def hsvProceed(img, camID):
single = cv.CreateImage(cv.GetSize(img), 8, 1)
single_c = cv.CreateImage(cv.GetSize(img), 8, 1)
if camID == 0:
v_min = 120
if camID == 1:
v_min = 100
width = 0
height = 0
while (1):
while (1):
if (img[height, width][0] >
95) and (img[height, width][0] <
140) and (img[height, width][1] > 40) and (
img[height, width][1] <
220) and (img[height, width][2] > v_min):
single[height, width] = 255.0
single_c[height, width] = 255.0
else:
single[height, width] = 0.0
single_c[height, width] = 0.0
height = height + 1
if (height == 480):
height = 0
break
width = width + 1
if (width == 640):
break
cv.Erode(single, single)
cv.Dilate(single, single)
# cv.Erode(single_c, single_c)
# cv.Dilate(single_c, single_c)
# cv.ShowImage("rgb",single)
# cv.WaitKey()
return single, single_c
def hsvProceed(img):
single = cv.CreateImage(cv.GetSize(img), 8, 1)
single_c = cv.CreateImage(cv.GetSize(img), 8, 1)
width = 0
height = 0
while (1):
while (1):
if (img[height, width][0] >
70) and (img[height, width][0] <
95) and (img[height, width][1] > 53) and (
img[height, width][1] <
230): #and(img[height,width][2]<200):
single[height, width] = 255
single_c[height, width] = 255
else:
single[height, width] = 0
single_c[height, width] = 0
height = height + 1
if (height == 480):
height = 0
break
width = width + 1
if (width == 640):
break
cv.Erode(single, single)
cv.Dilate(single, single)
cv.Erode(single_c, single_c)
cv.Dilate(single_c, single_c)
# cv.ShowImage("rgb",single)
# cv.WaitKey()
return single, single_c
def getIris(frame): iris = [] copyImg = cv.CloneImage(frame) resImg = cv.CloneImage(frame) grayImg = cv.CreateImage(cv.GetSize(frame), 8, 1) mask = cv.CreateImage(cv.GetSize(frame), 8, 1) storage = cv.CreateMat(frame.width, 1, cv.CV_32FC3) cv.CvtColor(frame,grayImg,cv.CV_BGR2GRAY) cv.Canny(grayImg, grayImg, 5, 70, 3) cv.Smooth(grayImg,grayImg,cv.CV_GAUSSIAN, 7, 7) circles = getCircles(grayImg) iris.append(resImg) for circle in circles: rad = int(circle[0][2]) global radius radius = rad cv.Circle(mask, centroid, rad, cv.CV_RGB(255,255,255), cv.CV_FILLED) cv.Not(mask,mask) cv.Sub(frame,copyImg,resImg,mask) x = int(centroid[0] - rad) y = int(centroid[1] - rad) w = int(rad * 2) h = w cv.SetImageROI(resImg, (x,y,w,h)) cropImg = cv.CreateImage((w,h), 8, 3) cv.Copy(resImg,cropImg) cv.ResetImageROI(resImg) return(cropImg) return (resImg)
def __init__(self, threshold=1, doRecord=True, showWindows=True):
self.writer = None
self.font = None
self.doRecord = doRecord # Either or not record the moving object
self.show = showWindows # Either or not show the 2 windows
self.frame = None
self.capture = cv.CaptureFromCAM(0)
self.frame = cv.QueryFrame(
self.capture) # Take a frame to init recorder
if doRecord:
self.initRecorder()
self.gray_frame = cv.CreateImage(cv.GetSize(self.frame),
cv.IPL_DEPTH_8U, 1)
self.average_frame = cv.CreateImage(cv.GetSize(self.frame),
cv.IPL_DEPTH_32F, 3)
self.absdiff_frame = None
self.previous_frame = None
self.surface = self.frame.width * self.frame.height
self.currentsurface = 0
self.currentcontours = None
self.threshold = threshold
self.isRecording = False
self.trigger_time = 0 # Hold timestamp of the last detection
if showWindows:
cv.NamedWindow("Image")
