def getContours(im, approx_value=1): #Return contours approximated
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(cv.CloneImage(im), storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
contourLow = cv.ApproxPoly(contours, storage, cv.CV_POLY_APPROX_DP,
approx_value, approx_value)
return contourLow
def find_leds(thresh_img):
"""
Given a binary image showing the brightest pixels in an image,
returns a result image, displaying found leds in from PIL import Image
a rectangle
"""
contours = cv2.FindContours(thresh_img,
cv2.CreateMemStorage(),
mode=cv2.CV_RETR_EXTERNAL,
method=cv2.CV_CHAIN_APPROX_NONE,
offset=(0, 0))
regions = []
while contours:
pts = [pt for pt in contours]
x, y = zip(*pts)
min_x, min_y = min(x), min(y)
width, height = max(x) - min_x + 1, max(y) - min_y + 1
regions.append((min_x, min_y, width, height))
contours = contours.h_next()
out_img = cv2.CreateImage(cv2.GetSize(grey_img), 8, 3)
for x, y, width, height in regions:
pt1 = x, y
pt2 = x + width, y + height
color = (0, 0, 255, 0)
cv2.Rectangle(out_img, pt1, pt2, color, 2)
return out_img, regions
def somethingHasMoved(self):
# Find contours
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(self.gray_frame, storage,
cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
self.currentcontours = contours # Save contours
while contours: # For all contours compute the area
self.currentsurface += cv.ContourArea(contours)
contours = contours.h_next()
avg = (
self.currentsurface * 100
) / self.surface # Calculate the average of contour area on the total size
self.currentsurface = 0 # Put back the current surface to 0
if avg > self.threshold:
return True
else:
return False
def getPupil(frame): pupilImg = cv.CreateImage(cv.GetSize(frame), 8, 1) cv.InRangeS(frame, (30,30,30), (80,80,80), pupilImg) contours = cv.FindContours(pupilImg, cv.CreateMemStorage(0), mode = cv.CV_RETR_EXTERNAL) del pupilImg pupilImg = cv.CloneImage(frame) while contours: moments = cv.Moments(contours) area = cv.GetCentralMoment(moments,0,0) if (area > 50): pupilArea = area x = cv.GetSpatialMoment(moments,1,0)/area y = cv.GetSpatialMoment(moments,0,1)/area pupil = contours global centroid centroid = (int(x),int(y)) cv.DrawContours(pupilImg, pupil, (0,0,0), (0,0,0), 2, cv.CV_FILLED) break contours = contours.h_next() return (pupilImg)
orig = cv.imread('lena.png')
#im = cv.CreateImage(cv.GetSize(orig), 8, 1)
im = cv.cvtColor(orig,cv.COLOR_BGR2GRAY)
#cv.CvtColor(orig, im, cv.CV_BGR2GRAY)
#Keep the original in colour to draw contours in the end
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY)
cv.ShowImage("Threshold 1", im)
element = cv.CreateStructuringElementEx(5*2+1, 5*2+1, 5, 5, cv.CV_SHAPE_RECT)
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_OPEN) #Open and close to make appear contours
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_CLOSE)
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage("After MorphologyEx", im)
# --------------------------------
vals = cv.CloneImage(im) #Make a clone because FindContours can modify the image
contours=cv.FindContours(vals, cv.CreateMemStorage(0), cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, (0,0))
_red = (0, 0, 255); #Red for external contours
_green = (0, 255, 0);# Gren internal contours
levels=2 #1 contours drawn, 2 internal contours as well, 3 ...
cv.DrawContours (orig, contours, _red, _green, levels, 2, cv.CV_FILLED) #Draw contours on the colour image
cv.ShowImage("Image", orig)
cv.WaitKey(0)
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
width = frame.width
height = frame.height
surface = width * height #Surface area of the image
cursurface = 0 #Hold the current surface that have changed
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
difference = None
while True:
color_image = cv.QueryFrame(self.capture)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3,
0) #Remove false positives
if not difference: #For the first time put values in difference, temp and moving_average
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
else:
cv.RunningAvg(color_image, moving_average, 0.020,
None) #Compute the average
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1.0, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
#Convert the image so that it can be thresholded
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
cv.Dilate(grey_image, grey_image, None, 18) #to get object blobs
cv.Erode(grey_image, grey_image, None, 10)
# Find contours
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(grey_image, storage,
cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
backcontours = contours #Save contours
while contours: #For all contours compute the area
cursurface += cv.ContourArea(contours)
contours = contours.h_next()
avg = (
cursurface * 100
) / surface #Calculate the average of contour area on the total size
if avg > self.ceil:
print("Something is moving !")
