说明：
这个方向并不想做深入的研究。仅仅是依靠现有的算法基础。做做比赛而已。文档也是对整个过程做个记录。
数据类会是下一个重点研究的方向。
一、网址：
https://www.dcjingsai.com/common/cmpt/AI%E6%88%98%E7%96%AB%C2%B7%E5%8F%A3%E7%BD%A9%E4%BD%A9%E6%88%B4%E6%A3%80%E6%B5%8B%E5%A4%A7%E8%B5%9B_%E6%88%91%E7%9A%84%E9%98%9F%E4%BC%8D.html
二、任务：
三、开始做啦
1、下载数据
20200317
训练数据：pos254，neg417
数据扩增：
import albumentations as albu import cv2 import pdb from glob import glob import os import numpy as np def train_transform0(image, p=1): aug1 = albu.Compose([albu.HorizontalFlip(p=1)], p=p) aug2 = albu.Compose([albu.Transpose(p=1)], p=p) return aug1(image=image)['image'],aug2(image=image)['image'] def train_transform1(image, p=1): aug1 = albu.Compose([albu.Blur(blur_limit=1)], p=p) aug2 = albu.Compose([albu.Blur(blur_limit=2)], p=p) aug3 = albu.Compose([albu.Blur(blur_limit=3)], p=p) aug4 = albu.Compose([albu.Blur(blur_limit=4)], p=p) aug5 = albu.Compose([albu.Blur(blur_limit=5)], p=p) aug6 = albu.Compose([albu.Blur(blur_limit=6)], p=p) aug7 = albu.Compose([albu.Blur(blur_limit=7)], p=p) aug8 = albu.Compose([albu.Blur(blur_limit=8)], p=p) aug9 = albu.Compose([albu.Blur(blur_limit=9)], p=p) aug10 = albu.Compose([albu.Blur(blur_limit=10)], p=p) aug11 = albu.Compose([albu.OpticalDistortion(distort_limit=0.05, shift_limit=0.05, interpolation=1, border_mode=4, p=1)], p=p) aug12 = albu.Compose([albu.GridDistortion(num_steps=5, distort_limit=0.3, interpolation=1, border_mode=4, p=1)], p=p) aug13 = albu.Compose([albu.ElasticTransform(alpha=1, sigma=50, alpha_affine=50, interpolation=1, border_mode=4, p=1)], p=p) aug14 = albu.Compose([albu.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1)], p=p) aug15 = albu.Compose([albu.RandomBrightnessContrast(brightness_limit=0.4, contrast_limit=0.2, p=1)], p=p) aug16 = albu.Compose([albu.RandomBrightnessContrast(brightness_limit=0.6, contrast_limit=0.2, p=1)], p=p) aug17 = albu.Compose([albu.RandomBrightnessContrast(brightness_limit=0.8, contrast_limit=0.2, p=1)], p=p) aug18 = albu.Compose([albu.RandomBrightnessContrast(brightness_limit=1, contrast_limit=0.2, p=1)], p=p) return aug1(image=image)['image'],aug2(image=image)['image'],aug3(image=image)['image'],aug4(image=image)['image'],aug5(image=image)['image'],aug6(image=image)['image'],aug7(image=image)['image'],aug8(image=image)['image'],aug9(image=image)['image'],aug10(image=image)['image'],aug11(image=image)['image'],aug12(image=image)['image'],aug13(image=image)['image'],aug14(image=image)['image'],aug15(image=image)['image'],aug16(image=image)['image'],aug17(image=image)['image'],aug18(image=image)['image'] def train_transform2(image, p=1): aug1 = albu.Compose([albu.HueSaturationValue(hue_shift_limit=15, sat_shift_limit=30, val_shift_limit=15, p=1)], p=p) aug2 = albu.Compose([albu.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=30, val_shift_limit=20, p=1)], p=p) aug3 = albu.Compose([albu.PadIfNeeded(min_height=4608, min_width=4608, border_mode=0, value=[0, 0, 0],p=1.0)], p=p) aug4 = albu.Compose([albu.RGBShift(r_shift_limit=20, g_shift_limit=20, b_shift_limit=20, p=1)], p=p) aug5 = albu.Compose([albu.RandomBrightness(limit=0.2, p=1)], p=p) aug6 = albu.Compose([albu.RandomContrast(limit=0.2, p=1)], p=p) aug7 = albu.Compose([albu.MotionBlur(blur_limit=7, p=1)], p=p) aug8 = albu.Compose([albu.MedianBlur(blur_limit=7, p=1)], p=p) #aug22 = albu.Compose([albu.GaussianBlur(blur_limit=7, p=1)], p=p) aug9 = albu.Compose([albu.GaussNoise(var_limit=(10.0, 50.0), p=1)], p=p) aug10 = albu.Compose([albu.CLAHE(clip_limit=4.0, tile_grid_size=(8, 8), p=1)], p=p) aug11 = albu.Compose([albu.ChannelShuffle(always_apply=False, p=1)], p=p) aug12 = albu.Compose([albu.InvertImg(always_apply=False, p=1)], p=p) aug13 = albu.Compose([albu.JpegCompression(quality_lower=99, quality_upper=100, p=1)], p=p) return