寻找早晨去上班的最佳出发时间——yolov8检测、追踪
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1. 视频/截图
https://www.bilibili.com/video/BV1CP411W74S/?spm_id_from=333.999.0.0&vd_source=39b1662212679b11469d17d3bee8df4e
2. 核心代码
'''
iou追踪示例
'''
from ultralytics import YOLO
import cv2
import numpy as np
import time
import random
import os
from shapely.geometry import Polygon, LineString
import json
# 命令行设置 export YOLO_VERBOSE=false
class IouTracker:
def __init__(self):
# 加载检测模型
self.detection_model = YOLO("./weights/best_n.pt")
# 获取类别
self.objs_labels = self.detection_model.names
# 打印类别
# {0: 'pedestrian', 1: 'people', 2: 'bicycle', 3: 'car', 4: 'van', 5: 'truck', 6: 'tricycle', 7: 'awning-tricycle', 8: 'bus', 9: 'motor'}
print(self.objs_labels)
# 只处理car, van, truck, bus，即：轿车，货车，卡车，公交车
self.track_classes = {3: 'car', 4: 'van', 5: 'truck', 8: 'bus'}
# 追踪的IOU阈值
self.sigma_iou = 0.5
# detection threshold
self.conf_thresh = 0.3
# 颜色列表
self.colors_list = self.getColorsList(len(self.objs_labels))
# 读取标注的json文件
self.area_json = self.read_labelme_json('./media/snap.json')
def read_labelme_json(self, json_file):
'''
读取labelme标注的json文件
@param json_file: json文件路径
@return: dict {'mask': mask, 'line': line}
'''
# 读取json文件
with open(json_file, 'r') as f:
area_json = json.load(f)
shapes = area_json['shapes']
mask_overlay = []
line = []
for shape in shapes:
if shape['shape_type'] == 'line':
start_pt, end_pt = shape['points'][0],shape['points'][1]
line.append(start_pt)
line.append(end_pt)
else:
mask_overlay.append(shape['points'])
return {'mask': mask_overlay, 'line': line}
def iou(sel,bbox1, bbox2):
"""
计算两个bounding box的IOU
@param bbox1: bounding box in format x1,y1,x2,y2
@param bbox2: bounding box in format x1,y1,x2,y2
@return: intersection-over-union (float)
"""
(x0_1, y0_1, x1_1, y1_1) = bbox1
(x0_2, y0_2, x1_2, y1_2) = bbox2
# 计算重叠的矩形的坐标
overlap_x0 = max(x0_1, x0_2)
overlap_y0 = max(y0_1, y0_2)
overlap_x1 = min(x1_1, x1_2)
overlap_y1 = min(y1_1, y1_2)
# 检查是否有重叠
if overlap_x1 - overlap_x0 <= 0 or overlap_y1 - overlap_y0 <= 0:
return 0
# 计算重叠矩形的面积以及两个矩形的面积
size_1 = (x1_1 - x0_1) * (y1_1 - y0_1)
size_2 = (x1_2 - x0_2) * (y1_2 - y0_2)
size_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0)
size_union = size_1 + size_2 - size_intersection
# 计算IOU
return size_intersection / size_union
def predict(self, frame):
'''
检测
@param frame: 图片
@return: 检测结果，格式：[{'bbox': [l,t,r,b], 'score': conf, 'class_id': class_id}, ...]
'''
result = list(self.detection_model(frame, stream=True, conf=self.conf_thresh))[0] # inference，如果stream=False，返回的是一个列表，如果stream=True，返回的是一个生成器
boxes = result.boxes # Boxes object for bbox outputs
boxes = boxes.cpu().numpy() # convert to numpy array
dets = [] # 检测结果
# 参考：https://docs.ultralytics.com/modes/predict/#boxes
# 遍历每个框
for box in boxes.data:
l,t,r,b = box[:4] # left, top, right, bottom
conf, class_id = box[4:] # confidence, class
# 排除不需要追踪的类别
if class_id not in self.track_classes:
continue
dets.append({'bbox': [l,t,r,b], 'score': conf, 'class_id': class_id })
return dets
def getColorsList(self, num_colors):
'''
生成颜色列表
'''
hexs = ('FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', '00C2FF',
'2C99A8', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7', 'FF3838', 'FF9D97')
# hex to bgr
bgr_list = []
for hex in hexs:
bgr_list.append(tuple(int(hex[i:i+2], 16) for i in (4, 2, 0)))
# 随机取num_colors个颜色
# final_list = [random.choice(bgr_list) for i in range(num_colors)]
return bgr_list
def is_cross_line(self,line, tracker):
'''
检查直线与检测框是否相交
'''
l,t,r,b = tracker['bboxes'][-1]
rectange = Polygon([(l, t), (r, t), (r, b), (l, b)])
path = LineString(line)
return path.intersects(rectange)
def apply_mask(self, raw_frame, raw_mask):
# 遮挡区域坐标
vertices_list = [np.array(n ,np.int32) for n in self.area_json['mask'] ]
# 创建一个空白的mask，与原图大小一致
mask = raw_mask.copy()
# 每块区域填充白色
for vertices in vertices_list:
cv2.fillPoly(mask, [vertices], (255, 255, 255))
# 使用mask覆盖原图，bitwise_not是取反操作，意思是将mask区域取反，即黑变白，白变黑
# bitwise_and是与操作，即将原图中mask区域以外的区域置为0
result = cv2.bitwise_and(raw_frame, cv2.bitwise_not(mask))
