def repeat():
global capture #declare as globals since we are assigning to them now
global camera_index
global done
frame = cv.QueryFrame(capture)
cv.Smooth(frame, frame, cv.CV_GAUSSIAN, 3, 3)
imgHsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, imgHsv, cv.CV_BGR2HSV)
#imgHsv2 = GetThresholdedImage(imgHsv)
#print(numpy.asarray(cv.GetMat(imgHsv)))
imgRGBA = cv.CreateImage(cv.GetSize(frame), 8, 4)
cv.CvtColor(frame, imgRGBA, cv.CV_BGR2RGBA)
cv.Smooth(imgRGBA, imgRGBA, cv.CV_GAUSSIAN, 3, 3)
(filteredImg, offsetX, offsetY) = parallelSumRed(imgRGBA, 640,
480) #3D array
d = numpy.sqrt(offsetX * offsetX + offsetY * offsetY)
if d != 0:
print("Distance = " + str(c1 / d + c2) + "cm")
print("OffsetX = " + str(offsetX) + "; OffsetY = " + str(offsetY))
print("")
imgRGB = cv.CreateImage(cv.GetSize(frame), 8, 3)
#cv.CvtColor(Image.fromarray(filteredImg), imgRGB, cv.CV_RGBA2RGB)
imgRGBA = cv.fromarray(numpy.reshape(filteredImg, (480, 640, 4)))
if offsetX != 0 or offsetY != 0:
cv.Rectangle(imgRGBA, (320 + offsetX - 6, 240 + offsetY - 6),
(320 + offsetX + 6, 240 + offsetY + 6),
(255, 0, 255, 255), 1, 8)
cv.Line(imgRGBA, (0, 240 + offsetY), (639, 240 + offsetY),
(255, 0, 255, 255), 1, 8)
cv.Line(imgRGBA, (320 + offsetX, 0), (320 + offsetX, 479),
(255, 0, 255, 255), 1, 8)
cv.ShowImage(HSVWindow, imgRGBA)
cv.ShowImage(original, frame)
cv.SetMouseCallback(original, onMouseMove, [
cv.CV_EVENT_MOUSEMOVE,
numpy.asarray(cv.GetMat(imgHsv)),
numpy.asarray(cv.GetMat(frame))
])
#cv.SetMouseCallback(HSVWindow, onMouseMove, [cv.CV_EVENT_MOUSEMOVE, numpy.asarray(cv.GetMat(imgHsv)), numpy.asarray(cv.GetMat(frame))])
#cv.ShowImage(filtered, imgHsv2)
c = cv.WaitKey(10)
if (str(c) == "27"): #if ESC is pressed
print("Thank You!")
done = True
if (str(c) == "99"): #'c' for calibration
calibration(int(input("How many data points: ")))
def difference_image(img1, img2):
print " simg1 = simplify(img1)"
simg1 = simplify(img1)
print " simg2 = simplify(img2)"
simg2 = simplify(img2)
#dbg_image('simg1',simg1)
#dbg_image('simg2',simg2)
#create image buffers
img3 = cv.CreateImage(cv.GetSize(img2), cv.IPL_DEPTH_8U, 1)
simg3 = cv.CloneImage(img3)
bitimage = cv.CreateImage(cv.GetSize(img2), cv.IPL_DEPTH_8U, 1)
eimg3 = cv.CloneImage(bitimage)
#process
print " cv.AbsDiff(simg2,simg1,img3)"
cv.AbsDiff(simg2, simg1, img3)
print " cv.Smooth(img3,simg3)"
cv.Smooth(img3, simg3)
#dbg_image('simg3',simg3)
# these threshold values must be calibrated
#cv.Threshold(simg3,bitimage,16,255,cv.CV_THRESH_TOZERO_INV)
print " cv.Threshold(simg3,bitimage,16,255,cv.CV_THRESH_BINARY)"
cv.Threshold(simg3, bitimage, 50, 255, cv.CV_THRESH_BINARY)
#dbg_image('bitimage',bitimage)
print " cv.Erode(bitimage,eimg3)"
cv.Erode(bitimage, eimg3)
#dbg_image('eimg3',eimg3)
return eimg3
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 laplacian():
cv.Smooth(src_image, dst_image, cv.CV_GAUSSIAN, 9, 9)
dst_img = cv.CreateImage((dst_image.width, dst_image.height), 8, 1)
cv.CvtColor(dst_image, dst_img, cv.CV_RGB2GRAY)
cv.Laplace(dst_img, dst_img, 5)
display(dst_img, "Laplacian")
cv.WaitKey(0)
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 post_process_distance_img(self, dist_img):
inverted_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
#Blur image
soft_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Smooth(dist_img, soft_img, cv.CV_GAUSSIAN, 21, 21)
#Apply threshold to have just black and white
thresh_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Threshold(soft_img, thresh_img, 1, 255, cv.CV_THRESH_BINARY)#CV_THRESH_OTSU is an adaptive thresholding method
# #Create inverse image
# for r in range(0,thresh_img.rows):
# for c in range(0,thresh_img.cols):
# inverted_img[r,c]=255-thresh_img[r,c]
#Erode the inverse map to get it's skeleton
eroded_img = cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Erode(inverted_img, eroded_img, iterations=10)
#Create inverse image
for r in range(0,eroded_img.rows):
for c in range(0,eroded_img.cols):
inverted_img[r,c]=255-eroded_img[r,c]
return inverted_img
def smooth(grayscale, gaussian_std=5.0):
"""
Smooths an image with a Gaussian filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param gaussian_std: Std-deviation of the Gaussian filter.
