基于Oxford-IIIT Pet数据集实现PaddleHub猫咪一键抠图

本文基于Oxford-IIIT Pet数据集，用PaddleSeg实现猫咪一键抠图。提取猫数据集并重新制作标签（背景为0、图像为1），划分训练、测试、评估集后，配置FCN模型（HRNet骨干）训练，2100轮时mIoU达0.7874，导出静态模型并通过PaddleHub部署，可实现猫咪抠图，用于制作证件照等。

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一、 基于Oxford-IIIT Pet数据集实现PaddleHub猫咪一键抠图

1.主要工作

从Oxford-IIIT Pet宠物数据集提取cat据集重新制作label，背景设置为0，图像设置为1最终iter 2100时， mIoU =0.7874，仍有上升空间，只是耗时较长，不再训练通过paddlehub部署导出的静态模型

模型文件见当前目录 model.gz ， paddlehub部署见 catseg_mobile.zip 。

最后就可以完美抠图了，可以制作猫猫证件照了。

2.PaddleSeg简介

PaddleSeg是基于飞桨PaddlePaddle开发的端到端图像分割开发套件，涵盖了高精度和轻量级等不同方向的大量高质量分割模型。通过模块化的设计，提供了配置化驱动和API调用两种应用方式，帮助开发者更便捷地完成从训练到部署的全流程图像分割应用。

特性

高精度模型： 基于百度自研的半监督标签知识蒸馏方案（SSLD）训练得到高精度骨干网络，结合前沿的分割技术，提供了50+的高质量预训练模型，效果优于其他开源实现。模块化设计： 支持15+主流 分割网络 ，结合模块化设计的 数据增强策略 、骨干网络、损失函数 等不同组件，开发者可以基于实际应用场景出发，组装多样化的训练配置，满足不同性能和精度的要求。高性能： 支持多进程异步I/O、多卡并行训练、评估等加速策略，结合飞桨核心框架的显存优化功能，可大幅度减少分割模型的训练开销，让开发者更低成本、更高效地完成图像分割训练。In [ ]

! git clone https://gitee.com/paddlepaddle/PaddleSeg.git  --depth=1

Cloning into 'PaddleSeg'...remote: Enumerating objects: 1589, done.remote: Counting objects: 100% (1589/1589), done.remote: Compressing objects: 100% (1354/1354), done.remote: Total 1589 (delta 309), reused 1117 (delta 142), pack-reused 0Receiving objects: 100% (1589/1589), 88.49 MiB | 5.57 MiB/s, done.Resolving deltas: 100% (309/309), done.Checking connectivity... done.

3.数据集制作

需要手动删除dataset/annotations/list.txt文件头，便于pandas读取，如麻烦，可以直接使用已制作好的数据集二，cat数据集。

In [1]

# 解压缩数据集!mkdir dataset!tar -xvf data/data50154/images.tar.gz -C dataset/!tar -xvf data/data50154/annotations.tar.gz -C dataset/

   In [3]

# 查看list文件!head -n 10 dataset/annotations/list.txt

#Image CLASS-ID SPECIES BREED ID#ID: 1:37 Class ids#SPECIES: 1:Cat 2:Dog#BREED ID: 1-25:Cat 1:12:Dog#All images with 1st letter as captial are cat images#images with small first letter are dog imagesAbyssinian_100 1 1 1Abyssinian_101 1 1 1Abyssinian_102 1 1 1Abyssinian_103 1 1 1

       In [13]

# 删除文件前6行描述头，方便pandas读取!sed -i '1,6d' dataset/annotations/list.txt

   In [14]

!head dataset/annotations/list.txt

Abyssinian_100 1 1 1Abyssinian_101 1 1 1Abyssinian_102 1 1 1Abyssinian_103 1 1 1Abyssinian_104 1 1 1Abyssinian_105 1 1 1Abyssinian_106 1 1 1Abyssinian_107 1 1 1Abyssinian_108 1 1 1Abyssinian_109 1 1 1

       In [ ]

import pandas as pdimport shutilimport os# Image CLASS-ID SPECIES BREED ID# ID: 1:37 Class ids# SPECIES: 1:Cat 2:Dog# BREED ID: 1-25:Cat 1:12:Dog# All images with 1st letter as captial are cat images# images with small first letter are dog images# ._Abyssinian_100.pngdef copyfile(animal, filename):    # imagelabel列表    file_list = []    image_file = filename + '.jpg'    label_file = filename + '.png'    if os.path.exists(os.path.join('dataset/images', image_file)):        shutil.copy(os.path.join('dataset/images', image_file), os.path.join(f'{animal}/images', image_file))        shutil.copy(os.path.join('dataset/annotations/trimaps', label_file),                    os.path.join(f'{animal}/labels', label_file))        temp = os.path.join('images/', image_file) + ' ' + os.path.join('labels/',label_file) + 'n'        file_list.append(temp)    with open(os.path.join(animal, animal + '.txt'), 'a') as f:        f.writelines(file_list)if __name__ == "__main__":    data = pd.read_csv('dataset/annotations/list.txt', header=None, sep=' ')    data.head()    cat = data[data[2] == 1]    dog = data[data[2] == 2]    for item in cat[0]:        copyfile('cat', item)    for item in dog[0]:        copyfile('dog', item)

