上传文件至 'BANK_atm/code_yolov8_atm'
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import cv2
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from pipeline_atm_getResult import atm_det
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def analysis_pph(imagepath,model_pp_hand,score_person):
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pph_re_list = []
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score_pp_hand = atm_det.get_pph_result(imagepath,model_pp_hand)
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for pp_dic in score_pp_hand:
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person_bbox = list(score_person[0].values())[0]
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pp_bbox = list(pp_dic.values())[0]
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# print(person_bbox,pp_bbox)
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# 判断检测到的手部动作是否是工作人员,防止误识别客户的手部动作
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# if int(pp_bbox[0]) and int(pp_bbox[2]) in range(int(person_bbox[0]),int(person_bbox[2])) and int(pp_bbox[1]) and int(pp_bbox[3]) in range(int(person_bbox[1]),int(person_bbox[3])):
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if int(pp_bbox[0]) and int(pp_bbox[2]) in range(int(person_bbox[0]),int(person_bbox[2])):
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# text_pph = ('Wrong action with',list(pp_dic.keys())[0])
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pph_re_dict = {list(pp_dic.keys())[0]:pp_bbox}
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pph_re_list.append(pph_re_dict)
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# 加入图片输出
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# print('Wrong action with',list(pp_dic.keys())[0])
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else:
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continue
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return pph_re_list
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def analysis_button(imagepath,score_button,score_person):
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img = cv2.imread(imagepath)
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w,h,t = img.shape
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x_mid = w/2
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re_list = []
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for score_blue in score_button:
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# 选择右侧按键
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# if ((score_blue[2] + score_blue[0])/2 + score_blue[0]) >= x_mid:
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person_bbox = list(score_person[0].values())[0]
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# 判断检测到的手部动作是否是工作人员,防止误识别客户的手部动作
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if int(score_blue[0]) and int(score_blue[2]) in range(int(person_bbox[0]),int(person_bbox[2])):
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# if int(score_blue[0]) and int(score_blue[2]) in range(int(person_bbox[0]),int(person_bbox[2])) or int(score_blue[1]) and int(score_blue[3]) in range(int(person_bbox[1]),int(person_bbox[3]))
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# text_pph = ('Wrong action with',list(score_blue.keys())[0])
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# pph_re_dict = {text_pph:pp_bbox}
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re_list.append(score_blue)
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pass
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# 加入图片输出
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# print('Wrong action with',list(pp_dic.keys())[0])
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else:
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# re_list.append('button action is right.')
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pass
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# else:
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# # button_dict ={'button_dict_right':{imagepath:score_blue}}
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# # re_list.append('button action is right.')
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# pass
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# print(re_list)
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# if len(re_list) == 0:
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# re_list.append('button action is right.')
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# print(re_list)
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return re_list
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import cv2
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import os
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from yolov8_det import analysis_yolov8
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class atm_det:
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# 根据bbox截取小图
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def cut_img_bbox(img,bbox):
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# blist = bbox.tolist()
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x_min = bbox[0]
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y_min = bbox[1]
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x_max = bbox[2]
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y_max = bbox[3]
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obj_img = img[int(y_min):int(y_max), int(x_min):int(x_max)] #cv2裁剪出目标框中的图片
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#保存图片
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# cut_images = obj_img_path + '\\' + '%s_%s'%(img_name, count) + '.jpg'
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return obj_img
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# 获得atm检测中工作人员数据、并截取小图
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def get_person_result(imgpath,model_person):
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imgname = os.path.basename(imgpath)
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images = cv2.imread(imgpath)
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per_result = analysis_yolov8(images=images,
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model_coco=model_person,
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confidence=0.5
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)
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return per_result
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# 检测手部动作是否有握笔签字、输入密码等动作
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def get_pph_result(imgpath,model_pp_hand):
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images = cv2.imread(imgpath)
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pph_result = analysis_yolov8(images=images,
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model_coco=model_pp_hand,
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confidence=0.5
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)
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return pph_result
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# 获得当前屏幕上按钮信息
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def get_blue_result(images,model_blue):
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blue_result = analysis_yolov8(images=images,
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model_coco=model_blue,
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confidence=0.5
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)
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blues_list = []
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for blues in blue_result:
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blue = list(blues.values())[0]
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blues_list.append(blue)
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return blues_list
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# 获得当前屏幕信息
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def get_screen_result(imgpath,model_screen):
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imgname = os.path.basename(imgpath)
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images = cv2.imread(imgpath)
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screen_result = analysis_yolov8(images=images,
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model_coco=model_screen,
