def dilate_image(image, v, h):
vImage = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
hImage = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
vElement = cv.CreateStructuringElementEx(1, v, 0, v / 2, cv.CV_SHAPE_RECT)
hElement = cv.CreateStructuringElementEx(h, 1, h / 2, 0, cv.CV_SHAPE_RECT)
cv.Dilate(image, vImage, vElement, 1)
cv.Dilate(vImage, hImage, hElement, 1)
return hImage
def processImage(self, curframe):
cv.Smooth(curframe, curframe) #Remove false positives
if not self.absdiff_frame: #For the first time put values in difference, temp and moving_average
self.absdiff_frame = cv.CloneImage(curframe)
self.previous_frame = cv.CloneImage(curframe)
cv.Convert(
curframe, self.average_frame
) #Should convert because after runningavg take 32F pictures
else:
cv.RunningAvg(curframe, self.average_frame,
0.05) #Compute the average
cv.Convert(self.average_frame,
self.previous_frame) #Convert back to 8U frame
cv.AbsDiff(curframe, self.previous_frame,
self.absdiff_frame) # moving_average - curframe
cv.CvtColor(
self.absdiff_frame, self.gray_frame,
cv.CV_RGB2GRAY) #Convert to gray otherwise can't do threshold
cv.Threshold(self.gray_frame, self.gray_frame, 50, 255,
cv.CV_THRESH_BINARY)
cv.Dilate(self.gray_frame, self.gray_frame, None,
15) #to get object blobs
cv.Erode(self.gray_frame, self.gray_frame, None, 10)
def dilationofimage():
display(src_image, "Source Image")
struc = cv.CreateStructuringElementEx(10, 10, 5, 5, cv.CV_SHAPE_RECT)
cv.Dilate(src_image, dst_image, struc, 1)
display(dst_image, "Dilation")
cv.WaitKey(0)
cv.DestroyWindow("Dilation")
def erodeImage(img):
kernel = cv.CreateStructuringElementEx(9, 9, 5, 5, cv.CV_SHAPE_CROSS)
# Erode- replaces pixel value with lowest value pixel in kernel
cv.Erode(img, img, kernel, 2)
# Dilate- replaces pixel value with highest value pixel in kernel
cv.Dilate(img, img, kernel, 2)
return img
def preproc_map_img(self, map_img):
""" Preprocesses the map image Soft, erode or whtaever it is necessary to improve the input"""
#Apply threshold to have just black and white
thresh_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Threshold(map_img, thresh_img, 250, 255, cv.CV_THRESH_BINARY)
#Blur map's thresholded image
soft_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Smooth(thresh_img, soft_img, cv.CV_GAUSSIAN, 9, 9)
#Dilate the inverse map to get it's skeleton
dilated_img = cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Dilate(soft_img, dilated_img, iterations=20)
#Create inverse image
# dilated_inverted_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
# for r in range(0,dilated_img.rows):
# for c in range(0,dilated_img.cols):
# dilated_inverted_img[r,c]=255-dilated_img[r,c]
#Enhance image edges for hough transformdilated_img
canny_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Canny(soft_img, canny_img, 200,220)
preprocessed_map = dilated_img
return preprocessed_map
def precornerdetect(image):
# assume that the image is floating-point
corners = cv.CloneMat(image)
cv.PreCornerDetect(image, corners, 3)
dilated_corners = cv.CloneMat(image)
cv.Dilate(corners, dilated_corners, None, 1)
corner_mask = cv.CreateMat(image.rows, image.cols, cv.CV_8UC1)
cv.Sub(corners, dilated_corners, corners)
cv.CmpS(corners, 0, corner_mask, cv.CV_CMP_GE)
return (corners, corner_mask)
def find_squares4(color_img):
"""
Finds multiple squares in image
Steps:
-Use Canny edge to highlight contours, and dilation to connect
the edge segments.
-Threshold the result to binary edge tokens
-Use cv.FindContours: returns a cv.CvSequence of cv.CvContours
-Filter each candidate: use Approx poly, keep only contours with 4 vertices,
enough area, and ~90deg angles.
Return all squares contours in one flat list of arrays, 4 x,y points each.
"""
#select even sizes only
width, height = (color_img.width & -2, color_img.height & -2)
timg = cv.CloneImage(color_img) # make a copy of input image
gray = cv.CreateImage((width, height), 8, 1)
# select the maximum ROI in the image
cv.SetImageROI(timg, (0, 0, width, height))
# down-scale and upscale the image to filter out the noise
pyr = cv.CreateImage((width / 2, height / 2), 8, 3)
cv.PyrDown(timg, pyr, 7)
cv.PyrUp(pyr, timg, 7)
tgray = cv.CreateImage((width, height), 8, 1)
squares = []