   In [ ]

# 删除无用数据!rm dataset/ -rf

4.训练自定义的数据集

4.1文件结构

├── cat.txt├── images│   ├── Abyssinian_100.jpg│   ├── Abyssinian_101.jpg│   ├── ...├── labels│   ├── Abyssinian_100.png│   ├── Abyssinian_101.png│   ├── ...

4.2 列表内容：

images/Abyssinian_1.jpg labels/Abyssinian_1.pngimages/Abyssinian_10.jpg labels/Abyssinian_10.pngimages/Abyssinian_100.jpg labels/Abyssinian_100.png...

4.3.数据查看

In [3]

%cd ~from PIL import Imageimg=Image.open('cat/images/Abyssinian_123.jpg')print(img)img

/home/aistudio

               In [5]

img=Image.open('cat/labels/Abyssinian_123.png')print(img)img

5.标签处理

标签是从0开始排序，本项目的数据提取自Oxford-IIIT Pet https://www.robots.ox.ac.uk/~vgg/data/pets 宠物数据集，该数据集是从1开始编码，所以需要重新编码。背景设置为0，图像设置为1.

In [ ]

# 执行一次即可import pandas as pdimport osimport matplotlib.pyplot as pltimport numpy as npfrom PIL import Imagedef re_label(filename):    img = plt.imread(filename) * 255.0    img_label = np.zeros((img.shape[0], img.shape[1]), np.uint8)    for i in range(img.shape[0]):        for j in range(img.shape[1]):            value = img[i, j]            if value == 2:                img_label[i, j] = 1    label0 = Image.fromarray(np.uint8(img_label))    label0.save( filename)data=pd.read_csv("cat/cat.txt", header=None, sep=' ') for item in data[1]:    re_label(os.path.join('cat', item))print('处理完毕！')

处理完毕！

二、数据集预处理

In [ ]

import osfrom sklearn.model_selection import train_test_splitimport pandas as pddef break_data(target, rate=0.2):    origin_dataset = pd.read_csv("cat/cat.txt", header=None, sep=' ')  # 加入参数    train_data, test_data = train_test_split(origin_dataset, test_size=rate)    train_data,eval_data=train_test_split(train_data, test_size=rate)    train_filename = os.path.join(target, 'train.txt')    test_filename = os.path.join(target, 'test.txt')    eval_filename = os.path.join(target, 'eval.txt')    train_data.to_csv(train_filename, index=False, sep=' ',  header=None)    test_data.to_csv(test_filename, index=False, sep=' ', header=None)    eval_data.to_csv(eval_filename, index=False, sep=' ', header=None)    print('train_data:',len(train_data))    print('test_data:',len(test_data))    print('eval_data:',len(eval_data))if __name__ == '__main__':    break_data(target='cat', rate=0.2)

train_data: 1516test_data: 475eval_data: 380

       In [ ]

# 查看!head cat/train.txt

images/Bengal_173.jpg labels/Bengal_173.pngimages/Siamese_179.jpg labels/Siamese_179.pngimages/British_Shorthair_201.jpg labels/British_Shorthair_201.pngimages/Russian_Blue_60.jpg labels/Russian_Blue_60.pngimages/British_Shorthair_93.jpg labels/British_Shorthair_93.pngimages/British_Shorthair_26.jpg labels/British_Shorthair_26.pngimages/British_Shorthair_209.jpg labels/British_Shorthair_209.pngimages/British_Shorthair_101.jpg labels/British_Shorthair_101.pngimages/British_Shorthair_269.jpg labels/British_Shorthair_269.pngimages/Ragdoll_59.jpg labels/Ragdoll_59.png

三、配置

In [ ]