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confidence=0.5
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)
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return screen_result
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import cv2
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from tqdm import tqdm
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from ultralytics import YOLO
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from ultralytics.yolo.utils.plotting import Annotator
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import os
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import cv2
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import time
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import os
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import queue
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import threading
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from pipeline_atm_getResult import atm_det
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from pipeline_atm_anaysis import analysis_pph,analysis_button
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class analysis_atm():
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def __init__(self,imgPath,savePath,modellist):
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self.imgPath = imgPath
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self.savePath = savePath
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self.imgList = os.listdir(self.imgPath)
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#加载模型
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self.model_person = YOLO(modellist[0])
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self.model_pp_hand = YOLO(modellist[1])
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self.model_blue = YOLO(modellist[2])
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self.model_screen = YOLO(modellist[3])
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# 图片检测后队列
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self.imgQueue1 = queue.Queue(maxsize=0)
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self.imgQueue2 = queue.Queue(maxsize=0)
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#线程
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self.get_img_listThread = threading.Thread(target=self.get_img_list)
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self.analysis_personThread = threading.Thread(target=self.analysis_person)
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self.draw_imagesThread = threading.Thread(target=self.draw_images)
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# 读图
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def get_img_list(self):
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for image_path in self.imgList:
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imagepath = os.path.join(self.imgPath,image_path)
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self.imgQueue1.put(imagepath)
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# 检测图
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def analysis_person(self):
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count = 0
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while True:
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if ~self.imgQueue1.empty():
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imagepath = self.imgQueue1.get()
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count = count + 1
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print('-----------------------------------------start',count,self.imgPath,'---------------------------------------------------------')
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# 检测工作人员信息
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score_person = atm_det.get_person_result(imagepath,self.model_person)
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# 检测当前屏幕信息
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score_screen = atm_det.get_screen_result(imagepath,self.model_screen)
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print('-----------------------------------------score_person:',score_person,'score_screen:',score_screen,'---------------------------------------------------------')
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# 屏幕信息列表
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screen_pph_list = ['Sign','Enter_Password_1','Enter_Password_2']
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screen_password_list = ['Enter_Password_1','Enter_Password_2']
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# screen_button_list = ['Select_Service_Item','Bank_Card','ID_Card',"Face_Verification"]
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imglist = []
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if score_person and score_screen:
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#检测屏幕中按钮情况
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score_button = atm_det.get_blue_result(imagepath,self.model_blue)
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# print('score_button:',score_button)
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# 屏幕编号
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screen_id = list(score_screen[0].keys())[0]
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# 检测人员类别
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person_labels = list(score_person[0].keys())[0]
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person_ppox = list(score_person[0].values())[0]
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# print('screen_id:',screen_id,'person_labels:',person_labels)
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#工作人员行为检测列表
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# person_re_list = []
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# 屏幕+工作人员
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screen_dict = {screen_id + "_wrong":list(score_screen[0].values())[0]}
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# person_re_list.append(screen_dict)
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person_dict = {person_labels + "_wrong":person_ppox}
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# person_re_list.append(person_dict)
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person_re_list = [screen_dict,person_dict]
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# print('person_re_list:',person_re_list)
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# # 检测到工作人员状态再检测签字情况
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# if person_labels == 'wrong' and screen_id == 'Sign' :
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if person_labels == 'wrong' and screen_id == 'Sign':
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# if person_labels == 'wrong' :
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score_pp_hand = analysis_pph(imagepath,self.model_pp_hand,score_person)
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if score_pp_hand:
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pp_hand_dict = {list(score_pp_hand[0].keys())[0] + '_hand_dict_wrong' :list(score_pp_hand[0].values())[0]}
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person_re_list.append(pp_hand_dict)
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else:
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pp_hand_dict = {'_hand_dict_right':[50,50]}
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# pp_hand_dict = {'pp_hand_dict_right':'sign and enter password is right action.'}
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person_re_list.append(pp_hand_dict)
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# 检测输入密码环节
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if person_labels == 'wrong' and screen_id in screen_password_list:
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# if person_labels == 'wrong' :
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score_pp_hand = analysis_pph(imagepath,self.model_pp_hand,score_person)
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if list(score_pp_hand[0].keys())[0] == 'password_area':
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pp_hand_dict = {list(score_pp_hand[0].keys())[0] + '_pp_dict_wrong' :list(score_pp_hand[0].values())[0]}
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person_re_list.append(pp_hand_dict)