#print avg,"%"
cursurface = 0 #Put back the current surface to 0
#Draw the contours on the image
_red = (0, 0, 255)
#Red for external contours
_green = (0, 255, 0)
# Gren internal contours
levels = 1 #1 contours drawn, 2 internal contours as well, 3 ...
cv.DrawContours(color_image, backcontours, _red, _green, levels, 2,
cv.CV_FILLED)
cv.ShowImage("Target", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
def showNaoImage(IP, PORT, camID): # 参数分别为IP、PORT、摄像头ID（区分上下摄像头）
#链接nao的摄像头
camProxy = ALProxy("ALVideoDevice", IP, PORT)
resolution = 2 # VGA``
colorSpace = 11 # RGB
videoClient = camProxy.subscribe("python_client", resolution, colorSpace,
5) # 设置分辨率、帧速、颜色空间
t0 = time.time()
camProxy.setParam(18, camID) # 设置摄像头
naoImage = camProxy.getImageRemote(videoClient) # 将获取的图像赋给naoImage
t1 = time.time()
camProxy.unsubscribe(videoClient)
imageWidth = naoImage[0]
imageHeight = naoImage[1]
imagechannls = naoImage[2] # naoImage[6]为imagedata
frameArray = numpy.frombuffer(naoImage[6], dtype=numpy.uint8).reshape(
imageHeight, imageWidth, imagechannls)
cimg = cv2.cvtColor(frameArray, cv2.COLOR_BGR2HSV)
gray = cv2.cvtColor(frameArray, cv2.COLOR_BGR2GRAY) # 转换为BGR图像
blurred = cv2.GaussianBlur(gray, (5, 5), 0) # 霍夫降噪 平滑处理
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1] # 取得二值图
# im_cv = numpy.zeros((imageHeight, imageWidth, 3), numpy.uint8)#初始化图像im_cv
#
# im_cv.data = array #将从摄像头获取的图像copy到im_cv，转为mat
#
# # 转化颜色空间由BGR到RGB
# b, g, r = cv2.split(im_cv)
# img1 = cv2.merge([r, g, b])
# # 转mat到cvmat
# img3 = cv2.fromarray(img1)
# cv2.SaveImage("test22.bmp",img3)
# #转换颜色空间到HSV
# imgHSV = cv2.CreateImage(cv2.GetSize(img3), 8, 3)
# cv2.CvtColor(img3, imgHSV, cv2.CV_RGB2HSV)
#
# cimg,cimg_c=hsvProceed(imgHSV,camID) # 调用hsvProceed处理图像，返回二值图
#圈取最小矩形框
#初始化
storage = cv2.CreateMemStorage(0)
cnts = cv2.FindContours(thresh, storage, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
currtnt = cnts
Area = 0
left_right = 0
up_down = 0
#为不同摄像头设置不同筛选条件
if camID == 0:
areamax = 2500
areamin = 40
valuemin = 25
value_w = 641
valuemax = 481
else:
areamax = 5000
areamin = 400
valuemin = 0
value_w = 500
valuemax = 400
while cnts:
rect = cv2.BoundingRect(cnts, 0) #获得单连通矩形框
area = rect[2] * rect[3] #获得矩形框面积
#获得矩形框中心点坐标
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
#调用choose0文件下的radio函数，筛选圆形部分
# radio_c = choose0.radio(cimg_c,rect)
radio = float(rect[2]) / rect[3] #计算矩形框的长宽比
#以下if语句均为筛选条件
if rect[1] >= valuemin:
if rect[1] <= valuemax:
if rect[0] <= value_w:
if area > areamin:
if area < areamax:
if radio > 0.6:
if radio < 1.6:
# if radio_c == 1:
cv2.DrawContours(frameArray, cnts,
(255, 255, 0),
(255, 255, 0), 0,
1) #画出单连通轮廓
cv2.Rectangle(
frameArray, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]),
(0, 0, 255), 1) #画出矩形框
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
#计算通过条件的矩形框的面积以及在图像中的位置
Area = rect[2] * rect[3]
left_right = rect_center_x - cimg.width / 2
up_down = rect_center_y - cimg.height / 2
cnts = cnts.h_next()
return Area, left_right, up_down #返回球的面积以及在图像中的位置
def process_image(self, slider_pos):
global cimg, source_image1, ellipse_size, maxf, maxs, eoc, lastcx,lastcy,lastr
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
cimg = cv.CloneImage(self.source_image)
cv.Zero(cimg)
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image), cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(self.source_image, image02, slider_pos, 255, cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02,
stor,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
(0, 0))
maxf = 0
maxs = 0
size1 = 0
for c in contour_iterator(cont):
if len(c) > ellipse_size:
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray,0,1,8,(0,0))
if iter == 0:
strng = segF + '/' + 'contour1.png'
cv.SaveImage(strng,image04)
color = (255,255,255)
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
if iter == 1:
if size[0] > size[1]:
size2 = size[0]
else:
size2 = size[1]
if size2 > size1:
