一、什么？猫主子突然连夜消失了？

什么？主子突然连夜消失了？

怎么办？快快贴出你的寻猫启示吧，什么？没有猫主子证件照？手动抠图？？？扣到半夜？那么整个一键抠图猫主子证件照就诞生了。。。。。。

1.主要工作

从Oxford-IIIT Pet宠物数据集提取cat、dog数据集
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。

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

Cloning into 'PaddleSeg'...
remote: Enumerating objects: 1589, done.[K
remote: Counting objects: 100% (1589/1589), done.[K
remote: Compressing objects: 100% (1354/1354), done.[K
remote: Total 1589 (delta 309), reused 1117 (delta 142), pack-reused 0[K
Receiving 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。

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

# 需要手动删除dataset/annotations/list.txt文件头，然后以pandas读取
import pandas as pd
import shutil
import 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.png
def copyfile(animal, filename):
    # image\label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)

# 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 cat.txt内容：

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

4.3.数据查看

%cd ~
from PIL import Image
img=Image.open('cat/images/Abyssinian_101.jpg')
print(img)
img

/home/aistudio
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=450x313 at 0x7FED044B0F10>

img=Image.open('cat/images/Abyssinian_123.jpg')
print(img)
img

<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7F174201AC90>

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 www.robots.ox.ac.uk/~vgg/data/p… 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.

# 执行一次即可
import pandas as pd
import os
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
def 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

import os
from sklearn.model_selection import train_test_split
import pandas as pd
def 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: 1516
test_data: 475
eval_data: 380

# 查看
!head cat/train.txt

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

三、配置

# 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: 600
iters: 5000
train_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: train
val_dataset: 
  type: Dataset
  dataset_root: /home/aistudio/cat/
  val_path: /home/aistudio/cat/eval.txt
  num_classes: 2
  transforms:
    - type: Normalize
  mode: val
optimizer:
  type: sgd
  momentum: 0.9
  weight_decay: 0.0005
lr_scheduler:
  type: PolynomialDecay
  learning_rate: 0.05
  end_lr: 0
  power: 0.9
loss:
  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

%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 06:50:02
2021-11-13 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 06:38:29
2021-11-13 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 06:50:11
2021-11-13 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 06:38:48
2021-11-13 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 06:42:00
2021-11-13 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 06:37:30
2021-11-13 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 06:35:59
2021-11-13 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 06:39:01
2021-11-13 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 06:43:30
2021-11-13 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 06:41:28
2021-11-13 19:44:02 [INFO]  Start evaluating (total_samples: 380, total_iters: 380)...
380/380 [==============================] - 15s 40ms/step - batch_cost: 0.0394 - reader cost: 0.001
2021-11-13 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

!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: 600
iters: 5000
loss:
  coef:
  - 1
  types:
  - type: CrossEntropyLoss
lr_scheduler:
  end_lr: 0
  learning_rate: 0.05
  power: 0.9
  type: PolynomialDecay
model:
  backbone:
    align_corners: false
    type: HRNet_W18_Small_V1
  num_classes: 2
  pretrained: null
  type: FCN
optimizer:
  momentum: 0.9
  type: sgd
  weight_decay: 0.0005
train_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: Dataset
val_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: 10.1
W1113 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.pdparams
2021-11-13 19:48:19 [INFO]  There are 363/363 variables loaded into FCN.
2021-11-13 19:48:19 [INFO]  Loaded trained params of model successfully
2021-11-13 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: 0.00
2021-11-13 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: 0.00
2021-11-13 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

!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:58
The 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

# 安装paddleseg
!pip install -e .

# 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

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

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

八、hub部署

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

学会了这个，天天换猫主子，天天可以任意合影了，啊哈哈哈！！！聪明如我。

什么？猫主子突然连夜消失了？

一、什么？猫主子突然连夜消失了？

1.主要工作

2.PaddleSeg简介

特性

3.数据集制作

4.训练自定义的数据集

4.1文件结构

4.2 cat.txt内容：

4.3.数据查看

5.标签处理

二、数据集预处理

三、配置

四、训练

五、测试

六、导出静态模型

七、预测

八、hub部署

热门文章

最新文章

相关课程

相关电子书

相关实验场景

什么？猫主子突然连夜消失了？

一、 什么？猫主子突然连夜消失了？

1.主要工作

2.PaddleSeg简介

特性

3.数据集制作

4.训练自定义的数据集

4.1文件结构

4.2 cat.txt内容：

4.3.数据查看

5.标签处理

二、数据集预处理

三、配置

四、训练

五、测试

六、导出静态模型

七、预测

八、hub部署

热门文章

最新文章

相关课程

相关电子书

相关实验场景

一、什么？猫主子突然连夜消失了？