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DataSet_Own/1. 图片预处理（内含使用手册）/4_deal_labels.py Executable file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*
import os,time,sys,threading, colorsys, argparse
import asyncio, cv2, multiprocessing, random
from PIL import Image
import numpy as np
from Tool_deal_labels import edge_detection, detect_connected_regions, Tool_color_connected_array, fill_white_regions, color_connected_regions
def getFileList(dir,Filelist=[], ext=None, Max_layer=1, layer=0, Donot_Search=['1_边缘检测并膨胀', '2_连通区域检测', '3_分水岭算法填充']):
"""
获取文件夹及其子文件夹中文件列表
输入 dir：文件夹根目录
输入 ext: 扩展名
返回： 文件路径列表
"""
newDir = dir
if os.path.isfile(dir):
if ext is None:
Filelist.append(dir)
else:
if ext in dir[-3:]:
Filelist.append(dir)
elif os.path.isdir(dir):
file_name = os.path.basename(dir)
# 判断是否在禁搜名单中
if file_name in Donot_Search:
return Filelist
for s in os.listdir(dir):
newDir=os.path.join(dir,s)
if layer <= Max_layer:
getFileList(newDir, Filelist, ext, Max_layer, layer+1)
return Filelist
class Deal_image():
def __init__(self, Annotate_CLASSES = ('肝脏','胆囊'), Annotate_PALETTE = [[255,91,0],[255,234,0]], src_label_fold = "./Label", save_pro_label_fold = "./LABEL_PNG_new", save_GT_label_fold = "./Label_Generate", GT_channel = 1, pro_append_name="_label", GT_append_name="_gtFine_labelTrainIds", ori_img_folder="./ORI_PNG", res_label_folder="./Result_label", save_merge_pic_folder="./Result_merge", back_gnd_color=0, first_class_color=1, pic_type="png", Max_width = 10000, Label_Max_Search_layer=1000, save_process_pics=False, bg_PALETTE = [0,0,0]):
# 背景最好放在最后
# self.src_CLASSES = ('肝脏','胆囊','分离钳','止血海绵','肝总管','胆总管','吸引器','剪刀','止血纱布','生物夹','无损伤钳','喷洒','胆囊管','胆囊动脉','电凝','标本袋','引流管','纱布','金属钛夹','术中超声','吻合器','乳胶管','推结器','肝带','钳夹','超声刀','脂肪','双极电凝','棉球','血管阻断夹','肿瘤','针','线','韧带','胆囊静脉','背景')
# self.src_PALETTE = np.array([[255,91,0],[255,234,0],[85, 111, 181],[181, 227, 14],[72, 0, 255],[0, 155, 33],[255,0,255],[29, 32, 136],[160, 15, 95],[0,160,233],[52,184,178],[90,120,41],[255,0,0],[177,0,0],[167,24,233],[112,113,150],[0,255,0],[255,255,255],[0,255,255],[138,251,213],[136,162,196],[197,83,181],[202,202,200],[113,102,140],[66,115,82],[240,16,116],[155,132,0],[155,62,0],[146,175,236],[255,172,159],[245,161,0],[134,124,118], [0,157,142], [181,85,105], [42,8,66],[0,0,0]])
# self.src_CLASSES_NUM = np.shape(self.src_CLASSES)[0]
self.bg_PALETTE = bg_PALETTE # 背景颜色 TODO
self.Annotate_CLASSES = Annotate_CLASSES # 待分类的类
self.Annotate_PALETTE = np.array(Annotate_PALETTE) # 每一类的像素直
self.Annotate_CLASSES_NUM = np.shape(Annotate_CLASSES)[0] # 类数量
self.save_process_pics = save_process_pics # 保存中间过程图片
self.src_label_fold = src_label_fold # 原始标签图片 保存位置
self.save_pro_label_fold = save_pro_label_fold # 优化后标签图片 保存位置
self.save_GT_label_fold = save_GT_label_fold # GT标签图片 保存位置
self.ori_img_folder = ori_img_folder # 最原始手术图片 保存位置
self.res_label_folder = res_label_folder # 训练出来的label 保存位置
self.save_merge_pic_folder = save_merge_pic_folder # 融合图像保存位置