cv.CreateTrackbar("Detection treshold: ", "Image", self.threshold,
100, self.onThresholdChange)
def getthresholdedimg(im):
imghsv = cv.CreateImage(cv.GetSize(im), 8, 3)
# Convert image from RGB to HSV
cv.CvtColor(im, imghsv, cv.CV_BGR2HSV)
imgthreshold = cv.CreateImage(cv.GetSize(im), 8, 1)
cv.InRangeS(imghsv, cv.Scalar(23, 100, 100), cv.Scalar(25, 255, 255),
imgthreshold) # catch the orange yellow blob
return imgthreshold
def hsvProceed(img, camID): #img为一三通道的HSV图像，camID
#初始化
v_min = 0
h_max = 0
h_min = 0
s_max = 0
s_min = 0
width = 0
height = 0
#创建两个单通道图像
single = cv2.CreateImage(cv2.GetSize(img), 8, 1)
single_c = cv2.CreateImage(cv2.GetSize(img), 8, 1)
#为不同的摄像头分别设置h,s,v的阈值区间
if camID == 0:
v_min = 120
h_max = 138
h_min = 95
s_max = 256
s_min = 30
if camID == 1:
v_min = 10
h_max = 140
h_min = 95
s_max = 256
s_min = 30
# 提取红色部分到single，组建单通道图像
while (1):
while (1):
if (img[height, width][0] >=
h_min) and (img[height, width][0] < h_max) and (
img[height, width][1] >=
s_min) and (img[height, width][1] < s_max) and (
img[height, width][2] >= v_min): #筛选红色部分
single[height, width] = 255.0
single_c[height, width] = 255.0
else:
single[height, width] = 0.0
single_c[height, width] = 0.0
height = height + 1
if (height == 480):
height = 0
break
width = width + 1
if (width == 640):
break
#形态学处理
cv2.Erode(single, single)
cv2.Dilate(single, single)
return single, single_c
def run(self):
while True:
img = self.capture.read()
#blur the source image to reduce color noise
cv2.blur(img, img, 3)
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv2.CreateImage(cv2.GetSize(img), 8, 3)
cv2.CvtColor(img, hsv_img, cv2.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
greenLower = (20, 190, 165)
greenUpper = (30, 225, 220)
thresholded_img = cv2.CreateImage(cv2.GetSize(hsv_img), 8, 1)
cv2.InRangeS(hsv_img, greenLower, greenUpper, thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
moments = cv2.Moments(thresholded_img, 0)
area = cv2.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 100000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv2.GetSpatialMoment(moments, 1, 0) / area
y = cv2.GetSpatialMoment(moments, 0, 1) / area
#print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area)
#create an overlay to mark the center of the tracked object
overlay = cv2.CreateImage(cv2.GetSize(img), 8, 3)
cv2.Circle(overlay, (x, y), 2, (255, 255, 255), 20)
cv2.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
cv2.Merge(thresholded_img, None, None, None, img)
#display the image
cv2.ShowImage(color_tracker_window, img)
if cv2.WaitKey(10) == 27:
break
def __produce_gradient_image(self, i, scale):
size = cv.GetSize(i)
grey_image = cv.CreateImage(size, 8, 1)
size = [s/scale for s in size]
grey_image_small = cv.CreateImage(size, 8, 1)
cv.CvtColor(i, grey_image, cv.CV_RGB2GRAY)
df_dx = cv.CreateImage(cv.GetSize(i), cv.IPL_DEPTH_16S, 1)
cv.Sobel( grey_image, df_dx, 1, 1)
cv.Convert(df_dx, grey_image)
cv.Resize(grey_image, grey_image_small)#, interpolation=cv.CV_INTER_NN)
cv.Resize(grey_image_small, grey_image)#, interpolation=cv.CV_INTER_NN)
return grey_image
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 extract_bright(grey_img, histogram=False):
"""
Extracts brightest part of the image.
