diff --git a/modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md b/modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md index 92808c8b4ba6ea7b4f1f0aad8aa6dd39268703c7..813a94eab04e23ab541e72514c55fb8f3f92531e 100644 --- a/modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md +++ b/modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md @@ -1,57 +1,90 @@ -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： +

+ +

-# 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 diff --git a/modules/image/semantic_segmentation/Extract_Line_Draft/function.py b/modules/image/semantic_segmentation/Extract_Line_Draft/function.py index bb83b2237b9bcff8cfdb730175bb2e4250868123..aa3fe8385fc05f7d3fe562eb686c6682fe3342e9 100644 --- a/modules/image/semantic_segmentation/Extract_Line_Draft/function.py +++ b/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 diff --git a/modules/image/semantic_segmentation/Extract_Line_Draft/module.py b/modules/image/semantic_segmentation/Extract_Line_Draft/module.py index f1aa37769f14281a9d1e4e99980c76494aa81af9..8096a33a78b01c781e3e4420523d3f14bbb7f19a 100644 --- a/modules/image/semantic_segmentation/Extract_Line_Draft/module.py +++ b/modules/image/semantic_segmentation/Extract_Line_Draft/module.py @@ -1,34 +1,30 @@ 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 = [] diff --git a/modules/image/semantic_segmentation/Extract_Line_Draft/test.py b/modules/image/semantic_segmentation/Extract_Line_Draft/test.py new file mode 100644 index 0000000000000000000000000000000000000000..6235b38ab95b11cd597054cd347856598d100080 --- /dev/null +++ b/modules/image/semantic_segmentation/Extract_Line_Draft/test.py @@ -0,0 +1,66 @@ +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()