# 已配置好，可以不用复制了# !cp PaddleSeg/configs/quick_start/bisenet_optic_disc_512x512_1k.yml ~/bisenet_optic_disc_512x512_1k.yml

修改 bisenet_optic_disc_512x512_1k.yml，要注意一下几点：

1.数据集路径配置2.num_classes设置，背景不算3.transforms设置4.loss设置

batch_size: 600iters: 5000train_dataset:     type: Dataset  dataset_root: /home/aistudio/cat/  train_path: /home/aistudio/cat/train.txt  num_classes: 2  transforms:    - type: ResizeStepScaling      min_scale_factor: 0.5      max_scale_factor: 2.0      scale_step_size: 0.25    - type: RandomPaddingCrop      crop_size: [224, 224]    - type: RandomHorizontalFlip    - type: RandomDistort      brightness_range: 0.4      contrast_range: 0.4      saturation_range: 0.4    - type: Normalize  mode: trainval_dataset:   type: Dataset  dataset_root: /home/aistudio/cat/  val_path: /home/aistudio/cat/eval.txt  num_classes: 2  transforms:    - type: Normalize  mode: valoptimizer:  type: sgd  momentum: 0.9  weight_decay: 0.0005lr_scheduler:  type: PolynomialDecay  learning_rate: 0.05  end_lr: 0  power: 0.9loss:  types:    - type: CrossEntropyLoss  coef: [1]model:  type: FCN  backbone:    type: HRNet_W18_Small_V1    align_corners: False  num_classes: 2  pretrained: Null

四、训练

In [1]

%cd ~/PaddleSeg/! python train.py --config  ../bisenet_optic_disc_512x512_1k.yml    --do_eval     --use_vdl     --save_interval 100     --save_dir output

2021-11-13 19:30:52 [INFO][TRAIN] epoch: 1105, iter: 2210/5000, loss: 0.1849, lr: 0.029586, batch_cost: 8.8180, reader_cost: 7.73956, ips: 68.0427 samples/sec | ETA 06:50:022021-11-13 19:32:18 [INFO][TRAIN] epoch: 1110, iter: 2220/5000, loss: 0.1768, lr: 0.029490, batch_cost: 8.6004, reader_cost: 7.52235, ips: 69.7641 samples/sec | ETA 06:38:292021-11-13 19:33:47 [INFO][TRAIN] epoch: 1115, iter: 2230/5000, loss: 0.1791, lr: 0.029395, batch_cost: 8.8851, reader_cost: 7.80702, ips: 67.5288 samples/sec | ETA 06:50:112021-11-13 19:35:14 [INFO][TRAIN] epoch: 1120, iter: 2240/5000, loss: 0.1835, lr: 0.029299, batch_cost: 8.6699, reader_cost: 7.59314, ips: 69.2053 samples/sec | ETA 06:38:482021-11-13 19:36:41 [INFO][TRAIN] epoch: 1125, iter: 2250/5000, loss: 0.1815, lr: 0.029204, batch_cost: 8.7713, reader_cost: 7.68169, ips: 68.4051 samples/sec | ETA 06:42:002021-11-13 19:38:08 [INFO][TRAIN] epoch: 1130, iter: 2260/5000, loss: 0.1833, lr: 0.029108, batch_cost: 8.7045, reader_cost: 7.62504, ips: 68.9299 samples/sec | ETA 06:37:302021-11-13 19:39:35 [INFO][TRAIN] epoch: 1135, iter: 2270/5000, loss: 0.1741, lr: 0.029013, batch_cost: 8.7032, reader_cost: 7.61708, ips: 68.9401 samples/sec | ETA 06:35:592021-11-13 19:41:03 [INFO][TRAIN] epoch: 1140, iter: 2280/5000, loss: 0.1810, lr: 0.028917, batch_cost: 8.8020, reader_cost: 7.72264, ips: 68.1664 samples/sec | ETA 06:39:012021-11-13 19:42:33 [INFO][TRAIN] epoch: 1145, iter: 2290/5000, loss: 0.1799, lr: 0.028821, batch_cost: 8.9336, reader_cost: 7.84692, ips: 67.1623 samples/sec | ETA 06:43:302021-11-13 19:44:02 [INFO][TRAIN] epoch: 1150, iter: 2300/5000, loss: 0.1756, lr: 0.028726, batch_cost: 8.9216, reader_cost: 7.84517, ips: 67.2524 samples/sec | ETA 06:41:282021-11-13 19:44:02 [INFO]Start evaluating (total_samples: 380, total_iters: 380)...380/380 [==============================] - 15s 40ms/step - batch_cost: 0.0394 - reader cost: 0.0012021-11-13 19:44:17 [INFO][EVAL] #Images: 380 mIoU: 0.7640 Acc: 0.8681 Kappa: 0.7330 2021-11-13 19:44:17 [INFO][EVAL] Class IoU: [0.7378 0.7902]2021-11-13 19:44:17 [INFO][EVAL] Class Acc: [0.7925 0.9347]2021-11-13 19:44:17 [INFO][EVAL] The model with the best validation mIoU (0.7874) was saved at iter 2100.