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else:
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pp_hand_dict = {'pp_dict_right':[50,50]}
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# pp_hand_dict = {'pp_hand_dict_right':'sign and enter password is right action.'}
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person_re_list.append(pp_hand_dict)
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# 检测屏幕中按钮与首部情况情况
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if score_button:
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# 读取图片，判断按钮的位置
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# print('score_button:',score_button)
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button_result = analysis_button(imagepath,score_button,score_person)
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if button_result:
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button_dict ={'button_dict_wrong':button_result[0]}
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person_re_list.append(button_dict)
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else:
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# 判别顾客的手误识别
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button_dict ={'button_dict_right':[60,60]}
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# button_dict ={'button_dict_right':'button action is right.'}
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person_re_list.append(button_dict)
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# 检测人员回避情况
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if screen_id in screen_password_list:
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if person_labels == 'wrong' or 'correct':
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avoid_dict = {'avoid_dict_wrong':person_ppox}
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person_re_list.append(avoid_dict)
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else:
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# print('person_labels:',person_labels)
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avoid_dict = {'avoid_dict_right':[70,70]}
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# avoid_dict = {'avoid_dict_right':person_ppox}
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person_re_list.append(avoid_dict)
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# print('person_re_list',person_re_list)
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print('person_re_list_2:',person_re_list)
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# else:
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# pass
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# print('person_re_list_2:',person_re_list)
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# imglist.append(person_re_list)
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img_dict = {imagepath:person_re_list}
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print('img_dict:',img_dict)
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self.imgQueue2.put(img_dict)
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def draw_images(self):
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while True:
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if ~self.imgQueue2.empty():
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imagedict = self.imgQueue2.get()
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print('---------------------------------------','imagedict:',imagedict,'---------------------------------------')
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imgpath = list(imagedict.keys())[0]
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img_result = list(imagedict.values())[0]
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imgcv = cv2.imread(imgpath)
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imgname = os.path.basename(imgpath)
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print('img_result:',img_result)
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for re_dic in img_result:
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# print(re_dic)
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re_txt = list(re_dic.keys())[0]
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re_right = re_txt.split('_')[-1]
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re_bbox = list(re_dic.values())[0]
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print('re_bbox:',re_bbox)
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print('---------------------------------------',re_right,'---------------------------------------')
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if re_right == "wrong":
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# re_bbox = list(re_dic.values())[0]
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cv2.rectangle(imgcv, (int(re_bbox[0]), int(re_bbox[1])),(int(re_bbox[2]), int(re_bbox[3])), (0, 0, 255), 1)
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cv2.putText(imgcv, re_txt, (int(re_bbox[0]) - 10, int(re_bbox[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 1)
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# cv2.imwrite(os.path.join(self.savePath,imgname),imgcv)
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else:
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print('re_txt:',re_txt)
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cv2.putText(imgcv, re_txt, (int(re_bbox[0]), int(re_bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 0, 255), 3)
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cv2.imwrite(os.path.join(self.savePath,imgname),imgcv)
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def run(self):
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self.get_img_listThread.start()
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self.analysis_personThread.start()
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self.draw_imagesThread.start()
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if __name__ == '__main__':
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modelList = ("model_files/bk1.pt","model_files/best_pph.pt","model_files/best_butten_h_01.pt","model_files/best_screen_02.pt")
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# imgPath= 'E:/BANK_XZ_yolov8/data_file'
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imgPath = 'E:/Images Data/XZ/DatasetId_1845281_1685322163/Images'
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# imgPath = 'E:/Images Data/XZ/DatasetId_1845279_1685326217/DatasetId_1845279_1685326217/Images'
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savepath = 'E:/BANK_XZ_yolov8/output_data_06'
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# imgList = os.listdir(imgPath)
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# for image_path in imgList:
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# imagepath = os.path.join(imgPath,image_path)
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# # imagepath = 'E:/BANK_XZ_yolov8/data_file/0001326.jpg'
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a = analysis_atm(imgPath,savepath,modelList)
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a.run()
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@ -0,0 +1,20 @@
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Select_Service_Item 选择服务项目
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Bank_Card 银行卡
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ID_Card 身份证
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Face_Verification 人脸确认
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Information_Writing 信息写入
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Statement 声明
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Information_Confirmation 信息确认
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Sign 签字
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Password_Reset 密码重置
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Enter_Password_1 输入密码
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Enter_Password_2 输入密码
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Successful_Transaction_1 交易成功_1
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Successful_Transaction_2 交易成功_2
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continue_Y_Or_N 是否继续
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Reference in New Issue