size1 = size2
size3 = size
# Fits ellipse to current contour.
if eoc == 0 and iter == 2:
rand_val = abs((lastr - ((size[0]+size[1])/2)))
if rand_val > 20 and float(max(size[0],size[1]))/float(min(size[0],size[1])) < 1.5:
lastcx = center[0]
lastcy = center[1]
lastr = (size[0]+size[1])/2
if rand_val > 20 and float(max(size[0],size[1]))/float(min(size[0],size[1])) < 1.4:
cv.Ellipse(cimg, center, size,
angle, 0, 360,
color,2, cv.CV_AA, 0)
cv.Ellipse(source_image1, center, size,
angle, 0, 360,
color,2, cv.CV_AA, 0)
elif eoc == 1 and iter == 2:
(int,cntr,rad) = cv.MinEnclosingCircle(PointArray2D32f)
cntr = (cv.Round(cntr[0]), cv.Round(cntr[1]))
rad = (cv.Round(rad))
if maxf == 0 and maxs == 0:
cv.Circle(cimg, cntr, rad, color, 1, cv.CV_AA, shift=0)
cv.Circle(source_image1, cntr, rad, color, 2, cv.CV_AA, shift=0)
maxf = rad
elif (maxf > 0 and maxs == 0) and abs(rad - maxf) > 30:
cv.Circle(cimg, cntr, rad, color, 2, cv.CV_AA, shift=0)
cv.Circle(source_image1, cntr, rad, color, 2, cv.CV_AA, shift=0)
maxs = len(c)
if iter == 1:
temp3 = 2*abs(size3[1] - size3[0])
if (temp3 > 40):
eoc = 1
def landmarkdetect(IP, PORT, camID):
camProxy = ALProxy("ALVideoDevice", IP, PORT)
resolution = 2 # VGA``
colorSpace = 11 # RGB
videoClient = camProxy.subscribe("python_client", resolution, colorSpace,
5)
camProxy.setParam(18, camID)
naoImage = camProxy.getImageRemote(videoClient)
camProxy.unsubscribe(videoClient)
imageWidth = naoImage[0]
imageHeight = naoImage[1]
array = naoImage[6]
im_cv = numpy.zeros((imageHeight, imageWidth, 3), numpy.uint8)
im_cv.data = array
#im_cv = cv2.imread("img3.jpg",1)
b, g, r = cv2.split(im_cv)
img1 = cv2.merge([r, g, b])
img3 = cv2.fromarray(img1)
#img3=cv.LoadImage("img3.jpg",1)
cv.SaveImage("save1.jpg", img3)
imgHSV = cv.CreateImage(cv.GetSize(img3), 8, 3)
cv.CvtColor(img3, imgHSV, cv.CV_RGB2HSV)
cimg, cimg_c = hsvProceed(imgHSV)
# cv.ShowImage("imgHSV", imgHSV)
# cv.ShowImage("cimg", cimg)
# cv.WaitKey(5)
#
# img3 = cv.LoadImage("save1.jpg",1)
# imgHSV = cv.CreateImage(cv.GetSize(img3), 8, 3)
#
# cv.CvtColor(img3, imgHSV, cv.CV_RGB2HSV)
# cimg, cimg_c=hsvProceed(imgHSV)
# cv.ShowImage("s",cimg_c)
storage = cv2.CreateMemStorage(0)
cnts = cv.FindContours(cimg, storage, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
currtnt = cnts
x = 0.0
Area = 0
left_right = 0
up_down = 0
if camID == 0:
areamax = 6000
areamin = 350
value = img3.height / 7
else:
areamax = 5000
areamin = 400
value = 0
while cnts:
rect = cv2.BoundingRect(cnts, 0)
area = rect[2] * rect[3]
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
# if camID == 1:
# radio_c = choose0.radio(cimg_c,rect)
# if camID == 0:
# radio_c = choose0.choose02(cimg_c,rect)
radio = float(rect[2]) / rect[3]
if rect[1] > 10:
if area > areamin:
if area < areamax:
if radio > 0.1:
if radio < 1.0:
#if radio_c == 1:
# cv2.cv.DrawContours(img3, cnts, (255, 255, 0), (255, 255, 0), 0, 1)
# cv2.cv.Rectangle(img3,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),(0,0,255),1)
#choose0.radio(cimg_c,rect)
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
Area = rect[2] * rect[3]
left_right = rect_center_x - cimg.width / 2
up_down = rect_center_y - cimg.height / 2
x, y = getloacation(rect[2], left_right)
cnts = cnts.h_next()
# cv2.cv.ShowImage("fsfs", img3)
# cv.WaitKey(1)
return Area, left_right, x
def main():
original = cv2.namedWindow("Original", cv2.WINDOW_KEEPRATIO)
hsv = cv2.namedWindow("HSV", cv2.WINDOW_KEEPRATIO)
mask = cv2.namedWindow("Mask", cv2.WINDOW_KEEPRATIO)
res1 = cv2.namedWindow("Avg1", cv2.WINDOW_KEEPRATIO)
res2 = cv2.namedWindow("Avg2", cv2.WINDOW_KEEPRATIO)
#res = cv2.namedWindow("Res", cv2.WINDOW_KEEPRATIO)
vc = cv2.VideoCapture(0)
cv2.moveWindow("Original", 0, 0)
cv2.moveWindow("HSV", 300, 0)
cv2.moveWindow("Mask", 700, 0)
cv2.moveWindow("Avg1", 300, 300)
cv2.moveWindow("Avg2", 700, 300)
# create trackbars for color change
cv2.createTrackbar('RL', 'HSV', 10, 255, nothing)
cv2.createTrackbar('RU', 'HSV', 10, 255, nothing)
cv2.createTrackbar('GL', 'HSV', 10, 255, nothing)
cv2.createTrackbar('GU', 'HSV', 10, 255, nothing)
cv2.createTrackbar('BL', 'HSV', 10, 255, nothing)
cv2.createTrackbar('BU', 'HSV', 10, 255, nothing)
# create switch for ON/OFF functionality
#switch = '0 : OFF \n1 : ON'
#cv2.createTrackbar(switch, 'Original',0,1,nothing)
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
else:
rval = False
avg1 = np.float32(frame)
avg2 = np.float32(frame)
while rval:
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
h = hsv[0]
s = hsv[1]
v = hsv[2]
print "H:", h, " S: ", s, "V: ", v
cv2.accumulateWeighted(frame, avg1, 0.1)
cv2.accumulateWeighted(frame, avg2, 0.01)
res1 = cv2.convertScaleAbs(avg1)
res2 = cv2.convertScaleAbs(avg2)
cv2.FindContours("Original", )
#set mouse callback to print out color values
cv2.setMouseCallback("Original", onMouse)
# get current positions of four trackbars
rl = cv2.getTrackbarPos('RL', 'HSV')
gl = cv2.getTrackbarPos('GL', 'HSV')
bl = cv2.getTrackbarPos('BL', 'HSV')
ru = cv2.getTrackbarPos('RU', 'HSV')
gu = cv2.getTrackbarPos('GU', 'HSV')
bu = cv2.getTrackbarPos('BU', 'HSV')
#s = cv2.getTrackbarPos(switch,'HSV')
# define range of blue color in HSV
lower_red = np.array([rl, gl, bl])
upper_red = np.array([ru, gu, bu])
lower_blue = np.array([rl, gl, bl])
upper_blue = np.array([ru, gu, bu])
lower_green = np.array([rl, gl, bl])
upper_green = np.array([ru, gu, bu])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_green, upper_green)
# Bitwise-AND mask and original image
#res = cv2.bitwise_and(frame, frame, mask=mask)
cv2.imshow("Original", frame)
cv2.imshow("HSV", hsv)
cv2.imshow("Mask", mask)
cv2.imshow("Avg1", res1)
cv2.imshow('Avg2', res2)
#cv2.imshow("Res", res)
rval, frame = vc.read()
key = cv2.waitKey(20)
if key == 27: # exit on ESC
break
cv2.destroyAllWindows()