基于PaddleClas的热轧钢带表面缺陷分类

最新推荐文章于 2025-05-21 21:08:24 发布

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最新推荐文章于 2025-05-21 21:08:24 发布

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本文链接：https://blog.youkuaiyun.com/qq_62022086/article/details/120789597

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基于PaddleClas的热轧钢带表面缺陷分类

!unzip -q data/data47421/PaddleClas.zip

!unzip -q data/data47260/NEU-CLS.zip
!mv NEU-CLS PaddleClas/dataset

import codecs
import os
import random
import shutil
from PIL import Image

train_ratio = 4.0 / 5
all_file_dir = 'PaddleClas/dataset/NEU-CLS'
class_list = [c for c in os.listdir(all_file_dir) if os.path.isdir(os.path.join(all_file_dir, c)) and not c.endswith('Set') and not c.startswith('.')]
class_list.sort()
print(class_list)
train_image_dir = os.path.join(all_file_dir, "trainImageSet")
if not os.path.exists(train_image_dir):
    os.makedirs(train_image_dir)
    
eval_image_dir = os.path.join(all_file_dir, "evalImageSet")
if not os.path.exists(eval_image_dir):
    os.makedirs(eval_image_dir)

train_file = codecs.open(os.path.join(all_file_dir, "train.txt"), 'w')
eval_file = codecs.open(os.path.join(all_file_dir, "eval.txt"), 'w')

with codecs.open(os.path.join(all_file_dir, "label_list.txt"), "w") as label_list:
    label_id = 0
    for class_dir in class_list:
        label_list.write("{0}\t{1}\n".format(label_id, class_dir))
        image_path_pre = os.path.join(all_file_dir, class_dir)
        for file in os.listdir(image_path_pre):
            try:
                img = Image.open(os.path.join(image_path_pre, file))
                if random.uniform(0, 1) <= train_ratio:
                    shutil.copyfile(os.path.join(image_path_pre, file), os.path.join(train_image_dir, file))
                    train_file.write("{0} {1}\n".format(os.path.join("trainImageSet", file), label_id))
                else:
                    shutil.copyfile(os.path.join(image_path_pre, file), os.path.join(eval_image_dir, file))
                    eval_file.write("{0} {1}\n".format(os.path.join("evalImageSet", file), label_id))
            except Exception as e:
                pass
                # 存在一些文件打不开，此处需要稍作清洗
        label_id += 1
            
train_file.close()
eval_file.close()

! head PaddleClas/dataset/NEU-CLS/train.txt

%cd PaddleClas
import os 
os.environ['PYTHONPATH']="/home/aistudio/PaddleClas"

!python ../download_model.py ResNet50_vd_pretrained
!mv ../ResNet50_vd_pretrained ./

!python -m paddle.distributed.launch --selected_gpus="0"  tools/train.py -c ../neu.yaml

!python -m paddle.distributed.launch --selected_gpus="0" tools/eval.py \
    -c ../eval.yaml \
    -o pretrained_model=output/ResNet50_vd/best_model/ppcls
    
!python tools/export_model.py \
    --model='ResNet50_vd' \
    --pretrained_model=output/ResNet50_vd/best_model/ppcls \
    --output_path=./inference
    
!python tools/infer/predict.py \
    -m inference/model \
    -p inference/params \
    -i "dataset/NEU-CLS/liewen_1.bmp" \
    --use_gpu=1

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0——Cr

1——Pa

2——PS

3——RS

4——Sc

5——In

轧制氧化皮（RS），斑块（Pa），开裂（Cr），点蚀表面（ PS），内含物（In）和划痕（Sc）