import cv2
from tqdm import tqdm
from ultralytics import YOLO
from ultralytics.yolo.utils.plotting import Annotator
import os
import cv2
from mediapipe.python.solutions import drawing_utils
from mediapipe.python.solutions import hands
import time
import os
import queue
import threading
from yolov8_det import analysis_yolov8
from cut_img_bbox import cut_img_bbox
from mediapipe_det import analysis_mediapipe
class atm_det:
def __init__(self,imgPath,savePath,modellist):
self.imgPath = imgPath
self.savePath = savePath
self.imgList = os.listdir(self.imgPath)
#定义加载好的模型
self.model_person = modellist[0]
self.model_pp_hand = modellist[1]
self.model_blue = modellist[2]
self.model_screen = modellist[3]
self.media_hands = modellist[4]
# 队列
self.imgQueue1 = queue.Queue(maxsize=len(self.imgList))
self.imgQueue2 = queue.Queue(maxsize=len(self.imgList))
self.imgQueue3 = queue.Queue(maxsize=len(self.imgList))
self.imgQueue4 = queue.Queue(maxsize=len(self.imgList))
self.imgQueue5 = queue.Queue(maxsize=len(self.imgList))
self.imgQueue6 = queue.Queue(maxsize=len(self.imgList))
#线程
self.get_imgThread = threading.Thread(target=self.get_img)
self.get_person_resultThread = threading.Thread(target=self.get_person_result)
self.get_hand_landmarkerThread = threading.Thread(target=self.get_hand_landmarker)
self.get_blue_resultThread = threading.Thread(target=self.get_blue_result)
self.get_pph_resultThread = threading.Thread(target=self.get_pph_result)
self.analysis_handThread = threading.Thread(target=self.analysis_hand)
self.draw_imagesThread = threading.Thread(target=self.draw_images)
self.analysis_hand_blueThread = threading.Thread(target=self.analysis_hand_blue)
self.get_screen_resultThread = threading.Thread(target=self.get_screen_result)
def get_img(self):
for img in self.imgList:
imgpath = os.path.join(self.imgPath,img)
images = cv2.imread(imgpath)
imagesDict = {img:images}
self.imgQueue1.put(imagesDict)
def get_person_result(self):
while True:
if ~self.imgQueue1.empty():
imagesDict = self.imgQueue1.get()
images = list(imagesDict.values())[0]
imgname = list(imagesDict.keys())[0]
per_result = analysis_yolov8(images=images,
model_coco=self.model_person,
confidence=0.5
)
for per in per_result:
per_bbox = list(per.values())[0]
imgcut = cut_img_bbox(images,per_bbox)
imgcutDict = {imgname:{"imgcut":imgcut,"per":per}}
self.imgQueue2.put(imgcutDict)
def get_blue_result(self):
while True:
if ~self.imgQueue1.empty():
imagesDict = self.imgQueue1.get()
images = list(imagesDict.values())[0]
imgname = list(imagesDict.keys())[0]
blue_result = analysis_yolov8(images=images,
model_coco=self.model_blue,
confidence=0.5
)
blues_list = []
for blues in blue_result:
blue = list(blues.values())[0]
blues_list.append(blue)
if blues_list:
bluesDict = {imgname:blues_list}
self.imgQueue4.put(bluesDict)
def get_pph_result(self):
while True:
if ~self.imgQueue1.empty():
imagesDict = self.imgQueue1.get()
images = list(imagesDict.values())[0]
imgname = list(imagesDict.keys())[0]
blue_result = analysis_yolov8(images=images,
model_coco=self.model_pp_hand,
confidence=0.5
)
pph_list = []
for blues in blue_result:
blue = list(blues.values())[0]
pph_list.append(blue)
if pph_list:
pphDict = {imgname:pph_list}
self.imgQueue5.put(pphDict)
def get_hand_landmarker(self):
while True:
if ~self.imgQueue2.empty():
imgcutDict = self.imgQueue2.get()
imgcut = list(imgcutDict.values())[0]["imgcut"]
hand_landmarker_result = analysis_mediapipe(images=imgcut,
hands=self.media_hands,
parameter=hands.HAND_CONNECTIONS)
handDict = {"hand_landmarker_result":hand_landmarker_result}
list(imgcutDict.values())[0].update(handDict)
self.imgQueue3.put(imgcutDict)
def get_screen_result(self):
while True:
if ~self.imgQueue1.empty():
imagesDict = self.imgQueue1.get()
images = list(imagesDict.values())[0]
imgname = list(imagesDict.keys())[0]
screen_result = analysis_yolov8(images=images,
model_coco=self.model_screen,
confidence=0.5
)
print('screen_result:',screen_result)
def analysis_hand(self):
while True:
if ~self.imgQueue3.empty():
imgcutDict2 = self.imgQueue3.get()
imgname = list(imgcutDict2.keys())[0]
re_list = list(imgcutDict2.values())[0]
pre_list = re_list['per']
pre_list = list(pre_list.values())[0]
# pre_x = int(pre_list[2] - pre_list[0])
pre_x = int(pre_list[0])
pre_y = int(pre_list[1])
# pre_y = int(pre_list[3] - pre_list[1])
hand_list = re_list['hand_landmarker_result']
point_list = []
for hand_point in hand_list:
for point in hand_point:
# print(point)
point_x = int(point[0]) + pre_x
point_y = int(point[1]) + pre_y
point_list.append((point_x,point_y))
if point_list:
imgcutDict2.update({imgname:point_list})
self.imgQueue6.put(imgcutDict2)
def analysis_hand_blue(self):
while True:
if ~self.imgQueue4.empty() and ~self.imgQueue6.empty():
blue_list = self.imgQueue4.get()
hand_list = self.imgQueue6.get()
print('blue_list:',blue_list)
print('hand_list:',hand_list)
while list(blue_list.keys())[0] == list(hand_list.keys())[0]:
print(list(blue_list.keys())[0])
def draw_images(self):
while True:
if ~self.imgQueue6.empty():
img_hand_point = self.imgQueue6.get()
imgname = list(img_hand_point.keys())[0]
img = cv2.imread(os.path.join(self.imgPath,imgname))
point_list = list(img_hand_point.values())[0]
for point in point_list:
cv2.circle(img, point, 1,(0,0,255), 2)
cv2.imwrite(os.path.join(self.savePath,imgname),img)
def run(self):
self.get_imgThread.start()
self.get_person_resultThread.start()
self.get_hand_landmarkerThread.start()
self.get_blue_resultThread.start()
# self.get_pph_resultThread.start()
self.analysis_handThread.start()
# self.draw_imagesThread.start()
self.analysis_hand_blueThread.start()
self.get_screen_resultThread.start()
if __name__ == '__main__':
model_person = YOLO("model_files/bk1.pt")
model_pp_hand = YOLO("model_files/best_pph.pt")
model_blue = YOLO("model_files/best_butten.pt")
model_screen = YOLO("model_files/best_screen.pt")
media_hands = hands.Hands(
static_image_mode=True,
max_num_hands=4,
min_detection_confidence=0.1,
min_tracking_confidence=0.1)
modelList = [model_person,model_pp_hand,model_blue,model_screen,media_hands]
q = atm_det(imgPath='E:/BANK_XZ/data_file',
savePath='E:/BANK_XZ/output_data',
modellist=modelList)
q.run()