python opencv 颜色判断_python+opencv实现颜色检测、轮廓检测、颜色追踪

准备工作

python配置numpy和openCv库

读取图像和视频图像cv2.imread(路径)

cv2.imshow(窗口名称，输出对象)

cv2.waitkey(等待时间)import cv2

img = cv2.imread("./Resources/3-1P316104441.jpg")//当前项目目录下

cv2.imshow("output", img)

cv2.waitKey(0)视频cv2.VideoCapture(路径或数字)

set()

(1) set(3,数字)设置显示区域宽

(2) set(4,数字)设置显示区域高

(3) set(10,数字)设置亮度read()

cv2.imshowimport cv2

cap = cv2.VideoCapture("./Resources/Ninja Track.mp4")

while True:

#success是bool值，用于判断是否读取成功

success, img = cap.read()

cv2.imshow("video", img)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

基础功能图像cv2.cvtColor(对象,cv2.COLOR_BGR2GRAY) 转换为灰度图像

cv2.GaussianBlur(对象,(奇数，奇数)，0)设置模糊，值越大，模糊程度越大

cv2.Canny(对象，数字，数字)设置Canny边缘检测器，数字越大，显示的越少

cv2.dilate(边缘对象，矩阵，iteration=数字) 图像膨胀，即：增加图像边缘厚度

cv2.erode(对象，矩阵而过，iteration=数字)图像腐蚀，即：减少图像边缘厚度

裁剪图像改变图像大小对象.shape用于显示图片大小print(img.shape)#先显示高度，再显示宽度cv2.resize(对象，(宽度，高度))改变图片大小imgResize = cv2.resize(img, (200,300))#先定义宽度，在定义高度裁剪图像

对象[数字：数字，数字：数字]先高度后宽度imgCropped = img[0:100,200:300]#先高度后宽度

绘制图形和文本创建简单图像numpy.zeros((512,512)) 建立矩阵，0表示黑色为图像添加颜色通道numpy.zeros((数字，数字，3),numpy.unint8)添加颜色三个颜色通道，颜色值为0~255为图像上色

对象[数字：数字，数字：数字]=rgb值img[200:300,:]=255,0,0绘制线条

cv2.line(对象，(起点坐标)，(终点坐标)，(rgb颜色值)，厚度)cv2.line(img,(0,0),(300,300),(0,255,0),3)绘制矩形

cv2.rectangle(对象，(起点坐标)，(终点坐标)，(rgb颜色值)，厚度)cv2.rectangle(img,(0,0),(100,100),(0,0,255),3)

cv2.rectangle(img,(0,0),(100,100),(0,0,255),cv2.FILLED)填充图形绘制圆

cv2.circle(对象，(圆心坐标)，半径，(rgb颜色值)，厚度)cv2.circle(img,(100,100),30,(255,255,0),2)显示文字

cv2.putText(对象，“文本内容"，(起始坐标)，字体样式，缩放比例，(rgb颜色值)，厚度)cv2.putText(img,"good",(200,200),cv2.FONT_HERSHEY_COMPLEX,1,(0,150,0),1)

视角转换设置待转换坐标

numpy.float32([[坐标1],[坐标2],[坐标3],[坐标4]])pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])设置转换后坐标numpy,float32([[0,0],[width,0],[0,height],[width,height]])透视图转换cv2.getPrespectiveTransform(待转换坐标，转换后坐标)转换后图像cv2.warpPerspective(对象，透视图转换，(width,height))img = cv2.imread("Resources/puke_zhipai-003.jpg")

width,height=250,250

pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])

pts2 = np.float32([[0,0],[width,0],[0,height],[width,height]])

matrix = cv2.getPerspectiveTransform(pts1,pts2)#透视图转换，将世界坐标系变为屏幕坐标系

imgoutput = cv2.warpPerspective(img,matrix,(width,height))

cv2.imshow("card",imgoutput)

cv2.waitKey(0)

