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OpenCV(九)-轮廓

发表于 更新于

一、图像轮廓

具有相同==颜色==或==强度==的==连续点==的曲线

image-20241108164945322

  • 图像轮廓的作用
    • 可以用于图形分析
    • 物体的识别与检测
  • 注意点
    • 为了检测的准确性，需要先对图像进行二值化或Canny操作
    • 画轮廓时会修改输入的图像

轮廓查找的API

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(function) def findContours(
    image: MatLike,
    mode: int,
    method: int,
    contours: Sequence[MatLike] | None = ...,
    hierarchy: MatLike | None = ...,
    offset: Point = ...
) -> tuple[Sequence[MatLike], MatLike]

# 两个返回值 contours和hierarchy
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# mode
RETR_EXTERNAL = 0,表示只检测外轮廓
RETR_LIST=1，检测的轮廓不建立等级关系
RETR_CCOMP=2，每层最多两级
RETR_TREE=3,按树形存储轮廓
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# method
CHAIN_APPROX_NONE,保存所有轮廓上的点
CHAIN_APPROX_SIMPLE,只保存角点

image-20241108165051241

image-20241108165106635

image-20241108165115197

image-20241108165132778

image-20241108165144100

二、查找轮廓

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import cv2
import numpy as np

# 读文件
img = 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)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

print(contours)
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(array([[[  0,   0]],

       [[  0, 435]],

       [[345, 435]],

       [[345,   0]]], dtype=int32),)
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# list
(array([[[ 36, 130]],

       [[ 37, 129]],

       [[310, 129]],

       [[311, 130]],

       [[311, 400]],

       [[310, 401]],

       [[ 37, 401]],

       [[ 36, 400]]], dtype=int32), array([[[ 36,  35]],

       [[ 37,  34]],

       [[308,  34]],

       [[309,  35]],

       [[309,  39]],

       [[308,  40]],

       [[ 37,  40]],

       [[ 36,  39]]], dtype=int32), array([[[  0,   0]],

       [[  0, 435]],

       [[345, 435]],

       [[345,   0]]], dtype=int32))
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# ccomp
(array([[[  0,   0]],

       [[  0, 435]],

       [[345, 435]],

       [[345,   0]]], dtype=int32), array([[[ 36, 130]],

       [[ 37, 129]],

       [[310, 129]],

       [[311, 130]],

       [[311, 400]],

       [[310, 401]],

       [[ 37, 401]],

       [[ 36, 400]]], dtype=int32), array([[[ 36,  35]],

       [[ 37,  34]],

       [[308,  34]],

       [[309,  35]],

       [[309,  39]],

       [[308,  40]],

       [[ 37,  40]],

       [[ 36,  39]]], dtype=int32))
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# tree
(array([[[  0,   0]],

       [[  0, 435]],

       [[345, 435]],

       [[345,   0]]], dtype=int32), array([[[ 36, 130]],

       [[ 37, 129]],

       [[310, 129]],

       [[311, 130]],

       [[311, 400]],

       [[310, 401]],

       [[ 37, 401]],

       [[ 36, 400]]], dtype=int32), array([[[ 36,  35]],

       [[ 37,  34]],

       [[308,  34]],

       [[309,  35]],

       [[309,  39]],

       [[308,  40]],

       [[ 37,  40]],

       [[ 36,  39]]], dtype=int32))

三、绘制轮廓

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(function)
def drawContours(
    image: MatLike,
    contours: Sequence[MatLike],
    contourIdx: int,	# -1表示绘制所有轮廓
    color: Scalar,		# 颜色（0，0，255）
    thickness: int = ...,	# 线宽，-1是全部填充
    lineType: int = ...,
    hierarchy: MatLike | None = ...,
    maxLevel: int = ...,
    offset: Point = ...
) -> MatLike: ...
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import cv2
import numpy as np

# 读文件
img = 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)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, -1, (0, 0, 255), 1)

print(contours)

cv2.imshow('img', img)
cv2.waitKey(0)

image-20241113153838976

四、轮廓的面积与周长

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# 面积
(function) def contourArea(
    contour: MatLike,	# 轮廓
    oriented: bool = ...
) -> float

