yolov8 区域多类别计数
- [1. 基础](#1. 基础)
- [2. 计数功能](#2. 计数功能)
-
- [2.1 计数模块](#2.1 计数模块)
- [2.2 判断模块](#2.2 判断模块)
- [3. 初始代码](#3. 初始代码)
- [4. 实验结果](#4. 实验结果)
- [5. 完整代码](#5. 完整代码)
- [6. 源码](#6. 源码)
1. 基础
本项目是在 Windows+YOLOV8环境配置 的基础上实现的，测距原理可见上边文章
2. 计数功能
2.1 计数模块
在指定区域内计数模块
region_points为指定区域，可设置为任意形状
python
region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)]
count_class1 = 0
count_class2 = 0
for i, box in enumerate(boxes):
x1, y1, x2, y2 = box[:4]
x_center = (x1 + x2) / 2
y_center = (y1 + y2) / 2
center_point = (int(x_center), int(y_center))
if is_inside_region(center_point, region_points):
if box[-1] == 0: # 类别1的标签.人
count_class1 += 1
elif box[-1] == 2: # 类别2的标签，车
count_class2 += 12.2 判断模块
python
def is_inside_region(point, region_points):
"""
判断点是否在指定区域内
"""
return cv2.pointPolygonTest(np.array(region_points), point, False) >= 03. 初始代码
python
import cv2
import numpy as np
from ultralytics import YOLO
def is_inside_region(point, region_points):
"""
判断点是否在指定区域内
"""
return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0
def detect():
model = YOLO("yolov8n.pt")
cv2.namedWindow('region couter', cv2.WINDOW_NORMAL)
cv2.resizeWindow('region couter', 1280, 360) # 设置宽高
cap = cv2.VideoCapture('ultralytics/assets/a3.mp4')
out_video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (2560, 720))
region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)]
region_points_np = np.array(region_points)
assert cap.isOpened(), "Error reading video file"
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True)
annotated_frame = tracks[0].plot()
boxes = tracks[0].boxes.data
count_class1 = 0
count_class2 = 0
for i, box in enumerate(boxes):
x1, y1, x2, y2 = box[:4]
x_center = (x1 + x2) / 2
y_center = (y1 + y2) / 2
center_point = (int(x_center), int(y_center))
if is_inside_region(center_point, region_points):
if box[-1] == 0: # 类别1的标签.人
count_class1 += 1
elif box[-1] == 2: # 类别2的标签，车
count_class2 += 1
cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2)
print("Number of objects in class 1:", count_class1)
print("Number of objects in class 2:", count_class2)
cv2.imshow("region couter", annotated_frame)
out_video.write(annotated_frame)
if cv2.waitKey(1) == ord('q'):
break
out_video.release()
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
detect()4. 实验结果
本实验可以计数多种类别
（1）区域计数实验
（2）如需把区域背景换成填充色，可将上边代码里的
python
cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2)替换为
python
region_mask = np.zeros_like(annotated_frame)
cv2.fillPoly(region_mask, [region_points_np], color=(255, 0, 0))
# 使用透明度将填充后的区域与原始帧混合
alpha = 0.2 # 调整透明度
annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)
（3）计数在图上显示
在annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)添加
python
text1 = "count_class1:%d" % count_class1
text2 = "count_class2:%d" % count_class2
cv2.putText(annotated_frame, text1, (20, 30), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2)
cv2.putText(annotated_frame, text2, (20, 60), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2)
5. 完整代码
python
import cv2
import numpy as np
from ultralytics import YOLO
def is_inside_region(point, region_points):
"""
判断点是否在指定区域内
"""
return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0
def detect():
model = YOLO("yolov8n.pt")
cv2.namedWindow('region couter', cv2.WINDOW_NORMAL)
cv2.resizeWindow('region couter', 1280, 360) # 设置宽高
cap = cv2.VideoCapture('ultralytics/assets/a3.mp4')
out_video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (2560, 720))
region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)]
region_points_np = np.array(region_points)
assert cap.isOpened(), "Error reading video file"
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True)
annotated_frame = tracks[0].plot()
boxes = tracks[0].boxes.data
count_class1 = 0
count_class2 = 0
for i, box in enumerate(boxes):
x1, y1, x2, y2 = box[:4]
x_center = (x1 + x2) / 2
y_center = (y1 + y2) / 2
center_point = (int(x_center), int(y_center))
if is_inside_region(center_point, region_points):
if box[-1] == 0: # 类别1的标签.人
count_class1 += 1
elif box[-1] == 2: # 类别2的标签，车
count_class2 += 1
# cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2)
region_mask = np.zeros_like(annotated_frame)
cv2.fillPoly(region_mask, [region_points_np], color=(255, 0, 0))
# 使用透明度将填充后的区域与原始帧混合
alpha = 0.2 # 调整透明度
annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)
text1 = "count_class1:%d" % count_class1
text2 = "count_class2:%d" % count_class2
cv2.putText(annotated_frame, text1, (20, 30), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2)
cv2.putText(annotated_frame, text2, (20, 60), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2)
print("Number of objects in class 1:", count_class1)
print("Number of objects in class 2:", count_class2)
cv2.imshow("region couter", annotated_frame)
out_video.write(annotated_frame)
if cv2.waitKey(1) == ord('q'):
break
out_video.release()
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
detect()6. 源码
可以去 Windows+YOLOV8环境配置 下载源码，然后把上边主代码贴进去运行即可