:type gaussian_std: float,>0
:return: smoothed: The smoothed image.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
grayscale = grayscale.astype('float32')
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
smoothed = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Smooth(im, smoothed, cv.CV_GAUSSIAN, 0, 0, gaussian_std)
result_a = cv_to_numpy(smoothed).squeeze()
return result_a
def smooth(image):
mat = cv.fromarray(image)
dest = cv.fromarray(np.zeros((len(image), len(image)), np.uint8))
#GaussianSmooth
#cv.Smooth(mat,dest)
cv.Smooth(mat, dest, smoothtype=cv.CV_MEDIAN)
#cv.Set(dest)
return np.asarray(dest[:][:], np.uint8)
def gaussianFilter(inputImage, filterSize):
"""
Apply gaussian filter of OpenCv to a given array
"""
# Convert from numpy array to CvMat
outputImage = cv.CreateImage(cv.GetSize(inputImage), cv.IPL_DEPTH_32F, 1)
cv.Smooth(inputImage, outputImage, cv.CV_GAUSSIAN, filterSize, filterSize)
return outputImage
def medianfiltering():
src = cv.LoadImageM(k, cv.CV_LOAD_IMAGE_COLOR)
dst = cv.CreateImage((src.width, src.height), 8, src.channels)
cv.SetZero(dst)
cv.NamedWindow("Median Filtering", 1)
cv.NamedWindow("After Filtering", 1)
cv.Smooth(src, dst, cv.CV_MEDIAN, 9, 9)
cv.ShowImage("Median Filtering", src)
cv.ShowImage("After Filtering", dst)
cv.WaitKey(0)
def processImage(self, frame):
cv.CvtColor(frame, self.frame2gray, cv.CV_RGB2GRAY)
#Absdiff to get the difference between to the frames
cv.AbsDiff(self.frame1gray, self.frame2gray, self.res)
#Remove the noise and do the threshold
cv.Smooth(self.res, self.res, cv.CV_BLUR, 5, 5)
cv.MorphologyEx(self.res, self.res, None, None, cv.CV_MOP_OPEN)
cv.MorphologyEx(self.res, self.res, None, None, cv.CV_MOP_CLOSE)
cv.Threshold(self.res, self.res, 10, 255, cv.CV_THRESH_BINARY_INV)
def findOffsetInOneFrame():
global capture #declare as globals since we are assigning to them now
global camera_index
frame = cv.QueryFrame(capture)
cv.Smooth(frame, frame, cv.CV_GAUSSIAN, 3, 3)
imgHsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, imgHsv, cv.CV_BGR2HSV)
#imgHsv2 = GetThresholdedImage(imgHsv)
#print(numpy.asarray(cv.GetMat(imgHsv)))
imgRGBA = cv.CreateImage(cv.GetSize(frame), 8, 4)
cv.CvtColor(frame, imgRGBA, cv.CV_BGR2RGBA)
cv.Smooth(imgRGBA, imgRGBA, cv.CV_GAUSSIAN, 3, 3)
(filteredImg, offsetX, offsetY) = parallelSumRed(imgRGBA, 640,
480) #3D array
return numpy.sqrt(offsetX * offsetX + offsetY * offsetY) #d
def smooth(self, image, param1=5, param2=16):
smoothed = cv.CreateImage(cv.GetSize(image), image.depth,
image.channels)
cv.Smooth(image,
smoothed,
smoothtype=cv.CV_GAUSSIAN_5x5,
param1=5,
param2=16)
return smoothed
def getThresholdImage(im):
newim = cv.CloneImage(im)
cv.Smooth(newim, newim, cv.CV_BLUR, 12) #Remove noise
hsv = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(newim, hsv, cv.CV_BGR2HSV) # Convert image to HSV
imThreshed = cv.CreateImage(cv.GetSize(im), 8, 1)
#Do the threshold on the hsv image, with the right range for the yellow color
cv.InRangeS(hsv, cv.Scalar(20, 100, 100), cv.Scalar(30, 255, 255),
imThreshed)
del hsv
return imThreshed
def Color_callibration(capture):
vals = []
bgr = []
mini = [255, 255, 255]
maxi = [0, 0, 0]
cv.NamedWindow("BGR", 0)
print 'Please Put Your color in the circular area.Press ESC to start Callibration:'
while 1:
image = cv.QueryFrame(capture)
cv.Flip(image, image, 1)
cv.Circle(image, (int(200), int(300)), 10, cv.CV_RGB(255, 255, 255), 4)
cv.ShowImage("BGR", image)
c = cv.WaitKey(33)
if c == 27:
break
print 'Starting Callibration...Analyzing the Object...'