# 周长
(function) def arcLength(
    curve: MatLike,		# 轮廓
    closed: bool		# 是否是闭合的轮廓
) -> float
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import cv2
import numpy as np

# 读文件
img = 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)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, 0, (0, 0, 255), 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("lenght=%d"%(len))

# print(contours)

cv2.imshow('img', img)
cv2.waitKey(0)
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area=150075
lenght=1560

五、多边形逼近与凸包

image-20241113154729831

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(function) def approxPolyDP(
    curve: MatLike,	# 轮廓
    epsilon: float,	# 精度
    closed: bool,	# 是否闭合
    approxCurve: MatLike | None = ...
) -> MatLike

(function)
def convexHull(
    points: MatLike,	# 轮廓
    hull: MatLike | None = ...,
    clockwise: bool = ...,	# 顺时针绘制
    returnPoints: bool = ...
) -> MatLike: ...
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import cv2
import numpy as np

def drawShape(src, points):
    i = 0
    while 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, 0, 255), 3)
        else:
            x,y = points[i][0]
            x1,y1 = points[i+1][0]
            cv2.line(src, (x,y), (x1,y1), (0, 0, 255), 3)
        i = i + 1

# 读文件
img = 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)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, 0, (0, 0, 255), 1)

e = 20
approx = cv2.approxPolyDP(contours[0], e, True)

drawShape(img, approx)


hull = cv2.convexHull(contours[0])
drawShape(img, hull)

cv2.imshow('img', img)
cv2.waitKey(0)

image-20241113155929896

image-20241113160349632

六、外接矩形

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import cv2
import numpy as np

# 读文件
img = 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)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, 0, (0, 0, 255), 1)

# 最小矩形
r = cv2.minAreaRect(contours[1])    # 得到的点是浮点型
box = cv2.boxPoints(r)          # 得到的点是带角度的，将角度去除
box = np.int0(box)              # 转换为int
cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

# 最大矩形
x,y,w,h = cv2.boundingRect(contours[1])
cv2.rectangle(img, (x,y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('img', img)
cv2.waitKey(0)

image-20241113161229355

七、项目总览（车辆统计）

image-20241113161744677

image-20241113161755405

image-20241113161800379

image-20241113161810280

八、视频加载（车辆统计）

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import cv2
import numpy as np

cap = cv2.VideoCapture('video.mp4')

while True:
    ret, frame = cap.read()

    if(ret == True):
        cv2.imshow('video', frame)

    key = cv2.waitKey(1)
    if (key == 27): # 27就是esc
        break

cap.release()
cv2.destroyAllWindows()

九、去背景（车辆统计）

image-20241113164238392

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import cv2
import numpy as np

cap = cv2.VideoCapture('video.mp4')

bgsubmog = cv2.bgsegm.createBackgroundSubtractorMOG()

while True:
    ret, frame = cap.read()

    if(ret == True):

        # 灰度
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 去噪（高斯）
        blur = cv2.GaussianBlur(frame, (3,3), 5)
        # 去背景
        mask = bgsubmog.apply(blur)
        cv2.imshow('video', mask)

    key = cv2.waitKey(1)
    if (key == 27): # 27就是esc
        break

cap.release()
cv2.destroyAllWindows()

image-20241113165933266

十、形态学处理（车辆统计）

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import cv2
import numpy as np

cap = cv2.VideoCapture('video.mp4')

bgsubmog = cv2.bgsegm.createBackgroundSubtractorMOG()

# 形态学kernel
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

while True:
    ret, frame = cap.read()

    if(ret == True):

        # 灰度
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 去噪（高斯）
        blur = cv2.GaussianBlur(frame, (3,3), 5)
        # 去背景
        mask = bgsubmog.apply(blur)

        # 腐蚀，去掉图中小斑块
        erode = cv2.erode(mask, kernel)

        # 膨胀，还原放大
        dilate = cv2.dilate(erode, kernel, iterations=3)

        # 闭操作，去掉物体内部的小块
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)

        cnts, h = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        for (i, c) in enumerate(cnts):
            (x,y,w,h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255), 2)
        cv2.imshow('video', frame)

    key = cv2.waitKey(1)
    if (key == 27): # 27就是esc
        break

cap.release()
cv2.destroyAllWindows()

image-20241113171250130

十一、逻辑处理（车辆统计）

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import cv2
import numpy as np

min_w = 90
min_h = 90

# 检测线的高度
line_high = 600

#线的偏移
offset = 7

#统计车的数量
carno =0

#存放有效车辆的数组
cars = []

def  center(x, y, w, h):
    x1 = int(w/2)
    y1 = int(h/2)
    cx = x + x1
    cy = y + y1

    return cx, cy

cap = cv2.VideoCapture('video.mp4')

bgsubmog = cv2.bgsegm.createBackgroundSubtractorMOG()