Expected to be the LEDs (provided that there is a dark background)
Returns a Thresholded image
histgram defines if we use the hist calculation to find the best margin
"""
## Searches for image maximum (brightest pixel)
# We expect the LEDs to be brighter than the rest of the image
[minVal, maxVal, minLoc, maxLoc] = cv2.MinMaxLoc(grey_img)
print "Brightest pixel val is %d" % (maxVal)
#We retrieve only the brightest part of the image
# Here is use a fixed margin (80%), but you can use hist to enhance this one
if 0:
## Histogram may be used to wisely define the margin
# We expect a huge spike corresponding to the mean of the background
# and another smaller spike of bright values (the LEDs)
hist = grey_histogram(img, nBins=64)
[hminValue, hmaxValue, hminIdx, hmaxIdx] = cv2.GetMinMaxHistValue(hist)
margin = 0 # statistics to be calculated using hist data
else:
margin = 0.8
thresh = int(maxVal * margin) # in pix value to be extracted
print "Threshold is defined as %d" % (thresh)
thresh_img = cv2.CreateImage(cv2.GetSize(img), img.depth, 1)
cv2.Threshold(grey_img, thresh_img, thresh, 255, cv2.CV_THRESH_BINARY)
return thresh_img
def maxValueGarylize(image):
grayimg = cv2.CreateImage(cv2.GetSize(image), image.depth, 1)
for i in range(image.height):
for j in range(image.width):
grayimg[i, j] = max(image[i, j][0], image[i, j][1], image[i, j][2])
# cv.ShowImage('srcImage', image)
cv2.ShowImage('maxGrayImage', grayimg)
def find_lines(frame, result):
gray = cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)
blur_g = cv2.bilateralFilter(gray, 9, 150, 150)
canny = cv2.Canny(blur_g, 50, 150, apertureSize = 3)
im2, contours, h = cv2.findContours(canny, 1, cv2.CHAIN_APPROX_SIMPLE)
# print("contours", len(contours))
guidingBoxes = []
if len(contours) > 0:
maxContour = max(contours, key=cv2.contourArea)
# ------------------------------------------------------------------------------------------
# ROTATE LEFT OR RIGHT OR IS_CENTERED
min_x = -1
max_x = -1
for point in maxContour:
pt = Point(point[0][0], point[0][1])
if min_x == -1 or pt.x < min_x:
min_x = pt.x
if max_x == -1 or pt.x > max_x:
max_x = pt.x
relative_center_x = math.ceil((max_x + min_x)/2)
width, height = cv2.GetSize(frame)
screen_center_x = math.ceil((width)/2)
# if it needs to be further focused
if not about_the_same(screen_center_x, relative_center_x):
if relative_center_x > screen_center_x:
return "right"
else:
return "left"
# ------------------------------------------------------------------------------------------
# DISTANCE FROM GOAL
min_y = -1
max_y = -1
for point in maxContour:
pt = Point(point[0][0], point[0][1])
if min_y == -1 or pt.y < min_y:
min_y = pt.y
if max_y == -1 or pt.y > max_y:
max_y = pt.y
size = max_y - min_y
#actual distance = CONSTANT_OF_AREA / size
dist = CONSTANT_OF_AREA / size
speed = get_speed_from_distance(dist) # need a field to test this
# ------------------------------------------------------------------------------------------
# JUST OUTPUT PROCESSED IMAGE
#for contour in contours:
approx = cv2.approxPolyDP(maxContour, 0.10 * cv2.arcLength(maxContour, True), True)
cv2.drawContours(result, [maxContour], 0, (0, 0, 255), -1)
cv2.imshow("DEBUG_SHAPE_FINDING", result)
def getPolar2CartImg(image, rad): imgSize = cv.GetSize(image) c = (float(imgSize[0]/2.0), float(imgSize[1]/2.0)) imgRes = cv.CreateImage((rad*3, int(360)), 8, 3) #cv.LogPolar(image,imgRes,c,50.0, cv.CV_INTER_LINEAR+cv.CV_WARP_FILL_OUTLIERS) cv.LogPolar(image,imgRes,c,60.0, cv.CV_INTER_LINEAR+cv.CV_WARP_FILL_OUTLIERS) return (imgRes)
def scale_image(input, factor):
w, h = cv2.GetSize(input)
# print('w=' + str(w))
# print('h=' + str(w))
output = cv2.resize(input,
((int(h) * int(factor)), (int(w) * int(factor))),
interpolation=cv2.INTER_CUBIC)
return output
def initRecorder(self): #Create the recorder
codec = cv.CV_FOURCC('M', 'J', 'P', 'G')
self.writer = cv.CreateVideoWriter(
datetime.now().strftime("%b-%d_%H_%M_%S") + ".wmv", codec, 5,
cv.GetSize(self.frame), 1)
#FPS set to 5 because it seems to be the fps of my cam but should be ajusted to your needs
self.font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 2,
8) #Creates a font
def weightedAverageValueGary(image):
grayimg = cv2.CreateImage(cv2.GetSize(image), image.depth, 1)
for i in range(image.height):
for j in range(image.width):
grayimg[i, j] = 0.3 * image[i, j][0] + 0.59 * image[
i, j][1] + 0.11 * image[i, j][2]
# cv2.ShowImage('srcImage', image)
cv2.ShowImage('weightedGrayImage', grayimg)
def averageValueGary(image):