for i in range(0, 100):
image = cv.QueryFrame(capture)
cv.Flip(image, image, 1)
cv.Smooth(image, image, cv.CV_MEDIAN, 3, 0)
imagehsv = cv.CreateImage(cv.GetSize(image), 8, 3)
cv.CvtColor(image, imagehsv, cv.CV_BGR2YCrCb)
vals = cv.Get2D(imagehsv, 300, 200)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 1, 0, 2, 8)
cv.PutText(
image,
" " + str(vals[0]) + "," + str(vals[1]) + "," + str(vals[2]),
(200, 300), font, (55, 25, 255))
for j in range(0, 3):
if (vals[j] < mini[j]): mini[j] = vals[j]
if (vals[j] > maxi[j]): maxi[j] = vals[j]
cv.Circle(image, (int(200), int(300)), 10, cv.CV_RGB(255, 255, 255), 4)
cv.ShowImage("BGR", image)
c = cv.WaitKey(33)
if c == 27:
break
print 'Analyzation Completed'
mini[0] -= 35
mini[1] -= 15
mini[2] -= 15
maxi[0] += 35
maxi[1] += 15
maxi[2] += 15
for i in range(0, 3):
if (mini[i] < 0):
mini[i] = 0
if (maxi[i] > 255):
maxi[i] = 255
cv.DestroyWindow("BGR")
bgr = (mini, maxi)
return bgr
def main():
# captured image size, change to whatever you want
width = 320
height = 240
capture = cv.CreateCameraCapture(0)
# Over-write default captured image size
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_WIDTH,width)
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_HEIGHT,height)
cv.NamedWindow( "output", 1 )
cv.NamedWindow( "processed", 1 )
while True:
frame = cv.QueryFrame(capture)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
imgColorProcessed = ColorProcess(frame)
mat = cv.GetMat(imgColorProcessed)
# Calculating the moments
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0,1)
# Finding a big enough blob
if(area > 60000):
# Calculating the center postition of the blob
posX = int(moment10 / area)
posY = int(moment01 / area)
# check slave status and send coordinates
state = readData()
if state == 1:
sendData(posX)
sendData(posY)
print 'x: ' + str(posX) + ' y: ' + str(posY)
# update video windows
cv.ShowImage("processed", imgColorProcessed)
cv.ShowImage("output", frame)
if cv.WaitKey(10) >= 0:
break
return;
def sobel():
cv.Smooth(src_image, dst_image, cv.CV_GAUSSIAN, 3, 3)
src_gray = cv.CreateImage((src_image.width, src_image.height), 8, 1)
dst_gray1 = cv.CreateImage((src_image.width, src_image.height), 8, 1)
dst_gray = cv.CreateImage((src_image.width, src_image.height), 8, 1)
cv.CvtColor(src_image, src_gray, cv.CV_BGR2GRAY)
cv.Sobel(src_gray, dst_gray1, 0, 1, 3)
cv.ConvertScaleAbs(dst_gray1, dst_gray1, 1, 0)
cv.Sobel(src_gray, dst_gray, 1, 0, 3)
cv.ConvertScaleAbs(dst_gray, dst_gray, 1, 0)
cv.AddWeighted(dst_gray, 0.5, dst_gray1, 0.5, 0, dst_gray)
cv.NamedWindow("Destination Image")
cv.ShowImage("Destination Image", dst_gray)
cv.WaitKey(0)
def extractEyeBrows(originalImage, pt1, centerX, centerY, eyeBallParams):
(eyeBallCenterX, eyeBallCenterY, eyeBallRadius) = eyeBallParams
# find good features
# eig_image = cv.CreateMat(gray_im.rows, gray_im.cols, cv.CV_32FC1)
# temp_image = cv.CreateMat(gray_im.rows, gray_im.cols, cv.CV_32FC1)
# for (x,y) in cv.GoodFeaturesToTrack(gray_im, eig_image, temp_image, 10, 0.04, 1.0, useHarris = True):
# print "good feature at", x,y
# cv.Rectangle(img, (int(x), int(y)),(int(x) + 20, int(y) + 20), cv.RGB(255, 255, 255))
#find color of the skin
#prepare histogram
eyebrow_Area = cv.GetSubRect(
originalImage, (int(pt1[0] * 1.1), int(
pt1[1] * 1.2), centerX - pt1[0], int((centerY - pt1[1]) * 0.6)))
eyebrow_Area2 = cv.CloneMat(eyebrow_Area)
cv.Smooth(eyebrow_Area2, eyebrow_Area2, cv.CV_GAUSSIAN, 9, 1)
hsv_image = cv.CreateMat(eyebrow_Area.height, eyebrow_Area.width,
cv.CV_8UC3)
imageArray = np.asarray(eyebrow_Area2, dtype=np.uint8)
hsv_image = cv2.cvtColor(imageArray, cv2.COLOR_BGR2HSV)