# 形态学kernel
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

while True:
    ret, frame = cap.read()

    if(ret == True):

        # 灰度
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 去噪（高斯）
        blur = cv2.GaussianBlur(frame, (3,3), 5)
        # 去背景
        mask = bgsubmog.apply(blur)

        # 腐蚀，去掉图中小斑块
        erode = cv2.erode(mask, kernel)

        # 膨胀，还原放大
        dilate = cv2.dilate(erode, kernel, iterations=3)

        # 闭操作，去掉物体内部的小块
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)

        cnts, h = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        cv2.line(frame, (10, line_high), (1200, line_high), (255, 255, 0), 3)
        for (i, c) in enumerate(cnts):
            (x,y,w,h) = cv2.boundingRect(c)

            # 对车辆的宽高进行判断
            # 以验证是否是有效的车辆
            isValid = (w >= min_w ) and (h >= min_h)
            if (not isValid):
                continue

            # 到这里都是有效的车
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0,255), 2)
            cpoint = center(x, y, w, h)
            cars.append(cpoint)
            cv2.circle(frame, (cpoint), 5, (0, 0, 255), -1)

            for (x,y) in cars:
                if (y > line_high - offset) and (y < line_high + offset):
                    carno += 1
                    cars.remove((x,y))
                    print(carno)

        cv2.imshow('video', frame)

    key = cv2.waitKey(1)
    if (key == 27): # 27就是esc
        break

cap.release()
cv2.destroyAllWindows()

image-20241113172805642

十二、显示信息（车辆统计）

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import cv2
import numpy as np

min_w = 90
min_h = 90

#检测线的高度
line_high = 550

#线的偏移
offset = 7

#统计车的数量
carno =0

#存放有效车辆的数组
cars = []

def center(x, y, w, h):
    x1 = int(w/2)
    y1 = int(h/2)
    cx = x + x1
    cy = y + y1

    return cx, cy

cap = cv2.VideoCapture('video.mp4')

bgsubmog = cv2.bgsegm.createBackgroundSubtractorMOG()

#形态学kernel
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

while True:
    ret, frame = cap.read()
    if(ret == True):     

        #灰度
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        #去噪（高斯）
        blur = cv2.GaussianBlur(frame, (3,3), 5)
        #去背影
        mask = bgsubmog.apply(blur)

        #腐蚀， 去掉图中小斑块
        erode = cv2.erode(mask, kernel) 

        #膨胀， 还原放大
        dilate = cv2.dilate(erode, kernel, iterations = 3)

        #闭操作，去掉物体内部的小块
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)

        cnts, h = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
        #画一条检测线
        cv2.line(frame, (10, line_high), (1200, line_high), (255, 255, 0), 3)

        for (i, c) in enumerate(cnts):
            (x,y,w,h) = cv2.boundingRect(c)

            #对车辆的宽高进行判断
            #以验证是否是有效的车辆
            isValid = ( w >= min_w ) and ( h >= min_h) 
            if( not isValid):
                continue

            #到这里都是有效的车 
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0,0,255), 2)
            cpoint = center(x, y, w, h)
            cars.append(cpoint) 
            cv2.circle(frame, (cpoint), 5, (0,0,255), -1)

            for (x, y) in cars:
                if( (y > line_high - offset) and (y < line_high + offset ) ):
                    carno +=1
                    cars.remove((x , y ))
                    print(carno)
        
        cv2.putText(frame, "Cars Count:" + str(carno), (500, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,0,0), 5)
        cv2.imshow('video', frame)
        #cv2.imshow('erode', close)
    
    key = cv2.waitKey(1)
    if(key == 27):
        break

cap.release()
cv2.destroyAllWindows()

image-20241113173259189

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