百度AI攻略：Paddlehub實現人體解析

PaddleHub能夠便捷地獲取PaddlePaddle生態下的預訓練模型，完成模型的管理和一鍵預測。配合使用Fine-tune API，能夠基於大規模預訓練模型快速完成遷移學習，讓預訓練模型能更好地服務於用戶特定場景的應用。

模型概述

人體解析(Human Parsing)是細粒度的語義分割任務，其旨在識別像素級別的人類圖像的組成部分（例如，身體部位和服裝）。ACE2P經過融合底層特徵，全局上下文信息和邊緣細節，端到端地訓練學習人體解析任務。該結構針對Intersection over Union指標進行鍼對性的優化學習，提高準確率。以ACE2P單人人體解析網絡爲基礎的解決方案在CVPR2019第三屆LIP挑戰賽中贏得了所有三我的體解析任務的第一名。該PaddleHub Module採用ResNet101做爲骨幹網絡，接受輸入圖片大小爲473x473x3。

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API

def segmentation(data)

用於人像分割

參數

data：dict類型，key爲image，str類型；value爲待分割的圖片路徑，list類型。

output_dir：生成圖片的保存路徑，默認爲ace2p_output

返回

result：list類型，每一個元素爲對應輸入圖片的預測結果。預測結果爲dict類型，有如下字段：

origin原輸入圖片路徑

processed分割圖片的路徑。

調色板

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代碼與案例

import paddlehub as hub

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

#ace2p

module = hub.Module(name="ace2p")

test_img_path = "./body2.jpg"

# 預測結果展現

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

# set input dict

input_dict = {"image": [test_img_path]}

# execute predict and print the result

results = module.segmentation(data=input_dict)

for result in results:

    print(result)

test_img_path = "./ace2p_output/body2_processed.png"

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

[2020-01-09 07:10:08,251] [    INFO] - Installing ace2p module

2020-01-09 07:10:08,251-INFO: Installing ace2p module

[2020-01-09 07:10:08,270] [    INFO] - Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

2020-01-09 07:10:08,270-INFO: Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

​

[2020-01-09 07:10:09,154] [    INFO] - 0 pretrained paramaters loaded by PaddleHub

2020-01-09 07:10:09,154-INFO: 0 pretrained paramaters loaded by PaddleHub

{'origin': './body2.jpg', 'processed': 'ace2p_output/body2_processed.png'}

​

In[4]

import paddlehub as hub

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

#ace2p

module = hub.Module(name="ace2p")

test_img_path = "./body1.jpg"

# 預測結果展現

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

# set input dict

input_dict = {"image": [test_img_path]}

# execute predict and print the result

results = module.segmentation(data=input_dict)

for result in results:

    print(result)

test_img_path = "./ace2p_output/body1_processed.png"

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

[2020-01-09 07:12:05,461] [    INFO] - Installing ace2p module

2020-01-09 07:12:05,461-INFO: Installing ace2p module

[2020-01-09 07:12:05,499] [    INFO] - Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

2020-01-09 07:12:05,499-INFO: Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

​

[2020-01-09 07:12:06,441] [    INFO] - 0 pretrained paramaters loaded by PaddleHub

2020-01-09 07:12:06,441-INFO: 0 pretrained paramaters loaded by PaddleHub

{'origin': './body1.jpg', 'processed': 'ace2p_output/body1_processed.png'}

​

In[7]

import paddlehub as hub

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

#ace2p

module = hub.Module(name="ace2p")

test_img_path = "./body3.jpg"

# 預測結果展現

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

# set input dict

input_dict = {"image": [test_img_path]}

# execute predict and print the result

results = module.segmentation(data=input_dict)

for result in results:

    print(result)

test_img_path = "./ace2p_output/body3_processed.png"

img = mpimg.imread(test_img_path)

plt.imshow(img)

plt.axis('off')

plt.show()

[2020-01-09 07:13:10,483] [    INFO] - Installing ace2p module

2020-01-09 07:13:10,483-INFO: Installing ace2p module

[2020-01-09 07:13:10,502] [    INFO] - Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

2020-01-09 07:13:10,502-INFO: Module ace2p already installed in /home/aistudio/.paddlehub/modules/ace2p

​

[2020-01-09 07:13:11,395] [    INFO] - 0 pretrained paramaters loaded by PaddleHub

2020-01-09 07:13:11,395-INFO: 0 pretrained paramaters loaded by PaddleHub

{'origin': './body3.jpg', 'processed': 'ace2p_output/body3_processed.png'}

​