若该文为原创文章,转载请注明原文出处。
一、介绍
在IPC监控视频中,很多IPC现在支持区域检测,当在区域内检测到有人闯入时,发送报警并联动报警系统,以保障生命和财产安全具有重大意义。它能够在第一时间检测到人员进入危险区域的行为,并发出及时警告,从而防止潜在事故的发生。
简单说是,在地图上标记出禁区(多边形),用计算机视觉技术监控进入禁区的物体。
现在很多摄像头模组,都自带了移动侦测功能,比如海思,君正,RK等。
以前有在RV1126上实现过类似的,现在想在RK3568上实现。
记录下PC端测试情况。
检测流程:
1、使用YOLOV5识别人物
2、使用ByteTrack实现多目标跟踪
3、使用射线法判断点是否在区域内
二、环境搭建
环境搭建参考AI项目二十二:行人属性识别-CSDN博客
项目结构
ByteTrack是git下载的源码
fonts存放了字体文件
weights存放yolov5s.pt模型
yolov5是git下载的源码
main.py主程序
mask_face.py是人脸遮挡代码
track.py是多目标根据和闯入识别代码
三、代码解析
代码功能不多,直接附上源码
main.py
import cv2
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageFont
print("0")
from mask_face import mask_face
print("2")
from track import PersonTrackprint("1")
def cv2_add_chinese_text(img, text, position, text_color=(0, 255, 0), tex_size=30):img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))draw = ImageDraw.Draw(img)font_style = ImageFont.truetype("./fonts/MSYH.ttc", tex_size, encoding="utf-8")draw.text(position, text, text_color, font=font_style)return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
print("2")
class BreakInDetection:def __init__(self):self.yolov5_model = torch.hub.load('yolov5', 'custom', path='./weights/yolov5s.pt', source='local')self.yolov5_model.conf = 0.7self.tracker = PersonTrack()@staticmethoddef yolo_pd_to_numpy(yolo_pd):box_list = yolo_pd.to_numpy()detections = []for box in box_list:l, t = int(box[0]), int(box[1])r, b = int(box[2]), int(box[3])conf = box[4]detections.append([l, t, r, b, conf])return np.array(detections, dtype=float)def plot_detection(self, person_track_dict, penalty_zone_point_list, frame, frame_idx):print(frame_idx)break_in_num = 0for track_id, detection in person_track_dict.items():l, t, r, b = detection.ltrbtrack_id = detection.track_idprint(track_id, detection.is_break_in)if detection.is_break_in:box_color = (0, 0, 255)id_color = (0, 0, 255)break_in_num += 1else:box_color = (0, 255, 0)id_color = (255, 0, 0)frame[t:b, l:r] = mask_face(frame[t:b, l:r])# 人体框cv2.rectangle(frame, (l, t), (r, b), box_color, 1)cv2.putText(frame, f'id-{track_id}', (l + 2, t - 3), cv2.FONT_HERSHEY_PLAIN, 3, id_color, 2)# 绘制禁区pts = np.array(penalty_zone_point_list, np.int32)pts = pts.reshape((-1, 1, 2))cv2.polylines(frame, [pts], True, (0, 0, 255), 2)cover = np.zeros((frame.shape[0], frame.shape[1], 3), np.uint8)cover = cv2.fillPoly(cover, [pts], (0, 0, 255))frame = cv2.addWeighted(frame, 0.9, cover, 0.3, 0)frame = cv2_add_chinese_text(frame, f'禁区', (600, 450), (255, 0, 0), 30)# 统计区info_frame_h, info_frame_w = 200, 400info_frame = np.zeros((info_frame_h, info_frame_w, 3), np.uint8)if_l, if_t = 100, 100if_r, if_b = if_l + info_frame_w, if_t + info_frame_hframe_part = frame[if_t:if_b, if_l:if_r]mixed_frame = cv2.addWeighted(frame_part, 0.6, info_frame, 0.7, 0)frame[if_t:if_b, if_l:if_r] = mixed_frameframe = cv2_add_chinese_text(frame, f'统计', (if_l + 150, if_t + 10), (255, 0, 0), 40)frame = cv2_add_chinese_text(frame, f'当前闯入禁区 {break_in_num} 人', (if_l + 60, if_t + 80), (255, 0, 0), 35)return framedef detect(self):cap = cv2.VideoCapture('./video.mp4')video_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))video_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))fps = round(cap.get(cv2.CAP_PROP_FPS))print(fps)video_writer = cv2.VideoWriter('./video_result.mp4', cv2.VideoWriter_fourcc(*'H264'), fps, (video_w, video_h))frame_idx = 0while cap.isOpened():frame_idx += 1success, frame = cap.read()if not success:print("Ignoring empty camera frame.")breakresults = self.yolov5_model(frame[:, :, ::-1])pd = results.pandas().xyxy[0]person_pd = pd[pd['name'] == 'person']person_det_boxes = self.yolo_pd_to_numpy(person_pd)if len(person_det_boxes) > 0:person_track_dict, penalty_zone_point_list = self.tracker.update_track(person_det_boxes, frame)frame = self.plot_detection(person_track_dict, penalty_zone_point_list, frame, frame_idx)cv2.imshow('Break in Detection', frame)video_writer.write(frame)if cv2.waitKey(1) & 0xFF == ord("q"):breakcap.release()cv2.destroyAllWindows()print("3")
