沧州建设网站公司,网络服务推广,做黑彩网站,wordpress is home文章目录 图像轮廓查找轮廓绘制轮廓轮廓的面积与周长多边形逼近与凸包外接矩形项目总览【车辆统计】视频加载【车辆统计】去背景【车辆统计】形态学处理【车辆统计】逻辑处理【车辆统计】显示信息【车辆统计】 图像轮廓 查找轮廓 # -*- coding: utf-8 -*- import cv2 import n… 文章目录 图像轮廓查找轮廓绘制轮廓轮廓的面积与周长多边形逼近与凸包外接矩形项目总览【车辆统计】视频加载【车辆统计】去背景【车辆统计】形态学处理【车辆统计】逻辑处理【车辆统计】显示信息【车辆统计】 图像轮廓 查找轮廓 # -*- coding: utf-8 -*- import cv2 import numpy as npimg cv2.imread(./contours1.jpeg) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)cv2.imshow(img, img) cv2.imshow(binary, binary)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)print(contours)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()绘制轮廓 # -*- coding: utf-8 -*- import cv2 import numpy as npimg cv2.imread(./contours1.jpeg) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)print(contours)# 绘制轮廓 cv2.drawContours(img, contours, -1, (0, 255, 0), 1)cv2.imshow(img, img) cv2.imshow(binary, binary)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()详细可参考官方资料 # -*- coding: utf-8 -*- import cv2 import numpy as npimg cv2.imread(./contours1.jpeg) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print(contours)# 绘制轮廓 cv2.drawContours(img, contours, -1, (0, 255, 0), 1)cv2.imshow(img, img) cv2.imshow(binary, binary)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()轮廓的面积与周长 # -*- coding: utf-8 -*- import cv2 import numpy as npimg cv2.imread(./contours1.jpeg) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print(contours)# 绘制轮廓 cv2.drawContours(img, contours, 0, (0, 255, 0), 1) # 计算面积 area cv2.contourArea(contours[0]) print(area%d%(area))# 计算周长 len cv2.arcLength(contours[0], True) print(len%d%(len))# cv2.imshow(img, img) # cv2.imshow(binary, binary)# key cv2.waitKey(0) 0xff # if key ord(q): # cv2.destroyAllWindows()多边形逼近与凸包 # -*- coding: utf-8 -*- import cv2 import numpy as npimg cv2.imread(./hand.png) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print(len(contours))# 绘制轮廓 cv2.drawContours(img, contours, 0, (0, 255, 0), 1) # 计算面积 # area cv2.contourArea(contours[0]) # print(area%d%(area))# 计算周长 # len cv2.arcLength(contours[0], True) # print(len%d%(len))cv2.imshow(img, img) # cv2.imshow(binary, binary)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()# -*- coding: utf-8 -*- import cv2 import numpy as npdef drawShape(src, points):i 0while i len(points):if(i len(points) - 1):x, y points[i][0]x1, y1 points[0][0]cv2.line(src, (x, y), (x1, y1), (0, 255, 0), 2)else:x, y points[i][0]x1, y1 points[i 1][0]cv2.line(src, (x, y), (x1, y1), (0, 255, 0), 2)i i 1img cv2.imread(./hand.png) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# print(len(contours))# 绘制轮廓 # cv2.drawContours(img, contours, 0, (0, 255, 0), 1) # 计算面积 # area cv2.contourArea(contours[0]) # print(area%d%(area))# 计算周长 # len cv2.arcLength(contours[0], True) # print(len%d%(len))e 5 approx cv2.approxPolyDP(contours[0], e, True) drawShape(img, approx)hull cv2.convexHull(contours[0]) drawShape(img, hull)cv2.imshow(img, img) # cv2.imshow(binary, binary)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()外接矩形 红框就是最小外接矩形绿框就是最大外接矩形。 