return result
def main(self):
'''
主函数
'''
# 读取视频
cap = cv2.VideoCapture(0)
# 获取视频帧率、宽、高
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(f"fps: {fps}, width: {width}, height: {height}")
tracks_active = [] # 活跃的跟踪器
frame_id = 1 # 帧ID
track_idx = 1 # 跟踪器ID
count_results = {'up': 0, 'down': 0 }
# 创建一个空白的mask，与原图大小一致
raw_mask = np.zeros((height, width, 3), np.uint8)
while True:
# 读取一帧
start_time = time.time()
ret, raw_frame = cap.read()
if ret:
# 对原图进行遮挡处理
frame = self.apply_mask(raw_frame, raw_mask)
# raw_frame = frame
# 缩放至720p
# frame = cv2.resize(frame, (720, 1280))
# 检测
dets = self.predict(frame)
# 更新后的跟踪器
updated_tracks = []
# 遍历活跃的跟踪器
for track in tracks_active:
if len(dets) > 0:
# 根据最大IOU更新跟踪器，先去explain.ipynb中看一下MAX用法
best_match = max(dets, key=lambda x: self.iou(track['bboxes'][-1], x['bbox'])) # 找出dets中与当前跟踪器（track['bboxes'][-1]）最匹配的检测框（IOU最大）
# 如果最大IOU大于阈值，则将本次检测结果加入跟踪器
if self.iou(track['bboxes'][-1], best_match['bbox']) > self.sigma_iou:
# 将本次检测结果加入跟踪器
track['bboxes'].append(best_match['bbox'])
track['max_score'] = max(track['max_score'], best_match['score'])
track['frame_ids'].append(frame_id)
# 更新跟踪器
updated_tracks.append(track)
# 删除已经匹配的检测框，避免后续重复匹配以及新建跟踪器
del dets[dets.index(best_match)]
# 如有未分配的目标，创建新的跟踪器
new_tracks = []
for det in dets: # 未分配的目标，已经分配的目标已经从dets中删除
new_track = {
'bboxes': [det['bbox']], # 跟踪目标的矩形框
'max_score': det['score'], # 跟踪目标的最大score
'start_frame': frame_id, # 目标出现的 帧id
'frame_ids': [frame_id], # 目标出现的所有帧id
'track_id': track_idx, # 跟踪标号
'class_id': det['class_id'], # 类别
'is_counted': False # 是否已经计数
}
track_idx += 1
new_tracks.append(new_track)
# 最终的跟踪器
tracks_active = updated_tracks + new_tracks
cross_line_color = (0,255,255) # 越界线的颜色
# 绘制跟踪器
for tracker in tracks_active:
# 绘制跟踪器的矩形框
l,t,r,b = tracker['bboxes'][-1]
# 取整
l,t,r,b = int(l), int(t), int(r), int(b)
class_id = tracker['class_id']
cv2.rectangle(raw_frame, (l,t), (r,b), self.colors_list[int(class_id)], 2)
# 绘制跟踪器的track_id + class_name + score（99.2%格式）
cv2.putText(raw_frame, f"{tracker['track_id']}", (l, t-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)
# cv2.putText(frame, f"{tracker['track_id']} {self.objs_labels[int(class_id)]} {tracker['max_score']:.2f}", (l, t-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
# 判断物体是否过线，根据跟踪起始位置y坐标判断方向
if self.is_cross_line(self.area_json['line'], tracker) and not tracker['is_counted']:
# 已经计数
tracker['is_counted'] = True
# 方法是判断车辆移动的方向，根据相隔两帧（起始位置和结束位置）的y坐标判断
bbox_end = tracker['bboxes'][-1] # 最新位置
bbox_start = tracker['bboxes'][0] # 起始位置
center_end_y = (bbox_end[1] + bbox_end[3]) / 2 # 最新位置的中心y坐标
center_start_y = (bbox_start[1] + bbox_start[3]) / 2 # 起始位置的中心y坐标
# 如果最新位置的中心y坐标大于起始位置的中心y坐标，则是向下移动，否则是向上移动
if center_end_y > center_start_y:
count_results['down'] += 1
cross_line_color = (255,0,255)
else:
count_results['up'] += 1
cross_line_color = (0,0,255)
# 设置半透明
color = (0,0,0)
alpha = 0.2
l,t = 0,0
r,b = l+240,t+120
raw_frame[t:b,l:r,0] = raw_frame[t:b,l:r,0] * alpha + color[0] * (1-alpha)
raw_frame[t:b,l:r,1] = raw_frame[t:b,l:r,1] * alpha + color[1] * (1-alpha)
raw_frame[t:b,l:r,2] = raw_frame[t:b,l:r,2] * alpha + color[2] * (1-alpha)
# end time
end_time = time.time()
# FPS
fps = 1 / (end_time - start_time)
# 绘制FPS
cv2.putText(raw_frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# 绘制直线
cv2.line(raw_frame, (int(self.area_json['line'][0][0]), int(self.area_json['line'][0][1])), (int(self.area_json['line'][1][0]), int(self.area_json['line'][1][1])), cross_line_color, 8)
# 绘制up和down人数
cv2.putText(raw_frame, f"up: {count_results['up']}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.putText(raw_frame, f"down: {count_results['down']}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# 显示
cv2.imshow("frame", raw_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# 实例化
iou_tracker = IouTracker()
# 运行
iou_tracker.main()
3.代码及附件
链接: https://pan.baidu.com/s/1F1skKzhwNFACbpJRMvcneA?pwd=8ujz 提取码: 8ujz
发表于 2023-8-12 12:46:16
发表于 2024-10-22 08:44:50