if __name__ == '__main__':BreakInDetection().detect()
mask_face.py
import cv2
import mediapipe as mpface_detection = mp.solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.3)def mask_face(frame):frame.flags.writeable = Falseframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)results = face_detection.process(frame)frame_h, frame_w = frame.shape[:2]frame.flags.writeable = Trueframe = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)if results.detections:for detection in results.detections:face_box = detection.location_data.relative_bounding_boxxmin, ymin, face_w, face_h = face_box.xmin, face_box.ymin, face_box.width, face_box.heightl, t = int(xmin*frame_w), int(ymin*frame_h)r, b = l+int(face_w*frame_w), t+int(face_h*frame_h)cv2.rectangle(frame, (l, t), (r, b), (203, 192, 255), -1)return frame
track.py
from dataclasses import dataclass
import numpy as np
from collections import deque
import cv2
import paddleclas
import sys
sys.path.append('./ByteTrack/')
from yolox.tracker.byte_tracker import BYTETracker, STrack@dataclass(frozen=True)
class BYTETrackerArgs:track_thresh: float = 0.25track_buffer: int = 30match_thresh: float = 0.8aspect_ratio_thresh: float = 3.0min_box_area: float = 1.0mot20: bool = Falseclass Detection(object):def __init__(self, ltrb, track_id, is_break_in):self.track_id = track_idself.ltrb = ltrbself.is_break_in = is_break_in # 是否闯入self.track_list = deque(maxlen=30)def update(self, ltrb, is_break_in):self.ltrb = ltrbself.is_break_in = is_break_inl, t, r, b = ltrbself.track_list.append(((l+r)//2, b))class PersonTrack(object):def __init__(self):self.byte_tracker = BYTETracker(BYTETrackerArgs())self.detection_dict = {}# 禁区多边形point1 = (400, 440)point2 = (460, 579)point3 = (920, 600)point4 = (960, 450)self.penalty_zone_point_list = [point1, point2, point3, point4]def is_point_in_polygon(self, vertices, point):"""判断点是否在多边形内:param vertices: 多边形顶点坐标列表 [(x1, y1), (x2, y2), ..., (xn, yn)]:param point: 需要判断的点坐标 (x, y):return: True or False"""n = len(vertices)inside = Falsep1x, p1y = vertices[0]for i in range(1, n + 1):p2x, p2y = vertices[i % n]if point[1] > min(p1y, p2y):if point[1] <= max(p1y, p2y):if point[0] <= max(p1x, p2x):if p1y != p2y:xints = (point[1] - p1y) * (p2x - p1x) / (p2y - p1y) + p1xif p1x == p2x or point[0] <= xints:inside = not insidep1x, p1y = p2x, p2yreturn insidedef update_track(self, boxes, frame):tracks = self.byte_tracker.update(output_results=boxes,img_info=frame.shape,img_size=frame.shape)new_detection_dict = {}for track in tracks:l, t, r, b = track.tlbr.astype(np.int32)track_id = track.track_id# 判断人是否闯入detect_point = ((l + r)//2, b)is_break_in = self.is_point_in_polygon(self.penalty_zone_point_list, detect_point)if track_id in self.detection_dict:detection = self.detection_dict[track_id]detection.update((l, t, r, b), is_break_in)else:detection = Detection((l, t, r, b), track_id, is_break_in)new_detection_dict[track_id] = detectionself.detection_dict = new_detection_dictreturn self.detection_dict, self.penalty_zone_point_list
代码需要注意的是:
一、区域位置
二、显示参数位置
这几个参数需要根据视频的大小,去调整位置,不然会报错。
三、检测点是否在区域内
转成C语言直接部署到RK3568上。
后续将部署到RK3568,参考git和讯为电子多目标检测已实现。
如有侵权,或需要完整代码,请及时联系博主。