图像拼接无法调整图像大小水平拼接

numpy.hstack((对象，对象))hor = np.hstack((img, img))垂直拼接

numpy.vstack((img,img))ver = np.vstack((img,img))能够调整图像大小编写stackImages函数def stackImages(scale,imgArray):

rows = len(imgArray)

cols = len(imgArray[0])

rowsAvailable = isinstance(imgArray[0], list)

width = imgArray[0][0].shape[1]

height = imgArray[0][0].shape[0]

if rowsAvailable:

for x in range ( 0, rows):

for y in range(0, cols):

if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:

imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)

else:

imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)

if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)

imageBlank = np.zeros((height, width, 3), np.uint8)

hor = [imageBlank]*rows

hor_con = [imageBlank]*rows

for x in range(0, rows):

hor[x] = np.hstack(imgArray[x])

ver = np.vstack(hor)

else:

for x in range(0, rows):

if imgArray[x].shape[:2] == imgArray[0].shape[:2]:

imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)

else:

imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)

if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)

hor= np.hstack(imgArray)

ver = hor

return verstackImages(缩放比例，水平拼接，垂直拼接)stackImages(0.5,([img,img,img]))

stackImages(0.5,([img,img,img],[img,img,img]))

颜色检测转换为HSV空间cv2.cvtColor(对象，cv2.COLOR_BGR2HSV)设置颜色调节器定义一个窗口cv2.namedWindow("窗口名")设置窗口大小cv2.resizeWindow("对应窗口名",宽度，高度)创建滑动控制器def empty():

pass

cv2.namedWindow("TrackBars")

cv2.resizeWindow("TrackBars", 640, 240)

cv2.createTrackbar("Hue Min", "TrackBars", 0, 179, empty)

cv2.createTrackbar("Hue Max", "TrackBars", 179, 179, empty)

cv2.createTrackbar("Sat Min", "TrackBars", 0, 255, empty)

cv2.createTrackbar("Sat Max", "TrackBars", 255, 255, empty)

cv2.createTrackbar("Val Min", "TrackBars", 0, 255, empty)

cv2.createTrackbar("Val Max", "TrackBars", 255, 255, empty)将颜色调节器和HSV图像进行关联获取控制器值cv2.getTrackbarPos("控制器名","相应窗口名")h_min = cv2.getTrackbarPos("Hue Min", "TrackBars")

h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")

s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")

s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")

v_min = cv2.getTrackbarPos("Val Min", "TrackBars")

v_max = cv2.getTrackbarPos("Val Max", "TrackBars")设置过滤图像

(1)最低阀值numpy.array([h_min, s_min, v_min])lower = np.array([h_min, s_min, v_min])

(2)最高阀值numpy.array([h_max, s_max, v_max])upper = np.array([h_max, s_max, v_max])

(3)阀值限制图像cv2.inRange(HSV图像对象，最低阀值，最高阀值)mask = cv2.inRange(imgHSV, lower, upper)获取检测颜色范围

将所有颜色调节器的最低值设置好

进行按位操作合成新图像cv2.bitwise_and(原图像对象，原图像对象，mask=阀值限制图像)imgResult = cv2.bitwise_and(img, img, mask=mask)图像拼接显示import cv2

import numpy as np

img = cv2.imread("Resources/3.png")

#图像拼接函数

def stackImages(scale,imgArray):

rows = len(imgArray)

cols = len(imgArray[0])

rowsAvailable = isinstance(imgArray[0], list)

width = imgArray[0][0].shape[1]

height = imgArray[0][0].shape[0]

if rowsAvailable:

for x in range ( 0, rows):

for y in range(0, cols):

if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:

imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)

else:

imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)

if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)

imageBlank = np.zeros((height, width, 3), np.uint8)

hor = [imageBlank]*rows

hor_con = [imageBlank]*rows

for x in range(0, rows):

hor[x] = np.hstack(imgArray[x])

ver = np.vstack(hor)

else:

for x in range(0, rows):

