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Duck_inject.py
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import os
import json
import numpy as np
import pandas as pd
import cv2
import glob
import random
from PIL import Image
import time
from sklearn import metrics as mr
random.seed(2019)
# defect_name2label = {
# '沾污': 1, '错花': 2, '水印': 3, '花毛': 4, '缝头': 5, '缝头印': 6, '虫粘': 7,
# '破洞': 8, '褶子': 9, '织疵': 10, '漏印': 11, '蜡斑': 12, '色差': 13, '网折': 14, '其他': 15
# }
defect_img_root='../../guangdong1_round2_train_part1_20190924/guangdong1_round2_train_part1_20190924/defect/'
save_dir='./normal_aug/'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
# normal_img_root='./normal_images/'
# aug_dir='./normal_images_aug/'
anno_file='./anno_train_20190925.json'
anno_result= pd.read_json(open(anno_file,"r"))
name_list=anno_result["name"].unique()
ring_width=10# default is 5
result=[]
last_result_length=0
img_name_count=0
for root,paths,files in os.walk(defect_img_root):
for path in paths:
img_name=path+'.jpg'
img_anno = anno_result[anno_result["name"] == img_name]
bboxs = img_anno["bbox"].tolist()
# print(bboxs)
img_name_count+=1
defect_names = img_anno["defect_name"].tolist()
# defect_names = [defect_name2label[x] for x in defect_names]
print(defect_names)
assert img_anno["name"].unique()[0] == img_name
# testimg=cv2.imread(root+path+'/'+img_name)
testimg=Image.open(root+path+'/'+img_name)
template_img_name='template_'+path.split('_')[0]+'.jpg'
# temp_img=cv2.imread(root+path+'/'+template_img_name)
temp_img=Image.open(root+path+'/'+template_img_name)
save_temp_name='template_'+path.split('_')[0]+str(img_name_count)+'.jpg'
for idx in range(len(bboxs)):
pts=bboxs[idx]
d_name=defect_names[idx]
xmin=pts[0]
ymin=pts[1]
xmax=pts[2]
ymax=pts[3]
defect_h=abs(xmax-xmin)
defect_w=abs(ymax-ymin)
w_h=round(defect_w/defect_h,2)
h_w=round(defect_h/defect_w,2)
# cv2.putText(testimg, str(d_name),(int(xmin),int(ymin)),cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 3)
# 用于长条判断
print('w_h',w_h)
print('h_w',h_w)
print('defect_size:',(ymax-ymin)*(xmax-xmin))
# cv2.putText(testimg, str(w_h),(int(xmin+10),int(ymin)),cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 3)
# cv2.putText(testimg, str(h_w),(int(xmin+30),int(ymin)),cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 3)
# cv2.rectangle(testimg, (int(xmin),int(ymin)), (int(xmax),int(ymax)), (0,0,255), 2)
# 筛选长条的采点位置
if w_h > 5 or h_w >5 or (ymax-ymin)*(xmax-xmin)>300000:#TODO: 这里的两个5感觉有问题，300000的应该丢弃
left_top_x=random.randint(1,3)
left_top_y=random.randint(1,testimg.size[1])
else:
left_top_x=random.randint(1,testimg.size[0])
left_top_y=random.randint(1,testimg.size[1])
print(left_top_x,left_top_y)
mask=np.zeros_like(temp_img)
mask[int(left_top_y-ring_width):int(left_top_y+defect_w+ring_width),int(left_top_x-ring_width):int(left_top_x+defect_h+ring_width)]=255
mask[int(left_top_y):int(left_top_y+defect_w),int(left_top_x):int(left_top_x+defect_h)]=0
# cv2.namedWindow("mask",0);
# cv2.resizeWindow("mask", 1200, 800);
# cv2.imshow('mask',mask)
# cv2.imwrite('mask.jpg',mask)
# cv2.waitKey(0)
patch=testimg.crop((xmin,ymin,xmax,ymax))
#====相似度计算==============================================================================================#
patch1=patch.copy()
patch2=temp_img.crop((left_top_x,left_top_y,int(left_top_x+patch1.size[0]),int(left_top_y+patch1.size[1])))
print('bbox:',(left_top_x,left_top_y,int(left_top_x+(xmax-xmin)),int(left_top_y+(ymax-ymin))))
print(patch1.size[0],patch1.size[1])
# print(patch1.size,patch2.size)
patch2.resize((patch1.size[0],patch1.size[1]))
patch1=np.resize(patch1,-1)
patch2=np.resize(patch2,-1)
# print(patch1.shape)
# print(patch2.shape)
mutual_infor=mr.mutual_info_score(patch1,patch2)
print(mutual_infor)
#==================================================================================================#
if mutual_infor>0.8:
temp_img.paste(patch,(left_top_x,left_top_y))
temp_img = cv2.cvtColor(np.asarray(temp_img),cv2.COLOR_RGB2BGR)
temp_img = cv2.inpaint(temp_img,mask[:,:,0],3,cv2.INPAINT_TELEA)
temp_img = Image.fromarray(cv2.cvtColor(temp_img,cv2.COLOR_BGR2RGB))
result.append({'name': save_temp_name, 'defect_name': d_name, 'bbox': [left_top_x,left_top_y,left_top_x+defect_h,left_top_y+defect_w]})
else:
continue
# cv2.rectangle(temp_img, (int(left_top_x),int(left_top_y)), (int(left_top_x+defect_h),int(left_top_y+defect_w)), (0,0,255), 2)
# TODO 这里会引入正常图
temp_img.save(save_dir+save_temp_name)
#test path
json_name='./Duck_inject_normal.json'
with open(json_name,'w') as fp:
json.dump(result, fp, indent = 4, separators=(',', ': '))
# json_name='./Duck_inject_normal.json'
# with open(json_name,'w') as fp:
# json.dump(result, fp, indent = 4, separators=(',', ': '))
# testimg.show()
# temp_img.show()
# # sys.pause(0)
# time.sleep(2)
# print(defect_img_root+defect_name[1]+'/'+defect_name[1]+'.jpg')
# cv2.namedWindow("testimg",0);
# cv2.resizeWindow("testimg", 1200, 800);
# cv2.imshow('testimg',testimg)
# cv2.namedWindow("temp_img",0);
# cv2.resizeWindow("temp_img", 1200, 800);
# cv2.imshow('temp_img',temp_img)
# cv2.waitKey(0)
# print(defect_name)
# testimg=cv2.imread(defect_img_root+defect_name[1]+'/'+defect_name[1]+'.jpg')
# print(defect_img_root+defect_name[1]+'/'+defect_name[1]+'.jpg')
# cv2.imshow('testimg',testimg)
# cv2.waitKey(0)