grayimg = cv2.CreateImage(cv2.GetSize(image), image.depth, 1)
for i in range(image.height):
for j in range(image.width):
grayimg[i,
j] = (image[i, j][0] + image[i, j][1] + image[i, j][2]) / 3
# cv2.ShowImage('srcImage', image)
cv2.ShowImage('averageGrayImage', grayimg)
def to_gray(img):
"""
Converts the input in grey levels
Returns a one channel image
"""
grey_img = cv2.CreateImage(cv2.GetSize(img), img.depth, 1)
cv2.CvtColor(img, grey_img, cv2.CV_RGB2GRAY)
return grey_img
def convert_inputs(self, img_mat, stand_size):
img_size = cv2.GetSize(img_mat)
img_string = img_mat.tostring()
img = self.external_convert(img_string, img_size)
img = tf.image.resize_bilinear(np.expand_dims(img, 0), stand_size)
img = img / 255.0
return img
def initRecorder(self): #Create the recorder
codec = cv.CV_FOURCC('D', 'I', 'V', 'X')
#codec = cv.CV_FOURCC("D", "I", "B", " ")
self.writer = cv.CreateVideoWriter(
datetime.now().strftime("%b-%d_%H:%M:%S") + ".avi", codec, 15,
cv.GetSize(self.frame), 1)
#FPS set at 15 because it seems to be the fps of my cam but should be ajusted to your needs
self.font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 2,
8) #Creates a font
def GetListOfPixelsInPolygon(self, input_image, polygon):
width, height = cv2.GetSize(input_image)
arrayOfInPolygonPixels = np.empty((0, 0))
for i in range(width):
for j in range(height):
point = Point(i, j)
if (polygon.contains(point)):
arrayOfInPolygonPixels.append(input_image[i, j, :])
return arrayOfInPolygonPixels
def draw_model_fitter(f):
cv.NamedWindow("Model Fitter", cv.CV_WINDOW_AUTOSIZE)
# Copy image
i = cv.CreateImage(cv.GetSize(f.image), f.image.depth, 3)
cv.Copy(f.image, i)
for pt_num, pt in enumerate(f.shape.pts):
# Draw normals
cv.Circle(i, (int(pt.x), int(pt.y)), 2, (0,0,0), -1)
cv.ShowImage("Shape Model",i)
cv.WaitKey()
def stream(label): # First Video
for image in video.iter_data():
while app.startButton.cget("text") == "Continue":
sleep(1)
width, height = cv2.GetSize(image)
img = Image.fromarray(image)
img2 = img.resize((500, 500), Image.ANTIALIAS)
img3 = ImageTk.PhotoImage(img2)
label.config(image=img3)
label.image = img3
def display_rgb(dev, data, timestamp):
global keep_running
cv.Image = frame_convert.video_cv(data)
img = cv.CreateImage(cv.GetSize(cv.Image), cv.IPL_DEPTH_16S, 3)
cv.ShowImage('RGB', cv.Image)
for x in range(1, 5):
name = "img%d" % (x)
cv.SaveImage('name.png', cv.Image)
time.sleep(1)
if cv.WaitKey(10) == 27:
keep_running = False
def ipl2tk_image(self, iplimg):
'''convert iplimage to PhotoImage via PIL image'''
pil_image = Image.fromstring(
'RGB',
cv.GetSize(iplimg),
iplimg.tostring(),
'raw',
'BGR',
iplimg.width * 3,
0)
return ImageTk.PhotoImage(pil_image)
def Bounding(binar,b):
contours, hierarchy = cv.findContours(binar,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
cnt = max(contours, key = lambda x: cv.contourArea(x))
else:
return
M = cv.moments(cnt)
#Centroid
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
#cv.rectangle(b, (cx-100, cy-100), (cx+130, cy+130), (255, 0, 0), 2)
x, y, w, h = cv.boundingRect(cnt)
mayor = 0
borde = 10
width, height = cv.GetSize(b)
#me quedo con la dimension del rectangulo mayor
if(w>h):
mayor = w
else:
mayor = h
#pongo el rectangulo
# if x <= borde:
# xlow = 1
# else:
# xlow = x-borde
# if x + borde > width:
# xhigh = width
# else:
# xhigh = x+borde
# if y <= borde:
# ylow = 1
# else:
# ylow = y-borde
# if y + borde > height:
# yhigh = height
# else:
# yhigh = y+borde
# cv.rectangle(b, (xlow, ylow), (xhigh, yhigh), (0, 255, 0), 2)
cv.rectangle(b, (x,y), (x+mayor, y+mayor), (0,255,0), 2)
#me quedo con la mano solo
squared = b[x:x+mayor,y:y+mayor]
#la cambio de tamanio
resized = cv.resize(squared,(160,160),interpolation=cv.INTER_AREA)
return resized, b
def applyEffect(self, image, width, height):
ipl_img = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]), cv.IPL_DEPTH_8U, 3)
cv2.cv.SetData(ipl_img, image.tostring(), image.dtype.itemsize * 3 * image.shape[1])
gray = cv.CreateImage((width, height), 8, 1) # tuple as the first arg
dst_img = cv.CreateImage(cv.GetSize(ipl_img), cv.IPL_DEPTH_8U, 3) # _16S => cv2.cv.iplimage
if self.effect == 'dilate':
cv.Dilate(ipl_img, dst_img, None, 5)
elif self.effect == 'laplace':
cv.Laplace(ipl_img, dst_img, 3)
elif self.effect == 'smooth':
cv.Smooth(ipl_img, dst_img, cv.CV_GAUSSIAN)
elif self.effect == 'erode':
cv.Erode(ipl_img, dst_img, None, 1)
cv.Convert(dst_img, ipl_img)
return self.ipl2tk_image(dst_img)
def get_hands(image):
""" Returns the hand as white on black. Uses value in HSV to determine
hands."""