if imgArray[x].shape[:2] == imgArray[0].shape[:2]:

imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)

else:

imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)

if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)

hor= np.hstack(imgArray)

ver = hor

return ver

#定义检测颜色的范围

#设置颜色调节器

def empty():

pass

cv2.namedWindow("TrackBars")

cv2.resizeWindow("TrackBars", 640, 240)

cv2.createTrackbar("Hue Min", "TrackBars", 0, 179, empty)

cv2.createTrackbar("Hue Max", "TrackBars", 19, 179, empty)

cv2.createTrackbar("Sat Min", "TrackBars", 110, 255, empty)

cv2.createTrackbar("Sat Max", "TrackBars", 240, 255, empty)

cv2.createTrackbar("Val Min", "TrackBars", 153, 255, empty)

cv2.createTrackbar("Val Max", "TrackBars", 255, 255, empty)

#关联颜色调节器和HSV图片

while True:

#转换为HSV空间

imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

h_min = cv2.getTrackbarPos("Hue Min", "TrackBars")

h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")

s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")

s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")

v_min = cv2.getTrackbarPos("Val Min", "TrackBars")

v_max = cv2.getTrackbarPos("Val Max", "TrackBars")

print(h_min, h_max, s_min, s_max, v_min, v_max)

lower = np.array([h_min, s_min, v_min])

upper = np.array([h_max, s_max, v_max])

mask = cv2.inRange(imgHSV, lower, upper)

imgResult = cv2.bitwise_and(img, img, mask=mask)

# cv2.imshow("windows", img)

# cv2.imshow("imgHSV", imgHSV)

# cv2.imshow("imgMask", mask)

# cv2.imshow("imgResult", imgResult)

imgStack = stackImages(0.6, ([img, imgHSV], [mask, imgResult]))

cv2.imshow("Stacked", imgStack)

cv2.waitKey(1)

轮廓检测转为灰度图像cv2.cvtColor(对象，cv2.COLOR_BGR2GRAY)对灰度图像进行模糊处理cv2.GaussianBlur(灰度图像对象,(7,7),1)边缘检测cv2.Canny(模糊图像对象，50,50)定义空白图像numpy.zeros_link(对象)定义轮廓处理函数检索轮廓cv2.findContours(对象，cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)遍历轮廓for cnt in contours定位区域cv2.contourArea(cnt)检索最小区域if area > 500绘制轮廓区域cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)计算曲线长度cv2.arcLength(cnt, True)计算拐角点cv2.approxPolyDP(cnt,0.02*peri,True)创建对象点objCor=len(approx)获取边界框边界cv2.boundingRect(approx)将对象进行分类if objCor == 3 : objectType = "Tri"

else: objectType="None"绘制矩形边界框cv2.rectangle(imgContour, (x, y), (x+w, y+h), (0, 255, 0), 3)贴上分类标签cv2.putText(imgContour, objectType, (x+(w//2)-10, y+(h//2)-10), cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 0, 0), 2)#轮廓处理函数

def getContours(img):

#检索轮廓

contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for cnt in contours:

#定位区域

area = cv2.contourArea(cnt)

print(area)

#检索最小区域

if area > 500:

# 绘制轮廓区域

cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)

#计算曲线长度

peri = cv2.arcLength(cnt, True)

#print(peri)

#计算拐角点

approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)

print(len(approx))

#创建对象角

objCor = len(approx)

#获取边界框边界

x, y, w, h = cv2.boundingRect(approx)

# 将对象进行分类

if objCor == 3 : objectType = "Tri"

elif objCor == 4:

aspRatio = w/float(h)

if aspRatio > 0.95 and aspRatio< 1.05: objectType = "Square"

else: objectType = "Rectangle"

elif objCor > 4: objectType = "Circles"

else: objectType="None"