self.pro_append_name = pro_append_name # 优化后标签图片后缀
self.GT_append_name = GT_append_name # GT标签图片后缀
self.GT_channel = GT_channel # GT标签图片通道数
self.Max_width = Max_width # 最大图片宽度（匹配时候用）
self.pic_type = pic_type # 图片类型
self.back_gnd_color = back_gnd_color # 背景颜色
self.first_class_color = first_class_color # 第一类上的颜色
self.Label_Max_Search_layer=Label_Max_Search_layer # 文件夹最大搜索深度
try:
self.labellist_src = getFileList(src_label_fold, [], pic_type, self.Label_Max_Search_layer)
print('本次执行检索到ori_label图片 '+str(len(self.labellist_src))+' 张图像')
except:
self.labellist_src = None
print("没有ori_label相关文件")
try:
# print(save_pro_label_fold)
self.labellist_pro = getFileList(save_pro_label_fold, [], pic_type, self.Label_Max_Search_layer)
print('本次执行检索到pro_label图片 '+str(len(self.labellist_pro))+' 张图像')
except:
self.labellist_pro = None
print("没有pro_label相关文件")
try:
self.imglist_src = getFileList(ori_img_folder, [], pic_type, self.Label_Max_Search_layer)
self.reslist_src = getFileList(res_label_folder, [], pic_type, self.Label_Max_Search_layer)
print('本次执行检索到ori原始图片 '+str(len(self.imglist_src))+' 张图像')
print('本次执行检索到训练train_result图片 '+str(len(self.reslist_src))+' 张图像')
except:
self.imglist_src = None
self.reslist_src = None
print("没有train_result和原始图片相关文件")
# 获取单张图片各个通路信息
def get_single_pic_rgb(self, imgpath):
print(imgpath)
image = Image.open(imgpath).convert('RGB') # 转为RGB图片
# 将 RGB 色值分离
image.load()
r, g, b = image.split()
r = np.array(r)
g = np.array(g)
b = np.array(b)
return image, r, g, b
# 将单个pro图片变成GT图片
def Conver_pro_label_pic_2_GT_pic(self, imgpath, imgname):
time_start=time.time() # 记录开始时间
# 获取单张图片各个通路信息
image, r,g,b = self.get_single_pic_rgb(imgpath)
result_gt = np.ones(np.shape(image))*self.back_gnd_color # 初始化填充内容为back_gnd_color
gt_number = self.first_class_color # 第一类上色颜色确定
# PALETTE中排除掉 '背景' [0,0,0]
PALETTE_No_Bg = self.Annotate_PALETTE[~np.all(self.Annotate_PALETTE == self.bg_PALETTE, axis=1)]
# 遍历所有待识别颜色
for [Annotate_PALETTE_r, Annotate_PALETTE_g, Annotate_PALETTE_b] in PALETTE_No_Bg:
# 查找三原色匹配位置
locate_r = np.where( r == Annotate_PALETTE_r )
locate_g = np.where( g == Annotate_PALETTE_g )
locate_b = np.where( b == Annotate_PALETTE_b )
# 查找都匹配位置（交集）
# 将矩阵换一种表示形式
locate_r = np.array(locate_r[0]) * Max_width + np.array(locate_r[1])
locate_g = np.array(locate_g[0]) * Max_width + np.array(locate_g[1])
locate_b = np.array(locate_b[0]) * Max_width + np.array(locate_b[1])
# 用自带函数寻找匹配项
matched = np.intersect1d(np.intersect1d(locate_r, locate_g), locate_b)
matched = np.concatenate(([matched // self.Max_width], [np.mod(matched, self.Max_width)]), 0)
result_gt[matched[0],matched[1], :] = gt_number
gt_number = gt_number + 1
# 输出GT图片
if(int(self.GT_channel) == 1):
result_gt = result_gt[:,:,0]
elif(int(self.GT_channel) == 3):
result_gt = cv2.cvtColor(np.float32(result_gt), cv2.COLOR_RGB2BGR) # rgb颜色互换
else:
print("GT_channel 必须为1或3")
quit
try: # 新建文件夹
os.mkdir(self.save_GT_label_fold)
except:
print("已有"+self.save_GT_label_fold)
if imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_GT_label_fold, os.path.basename(imgname).rpartition('.')[0]+self.GT_append_name+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_GT_label_fold, os.path.basename(imgname)+self.GT_append_name+'.'+self.pic_type)
cv2.imwrite(save_dir, result_gt)
print("GT图片已保存", save_dir)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 将处理好的图片转化为GT图片
def Conver_pro_label_pic_2_GT_pic_all(self):
print("\033[33m**** 进行转换将Pro_label_pic转换为GT_label_pic ****\033[0m")
print("\033[33mPro_label_pic存储位置为：\033[0m", self.save_pro_label_fold)
print("\033[33mGT_label_pic生成位置为：\033[0m", self.save_GT_label_fold)
try:
# print(save_pro_label_fold)
self.labellist_pro = getFileList(save_pro_label_fold, [], pic_type, self.Label_Max_Search_layer)
print('本次执行检索到pro_label图片 '+str(len(self.labellist_pro))+' 张图像')
except:
self.labellist_pro = None
print("没有pro_label相关文件")
try:
os.mkdir(self.save_GT_label_fold) # 新建存储文件夹
except:
print("已有"+self.save_GT_label_fold)
# 指定最大进程数为 3
max_processes = 20
# 创建Pool对象
pool = multiprocessing.Pool(processes=max_processes)
# 创建并启动进程
args_list1 = []
args_list2 = []
# 遍历整个文件夹
for imgpath in self.labellist_pro:
imgname = os.path.basename(imgpath).rpartition('.')[0].replace(self.pro_append_name,"")
args_list1.append(imgpath)
args_list2.append(imgname)
args_list = zip(args_list1, args_list2)
# 使用进程池并行执行任务
pool.starmap(self.Conver_pro_label_pic_2_GT_pic, args_list)
# 关闭进程池
pool.close()
pool.join()
def Conver_ori_label_pic_2_pro_pic(self, imgpath, imgname):
time_start=time.time() # 记录开始时间
# 获取单张图片各个通路信息
image = cv2.imread(imgpath)
# 1. 边缘检测并膨胀
dilated_image = edge_detection(image)
# 如果需要存储中间态图片
if(self.save_process_pics == True):
if imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_pro_label_fold, '1_边缘检测并膨胀', os.path.basename(imgname).rpartition('.')[0]+self.pro_append_name+'_Edge'+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_pro_label_fold, '1_边缘检测并膨胀', os.path.basename(imgname)+self.pro_append_name+'_Edge'+'.'+self.pic_type)
cv2.imwrite(save_dir, dilated_image)
print("中间态-边缘检测并膨胀 图片已保存", save_dir)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 2. 检测连通区域
filtered_labeled_array, _ = detect_connected_regions(dilated_image)
colored_image_filtered = Tool_color_connected_array(filtered_labeled_array)
# 如果需要存储中间态图片
if(self.save_process_pics == True):
if imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_pro_label_fold, '2_连通区域检测', os.path.basename(imgname).rpartition('.')[0]+self.pro_append_name+'_Region'+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_pro_label_fold, '2_连通区域检测', os.path.basename(imgname)+self.pro_append_name+'_Region'+'.'+self.pic_type)
cv2.imwrite(save_dir, colored_image_filtered)
print("中间态-连通区域检测 图片已保存", save_dir)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 3. 分水岭填充白色区域
filled_labeled_array = fill_white_regions(filtered_labeled_array)
colored_image_filled = Tool_color_connected_array(filled_labeled_array)
# 如果需要存储中间态图片
if(self.save_process_pics == True):
if imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_pro_label_fold, '3_分水岭算法填充', os.path.basename(imgname).rpartition('.')[0]+self.pro_append_name+'_FillEdge'+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_pro_label_fold, '3_分水岭算法填充', os.path.basename(imgname)+self.pro_append_name+'_FillEdge'+'.'+self.pic_type)
cv2.imwrite(save_dir, colored_image_filled)
print("中间态-分水岭算法填充 图片已保存", save_dir)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 4. 对连通区域最终上色
ori_labeled_image = image
result_pro = color_connected_regions(filled_labeled_array, filtered_labeled_array, ori_labeled_image, self.Annotate_PALETTE)
if imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_pro_label_fold, os.path.basename(imgname).rpartition('.')[0]+self.pro_append_name+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_pro_label_fold, os.path.basename(imgname)+self.pro_append_name+'.'+self.pic_type)
print("Pro图片已保存", save_dir)
cv2.imwrite(save_dir, result_pro)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 将原始src图片转化为处理好的pro图片
def Conver_ori_label_pic_2_pro_pic_all(self):
print("\033[33m**** 进行转换将Ori_label_pic转换为Pro_label_pic ****\033[0m")
print("\033[33mOri_label_pic存储位置为：\033[0m", self.src_label_fold)
print("\033[33mPro_label_pic生成位置为：\033[0m", self.save_pro_label_fold)
# 输出颜色预处理图片
try:
os.mkdir(self.save_pro_label_fold) # 新建存储文件夹
except:
print("已有"+self.save_pro_label_fold)
if(self.save_process_pics == True):
try:
os.mkdir(os.path.join(self.save_pro_label_fold, '1_边缘检测并膨胀')) # 新建存储1_边缘检测并膨胀文件夹
except:
print("已有"+os.path.join(self.save_pro_label_fold, '1_边缘检测并膨胀'))
try:
os.mkdir(os.path.join(self.save_pro_label_fold, '2_连通区域检测')) # 新建存储2_连通区域检测文件夹
except:
print("已有"+os.path.join(self.save_pro_label_fold, '2_连通区域检测'))
try:
os.mkdir(os.path.join(self.save_pro_label_fold, '3_分水岭算法填充')) # 新建存储1_边缘检测并膨胀文件夹
except:
print("已有"+os.path.join(self.save_pro_label_fold, '3_分水岭算法填充'))
# 指定最大进程数为 20，多参数函数并行
max_processes = 20
# 创建Pool对象
pool = multiprocessing.Pool(processes=max_processes)
# 创建并启动进程
args_list1 = []
args_list2 = []
# 遍历整个文件夹
for imgpath in self.labellist_src:
if imgpath.lower().endswith(('.jpg', '.png')):
imgname= os.path.basename(imgpath).rpartition('.')[0].replace(self.pro_append_name,"")
else:
imgname= os.path.basename(imgpath).replace(self.pro_append_name,"")
try:
print("Processing: ", imgname, "...")
# self.Conver_ori_label_pic_2_pro_pic(imgpath, imgname)s
# args_list.append({'imgpath': imgpath, 'imgname': imgname})
args_list1.append(imgpath)
args_list2.append(imgname)
except:
os.system("echo "+imgname+" >> error_1.txt")
args_list = zip(args_list1, args_list2)
# 使用进程池并行执行任务
pool.starmap(self.Conver_ori_label_pic_2_pro_pic, args_list) # 使用starmap进行多参数并行
# 关闭进程池
pool.close()
pool.join()
# 图片堆叠
def Merge_ori_pic_and_label_pic(self, res_img_path, res_imgname):
time_start=time.time() # 记录开始时间
# 获取单张图片各个通路信息
ori_img_path = os.path.join(self.ori_img_folder, res_imgname+'.'+self.pic_type)
if not os.path.exists(ori_img_path):