最小外接矩形可以看图形有没有旋转 详情见参考文档 # -*- coding: utf-8 -*- import cv2 import numpy as npdef drawShape(src, points):i 0while i len(points):if(i len(points) - 1):x, y points[i][0]x1, y1 points[0][0]cv2.line(src, (x, y), (x1, y1), (0, 255, 0), 2)else:x, y points[i][0]x1, y1 points[i 1][0]cv2.line(src, (x, y), (x1, y1), (0, 255, 0), 2)i i 1img cv2.imread(./hello.jpeg) # print(img.shape)# 转变为单通道 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 二值化 ret, binary cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) # print(gray.shape)# 轮廓查找 contours, hierarchy cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# print(len(contours))# 绘制轮廓 # cv2.drawContours(img, contours, 1, (0, 255, 0), 1) # 计算面积 # area cv2.contourArea(contours[0]) # print(area%d%(area))# 计算周长 # len cv2.arcLength(contours[0], True) # print(len%d%(len))# e 5 # 多边形逼近 # approx cv2.approxPolyDP(contours[0], e, True) # drawShape(img, approx)# 凸包 # hull cv2.convexHull(contours[0]) # drawShape(img, hull)r cv2.minAreaRect(contours[1]) box cv2.boxPoints(r) box np.int0(box) cv2.drawContours(img, [box], 0, (0, 255, 0), 2)x, y, w, h cv2.boundingRect(contours[1]) cv2.rectangle(img, (x, y), (x w, y h), (0, 0, 255), 2)cv2.imshow(img, img) # cv2.imshow(binary, binary)key cv2.waitKey(0) 0xff if key ord(q):cv2.destroyAllWindows()项目总览【车辆统计】 视频加载【车辆统计】 # -*- coding: utf-8 -*- import cv2 import numpy as npcap cv2.VideoCapture(./video.mp4)while True:ret, frame cap.read()if(ret True):cv2.imshow(video, frame)key cv2.waitKey(1)if(key 27):breakcap.release() cv2.destroyAllWindows()去背景【车辆统计】 如果视频是25fps即每秒25帧即1000ms过25帧那么200ms就走5帧25/1000*2005) 运动的物体为前景静止的物体就是背景 详细可以参考官方文档 # -*- coding: utf-8 -*- import cv2 import numpy as npcap cv2.VideoCapture(./video.mp4)bgsubmog cv2.createBackgroundSubtractorMOG2() while True:ret, frame cap.read()if(ret True):mask bgsubmog.apply(frame)cv2.imshow(video, mask)key cv2.waitKey(1)if(key 27):breakcap.release() cv2.destroyAllWindows()# -*- coding: utf-8 -*- import cv2 import numpy as npcap cv2.VideoCapture(./video.mp4)bgsubmog cv2.createBackgroundSubtractorMOG2() tst cv2.createBackgroundSubtractorMOG2()while True:ret, frame cap.read()if(ret True):# 灰度图cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 去噪高斯blur cv2.GaussianBlur(frame, (3, 3), 5)# 去背景mask bgsubmog.apply(blur)t tst.apply(frame)cv2.imshow(video, mask)cv2.imshow(t, t)key cv2.waitKey(40)if(key 27):breakcap.release() cv2.destroyAllWindows()形态学处理【车辆统计】 # -*- coding: utf-8 -*- import cv2 import numpy as npcap cv2.VideoCapture(./video.mp4)bgsubmog cv2.createBackgroundSubtractorMOG2() # tst cv2.createBackgroundSubtractorMOG2()# 形态学kernel kernel cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) kernel2 cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))while True:ret, frame cap.read()if(ret True):# 灰度图cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 去噪高斯blur cv2.GaussianBlur(frame, (3, 3), 1)# 去背景mask bgsubmog.apply(blur)# t tst.apply(frame)# 腐蚀去掉图中小斑块erode cv2.erode(mask, kernel, iterations1)# 膨胀还原放大dilate cv2.dilate(erode, kernel2, iterations5)# 闭操作去掉物体内部噪声close cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)close cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)contours, hierarchy cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)for i, c in enumerate(contours):x, y, w, h cv2.boundingRect(c)cv2.rectangle(frame, (x, y), (x w, y h), (0, 255, 0), 2)cv2.imshow(video, frame)# cv2.imshow(erode, close)key cv2.waitKey(40)if(key 27):breakcap.release() cv2.destroyAllWindows()逻辑处理【车辆统计】 # -*- coding: utf-8 -*- import cv2 import numpy as npmin_w 50 min_h 50 max_w 800 max_h 800# 检测线的高度 line_high 480# 存放有效车辆数组 cars []# 统计车的数量 carnums 0# 线的偏移 offset 10# 求中心点 def center(x, y, w, h):x1 int(w / 