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modules/image/semantic_segmentation/Extract_Line_Draft/Readme.md
浏览文件 @ 939258c6
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
浏览文件 @ 939258c6
......@@ -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
浏览文件 @ 939258c6
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 0 → 100644
浏览文件 @ 939258c6
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()
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