# Find squares in every color plane of the image
# Two methods, we use both:
# 1. Canny to catch squares with gradient shading. Use upper threshold
# from slider, set the lower to 0 (which forces edges merging). Then
# dilate canny output to remove potential holes between edge segments.
# 2. Binary thresholding at multiple levels
N = 11
for c in [0, 1, 2]:
#extract the c-th color plane
cv.SetImageCOI(timg, c + 1)
cv.Copy(timg, tgray, None)
cv.Canny(tgray, gray, 0, 50, 5)
cv.Dilate(gray, gray)
squares = squares + find_squares_from_binary(gray)
# Look for more squares at several threshold levels
for l in range(1, N):
cv.Threshold(tgray, gray, (l + 1) * 255 / N, 255,
cv.CV_THRESH_BINARY)
squares = squares + find_squares_from_binary(gray)
return squares
def do_loop(self):
# image processing
if self.config.threshold:
cv.Threshold(self.img_original, self.img_target,
self.config.pix_thresh_min, 0xff, cv.CV_THRESH_BINARY)
cv.And(self.img_target, self.img_mask, self.img_target)
if self.config.dilate:
cv.Dilate(self.img_target,
self.img_target,
iterations=self.config.dilate)
if self.config.erode:
cv.Erode(self.img_target,
self.img_target,
iterations=self.config.erode)
show_image(self)
sys.stdout.write('> ')
sys.stdout.flush()
# keystroke processing
ki = cv.WaitKey(0)
# Simple character value, if applicable
kc = None
# Char if a common char, otherwise the integer code
k = ki
if 0 <= ki < 256:
kc = chr(ki)
k = kc
elif 65506 < ki < 66000 and ki != 65535:
ki2 = ki - 65506 - 30
# modifier keys
if ki2 >= 0:
kc = chr(ki2)
k = kc
if kc:
print '%d (%s)\n' % (ki, kc)
else:
print '%d\n' % ki
if ki > 66000:
return
if ki < 0:
print "Exiting on closed window"
self.running = False
return
on_key(self, k)
def findImage(img):
#Set up storage for images
frame_size = cv.GetSize(img)
img2 = cv.CreateImage(frame_size,8,3)
tmp = cv.CreateImage(frame_size,8,cv.CV_8U)
h = cv.CreateImage(frame_size,8,1)
#copy original image to do work on
cv.Copy(img,img2)
#altering the image a bit for smoother processing
cv.Smooth(img2,img2,cv.CV_BLUR,3)
cv.CvtColor(img2,img2,cv.CV_BGR2HSV)
#make sure temp is empty
cv.Zero(tmp)
#detection based on HSV value
#30,100,90 lower limit on pic 41,255,255 on pic
#cv.InRangeS(img2,cv.Scalar(25,100,87),cv.Scalar(50,255,255),tmp)
#Range for green plate dot in my Living room
#cv.InRangeS(img2,cv.Scalar(55,80,60),cv.Scalar(65,95,90),tmp)
#classroom
#cv.InRangeS(img2,cv.Scalar(55,80,60),cv.Scalar(70,110,70),tmp)
#Kutztowns Gym
cv.InRangeS(img2,cv.Scalar(65,100,112),cv.Scalar(85,107,143),tmp)
elmt_shape=cv.CV_SHAPE_ELLIPSE
pos = 3
element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, elmt_shape)
cv.Dilate(tmp,tmp,element,6)
cv.Erode(tmp,tmp,element,2)
cv.Split(tmp,h,None,None,None)
storage = cv.CreateMemStorage()
scan = sc.FindContours(h,storage)
xyImage=drawCircles(scan,img)
if xyImage != None:
return (xyImage,tmp)
else:
return None
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 motionDetect(self, img):
cv.Smooth(img, img, cv.CV_GAUSSIAN, 3, 0)
cv.RunningAvg(img, self.movingAvg, 0.020, None)
cv.ConvertScale(self.movingAvg, self.tmp, 1.0, 0.0)
cv.AbsDiff(img, self.tmp, self.diff)
cv.CvtColor(self.diff, self.grayImage, cv.CV_RGB2GRAY)
cv.Threshold(self.grayImage, self.grayImage, 70,255, cv.CV_THRESH_BINARY)
cv.Dilate(self.grayImage, self.grayImage, None, 18)#18
cv.Erode(self.grayImage, self.grayImage, None, 10)#10
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(self.grayImage, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
# points = []
while contour:
boundRect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (boundRect[0], boundRect[1])
pt2 = (boundRect[0] + boundRect[2], boundRect[1] + boundRect[3])
cv.Rectangle(img, pt1, pt2, cv.CV_RGB(255,255,0), 1)
return img
global Data
global Grid_Intersections
if Data[Grid_Intersections.index((x, y))] == '0':
Data[Grid_Intersections.index((x, y))] = '1'
else:
Data[Grid_Intersections.index((x, y))] = '0'
return get_data(x, y)
# main loop
Target = cv.CloneImage(Img)
while True:
# image processing
if Dilate:
cv.Dilate(Target, Target, iterations=Dilate)
Dilate = 0
if Erode:
cv.Erode(Target, Target, iterations=Erode)
Erode = 0
if Threshold:
cv.Threshold(Img, Target, Threshold_Min, 0xff, cv.CV_THRESH_BINARY)
cv.And(Target, Mask, Target)
show_image()
# keystroke processing
k = cv.WaitKey(0)
print k
if k > 66000:
continue
if k < 256:
i = 0
for files in glob.glob('D:/pic/car/*.jpg'):
filepath,filename = os.path.split(files)
image = cv2.imread(filepath + '/' + filename)
# image = cv2.imread('D:/pic/car2.jpg')
h,w = image.shape[:2]
#灰度化
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
grayIPimage = cv.GetImage(cv.fromarray(gray))
sobel = cv.CreateImage((w, h),cv2.IPL_DEPTH_16S, 1) #创建一张深度为16位有符号（-65536~65535）的的图像区域保持处理结果
cv.Sobel(grayIPimage,sobel,2,0,7) # 进行x方向的sobel检测
temp = cv.CreateImage(cv.GetSize(sobel),cv2.IPL_DEPTH_8U, 1) #图像格式转换回8位深度已进行下一步处理
cv.ConvertScale(sobel, temp,0.00390625, 0)
cv.Threshold(temp, temp, 0, 255, cv2.THRESH_OTSU)
