import cv2 import torch import time import math from shapely.geometry import box, Polygon import threading import numpy as np from datetime import datetime from ultralytics import YOLO from utils.tool import IoU from globals import stop_event,redis_client from config import SAVE_IMG_PATH,POST_IMG_PATH4,PLATFORM_SETUP_MODEL,PLATFORM_SETUP_VIDEO_SOURCES from globals import platform_remove_steps_detect_num,platform_remove_final_result,platform_remove_steps_img,remove_detection_status,remove_detection_timers def update_detection_status(platform_remove_steps): global remove_detection_status,remove_detection_timers current_time = time.time() for i,nums in enumerate(platform_remove_steps): if nums>0: remove_detection_timers[i] = current_time # 更新物体的最后检测时间 remove_detection_status[i] = False # 重置状态为 False def check_timeout(): current_time = time.time() for i in range(14): if current_time - remove_detection_timers[i] > 10: # 如果超过10秒未检测到 remove_detection_status[i] = True # 将状态置为 True # return True # return False def init_platform_remove_detection(): global remove_detection_timers,remove_detection_status remove_detection_timers = [time.time()] * 14 # 初始化计时器 remove_detection_status = [False]*14 # 初始化检 redis_client.delete("platform_remove_order") for i in range(1, 14): redis_client.delete(f"platform_remove_{i}_img") def start_platform_remove_detection(start_events): threads = [] for video_source in PLATFORM_SETUP_VIDEO_SOURCES: event = threading.Event() start_events.append(event) thread = threading.Thread(target=process_video, args=(PLATFORM_SETUP_MODEL,video_source,event)) threads.append(thread) thread.daemon=True thread.start() # Wait for all threads to complete for thread in threads: thread.join() print("平台拆除子线程启动") # Function to process video with YOLO model def process_video(model_path, video_source,start_event): # Load YOLO model model = YOLO(model_path) cap = cv2.VideoCapture(video_source) while cap.isOpened(): if stop_event.is_set():#控制停止推理 #del model break # Read a frame from the video success, frame = cap.read() if success: # Run YOLOv8 inference on the frame if cap.get(cv2.CAP_PROP_POS_FRAMES) % 25 != 0: continue results = model.predict(frame,conf=0.6,verbose=False,task='obb') LEVLEL0_REGION = np.array([[1167, 908], [931, 1153], [1962, 1187], [2034, 936]], np.int32) LEVLEL1_REGION = np.array([[1163, 574], [859, 818], [1969, 828], [2060, 588]], np.int32) LEVLEL2_REGION = np.array([[1147, 263], [778, 438], [1953, 442], [2044, 248]], np.int32) LEVLEL3_REGION = np.array([[1142, 34], [793, 163], [1945, 112], [2050, 17]], np.int32) DIAGONAL_REGION = np.array([[838, 147], [845, 1145], [1935, 1147], [1943, 147]], np.int32) global platform_remove_steps_detect_num,remove_detection_status,platform_remove_steps_img # 每十秒归零 platform_remove_steps_detect_num # current_time = time.time() # if not hasattr(process_video, 'last_reset_time'): # process_video.last_reset_time = current_time # if current_time - process_video.last_reset_time >= 10: # platform_remove_steps_detect_num = [0] * 14 # process_video.last_reset_time = current_time for r in results: boxes=r.obb.xyxyxyxy boxes1=r.obb.xywhr confidences=r.obb.conf classes=r.obb.cls # 0: montant # 1: diagonal # 2: wheel # 3: vertical_bar # 4: horizontal_bar # 5: ladder # 6: toe_board # 7: scaffold # 1=轮子 # 2=立杆 # 3=纵向扫地杆 # 4=横向扫地杆 # 5=纵向水平杆1 # 6=横向水平杆1 # 7=纵向水平杆2 # 8=横向水平杆2 # 9=斜撑 # 10=爬梯 # 11=脚手板 # 12=挡脚板 # 13=纵向水平杆3 # 14=横向水平杆3 platform_remove_steps = [0] * 14 hengxianggan=0 zongxinaggan=0 for i in range(len(boxes)): confidence = confidences[i].item() cls = int(classes[i].item()) label = model.names[cls] #print(boxes[i].tolist()) box_coords = boxes[i].tolist() x_center = (box_coords[0][0] + box_coords[1][0]+box_coords[2][0]+box_coords[3][0]) / 4 y_center=(box_coords[0][1] + box_coords[1][1]+box_coords[2][1]+box_coords[3][1]) / 4 center_point = (int(x_center), int(y_center)) rotation= math.degrees(boxes1[i][4]) if label=="wheel":#轮子 # platform_remove_steps_detect_num[0]+=1 # if platform_remove_steps_detect_num[0]>3: platform_remove_steps[0]+=1 elif label=="bar":#立杆 # platform_setup_steps_detect_num[1]+=1 # if platform_setup_steps_detect_num[1]>3: # platform_setup_steps[1]+=1 if video_source == PLATFORM_SETUP_VIDEO_SOURCES[0]: if 70 < rotation < 110: platform_remove_steps[1] += 1 # 立杆 elif rotation < 10 or rotation > 170: hengxianggan += 1 # 横向杆 elif 135 < rotation < 150: platform_remove_steps[8] += 1 # 斜杆 else: zongxinaggan += 1 # 纵向杆 elif video_source == PLATFORM_SETUP_VIDEO_SOURCES[1]: if 70 < rotation < 110: platform_remove_steps[1] += 1 # 立杆 elif 5 < rotation < 25: hengxianggan += 1 # 横向杆 elif 40 < rotation < 60: platform_remove_steps[8] += 1 # 斜杆 else: zongxinaggan += 1 # 纵向杆 # elif label=="montant":#立杆 # platform_remove_steps_detect_num[1]+=1 # if platform_remove_steps_detect_num[1]>3: # platform_remove_steps[1]+=1 # elif label=="vertical_bar":#水平横杆,纵杆 # is_inside0 = cv2.pointPolygonTest(LEVLEL0_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside1 = cv2.pointPolygonTest(LEVLEL1_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside2 = cv2.pointPolygonTest(LEVLEL2_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside3 = cv2.pointPolygonTest(LEVLEL3_REGION.reshape((-1, 1, 2)), center_point, False) # #print(is_inside) # if is_inside0>=0 : # platform_remove_steps_detect_num[2]+=1 # if platform_remove_steps_detect_num[2]>3: # platform_remove_steps[2]+=1 #表示纵向扫地杆 # elif is_inside1>=0: # platform_remove_steps_detect_num[4]+=1 # if platform_remove_steps_detect_num[4]>3: # platform_remove_steps[4]+=1#5=纵向水平杆1 # elif is_inside2>=0: # platform_remove_steps_detect_num[6]+=1 # if platform_remove_steps_detect_num[6]>3: # platform_remove_steps[6]+=1#7=纵向水平杆2 # elif is_inside3>=0: # platform_remove_steps_detect_num[12]+=1 # if platform_remove_steps_detect_num[12]>3: # platform_remove_steps[12]+=1#13=纵向水平杆3 # elif label=="horizontal_bar":#水平纵杆 # is_inside0 = cv2.pointPolygonTest(LEVLEL0_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside1 = cv2.pointPolygonTest(LEVLEL1_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside2 = cv2.pointPolygonTest(LEVLEL2_REGION.reshape((-1, 1, 2)), center_point, False) # is_inside3 = cv2.pointPolygonTest(LEVLEL3_REGION.reshape((-1, 1, 2)), center_point, False) # #print(is_inside) # if is_inside0>=0 : # platform_remove_steps_detect_num[3]+=1 # if platform_remove_steps_detect_num[3]>3: # platform_remove_steps[3]+=1#4=横向扫地杆 # elif is_inside1>=0: # platform_remove_steps_detect_num[5]+=1 # if platform_remove_steps_detect_num[5]>3: # platform_remove_steps[5]+=1#6=横向水平杆1 # elif is_inside2>=0: # platform_remove_steps_detect_num[7]+=1 # if platform_remove_steps_detect_num[7]>3: # platform_remove_steps[7]+=1# 8=横向水平杆2 # elif is_inside3>=0: # platform_remove_steps_detect_num[13]+=1 # if platform_remove_steps_detect_num[13]>3: # platform_remove_steps[13]+=1#14=横向水平杆3 # elif label=="diagonal":#斜撑 # is_inside = cv2.pointPolygonTest(DIAGONAL_REGION.reshape((-1, 1, 2)), center_point, False) # if is_inside>=0: # platform_remove_steps_detect_num[8]+=1 # if platform_remove_steps_detect_num[8]>3: # platform_remove_steps[8]+=1# 9=斜撑 elif label=="ladder":#梯子 #10=爬梯 # platform_remove_steps_detect_num[9]+=1 # if platform_remove_steps_detect_num[9]>3: platform_remove_steps[9]+=1 elif label=="scaffold":#脚手板 #11=脚手板 # platform_remove_steps_detect_num[10]+=1 # if platform_remove_steps_detect_num[10]>3: platform_remove_steps[10]+=1 elif label=="toe_board":#档脚板 #12=挡脚板 # platform_remove_steps_detect_num[11]+=1 # if platform_remove_steps_detect_num[11]>3: platform_remove_steps[11]+=1 if hengxianggan >= 2: platform_remove_steps[3] = 2 if hengxianggan >= 4: platform_remove_steps[5] = 2 if hengxianggan >= 6: platform_remove_steps[7] = 2 if hengxianggan >= 8: platform_remove_steps[13] = 2 if zongxinaggan>=2: platform_remove_steps[2]=2 if zongxinaggan>=4: platform_remove_steps[4]=2 if zongxinaggan>=6: platform_remove_steps[6]=2 if zongxinaggan>=8: platform_remove_steps[12]=2 update_detection_status(platform_remove_steps) check_timeout()#返回True表示10秒内未检测到某物体，表示该物体拆除完成 for i, status in reversed(list(enumerate(remove_detection_status))): if status and platform_remove_steps_img[i]==False: redis_client.rpush("platform_remove_order", i+1) platform_remove_steps_img[i]=True save_time=datetime.now().strftime('%Y%m%d_%H%M') imgpath = f"{SAVE_IMG_PATH}/platform_remove{i+1}_{save_time}.jpg" post_path= f"{POST_IMG_PATH4}/platform_remove{i+1}_{save_time}.jpg" annotated_frame = results[0].plot() cv2.imwrite(imgpath, annotated_frame) redis_client.set(f"platform_remove_{i+1}_img",post_path) #redis_client.set(f"platform_remove_{14-i-1}", "1") start_event.set() else: # Break the loop if the end of the video is reached break # Release the video capture object and close the display window cap.release() if torch.cuda.is_available(): torch.cuda.empty_cache() del model