2)y1 int(h / 2)cx x x1cy y y1return cx, cycap cv2.VideoCapture(./video.mp4)bgsubmog cv2.createBackgroundSubtractorMOG2() # tst cv2.createBackgroundSubtractorMOG2()# 形态学kernel kernel cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) kernel2 cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))while True:ret, frame cap.read()if(ret True):# print(frame.shape)# (584, 1280, 3)# 灰度图cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 去噪高斯blur cv2.GaussianBlur(frame, (3, 3), 1)# 去背景mask bgsubmog.apply(blur)# t tst.apply(frame)# 腐蚀去掉图中小斑块erode cv2.erode(mask, kernel, iterations1)# 膨胀还原放大dilate cv2.dilate(erode, kernel2, iterations3)# 闭操作去掉物体内部噪声close cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)close cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)contours, hierarchy cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)cv2.line(frame, (100, line_high), (1100, line_high), (255, 255, 0), 2)for i, c in enumerate(contours):x, y, w, h cv2.boundingRect(c)# 对车辆的宽高进行判断以验证是否是有效车辆isValid (x min_w) and (h min_h) and (x max_w) and (h max_h)if(not isValid):continue# 有效的车cpoint center(x, y, w, h)cars.append(cpoint)cv2.rectangle(frame, (x, y), (x w, y h), (0, 255, 0), 2)for xc, yc in cars:if(yc line_high - offset and yc line_high offset):carnums 1cars.remove((xc, yc))print(carnums)cv2.imshow(video, frame)# cv2.imshow(erode, close)key cv2.waitKey(40)if(key 27):breakcap.release() cv2.destroyAllWindows()显示信息【车辆统计】 # -*- coding: utf-8 -*- import cv2 import numpy as npmin_w 50 min_h 50 max_w 800 max_h 800# 检测线的高度 line_high 480# 存放有效车辆数组 cars []# 统计车的数量 carnums 0# 线的偏移 offset 10# 求中心点 def center(x, y, w, h):x1 int(w / 2)y1 int(h / 2)cx x x1cy y y1return cx, cycap cv2.VideoCapture(./video.mp4)bgsubmog cv2.createBackgroundSubtractorMOG2() # tst cv2.createBackgroundSubtractorMOG2()# 形态学kernel kernel cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) kernel2 cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))while True:ret, frame cap.read()if(ret True):# print(frame.shape)# (584, 1280, 3)# 灰度图cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 去噪高斯blur cv2.GaussianBlur(frame, (3, 3), 1)# 去背景mask bgsubmog.apply(blur)# t tst.apply(frame)# 腐蚀去掉图中小斑块erode cv2.erode(mask, kernel, iterations1)# 膨胀还原放大dilate cv2.dilate(erode, kernel2, iterations3)# 闭操作去掉物体内部噪声close cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)close cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)contours, hierarchy cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 画一条检测线cv2.line(frame, (100, line_high), (1100, line_high), (255, 255, 0), 2)for i, c in enumerate(contours):x, y, w, h cv2.boundingRect(c)# 对车辆的宽高进行判断以验证是否是有效车辆isValid (x min_w) and (h min_h) and (x max_w) and (h max_h)if(not isValid):continue# 有效的车cpoint center(x, y, w, h)cars.append(cpoint)cv2.rectangle(frame, (x, y), (x w, y h), (0, 255, 0), 2)cv2.circle(frame, (cpoint), 5, (0, 0, 255), -1)for xc, yc in cars:if(yc line_high - offset and yc line_high offset):carnums 1cars.remove((xc, yc))print(carnums)cv2.putText(frame, Cars Count: str(carnums), (500, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)cv2.imshow(video, frame)# cv2.imshow(erode, close)key cv2.waitKey(40)if(key 27):breakcap.release() cv2.destroyAllWindows()通过实践可以发现传统的目标检测缺点很多比方说可能重复计数…所以之后我们需要结合深度学习来提高目标检测的精度 之后我会持续更新如果喜欢我的文章请记得一键三连哦点赞关注收藏你的每一个赞每一份关注每一次收藏都将是我前进路上的无限动力 ↖(▔▽▔)↗感谢支持