size = cv2.GetSize(image)
hsv = cv2.CreateImage(size, 8, 3)
hue = cv2.CreateImage(size, 8, 1)
sat = cv2.CreateImage(size, 8, 1)
val = cv2.CreateImage(size, 8, 1)
hands = cv2.CreateImage(size, 8, 1)
cv2.Cv2tColor(image, hsv, cv2.CV2_BGR2HSV)
cv2.Split(hsv, hue, sat, val, None)
cv2.ShowImage('Live', image)
cv2.ShowImage('Hue', hue)
cv2.ShowImage('Saturation', sat)
cv2.Threshold(
hue, hue, 10, 255,
cv2.CV2_THRESH_TOZERO) #set to 0 if <= 10, otherwise leave as is
cv2.Threshold(
hue, hue, 244, 255,
cv2.CV2_THRESH_TOZERO_INV) #set to 0 if > 244, otherwise leave as is
cv2.Threshold(hue, hue, 0, 255,
cv2.CV2_THRESH_BINARY_INV) #set to 255 if = 0, otherwise 0
cv2.Threshold(
sat, sat, 64, 255,
cv2.CV2_THRESH_TOZERO) #set to 0 if <= 64, otherwise leave as is
cv2.EqualizeHist(sat, sat)
cv2.Threshold(sat, sat, 64, 255,
cv2.CV2_THRESH_BINARY) #set to 0 if <= 64, otherwise 255
cv2.ShowImage('Saturation threshold', sat)
cv2.ShowImage('Hue threshold', hue)
cv2.Mul(hue, sat, hands)
#smooth + threshold to filter noise
# cv2.Smooth(hands, hands, smoothtype=cv2.CV2_GAUSSIAN, param1=13, param2=13)
# cv2.Threshold(hands, hands, 200, 255, cv2.CV2_THRESH_BINARY)
cv2.ShowImage('Hands', hands)
return hands
def meanshiftUsingILM(path):
# Load original image given the image path
im = cv2.LoadImageM(path)
# Load bank of filters
filterBank = lmfilters.loadLMFilters()
# Resize image to decrease dimensions during clustering
resize_factor = 1
thumbnail = cv2.CreateMat(im.height / resize_factor,
im.width / resize_factor, cv2.CV_8UC3)
cv2.Resize(im, thumbnail)
# now work with resized thumbnail image
response = np.zeros(shape=((thumbnail.height) * (thumbnail.width), 4),
dtype=float)
for f in range(0, 48):
filter = filterBank[f]
# Resize the filter with the same factor for the resized image
dst = cv2.CreateImage(cv2.GetSize(thumbnail), cv2.IPL_DEPTH_32F, 3)
resizedFilter = cv2.CreateMat(filter.height / resize_factor,
filter.width / resize_factor,
filter.type)
cv2.Resize(filter, resizedFilter)
# Apply the current filter
cv2.Filter2D(thumbnail, dst, resizedFilter)
featureIndex = getFilterTypeIndex(f)
for j in range(0, thumbnail.height):
for i in range(0, thumbnail.width):
# Select the max. along the three channels
maxRes = max(dst[j, i])
if math.isnan(maxRes):
maxRes = 0.0
if maxRes > response[thumbnail.width * j + i, featureIndex]:
# Store the max. response for the given feature index
response[thumbnail.width * j + i, featureIndex] = maxRes
# Create new mean shift instance
ms = MeanShift(bandwidth=10, bin_seeding=True)
# Apply the mean shift clustering algorithm
ms.fit(response)
labels = ms.labels_
n_clusters_ = np.unique(labels)
print("Number of clusters: ", len(n_clusters_))
repaintImage(thumbnail, labels)
cv2.Resize(thumbnail, im)
return im
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)