kernal = cv.CreateStructuringElementEx(3,1, 1, 0, 0)
cv.Dilate(temp, temp,kernal,2)
cv.Erode(temp, temp,kernal,4)
cv.Dilate(temp, temp,kernal,2)
# cv.ShowImage('1', temp)
kernal = cv.CreateStructuringElementEx(1,3, 0, 1, 0)
cv.Erode(temp, temp,kernal,1)
cv.Dilate(temp, temp,kernal,3)
# cv.ShowImage('2', temp)
temp = np.asarray(cv.GetMat(temp))
contours, heirs = cv2.findContours(temp,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for tours in contours:
rc = cv2.boundingRect(tours)
if rc[2]/rc[3] >= 2:
#rc[0] 表示图像左上角的纵坐标，rc[1] 表示图像左上角的横坐标，rc[2] 表示图像的宽度，rc[3] 表示图像的高度，
cv2.rectangle(image, (rc[0],rc[1]),(rc[0]+rc[2],rc[1]+rc[3]),(255,0,255))
def dilateImage(im, nbiter=0):
for i in range(nbiter):
cv.Dilate(im, im)
def run(self):
logging.debug(' starting run ')
global samecolorclient
global capture
global centroidList #abh
global lock #abh
global lock2 #abh
global lock3 #abh
global lock4 #abh
mydata = threading.local()
#window1=" Color Detection"
mydata.window2 = str(self.name) + " Threshold"
#cv.NamedWindow(window1,0)
lock4.acquire() #abh
cv.NamedWindow(mydata.window2, 0)
lock4.release() #abh
mydata.centroidold = [0, 0]
mydata.flag = 0
mydata.roi = [100, 22, 390, 390]
#mydata.roi=[95,40,380,350]
while True:
lock2.acquire() #abh
lock4.acquire() #abh
mydata.color_image = cv.QueryFrame(capture)
lock4.release() #abh
lock2.release() #abh
if (mydata.flag == 0):
lock4.acquire #abh lock4.release #abh
mydata.color_image = cv.GetSubRect(mydata.color_image,
(100, 22, 390, 390))
lock4.release #abh
else:
lock4.acquire #abh lock4.release #abh
mydata.color_image = cv.GetSubRect(
mydata.color_image,
(int(mydata.roi[0]), int(mydata.roi[1]), int(
mydata.roi[2]), int(mydata.roi[3])))
lock4.release #abh
lock4.acquire #abh lock4.release #abh
cv.Flip(mydata.color_image, mydata.color_image, 1)
cv.Smooth(mydata.color_image, mydata.color_image, cv.CV_MEDIAN, 3,
0)
#logging.debug(' Starting getthresholdedimg ')
mydata.imghsv = cv.CreateImage(cv.GetSize(mydata.color_image), 8,
3)
cv.CvtColor(mydata.color_image, mydata.imghsv,
cv.CV_BGR2YCrCb) # Convert image from RGB to HSV
mydata.imgnew = cv.CreateImage(cv.GetSize(mydata.color_image),
cv.IPL_DEPTH_8U, 1)
mydata.imgthreshold = cv.CreateImage(
cv.GetSize(mydata.color_image), 8, 1)
lock4.release #abh
mydata.c = self.color[0]
mydata.minc = (float(mydata.c[0]), float(mydata.c[1]),
float(mydata.c[2]))
mydata.c = self.color[1]
mydata.maxc = (float(mydata.c[0]), float(mydata.c[1]),
float(mydata.c[2]))
lock4.acquire #abh lock4.release #abh
cv.InRangeS(mydata.imghsv, cv.Scalar(*(mydata.minc)),
cv.Scalar(*(mydata.maxc)), mydata.imgnew)
cv.Add(mydata.imgnew, mydata.imgthreshold, mydata.imgthreshold)
#logging.debug(' Exiting getthreasholdedimg')
#logging.debug('function returned from thresholdedimg')
cv.Erode(mydata.imgthreshold, mydata.imgthreshold, None, 1)
cv.Dilate(mydata.imgthreshold, mydata.imgthreshold, None, 4)
mydata.img2 = cv.CloneImage(mydata.imgthreshold)
mydata.storage = cv.CreateMemStorage(0)
mydata.contour = cv.FindContours(mydata.imgthreshold,
mydata.storage,
cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
lock4.release #abh
mydata.points = []
#logging.debug('Starting while contour')
while mydata.contour:
# Draw bounding rectangles
lock4.acquire #abh lock4.release #abh
mydata.bound_rect = cv.BoundingRect(list(mydata.contour))
lock4.release #abh
mydata.contour = mydata.contour.h_next()
mydata.pt1 = (mydata.bound_rect[0], mydata.bound_rect[1])
mydata.pt2 = (mydata.bound_rect[0] + mydata.bound_rect[2],
mydata.bound_rect[1] + mydata.bound_rect[3])
mydata.points.append(mydata.pt1)
mydata.points.append(mydata.pt2)
lock4.acquire #abh lock4.release #abh
cv.Rectangle(
mydata.color_image, mydata.pt1, mydata.pt2,
cv.CV_RGB(mydata.maxc[0], mydata.maxc[1], mydata.maxc[2]),
1)
lock4.release #abh
# Calculating centroids
if (((mydata.bound_rect[2]) * (mydata.bound_rect[3])) < 3500):
#logging.debug('Inside iffffffffffffffffffffffff')
lock4.acquire #abh lock4.release #abh
mydata.centroidx = cv.Round(
(mydata.pt1[0] + mydata.pt2[0]) / 2)
mydata.centroidy = cv.Round(
(mydata.pt1[1] + mydata.pt2[1]) / 2)
lock4.release #abh
if (mydata.flag == 1):
#logging.debug("inside flag1")
mydata.centroidx = mydata.roi[0] + mydata.centroidx
mydata.centroidy = mydata.roi[1] + mydata.centroidy
mydata.centroidnew = [mydata.centroidx, mydata.centroidy]
#logging.debug('mydataroi[0] '+str(mydata.roi[0]) + ';centroidx ' + str(mydata.centroidx))
#logging.debug('mydataroi[1] '+str(mydata.roi[1]) + ';centroidy ' + str(mydata.centroidy))
#print mydata.centroidx #abh
#print mydata.centroidy #abh
mydata.tmpclient = []
lock3.acquire() #abh
mydata.tmpclient = samecolorclient[self.i]
lock3.release() #abh
mydata.distance = math.sqrt(
math.pow((mydata.centroidnew[0] -
mydata.centroidold[0]), 2) +
math.pow((mydata.centroidnew[1] -
mydata.centroidold[1]), 2))
#lock.acquire() #abh #abh commented
for mydata.j in range(len(mydata.tmpclient)):
mydata.client_socket = mydata.tmpclient[mydata.j]
#logging.debug('before centroid send...')