print("****照片不存在：****", ori_img_path)
return -1
ori_image, ori_r, ori_g, ori_b = self.get_single_pic_rgb(ori_img_path)
res_image, res_r, res_g, res_b = self.get_single_pic_rgb(res_img_path)
merge_img = np.array(ori_image) # merge图片初始化，默认图片背景为0.0.0
# 遍历所有待识别颜色
for [Annotate_PALETTE_r, Annotate_PALETTE_g, Annotate_PALETTE_b] in self.Annotate_PALETTE:
# 查找三原色匹配位置
locate_r = np.where( res_r == Annotate_PALETTE_r )
locate_g = np.where( res_g == Annotate_PALETTE_g )
locate_b = np.where( res_b == Annotate_PALETTE_b )
# 查找都匹配位置（交集）
# 将矩阵换一种表示形式
locate_r = np.array(locate_r[0]) * self.Max_width + np.array(locate_r[1])
locate_g = np.array(locate_g[0]) * self.Max_width + np.array(locate_g[1])
locate_b = np.array(locate_b[0]) * self.Max_width + np.array(locate_b[1])
# 用自带函数寻找匹配项
matched = np.intersect1d(np.intersect1d(locate_r, locate_g), locate_b)
matched = np.concatenate(([matched // self.Max_width], [np.mod(matched, self.Max_width)]), 0)
merge_img[matched[0],matched[1], 0] = Annotate_PALETTE_r
merge_img[matched[0],matched[1], 1] = Annotate_PALETTE_g
merge_img[matched[0],matched[1], 2] = Annotate_PALETTE_b
# 转成cv2形式
merge_img = cv2.cvtColor(np.float32(merge_img), cv2.COLOR_RGB2BGR)
try: # 新建文件夹
os.mkdir(self.save_merge_pic_folder)
except:
print("已有"+self.save_merge_pic_folder)
if res_imgname.lower().endswith(('.jpg', '.png')):
save_dir = os.path.join(self.save_merge_pic_folder, os.path.basename(res_imgname).rpartition('.')[0]+'.'+self.pic_type)
else:
save_dir = os.path.join(self.save_merge_pic_folder, os.path.basename(res_imgname)+'.'+self.pic_type)
cv2.imwrite(save_dir, merge_img)
print("Merge图片已保存", save_dir)
time_end=time.time() # 输出结束时间
print('time cost',time_end-time_start,'s')
# 将label图片与原图片重合
def Merge_ori_pic_and_label_pic_all(self):
# 遍历整个文件夹
for res_img_path in self.reslist_src:
if res_img_path.lower().endswith(('.jpg', '.png')):
res_imgname = os.path.basename(res_img_path).rpartition('.')[0].replace(self.pro_append_name,"")
else:
res_imgname = os.path.basename(res_img_path).replace(self.pro_append_name,"")
print("Processing: ", res_imgname, "...")
self.Merge_ori_pic_and_label_pic(res_img_path, res_imgname)
if __name__ == "__main__":
Annotate_CLASSES = ('肝脏','胆囊','分离钳','止血海绵','肝总管','胆总管','吸引器','剪刀','止血纱布','生物夹','无损伤钳','喷洒','胆囊管','胆囊动脉','电凝','标本袋','引流管','纱布','金属钛夹','术中超声','吻合器','乳胶管','推结器','肝带','钳夹','超声刀','脂肪','双极电凝','棉球','血管阻断夹','肿瘤','针','线','韧带','胆囊静脉','背景') # 待分类的类
Annotate_PALETTE = [[255,91,0],[255,234,0],[85, 111, 181],[181, 227, 14],[72, 0, 255],[0, 155, 33],[255,0,255],[29, 32, 136],[160, 15, 95],[0,160,233],[52,184,178],[90,120,41],[255,0,0],[177,0,0],[167,24,233],[112,113,150],[0,255,0],[255,255,255],[0,255,255],[138,251,213],[136,162,196],[197,83,181],[202,202,200],[113,102,140],[66,115,82],[240,16,116],[155,132,0],[155,62,0],[146,175,236],[255,172,159],[245,161,0],[134,124,118], [0,157,142], [181,85,105], [42,8,66],[0,0,0]] # 每一类的像素直
bg_PALETTE = [0,0,0] # 背景的RGB
# 创建参数解析器
parser = argparse.ArgumentParser(description='Process some files.')
