 'output.png')
         else:
             os.makedirs('./output/')
-            show_active_img_and_save_denoise(line_mat, './output/' + 'output.png')
+            show_active_img_and_save_denoise(
+                line_mat, './output/' + 'output.png')
         print('图片已经完成')
 
     @runnable
@@ -154,9 +159,11 @@ class ExtractLineDraft(hub.Module):
             usage='%(prog)s',
             add_help=True)
 
-        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
+        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(
-            title="Config options", description="Run configuration for controlling module behavior, not required.")
+            title="Config options",
+            description="Run configuration for controlling module behavior, not required.")
 
         self.add_module_input_arg()
 
@@ -175,8 +182,16 @@ class ExtractLineDraft(hub.Module):
         """
         Add the command input options
         """
-        self.arg_input_group.add_argument('--image', type=str, default=None, help="file contain input data")
-        self.arg_input_group.add_argument('--use_gpu', type=ast.literal_eval, default=None, help="weather to use gpu")
+        self.arg_input_group.add_argument(
+            '--image',
+            type=str,
+            default=None,
+            help="file contain input data")
+        self.arg_input_group.add_argument(
+            '--use_gpu',
+            type=ast.literal_eval,
+            default=None,
+            help="weather to use gpu")
 
     def check_input_data(self, args):
         input_data = []

modules/image/semantic_segmentation/Extract_Line_Draft/test.py

+import os
+import shutil
+import unittest
+
+import cv2
+import requests
+import paddlehub as hub
+
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+class TestHubModule(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        img_url = 'https://ai-studio-static-online.cdn.bcebos.com/1c30757e069541a18dc89b92f0750983b77ad762560849afa0170046672e57a3'
+        if not os.path.exists('tests'):
+            os.makedirs('tests')
+        response = requests.get(img_url)
+        assert response.status_code == 200, 'Network Error.'
+        with open('tests/test.jpg', 'wb') as f:
+            f.write(response.content)
+        cls.module = hub.Module(name="Extract_Line_Draft")
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        shutil.rmtree('tests')
+        shutil.rmtree('inference')
+        shutil.rmtree('output')
+
+    def test_ExtractLine1(self):
+        self.module.ExtractLine(
+            image='tests/test.jpg',
+            use_gpu=False
+        )
+        self.assertTrue(os.path.exists('output/output.png'))
+
+    def test_ExtractLine2(self):
+        self.module.ExtractLine(
+            image='tests/test.jpg',
+            use_gpu=True
+        )
+        self.assertTrue(os.path.exists('output/output.png'))
+
+    def test_ExtractLine3(self):
+        self.assertRaises(
+            AttributeError,
+            self.module.ExtractLine,
+            image='no.jpg'
+        )
+
+    def test_ExtractLine4(self):
+        self.assertRaises(
+            TypeError,
+            self.module.ExtractLine,
+            image=['tests/test.jpg']
+        )
+
+    def test_save_inference_model(self):
+        self.module.save_inference_model('./inference/model')
+
+        self.assertTrue(os.path.exists('./inference/model.pdmodel'))
+        self.assertTrue(os.path.exists('./inference/model.pdiparams'))
+
+
+if __name__ == "__main__":
+    unittest.main()