系列文章目录
目标跟踪——SORT算法原理浅析
目标跟踪——Deep Sort算法原理浅析
基于yolov5与Deep Sort的流量统计与轨迹跟踪
文章目录
前言
先来看下实现效果：
上图展示了用yolov5作为检测器，Deep Sort为追踪器实现了对车流量的统计并绘制了每辆车的运行轨迹。
; 一、整体目录结构
下图展示了项目的整体目录结构：
其中：
deep_sort文件下为目标跟踪相关代码；weights文件夹下存放yolov5检测模型；demo.py针对读取的视频进行目标追踪objdetector.py封装的一个目标检测器，对视频中的物体进行检测objtracker.py封装了一个目标追踪器，对检测的物体进行追踪
二、Deep Sort代码参数解释
deep_sort/configs/deep_sort.yaml文件里保存了Deep Sort算法的配置参数：
这些参数依次的含义为：
REID_CKPT:特征提取权重的目录路径MAX_DIST:最大余弦距离，用于级联匹配，如果大于该阈值，则忽略MIN_CONFIDENCE:检测结果置信度阈值NMS_MAX_OVERLAP:非极大抑制阈值，设置为1代表不进行抑制MAX_IOU_DISTANCE:最大IOU阈值MAX_AGE:最大寿命，也就是经过MAX_AGE帧没有追踪到该物体，就将该轨迹变为删除态N_INIT:最高击中次数，如果击中该次数，就由不确定态转为确定态NN_BUDGET:最大保存特征帧数，如果超过该帧数，将进行滚动保存
; 三、代码展示
下面给出 demo.py的代码：
import numpy as np
import objtracker
from objdetector import Detector
import cv2
VIDEO_PATH = './video/test_traffic.mp4'
if __name__ == '__main__':
width = 1920
height = 1080
mask_image_temp = np.zeros((height, width), dtype=np.uint8)
pts = {}
list_pts_blue = [[204, 305], [227, 431], [605, 522], [1101, 464], [1900, 601], [1902, 495], [1125, 379], [604, 437],
[299, 375], [267, 289]]
ndarray_pts_blue = np.array(list_pts_blue, np.int32)
polygon_blue_value_1 = cv2.fillPoly(mask_image_temp, [ndarray_pts_blue], color=1)
polygon_blue_value_1 = polygon_blue_value_1[:, :, np.newaxis]
mask_image_temp = np.zeros((height, width), dtype=np.uint8)
list_pts_yellow = [[181, 305], [207, 442], [603, 544], [1107, 485], [1898, 625], [1893, 701], [1101, 568],
[594, 637], [118, 483], [109, 303]]
ndarray_pts_yellow = np.array(list_pts_yellow, np.int32)
polygon_yellow_value_2 = cv2.fillPoly(mask_image_temp, [ndarray_pts_yellow], color=2)
polygon_yellow_value_2 = polygon_yellow_value_2[:, :, np.newaxis]
polygon_mask_blue_and_yellow = polygon_blue_value_1 + polygon_yellow_value_2
polygon_mask_blue_and_yellow = cv2.resize(polygon_mask_blue_and_yellow, (width // 2, height // 2))
blue_color_plate = [255, 0, 0]
blue_image = np.array(polygon_blue_value_1 * blue_color_plate, np.uint8)
yellow_color_plate = [0, 255, 255]
yellow_image = np.array(polygon_yellow_value_2 * yellow_color_plate, np.uint8)
color_polygons_image = blue_image + yellow_image
color_polygons_image = cv2.resize(color_polygons_image, (width // 2, height // 2))
list_overlapping_blue_polygon = []
list_overlapping_yellow_polygon = []
down_count = 0
up_count = 0
font_draw_number = cv2.FONT_HERSHEY_SIMPLEX
draw_text_postion = (int((width / 2) * 0.01), int((height / 2) * 0.05))
detector = Detector()
capture = cv2.VideoCapture(VIDEO_PATH)
while True:
_, im = capture.read()
if im is None:
break
im = cv2.resize(im, (width // 2, height // 2))
list_bboxs = []
output_image_frame, list_bboxs = objtracker.update(detector, im)
output_image_frame = cv2.add(output_image_frame, color_polygons_image)
if len(list_bboxs) > 0:
for item_bbox in list_bboxs:
x1, y1, x2, y2, _, track_id = item_bbox
y1_offset = int(y1 + ((y2 - y1) * 0.5))
x1_offset = int(x1 + ((x2 - x1) * 0.5))
y = y1_offset
x = x1_offset
center = (x, y)
if track_id in pts:
pts[track_id].append(center)
else:
pts[track_id] = []
pts[track_id].append(center)
thickness = 2
cv2.circle(output_image_frame, (center), 1, [255, 255, 255], thickness)
for j in range(1, len(pts[track_id])):
if pts[track_id][j - 1] is None or pts[track_id][j] is None:
continue
cv2.line(output_image_frame, (pts[track_id][j - 1]), (pts[track_id][j]), [255, 255, 255], thickness)
if polygon_mask_blue_and_yellow[y, x] == 1:
if track_id not in list_overlapping_blue_polygon:
list_overlapping_blue_polygon.append(track_id)
if track_id in list_overlapping_yellow_polygon:
up_count += 1
print('up count:', up_count, ', up id:', list_overlapping_yellow_polygon)
list_overlapping_yellow_polygon.remove(track_id)
elif polygon_mask_blue_and_yellow[y, x] == 2:
if track_id not in list_overlapping_yellow_polygon:
list_overlapping_yellow_polygon.append(track_id)
if track_id in list_overlapping_blue_polygon:
down_count += 1
print('down count:', down_count, ', down id:', list_overlapping_blue_polygon)
list_overlapping_blue_polygon.remove(track_id)
list_overlapping_all = list_overlapping_yellow_polygon + list_overlapping_blue_polygon
for id1 in list_overlapping_all:
is_found = False
for _, _, _, _, _, bbox_id in list_bboxs:
if bbox_id == id1:
is_found = True
if not is_found:
if id1 in list_overlapping_yellow_polygon:
list_overlapping_yellow_polygon.remove(id1)
if id1 in list_overlapping_blue_polygon:
list_overlapping_blue_polygon.remove(id1)
list_overlapping_all.clear()
list_bboxs.clear()
else:
list_overlapping_blue_polygon.clear()
list_overlapping_yellow_polygon.clear()
text_draw = 'DOWN: ' + str(down_count) + \
' , UP: ' + str(up_count)
output_image_frame = cv2.putText(img=output_image_frame, text=text_draw,
org=draw_text_postion,
fontFace=font_draw_number,
fontScale=0.75, color=(0, 0, 255), thickness=2)
cv2.imshow('Counting Demo', output_image_frame)
cv2.waitKey(1)
capture.release()
cv2.destroyAllWindows()
若需要更改模型，只需要更改 objdetector.py下面的给出的部分：
OBJ_LIST = ['person', 'car', 'bus', 'truck']
DETECTOR_PATH = 'weights/yolov5m.pt'
总结
本篇文章给出了基于yolov5与Deep Sort的流量统计与轨迹跟踪的实例，在项目中有着实际的应用场景。
下面给出源码地址，欢迎 star：
https://github.com/JulyLi2019/yolov5-deepsort/releases/tag/V1.0，yolov5-deepsort.zip文件
如果阅读本文对你有用，欢迎一键三连呀！！！
2022年4月15日09:59:53
Original: https://blog.csdn.net/JulyLi2019/article/details/124047020
Author: JulyLi2019
Title: 基于yolov5与Deep Sort的流量统计与轨迹跟踪
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