if (mydata.distance >= 1.50):
print 'inside 1.50 '
#self.server_socket.sendto(str(mydata.centroidnew),mydata.client_socket) #abh
lock.acquire() #abh
centroidList[colorlist.index(
self.color)] = mydata.centroidnew #abh
del mydata.centroidold[:]
#logging.debug(str(centroidList))
self.server_socket.sendto(
str(centroidList), mydata.client_socket) #abh
lock.release() #abh
#logging.debug ('updating done.') #abh
#print centroidList #abh
mydata.centroidold = mydata.centroidnew[:]
else:
#self.server_socket.sendto(str(mydata.centroidold),mydata.client_socket) #abh
lock.acquire() #abh
centroidList[colorlist.index(
self.color)] = mydata.centroidold #abh
#logging.debug(str(centroidList))
self.server_socket.sendto(
str(centroidList), mydata.client_socket) #abh
lock.release() #abh
#logging.debug ('updating done2.') #abh
#print centroidList #abh
# logging.debug('byte sent to client')
#lock.release() #abh
mydata.roi[0] = mydata.centroidx - 50
mydata.roi[1] = mydata.centroidy - 50
if (mydata.roi[0] < 95):
mydata.roi[0] = 95
if (mydata.roi[1] < 40):
mydata.roi[1] = 40
mydata.roi[2] = 100
mydata.roi[3] = 100
if ((mydata.roi[0] + mydata.roi[2]) > 475):
mydata.roi[0] = mydata.roi[0] - (
(mydata.roi[0] + mydata.roi[2]) - 475)
if ((mydata.roi[1] + mydata.roi[3]) > 390):
mydata.roi[1] = mydata.roi[1] - (
(mydata.roi[1] + mydata.roi[3]) - 390)
#del mydata.centroidnew[:]
mydata.flag = 1
if mydata.contour is None:
mydata.flag = 0
#cv.ShowImage(window1,mydata.color_image)
lock4.acquire #abh lock4.release #abh
cv.ShowImage(mydata.window2, mydata.img2)
lock4.release #abh
if cv.WaitKey(33) == 27: #here it was 33 instead of 10
#cv.DestroyWindow(mydata.window1)
#cv.DestroyWindow(mydata.window2)
break
#cv.CvtColor(thumb, cvt, cv.CV_RGB2BGR)
#cv.NamedWindow('Image', cv.CV_WINDOW_AUTOSIZE)
grey = cv.CreateImage(cv.GetSize(thumb), 8, 1)
cv.CvtColor(thumb, grey, cv.CV_RGB2GRAY)
cv.ShowImage('Greyed', grey)
smoothed = cv.CloneImage(thumb)
cv.Smooth(thumb, smoothed, cv.CV_MEDIAN)
cv.ShowImage('Smoothed', smoothed)
cv.EqualizeHist(grey, grey)
cv.ShowImage('Equalized', grey)
threshold1 = cv.CloneImage(grey)
cv.Threshold(threshold1, threshold1, 100, 255, cv.CV_THRESH_BINARY)
cv.ShowImage('Threshold1', threshold1)
threshold2 = cv.CloneImage(grey)
cv.Threshold(threshold2, threshold2, 100, 255, cv.CV_THRESH_OTSU)
cv.ShowImage('Threshold2', threshold2)
element_shape = cv.CV_SHAPE_RECT
pos = 3
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(grey, grey, element, 2)
cv.ShowImage('Dilated', grey)
cv.WaitKey(0)
dst_32f = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_32F, 1)
neighbourhood = 3
aperture = 3
k = 0.01
maxStrength = 0.0
threshold = 0.01
nonMaxSize = 3
cv.CornerHarris(im, dst_32f, neighbourhood, aperture, k)
minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)
dilated = cv.CloneImage(dst_32f)
cv.Dilate(
dst_32f, dilated
) # By this way we are sure that pixel with local max value will not be changed, and all the others will
localMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Cmp(
dst_32f, dilated, localMax, cv.CV_CMP_EQ
) #compare allow to keep only non modified pixel which are local maximum values which are corners.