# 添加参数选项
parser.add_argument('-src_fold', dest='src_label_fold', default='', help='source label folder')
parser.add_argument('-save_pro_fold', dest='save_pro_label_fold', default='./save_pro_label_fold', help='processed label folder')
parser.add_argument('-save_GT_fold', dest='save_GT_label_fold', default='./save_GT_label_fold', help='ground truth folder')
parser.add_argument('-fold_search_depth', dest='Label_Max_Search_layer', default='1000', type=int, help='Folder Search Depth')
parser.add_argument('-pro_suffix_name', dest='pro_append_name', default='_label', help='Pro file suffix')
parser.add_argument('-GT_suffix_name', dest='GT_append_name', default='_gtFine_labelTrainIds', help='GT file suffix')
parser.add_argument('-GT_channel', dest='GT_channel', default='1', type=int, help='GT file channel(1 or 3)')
parser.add_argument('-back_gnd_color', dest='back_gnd_color', default='0', type=int, help='Color of "Back ground"(0 or 255)')
parser.add_argument('-first_class_color', dest='first_class_color', default='1', type=int, help='Color of "First Class"')
parser.add_argument('-pic_type', dest='pic_type', default='png', help='type of picture(Do not add ".")')
parser.add_argument('-Max_width', dest='Max_width', default='10000', type=int, help='Max width of picture')
parser.add_argument('-Rebuild_from', dest='Rebuild_from', default='label', help='Source to Rebuild Labels(label/pro)')
parser.add_argument('-Rebuild_to', dest='Rebuild_to', default='GT', help='Destination of Rebuild Labels(pro/GT)')
parser.add_argument('-save_process_pics', dest='save_process_pics', default='False', help='Save the processed pics(e.g.Gray_pics,Color_pics) in generating pro_pics')
# 解析命令行参数
args = parser.parse_args()
src_label_fold = args.src_label_fold
save_pro_label_fold = args.save_pro_label_fold
save_GT_label_fold = args.save_GT_label_fold
Label_Max_Search_layer = args.Label_Max_Search_layer
pro_append_name = args.pro_append_name
GT_append_name = args.GT_append_name
GT_channel = args.GT_channel
back_gnd_color = args.back_gnd_color
first_class_color = args.first_class_color
pic_type = args.pic_type
Max_width = args.Max_width
Rebuild_from = args.Rebuild_from
Rebuild_to = args.Rebuild_to
save_process_pics = args.save_process_pics
try: # 遍历文件深度，最小为1
Label_Max_Search_layer=int(Label_Max_Search_layer)
except:
Label_Max_Search_layer=1000
try: # GT标签图片通道数
GT_channel=int(GT_channel)
except:
GT_channel=1
try: # 背景颜色（背景选择0或255)
back_gnd_color=int(back_gnd_color)
except:
back_gnd_color=0
try: # 第一类上的颜色(如果背景为0,选择1；)
first_class_color=int(first_class_color)
except:
first_class_color=1
try: # 最大图片宽度（匹配时候用）
Max_width=int(Max_width)
except:
Max_width=10000
if(save_process_pics.lower() == 'false'):
save_process_pics = False
elif(save_process_pics.lower() == 'true'):
save_process_pics = True
else:
save_process_pics = False
D = Deal_image(Annotate_CLASSES=Annotate_CLASSES, Annotate_PALETTE=Annotate_PALETTE, src_label_fold=src_label_fold, save_pro_label_fold=save_pro_label_fold, save_GT_label_fold=save_GT_label_fold, GT_channel=GT_channel, pro_append_name=pro_append_name, GT_append_name=GT_append_name, back_gnd_color=back_gnd_color, first_class_color=first_class_color, pic_type=pic_type, Max_width=Max_width, Label_Max_Search_layer=Label_Max_Search_layer, save_process_pics=save_process_pics, bg_PALETTE = bg_PALETTE)
# print(D.src_CLASSES_NUM)
if Rebuild_from == 'label':
# 1.先将所有原始图片转为pro图片
D.Conver_ori_label_pic_2_pro_pic_all()
pass
if Rebuild_to == 'GT':
# 2.再将pro图片转为GT图片
D.Conver_pro_label_pic_2_GT_pic_all()
pass