aug1(image=image)['image'],aug2(image=image)['image'],aug3(image=image)['image'],aug4(image=image)['image'],aug5(image=image)['image'],aug6(image=image)['image'],aug7(image=image)['image'],aug8(image=image)['image'],aug9(image=image)['image'],aug10(image=image)['image'],aug11(image=image)['image'],aug12(image=image)['image'],aug13(image=image)['image'] def train_transform3(image, p=1): aug11 = albu.Compose([albu.OpticalDistortion(distort_limit=0.05, shift_limit=0.05, interpolation=1, border_mode=0, p=1)], p=p) aug12 = albu.Compose([albu.GridDistortion(num_steps=5, distort_limit=0.3, interpolation=1, border_mode=0, p=1)], p=p) aug13 = albu.Compose([albu.ElasticTransform(alpha=1, sigma=50, alpha_affine=50, interpolation=1, border_mode=0, p=1)], p=p) return aug11(image=image)['image'],aug12(image=image)['image'],aug13(image=image)['image'] old_dir='./train/pos/' new_dir='./train/pos2/' if not os.path.exists(new_dir): new_dir=os.mkdir(new_dir) train_dataset = glob(old_dir+'*.*') num=0 for wj in range(0,len(train_dataset)): print (wj,num,len(train_dataset)) img_name=train_dataset[wj].split('\')[1] img=cv2.imread(old_dir+img_name) num+=1 I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,I11,I12,I13,I14,I15,I16,I17,I18=train_transform1(img, p=1) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I1.jpg',I1) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I2.jpg',I2) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I3.jpg',I3) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I4.jpg',I4) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I5.jpg',I5) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I6.jpg',I6) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I7.jpg',I7) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I8.jpg',I8) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I9.jpg',I9) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I10.jpg',I10) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I11.jpg',I11) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I12.jpg',I12) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I13.jpg',I13) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I14.jpg',I14) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I15.jpg',I15) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I16.jpg',I16) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I17.jpg',I17) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I18.jpg',I18) I01,I02,I03,I04,I05,I06,I07,I08,I09,I010,I011,I012,I013=train_transform2(img, p=1) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I01.jpg',I01) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I02.jpg',I02) #cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I03.jpg',I03) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I04.jpg',I04) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I05.jpg',I05) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I06.jpg',I06) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I07.jpg',I07) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I08.jpg',I08) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I09.jpg',I09) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I010.jpg',I010) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I011.jpg',I011) #cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I012.jpg',I012) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'I013.jpg',I013) Ij1,Ij2,Ij3=train_transform3(img, p=1) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'Ij1.jpg',Ij1) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'Ij2.jpg',Ij2) cv2.imwrite(new_dir+img_name.split('.')[0]+'_'+'Ij3.jpg',Ij3)
获得label
#--coding:utf-8-- import os import numpy as np First_level_dir = './train/' f=open('list.txt','w') for Second_level_dir in (First_level_dir): for img_name in (First_level_dir + Second_level_dir): if 'neg' in Second_level_dir: f.write(img_name+' '+'0'+' ') else: f.write(img_name+' '+'1'+' ') f.close()
2、下载分类模型并训练
git clone --recursive https://github.com/bearpaw/pytorch-classification.git