threshold = 0.01 * maxv
cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)
cornerMap = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Convert(dst_32f, cornerMap) #Convert to make the and
cv.And(cornerMap, localMax, cornerMap) #Delete all modified pixels
radius = 3
def findSquares4(img, storage):
N = 11
sz = (img.width & -2, img.height & -2)
timg = cv.CloneImage(img); # make a copy of input image
gray = cv.CreateImage(sz, 8, 1)
pyr = cv.CreateImage((sz.width/2, sz.height/2), 8, 3)
# create empty sequence that will contain points -
# 4 points per square (the square's vertices)
squares = cv.CreateSeq(0, sizeof_CvSeq, sizeof_CvPoint, storage)
squares = CvSeq_CvPoint.cast(squares)
# select the maximum ROI in the image
# with the width and height divisible by 2
subimage = cv.GetSubRect(timg, cv.Rect(0, 0, sz.width, sz.height))
# down-scale and upscale the image to filter out the noise
cv.PyrDown(subimage, pyr, 7)
cv.PyrUp(pyr, subimage, 7)
tgray = cv.CreateImage(sz, 8, 1)
# find squares in every color plane of the image
for c in range(3):
# extract the c-th color plane
channels = [None, None, None]
channels[c] = tgray
cv.Split(subimage, channels[0], channels[1], channels[2], None)
for l in range(N):
# hack: use Canny instead of zero threshold level.
# Canny helps to catch squares with gradient shading
if(l == 0):
# apply Canny. Take the upper threshold from slider
# and set the lower to 0 (which forces edges merging)
cv.Canny(tgray, gray, 0, thresh, 5)
# dilate canny output to remove potential
# holes between edge segments
cv.Dilate(gray, gray, None, 1)
else:
# apply threshold if l!=0:
# tgray(x, y) = gray(x, y) < (l+1)*255/N ? 255 : 0
cv.Threshold(tgray, gray, (l+1)*255/N, 255, cv.CV_THRESH_BINARY)
# find contours and store them all as a list
count, contours = cv.FindContours(gray, storage, sizeof_CvContour,
cv.CV_RETR_LIST, cv. CV_CHAIN_APPROX_SIMPLE, (0, 0))
if not contours:
continue
# test each contour
for contour in contours.hrange():
# approximate contour with accuracy proportional
# to the contour perimeter
result = cv.ApproxPoly(contour, sizeof_CvContour, storage,
cv.CV_POLY_APPROX_DP, cv.ContourPerimeter(contours)*0.02, 0)
# square contours should have 4 vertices after approximation
# relatively large area (to filter out noisy contours)
# and be convex.
# Note: absolute value of an area is used because
# area may be positive or negative - in accordance with the
# contour orientation
if(result.total == 4 and
abs(cv.ContourArea(result)) > 1000 and
cv.CheckContourConvexity(result)):
s = 0
for i in range(5):
# find minimum angle between joint
# edges (maximum of cosine)
if(i >= 2):
t = abs(angle(result[i], result[i-2], result[i-1]))
if s<t:
s=t
# if cosines of all angles are small
# (all angles are ~90 degree) then write quandrange
# vertices to resultant sequence
if(s < 0.3):
for i in range(4):
squares.append(result[i])
return squares
def findBrightObjects(self):
cv.NamedWindow("camera")
while True :
frame = cv.QueryFrame(self.video1)
# print type(frame)
[rows, cols] = cv.GetSize(frame)
# image = cv.CreateMat(rows, cols, cv.CV_8UC3)
image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, frame.nChannels)
cv.Copy(frame, image)
# image = cv.GetMat(frame)
cv.ShowImage("camera", image)
# grayScaleFullImage = cv.CreateImage((image.width, image.height), 8, 1)
# cv.CvtColor(image, grayScaleFullImage, cv.CV_BGR2GRAY)
# convert to hsv
hsvImage = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, frame.nChannels)
cv.CvtColor(image, hsvImage, cv.CV_BGR2HSV)
cv.ShowImage("hsv", hsvImage)