#绘制矩形边界框

cv2.rectangle(imgContour, (x, y), (x+w, y+h), (0, 255, 0), 3)

cv2.putText(imgContour, objectType, (x+(w//2)-10, y+(h//2)-10), cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 0, 0), 2)import cv2

import numpy as np

#图像拼接函数

def stackImages(scale,imgArray):

rows = len(imgArray)

cols = len(imgArray[0])

rowsAvailable = isinstance(imgArray[0], list)

width = imgArray[0][0].shape[1]

height = imgArray[0][0].shape[0]

if rowsAvailable:

for x in range ( 0, rows):

for y in range(0, cols):

if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:

imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)

else:

imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)

if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)

imageBlank = np.zeros((height, width, 3), np.uint8)

hor = [imageBlank]*rows

hor_con = [imageBlank]*rows

for x in range(0, rows):

hor[x] = np.hstack(imgArray[x])

ver = np.vstack(hor)

else:

for x in range(0, rows):

if imgArray[x].shape[:2] == imgArray[0].shape[:2]:

imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)

else:

imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)

if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)

hor= np.hstack(imgArray)

ver = hor

return ver

#轮廓处理函数

def getContours(img):

#检索轮廓

contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for cnt in contours:

#定位区域

area = cv2.contourArea(cnt)

print(area)

#检索最小区域

if area > 500:

# 绘制轮廓区域

cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)

#计算曲线长度

peri = cv2.arcLength(cnt, True)

#print(peri)

#计算拐角点

approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)

print(len(approx))

#创建对象角

objCor = len(approx)

#获取边界框边界

x, y, w, h = cv2.boundingRect(approx)

# 将对象进行分类

if objCor == 3 : objectType = "Tri"

elif objCor == 4:

aspRatio = w/float(h)

if aspRatio > 0.95 and aspRatio< 1.05: objectType = "Square"

else: objectType = "Rectangle"

elif objCor > 4: objectType = "Circles"

else: objectType="None"

#绘制矩形边界框

cv2.rectangle(imgContour, (x, y), (x+w, y+h), (0, 255, 0), 3)

cv2.putText(imgContour, objectType, (x+(w//2)-10, y+(h//2)-10), cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 0, 0), 2)

img = cv2.imread("Resources/3s.png")

imgContour = img.copy()

#转换为灰度图像

imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#模糊图像

imgBlur = cv2.GaussianBlur(imgGray, (7, 7), 1)

#边缘检测

imgCanny = cv2.Canny(imgBlur, 50, 50)

getContours(imgCanny)

#定义空白图像

imgBlank = np.zeros_like(img)

imgStack = stackImages(0.6, ([img, imgGray, imgBlur], [imgCanny, imgContour, imgBlank]))

cv2.imshow("imgStack", imgStack)

cv2.waitKey(0)

颜色追踪引入摄像头监控import cv2

frameWidth = 640

frameHeight = 480

cap = cv2.VideoCapture(0)

cap.set(3, frameWidth)

cap.set(4, frameHeight)

cap.set(10, 150)

while True:

success, img = cap.read()

cv2.imshow("Result", img)

if cv2.waitKey(1) & 0xFF == ord('q'):

break获取检测颜色值import cv2

import numpy as np

frameWidth = 640

frameHeight = 480

cap = cv2.VideoCapture(0)

cap.set(3, frameWidth)

cap.set(4, frameHeight)

cap.set(10,150)

def empty(a):

pass

cv2.namedWindow("HSV")

cv2.resizeWindow("HSV",640,240)

cv2.createTrackbar("HUE Min","HSV",0,179,empty)

cv2.createTrackbar("SAT Min","HSV",0,255,empty)

cv2.createTrackbar("VALUE Min","HSV",0,255,empty)

cv2.createTrackbar("HUE Max","HSV",179,179,empty)

cv2.createTrackbar("SAT Max","HSV",255,255,empty)

cv2.createTrackbar("VALUE Max","HSV",255,255,empty)

while True:

_, img = cap.read()

imgHsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

h_min = cv2.getTrackbarPos("HUE Min","HSV")

h_max = cv2.getTrackbarPos("HUE Max", "HSV")

s_min = cv2.getTrackbarPos("SAT Min", "HSV")

s_max = cv2.getTrackbarPos("SAT Max", "HSV")

v_min = cv2.getTrackbarPos("VALUE Min", "HSV")

v_max = cv2.getTrackbarPos("VALUE Max", "HSV")

print(h_min)

lower = np.array([h_min,s_min,v_min])

upper = np.array([h_max,s_max,v_max])

mask = cv2.inRange(imgHsv,lower,upper)

result = cv2.bitwise_and(img,img, mask = mask)

mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)

hStack = np.hstack([img,mask,result])

cv2.imshow('Horizontal Stacking', hStack)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

cv2.destroyAllWindows()颜色检测函数myColors = [[0, 118, 140, 179, 188, 255],

[133, 56, 0, 159, 156, 255],

[57, 56, 0, 100, 255, 255]]

def findColor(img, myColors):

imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

for color in myColors:

lower = np.array(color[0:3])

upper = np.array(color[3:6])

mask = cv2.inRange(imgHSV, lower, upper)

cv2.imshow("img", mask)将颜色检测和摄像头监控结合success, img = cap.read()

findColor(img, myColors)获取检测对象轮廓def getContours(img):

contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

x, y, w, h = 0, 0, 0, 0

for cnt in contours:

area = cv2.contourArea(cnt)

if area>500:

cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)

peri = cv2.arcLength(cnt,True)

approx = cv2.approxPolyDP(cnt,0.02*peri,True)

x, y, w, h = cv2.boundingRect(approx)

return x+w//2, y绘制物体运动轨迹def drawOnCanvas(myPoints, myColorValues):

for point in myPoints:

cv2.circle(imgResult, (point[0], point[1]), 10, myColorValues[point[2]], cv2.FILLED)import cv2

import numpy as np

frameWidth = 640

frameHeight = 480

cap = cv2.VideoCapture(0)

cap.set(3, frameWidth)

cap.set(4, frameHeight)

cap.set(10, 150)

myColors = [[0, 118, 140, 179, 188, 255],

[133, 56, 0, 159, 156, 255],

[57, 56, 0, 100, 255, 255]]

myColorValues = [[51, 51, 255], ##BGR

[255, 0, 255],

[0, 255, 0]]

myPoints = [] ##[x, y, colorId]

def findColor(img, myColors, myColorValues):

imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

count = 0

newPoints = []

for color in myColors:

lower = np.array(color[0:3])

upper = np.array(color[3:6])

mask = cv2.inRange(imgHSV, lower, upper)

x, y = getContours(mask)

cv2.circle(imgResult, (x, y), 10, myColorValues[count], cv2.FILLED)

if x!= 0 and y!= 0:

newPoints.append([x, y, count])

count += 1

# cv2.imshow(str(color[0]), mask)

return newPoints

def drawOnCanvas(myPoints, myColorValues):

for point in myPoints:

cv2.circle(imgResult, (point[0], point[1]), 10, myColorValues[point[2]], cv2.FILLED)

def getContours(img):

contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

x, y, w, h = 0, 0, 0, 0

for cnt in contours:

area = cv2.contourArea(cnt)

if area>500:

cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)

peri = cv2.arcLength(cnt,True)

approx = cv2.approxPolyDP(cnt,0.02*peri,True)

x, y, w, h = cv2.boundingRect(approx)

return x+w//2, y

while True:

success, img = cap.read()

imgResult = img.copy()

newPoints = findColor(img, myColors, myColorValues)

if len(newPoints) != 0:

for newP in newPoints:

myPoints.append(newP)

for i in myPoints:

print("myPoints"+str(i))

if len(myPoints) !=0:

drawOnCanvas(myPoints, myColorValues)

cv2.imshow("Result", imgResult)

if cv2.waitKey(1) & 0xFF == ord('q'):

break