五、测试

In [2]

!python val.py        --config  /home/aistudio/bisenet_optic_disc_512x512_1k.yml       --model_path output/best_model/model.pdparams

2021-11-13 19:48:13 [INFO]---------------Config Information---------------batch_size: 600iters: 5000loss:  coef:  - 1  types:  - type: CrossEntropyLosslr_scheduler:  end_lr: 0  learning_rate: 0.05  power: 0.9  type: PolynomialDecaymodel:  backbone:    align_corners: false    type: HRNet_W18_Small_V1  num_classes: 2  pretrained: null  type: FCNoptimizer:  momentum: 0.9  type: sgd  weight_decay: 0.0005train_dataset:  dataset_root: /home/aistudio/cat/  mode: train  num_classes: 2  train_path: /home/aistudio/cat/train.txt  transforms:  - max_scale_factor: 2.0    min_scale_factor: 0.5    scale_step_size: 0.25    type: ResizeStepScaling  - crop_size:    - 224    - 224    type: RandomPaddingCrop  - type: RandomHorizontalFlip  - brightness_range: 0.4    contrast_range: 0.4    saturation_range: 0.4    type: RandomDistort  - type: Normalize  type: Datasetval_dataset:  dataset_root: /home/aistudio/cat/  mode: val  num_classes: 2  transforms:  - type: Normalize  type: Dataset  val_path: /home/aistudio/cat/eval.txt------------------------------------------------W1113 19:48:13.707370  4265 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1W1113 19:48:13.707428  4265 device_context.cc:422] device: 0, cuDNN Version: 7.6.2021-11-13 19:48:19 [INFO]Loading pretrained model from output/best_model/model.pdparams2021-11-13 19:48:19 [INFO]There are 363/363 variables loaded into FCN.2021-11-13 19:48:19 [INFO]Loaded trained params of model successfully2021-11-13 19:48:19 [INFO]Start evaluating (total_samples: 380, total_iters: 380).../opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:239: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.int32, but right dtype is paddle.bool, the right dtype will convert to paddle.int32  format(lhs_dtype, rhs_dtype, lhs_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:239: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.int64, but right dtype is paddle.bool, the right dtype will convert to paddle.int64  format(lhs_dtype, rhs_dtype, lhs_dtype))380/380 [==============================] - 15s 41ms/step - batch_cost: 0.0405 - reader cost: 0.002021-11-13 19:48:35 [INFO][EVAL] #Images: 380 mIoU: 0.7874 Acc: 0.8838 Kappa: 0.7616 2021-11-13 19:48:35 [INFO][EVAL] Class IoU: [0.7566 0.8181]2021-11-13 19:48:35 [INFO][EVAL] Class Acc: [0.8349 0.9211]

380/380 [==============================] - 15s 41ms/step - batch_cost: 0.0405 - reader cost: 0.002021-11-13 19:48:35 [INFO][EVAL] #Images: 380 mIoU: 0.7874 Acc: 0.8838 Kappa: 0.7616 2021-11-13 19:48:35 [INFO][EVAL] Class IoU: [0.7566 0.8181]2021-11-13 19:48:35 [INFO][EVAL] Class Acc: [0.8349 0.9211]

六、导出静态模型

In [3]

!python export.py        --config /home/aistudio/bisenet_optic_disc_512x512_1k.yml       --model_path output/best_model/model.pdparams

 op_type, op_type, EXPRESSION_MAP[method_name]))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:322: UserWarning: /tmp/tmp_l3u6xjv.py:58The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.  op_type, op_type, EXPRESSION_MAP[method_name]))2021-11-03 01:01:11 [INFO]Model is saved in ./output.

七、预测

deploy.yaml

Deploy:  model: model.pdmodel  params: model.pdiparams  transforms:  - type: Normalize

   In [3]

# 安装paddleseg!pip install -e .

   In [5]

# 预测%cd ~/PaddleSeg/!python deploy/python/infer.py --config output/deploy.yaml  --image_path /home/aistudio/cat/images/Bombay_130.jpg

/home/aistudio/PaddleSeg

       In [6]

# 打印原图from PIL import Imageimg=Image.open('/home/aistudio/cat/images/Bombay_130.jpg')img

               In [7]

# 打印输出图，颜色可调from PIL import Imageimg=Image.open('/home/aistudio/PaddleSeg/output/Bombay_130.png')img

八、hub部署

hub部署可参考：PaddleHub Module转换

已用hub部署，可通过命令行或者python来抠图啦！，具体hub文件见目录压缩包catseg_mobile.zip

hub run catseg_mobile --input_path .cat1.jpg

In [ ]

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