# hsvImage = cv2.cvtColor(imageArray, cv.CV_BGR2HSV)
# h_plane = cv.CreateImage(cv.GetSize(image), 8, 1)
# s_plane = cv.CreateImage(cv.GetSize(image), 8, 1)
# v_plane = cv.CreateImage(cv.GetSize(image), 8, 1)
# Split HSV into two of it's three channels. V channel is same as greyscale image so ignore.
# cv.Split(hsvImage, h_plane, s_plane, v_plane, None)
# http://robbierickman.blogspot.co.uk/2011/11/laserduckpy-coloured-object-tracking.html
# planes = [h_plane, s_plane]
#
# h_bins = 30
# s_bins = 32
# hist_size = [h_bins, s_bins]
# # hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
# h_ranges = [0, 180]
# # saturation varies from 0 (black-gray-white) to
# # 255 (pure spectrum color)
# s_ranges = [0, 255]
# ranges = [h_ranges, s_ranges]
# scale = 10
# hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
# cv.CalcHist([cv.GetImage(i) for i in planes], hist)
# (_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
#
# hist_img = cv.CreateImage((h_bins*scale, s_bins*scale), 8, 3)
#
# for h in range(h_bins):
# for s in range(s_bins):
# bin_val = cv.QueryHistValue_2D(hist, h, s)
# intensity = cv.Round(bin_val * 255 / max_value)
# cv.Rectangle(hist_img,
# (h*scale, s*scale),
# ((h+1)*scale - 1, (s+1)*scale - 1),
# cv.RGB(intensity, intensity, intensity),
# cv.CV_FILLED)
# http://uvhar.googlecode.com/hg/test/laser_tracker.py
# Threshold ranges of HSV components.
# cv.InRangeS(h_plane, hmin, hmax, h_plane)
# # cv.InRangeS(s_plane, smin, smax, s_plane)
# # cv.InRangeS(v_plane, vmin, vmax, v_plane)
#
# finalImage = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
#
# # Perform an AND on HSV components to identify the laser!
# cv.And(h_plane, s_plane, finalImage)
# # This actually Worked OK for me without using Saturation.
# # cv.cvAnd(laser_img, s_img,laser_img)
#
# # Merge the HSV components back together.
# cv.Merge(h_plane, s_plane, v_plane, None, hsvImage)
# cv.ShowImage("hue", h_plane)
# cv.ShowImage("saturation", s_plane)
# cv.ShowImage("value", v_plane)
thresholdImage = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
cv.InRangeS(hsvImage, cv.Scalar(0, 100, 100), cv.Scalar(50, 255, 255), thresholdImage)
# thresholdImage = cv2.threshold(hsvImage, [0, 100, 100], [50, 255, 255], cv2.THRESH_BINARY)
cv.ShowImage("threshold image", thresholdImage)
# remove noise from threshold image
kernel = cv.CreateStructuringElementEx(9, 9, 5, 5, cv.CV_SHAPE_CROSS)
# Dilate- replaces pixel value with highest value pixel in kernel
cv.Dilate(thresholdImage, thresholdImage, kernel, 2)
# Erode- replaces pixel value with lowest value pixel in kernel
cv.Erode(thresholdImage, thresholdImage, kernel, 2)
# element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
# cv.Dilate(thresholdImage, element, thresholdImage)
# cv2.erode(thresholdImage, element, thresholdImage)
cv.ShowImage("cleaned image ", thresholdImage)
# contour detection
imageArray = np.asarray(cv.GetMat(thresholdImage), np.uint8, 1)
print type(imageArray)
imageArray = imageArray.T
# contours, hier = cv2.findContours(imageArray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print "TYPE " + str(type(contours)) + " " + str(len(contours))
#
# # for i in contours:
# # print i
# maxArea = -1
# contourIndex = -1
# if contours:
# for i in range(0, len(contours)):
# cnt = contours[i].astype('int')
# print type(cnt)
# area = cv2.contourArea(cnt)
# print area
# if area > maxArea:
# maxArea = area
# contourIndex = i
#
# if contourIndex != -1:
# cv2.drawContours(imageArray, contours, contourIndex, (0, 0 , 255), 10)
params = cv2.SimpleBlobDetector_Params()
# params.minDistBetweenBlobs = 1.0 # minimum 10 pixels between blobs
# params.filterByArea = True # filter my blobs by area of blob
# params.minArea = 5.0 # min 20 pixels squared
# params.maxArea = 500.0 # max 500 pixels squared
params.minDistBetweenBlobs = 50.0
params.filterByInertia = False
params.filterByConvexity = False
params.filterByColor = False
params.filterByCircularity = False
params.filterByArea = True
params.minArea = 20.0
params.maxArea = 500.0
myBlobDetector = cv2.SimpleBlobDetector(params)
keypoints = myBlobDetector.detect(imageArray)
print "blobs " + str(keypoints)
# extract the x y coordinates of the keypoints:
for i in range(0, len(keypoints) - 1):
print keypoints[i].pt
pt1 = (int(keypoints[i].pt[0]), int(keypoints[i].pt[1]))
pt2 = (int(keypoints[i + 1].pt[0]), int(keypoints[i + 1].pt[1]))
cv2.line(imageArray, pt1, pt2, (255, 0, 0))
# float X=keypoints[i].pt.x;
# float Y=keypoints[i].pt.y;
cv2.imshow("final detection ", imageArray)
#
if cv.WaitKey(10) == 27:
break
def run(self):
# Capture first frame to get size
frame = self.get_image2()
frame_size = cv.GetSize(frame)
color_image = cv.CreateImage(frame_size, 8, 3)
grey_image = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(frame_size, cv.IPL_DEPTH_32F, 3)
first = True
while True:
closest_to_left = cv.GetSize(frame)[0]
closest_to_right = cv.GetSize(frame)[1]
print "getting Image"
color_image = self.get_image2()
print "got image"
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
if first:
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
first = False
else:
cv.RunningAvg(color_image, moving_average, 0.30, None)
# 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 to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while contour:
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1)
if len(points):
center_point = reduce(lambda a, b: ((a[0] + b[0]) / 2, (a[1] + b[1]) / 2), points)
cv.Circle(color_image, center_point, 40, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, center_point, 30, cv.CV_RGB(255, 100, 0), 1)
cv.Circle(color_image, center_point, 20, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, center_point, 10, cv.CV_RGB(255, 100, 0), 1)
cv.ShowImage("Target", color_image)
# Listen for ESC key
c = cv.WaitKey(7) % 0x100
if c == 27:
break
capture = cv.CaptureFromCAM(0)
sleep(5)
frame = cv.QueryFrame(capture)
frame_size = cv.GetSize(frame)
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
test = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.NamedWindow("Real")
cv.NamedWindow("Threshold")
while (1):
color_image = cv.QueryFrame(capture)
imdraw = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Flip(color_image, color_image, 1)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
imgyellowthresh = getthresholdedimg(color_image)
cv.Erode(imgyellowthresh, imgyellowthresh, None, 3)
cv.Dilate(imgyellowthresh, imgyellowthresh, None, 10)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(imgyellowthresh, storage, cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
# This is the new part here. ie Use of cv.BoundingRect()
while contour:
# Draw bounding rectangles
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
'''if contour!=None and contour.h_next()!=None:
contour=contour.h_next()[0]
print contour.h_next()[0]'''
# for more details about cv.BoundingRect,see documentation
dst_32f = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
neighbourhood = 3
aperture = 3
k = 0.01
maxStrength = 0.0
threshold = 0.01
nonMaxSize = 3
cv.CornerHarris(img, dst_32f, neighbourhood, aperture, k)
minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)
dilated = cv.CloneImage(dst_32f)
cv.Dilate(
dst_32f, dilated
) #nos aseguramos que el pixel local su maximo valor no cambia y todos los otros pixeles si
localMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Cmp(
dst_32f, dilated, localMax, cv.CV_CMP_EQ
) #comparamos y guardamos solo los pixeles modificados los que son los maximos valores locales que son esquinas
threshold = 0.01 * maxv
cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)
cornerMap = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Convert(dst_32f, cornerMap) #convertir y utilizar la operacion logica AND
cv.And(cornerMap, localMax, cornerMap) #Borrar todos los pixeles modificados
radius = 3
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 !")
