1.颜色识别
cv2.imread()和cv2.cvtColor() 的使用
cv2.imread()和cv2.cvtColor() 的使用
1、cv2.imread()接口读图像，读进来直接是BGR 格式数据格式在 0~255
需要特别注意的是图片读出来的格式是BGR，不是我们最常见的RGB格式，颜色肯定有区别。
2、cv2.cvtColor(p1,p2) 是颜色空间转换函数，p1是需要转换的图片，p2是转换成何种格式。
cv2.COLOR_BGR2RGB 将BGR格式转换成RGB格式
cv2.COLOR_BGR2GRAY 将BGR格式转换成灰度图片
cv2.circle()
cv2.circle(img, center, radius, color[, thickness[, lineType[, shift]]])
作用
根据给定的圆心和半径等画圆
参数说明
img：输入的图片data
center：圆心位置
radius：圆的半径
color：圆的颜色
thickness：圆形轮廓的粗细（如果为正）。负厚度表示要绘制实心圆。
lineType： 圆边界的类型。
shift：中心坐标和半径值中的小数位数。
完整代码：
import cv2
import numpy as np
frameWidth = 640
frameHeight = 480
cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10,150)
#添加可以识别的颜色
myColors = [[5,107,0,19,255,255],
[133,56,0,159,156,255],
[57,76,0,100,255,255]]
myColorValues = [[51,153,255],
[255,0,255],
[0,255,0]]
def findColor(img,myColors,myColorValues):
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
count = 0
for color in myColors:
lower = np.array(color[0:3]) #返回集合中，下标0至3的集合
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)
count += 1
#cv2.imshow(str(color[0]),mask)
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)
print(area)
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()
findColor(img, myColors, myColorValues)
cv2.imshow("Result", imgResult)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
2.识别+提取 图片内容
完整代码：
import cv2
import numpy as np
###################################
widthImg=540
heightImg =640
#####################################
cap = cv2.VideoCapture(0)
cap.set(10,150)
def preProcessing(img):
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
imgCanny = cv2.Canny(imgBlur,200,200) #边缘检测
# 给边缘加粗
kernel = np.ones((5,5))
imgDial = cv2.dilate(imgCanny,kernel,iterations=2)
imgThres = cv2.erode(imgDial,kernel,iterations=1)
return imgThres
def getContours(img):
biggest = np.array([])
maxArea = 0
contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area>5000:
#cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
peri = cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,0.02*peri,True) #计算边数
if area >maxArea and len(approx) == 4:
biggest = approx
maxArea = area
cv2.drawContours(imgContour, biggest, -1, (255, 0, 0), 20)
return biggest
def reorder (myPoints):
myPoints = myPoints.reshape((4,2))
myPointsNew = np.zeros((4,1,2),np.int32)
add = myPoints.sum(1)
#print("add", add)
myPointsNew[0] = myPoints[np.argmin(add)]
myPointsNew[3] = myPoints[np.argmax(add)]
diff = np.diff(myPoints,axis=1)
myPointsNew[1]= myPoints[np.argmin(diff)]
myPointsNew[2] = myPoints[np.argmax(diff)]
#print("NewPoints",myPointsNew)
return myPointsNew
def getWarp(img,biggest):
biggest = reorder(biggest)
pts1 = np.float32(biggest) # 获取最大轮廓
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
imgOutput = cv2.warpPerspective(img, matrix, (widthImg, heightImg))
imgCropped = imgOutput[20:imgOutput.shape[0]-20,20:imgOutput.shape[1]-20]
imgCropped = cv2.resize(imgCropped,(widthImg,heightImg))
return imgCropped
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
while True:
#success, img = cap.read()
#使用摄像头
#img = cv2.resize(img,(widthImg,heightImg))
#使用图片
img = cv2.imread("Resources/paper.jpg")
imgContour = img.copy()
imgThres = preProcessing(img)
biggest = getContours(imgThres)
if biggest.size !=0:
imgWarped=getWarp(img,biggest)
# imageArray = ([img,imgThres],
# [imgContour,imgWarped])
imageArray = ([imgContour, imgWarped])
cv2.imshow("ImageWarped", imgWarped)
else:
# imageArray = ([img, imgThres],
# [img, img])
imageArray = ([imgContour, img])
stackedImages = stackImages(0.6,imageArray)
cv2.imshow("WorkFlow", stackedImages)
if cv2.waitKey(1) & 0xFF == ord('q'):
break