opencv笔记(车辆计数实现)
注意：更准确的车辆计数实现应考虑深度学习。
目录基本实现思路
- 加载视频
- 通过形态学识别车辆
- 对车辆进行统计
- 显示车辆统计信息
涉及知识
- 窗口展示
- 图像/视频加载
- 基本图形的绘制
- 车辆识别
- 基本图像运算与处理
- 形态学
- 轮廓查找
分步骤代码
加载视频
import cv2
# 创建窗口对象
cv2.namedWindow('video', cv2.WINDOW_NORMAL)
cap = cv2.VideoCapture('2.mp4')
while 1:
ok, frame = cap.read()
if ok:
# 窗口显示
cv2.imshow('video', frame)
key = cv2.waitKey(1)
if key == 27 or cv2.getWindowProperty('color', cv2.WND_PROP_VISIBLE) < 1.0:
break
else:
break
# 释放资源
cap.release()
cv2.destroyAllWindows()
通过形态学识别车辆
import cv2
# 车辆过滤条件应根据参照物适配，此处仅为演示原理
min_w = 100
min_h = 100
line_high = 200
line_width = 6
cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
cars = []
while 1:
ok, frame = cap.read()
if ok:
# 转换绘图图
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 高斯去噪
blur = cv2.GaussianBlur(frame, (3, 3), 5)
# 去除背景
mask = bs.apply(blur)
# 腐蚀
erode = cv2.erode(mask, kernel, iterations=2)
# 膨胀
dilate = cv2.dilate(erode, kernel, iterations=2)
# 闭运算
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
# 查找轮廓
contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 过滤、绘制轮廓，车辆计数
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
# 过滤
if w < min_w or h < min_h:
continue
# 绘制轮廓
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('video', frame)
key = cv2.waitKey(42)
if key == 27:
break
else:
break
# 释放资源
cap.release()
cv2.destroyAllWindows()
对车辆进行统计
import cv2
# 车辆过滤条件应根据参照物适配，此处仅为演示原理
min_w = 100
min_h = 100
# 检测线高度、误差
line_high = 190
line_offset = 5
cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
car_count = 0
while 1:
ok, frame = cap.read()
if ok:
# 转换绘图图
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 高斯去噪
blur = cv2.GaussianBlur(frame, (3, 3), 3)
# 去除背景
mask = bs.apply(blur)
# 腐蚀
erode = cv2.erode(mask, kernel, iterations=3)
# 膨胀
dilate = cv2.dilate(erode, kernel, iterations=3)
# 闭运算
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
# 查找轮廓
contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 绘制检测线
cv2.line(frame, (0, line_high), (frame.shape[1], line_high), (255, 0, 0), 3)
# 过滤、绘制轮廓，车辆计数
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
# 过滤
if w < min_w or h < min_h:
continue
# 搜集有效车辆（此处有逻辑限制：车辆不能停止于检测线区域内）
center = y + h / 2
if line_high - line_offset < center < line_high + line_offset:
car_count += 1
# 绘制轮廓
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('video', frame)
key = cv2.waitKey(42)
if key == 27:
break
else:
break
# 释放资源
cap.release()
cv2.destroyAllWindows()
显示车辆统计信息
import cv2
# 车辆过滤条件应根据参照物适配，此处仅为演示原理
min_w = 100
min_h = 100
# 检测线高度、误差
line_high = 150
line_offset = 3
cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
car_count = 0
while 1:
ok, frame = cap.read()
if ok:
# 转换绘图图
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 高斯去噪
blur = cv2.GaussianBlur(frame, (3, 3), 3)
# 去除背景
mask = bs.apply(blur)
# 腐蚀
erode = cv2.erode(mask, kernel, iterations=3)
# 膨胀
dilate = cv2.dilate(erode, kernel, iterations=3)
# 闭运算
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
# 查找轮廓
contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 绘制检测线
cv2.line(frame, (0, line_high), (frame.shape[1], line_high), (255, 0, 0), 3)
# 过滤、绘制轮廓，车辆计数
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
# 过滤
if w < min_w or h < min_h:
continue
# 搜集有效车辆（此处有逻辑限制：车辆不能停止于检测线区域内，准确计数应该为每辆车提供唯一标识）
center = y + h / 2
if line_high - line_offset < center < line_high + line_offset:
car_count += 1
# 绘制轮廓
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame, f'Cars: {car_count}', (300, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)
cv2.imshow('video', frame)
key = cv2.waitKey(42)
if key == 27:
break
else:
break
# 释放资源
cap.release()
cv2.destroyAllWindows()