ring = IntrusionAlarm()
ring.run()
# 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("Virtual Eye", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
elif c == 99:
cv2.destroyWindow('Warning!!!')
def findBlob(rgbimage, hsvimage, maskimage, blobimage, hsvcolorrange, hsvmin,
hsvmax):
cv.CvtColor(rgbimage, hsvimage, cv.CV_BGR2HSV)
hsvmin = [
hsvmin[0] - hsvcolorrange[0], hsvmin[1] - hsvcolorrange[1],
hsvmin[2] - hsvcolorrange[2]
]
hsvmax = [
hsvmax[0] + hsvcolorrange[0], hsvmax[1] + hsvcolorrange[1],
hsvmax[2] + hsvcolorrange[2]
]
if hsvmin[0] < 0:
hsvmin[0] = 0
if hsvmin[1] < 0:
hsvmin[1] = 0
if hsvmin[2] < 0:
hsvmin[2] = 0
if hsvmax[0] > 255:
hsvmax[0] = 255
if hsvmax[1] > 255:
hsvmax[1] = 255
if hsvmax[2] > 255:
hsvmax[2] = 255
cv.InRangeS(hsvimage, cv.Scalar(hsvmin[0], hsvmin[1], hsvmin[2]),
cv.Scalar(hsvmax[0], hsvmax[1], hsvmax[2]), maskimage)
element = cv.CreateStructuringElementEx(5, 5, 2, 2, cv.CV_SHAPE_RECT)
cv.Erode(maskimage, maskimage, element, 1)
cv.Dilate(maskimage, maskimage, element, 1)
storage = cv.CreateMemStorage(0)
cv.Copy(maskimage, blobimage)
contour = cv.FindContours(maskimage, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
trackedpoint = None
maxtrackedpoint = None
maxareasize = 0
#You can tune these value to improve tracking
maxarea = 0
minarea = 1
areasize = 0
while contour:
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
areasize = fabs(bound_rect[2] * bound_rect[3])
if (areasize > maxareasize):
maxareasize = areasize
maxtrackedpoint = (int(
(pt1[0] + pt2[0]) / 2), int((pt1[1] + pt2[1]) / 2), 1.0)
cv.Rectangle(rgb_image, pt1, pt2, cv.CV_RGB(255, 0, 0), 1)
trackedpoint = maxtrackedpoint
if (trackedpoint != None):
cv.Circle(rgb_image, (trackedpoint[0], trackedpoint[1]), 5,
cv.CV_RGB(255, 0, 0), 1)
return trackedpoint
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
#nframes =+ 1
frame_size = cv.GetSize(frame)
color_image = cv.CreateImage(cv.GetSize(frame), 8, 3)
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
def totuple(a):
try:
return tuple(totuple(i) for i in a)
except TypeError:
return a
first = True
while True:
closest_to_left = cv.GetSize(frame)[0]
closest_to_right = cv.GetSize(frame)[1]
color_image = cv.QueryFrame(self.capture)
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
if first:
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
first = False
else:
cv.RunningAvg(color_image, moving_average, .1, None)
cv.ShowImage("BG", moving_average)
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
#cv.ShowImage("BG",difference)
# Convert the image to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.ShowImage("BG1", grey_image)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 40, 255, cv.CV_THRESH_BINARY)
#cv.ShowImage("BG2", grey_image)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 8)
cv.Erode(grey_image, grey_image, None, 3)
cv.ShowImage("BG3", grey_image)
storage = cv.CreateMemStorage(0)
global contour
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while contour:
global bound_rect
bound_rect = cv.BoundingRect(list(contour))
polygon_points = cv.ApproxPoly(list(contour), storage,
cv.CV_POLY_APPROX_DP)
contour = contour.h_next()
global pt1, pt2
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
#size control
if (bound_rect[0] - bound_rect[2] >
10) and (bound_rect[1] - bound_rect[3] > 10):
points.append(pt1)
points.append(pt2)
#points += list(polygon_points)
global box, box2, box3, box4, box5
box = cv.MinAreaRect2(polygon_points)
box2 = cv.BoxPoints(box)
box3 = np.int0(np.around(box2))
box4 = totuple(box3)
box5 = box4 + (box4[0], )
cv.FillPoly(grey_image, [
list(polygon_points),
], cv.CV_RGB(255, 255, 255), 0, 0)
cv.PolyLine(color_image, [
polygon_points,
], 0, cv.CV_RGB(255, 255, 255), 1, 0, 0)
cv.PolyLine(color_image, [list(box5)], 0, (0, 0, 255), 2)
#cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1)
if len(points):
#center_point = reduce(lambda a, b: ((a[0] + b[0]) / 2, (a[1] + b[1]) / 2), points)
center1 = (pt1[0] + pt2[0]) / 2
center2 = (pt1[1] + pt2[1]) / 2
#print center1, center2, center_point
#cv.Circle(color_image, center_point, 40, cv.CV_RGB(255, 255, 255), 1)
#cv.Circle(color_image, center_point, 30, cv.CV_RGB(255, 100, 0), 1)
#cv.Circle(color_image, center_point, 20, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, (center1, center2), 5,
cv.CV_RGB(0, 0, 255), -1)
cv.ShowImage("Target", color_image)
# Listen for ESC key
c = cv.WaitKey(7) % 0x100
if c == 27:
#cv.DestroyAllWindows()
break
def procImg(img, sideName, dispFlag):
#creates empty images of the same size
imdraw = cv.CreateImage(cv.GetSize(img), 8, 3)
#put the smoothed image here
imgSmooth = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.SetZero(imdraw)
cv.Smooth(img, imgSmooth, cv.CV_GAUSSIAN, 3, 0) #Gaussian filter the image
imgbluethresh = getthresholdedimg(
imgSmooth) #Get a color thresholed binary image
cv.Erode(imgbluethresh, imgbluethresh, None, 3)
cv.Dilate(imgbluethresh, imgbluethresh, None, 10)
#img2 = cv.CloneImage(imgbluethresh)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(imgbluethresh, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
centroidx = 0
centroidy = 0
prevArea = 0
pt1 = (0, 0)
pt2 = (0, 0)
while contour:
#find the area of each collection of contiguous points (contour)
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
#get the largest contour
area = bound_rect[2] * bound_rect[3]
if dispFlag:
print("Area= " + str(area))
if (area > prevArea and area > 3000):
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
# Draw bounding rectangle
cv.Rectangle(img, pt1, pt2, cv.CV_RGB(255, 0, 0), 3)
# calculating centroid
centroidx = cv.Round((pt1[0] + pt2[0]) / 2)
centroidy = cv.Round((pt1[1] + pt2[1]) / 2)
if (centroidx == 0 or centroidy == 0):
print("no blimp detected from " + sideName)
else:
print(sideName + " centroid x:" + str(centroidx))
print(sideName + " centroid y:" + str(centroidy))
print("")
if dispFlag:
small_thresh = cv.CreateImage(
(int(0.25 * cv.GetSize(imgbluethresh)[0]),
int(0.25 * cv.GetSize(imgbluethresh)[1])), 8, 1)
cv.Resize(imgbluethresh, small_thresh)
cv.ShowImage(sideName + "_threshold", small_thresh)
cv.WaitKey(100)
small_hsv = cv.CreateImage((int(
0.25 * cv.GetSize(imghsv)[0]), int(0.25 * cv.GetSize(imghsv)[1])),
8, 3)
cv.Resize(imghsv, small_hsv)
cv.ShowImage(sideName + "_hsv", small_hsv)
cv.WaitKey(100)
return (centroidx, centroidy)
def Closing(pos):
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(src, image, element, 1)
cv.Erode(image, dest, element, 1)
cv.ShowImage("Opening & Closing", dest)
1) #8depth, 1 channel so grayscale
cv.CvtColor(image, grey, cv.CV_RGBA2GRAY) #Convert to gray so act as a filter
cv.ShowImage('Greyed', grey)
smoothed = cv.CloneImage(image)
cv.Smooth(image, smoothed, cv.CV_MEDIAN
) #Apply a smooth alogrithm with the specified algorithm cv.MEDIAN
cv.ShowImage("Smoothed", smoothed)
cv.EqualizeHist(grey, grey) #Work only on grayscaled pictures
cv.ShowImage('Equalized', grey)
threshold1 = cv.CloneImage(grey)
cv.Threshold(threshold1, threshold1, 100, 255, cv.CV_THRESH_BINARY)
cv.ShowImage("Threshold Binary", threshold1)
threshold2 = cv.CloneImage(grey)
cv.Threshold(threshold2, threshold2, 100, 255, cv.CV_THRESH_OTSU)
cv.ShowImage("Threshold OTSU", threshold2)
element_shape = cv.CV_SHAPE_RECT
pos = 3
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(grey, grey, element,
2) #Replace a pixel value with the maximum value of neighboors
#There is others like Erode which replace take the lowest value of the neighborhood
#Note: The Structuring element is optionnal
cv.ShowImage("Dilated", grey)
cv.WaitKey(0)
while(1): # captures feed from video in color color_image = cv.QueryFrame(capture) # ?? imdraw = cv.CreateImage(cv.GetSize(frame), 8, 3) # ?? cv.SetZero(imdraw) cv.Flip(color_image,color_image, 1) cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0) # ?? imgbluethresh = getthresholdedimg(color_image) cv.Erode(imgbluethresh, imgbluethresh, None, 3) cv.Dilate(imgbluethresh, imgbluethresh, None, 10) # ?? img2 = cv.CloneImage(imgbluethresh) # ?? storage = cv.CreateMemStorage(0) contour = cv.FindContours(imgbluethresh, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE) # blank list into which points for bounding rectangles around blobs are appended points = [] # this is the new part here. ie use of cv.BoundingRect() while contour: # Draw bounding rectangles bound_rect = cv.BoundingRect(list(contour)) contour = contour.h_next()
def Dilation(pos):
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(src, dest, element, 1)
cv.ShowImage("Erosion & Dilation", dest)