# histogram2 = hs_histogram(leftEyeArea)
# print(histogram2)
# imageArray2 = np.asarray(histogram2, dtype=np.uint8)
# cv2.imshow("histo " , histogram2)
#
#dark = imageArray[...,2] < 32
#set not frequent to dark
#imageArray[dark] = 0
#histogram = cv.CreateHist(2, cv.CV_HIST_ARRAY)
histogram = cv2.calcHist([hsv_image], [0, 1], None, [180, 256],
[0, 180, 0, 256])
h1 = np.clip(histogram * 0.005 * hist_scale, 0, 1)
vis = hsv_map * h1[:, :, np.newaxis] / 255.0
#print type(vis)
#cv2.imshow('hist', vis)
#backproj = None
#cv.CalcBackProject(hsv_image, backproj, histogram)
ranges = [0, 180, 0, 256]
backproj = cv2.calcBackProject([hsv_image], [0, 1], histogram, ranges, 10)
cv2.imshow("back proj ", backproj)
def reduce_image_noise(self, image): """Reduce noise from image, etc. Prameters: image The image to be processed """ cv.Smooth(image, image, cv.CV_BLUR, 5, 5) #cv.MorphologyEx(image, image, None, None, cv.CV_MOP_OPEN) #cv.MorphologyEx(image, image, None, None, cv.CV_MOP_CLOSE) return image
def run(self):
while True:
img = cv.QueryFrame(self.capture)
#blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3)
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.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
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
cv.InRangeS(hsv_img, (120, 80, 80), (140, 255, 255),
thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
moments = cv.Moments(cv.GetMat(thresholded_img), 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 10000):
#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 = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.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 = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.Circle(overlay, (int(x), int(y)), 2, (255, 255, 255), 20)
#cv.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
#cv.Merge(thresholded_img, None, None, None, img)
#display the image
cv.ShowImage(color_tracker_window, img)
if cv.WaitKey(10) == 27:
break
def on_trackbar(position):
cv.Smooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.Not(gray, edge)
# run the edge dector on gray scale
cv.Canny(gray, edge, position, position * 3, 3)
# reset
cv.SetZero(col_edge)
# copy edge points
cv.Copy(im, col_edge, edge)
# show the im
cv.ShowImage(win_name, col_edge)
def run():
# Capture image as CvCapture object
capture = cv.CaptureFromFile('blob.jpeg')
# get image from capture
image = cv.QueryFrame(capture)
# Smooth to get rid of false positives
cv.Smooth(image,image,cv.CV_GAUSSIAN,19,0)
# Display smoothed image
cv.ShowImage('Blobs',image)
findBlobs(image)
cv.WaitKey(10000)
def on_trackbar(self, position):
cv.Smooth(self.source_image, self.edge, cv.CV_BLUR, 3, 3, 0)
cv.Not(self.source_image, self.edge)
# run the edge dector on gray scale
cv.Canny(self.source_image, self.edge, position, position * 3, 3)
# reset
cv.SetZero(self.col_edge)
# copy edge points
cv.Copy(self.source_color, self.col_edge, self.edge)
# show the im
cv.ShowImage(win_name, self.col_edge)
self.process_image(position)
def test_tangent_45plusdegree(self):
degree45 = cv.LoadImage(
os.path.abspath(os.environ['BORG'] +
'/Brain/data/hog_test/45degree.jpg'))
smoothed = cv.CreateImage(cv.GetSize(degree45), degree45.depth,
degree45.channels)
cv.Smooth(degree45, smoothed)
image = smoothed
(dx, dy) = self.gradient.sobelGradient(image)
canny = self.gradient.cannyGradient(image)
tangent = self.gradient.tangent(dx, dy, canny)
size = cv.GetSize(tangent)
for x in range(size[0]):
for y in range(size[1]):
if canny[y, x] > 0:
tang = math.atan2(dy[y, x], dx[y, x]) * self.constant
tang = int(tang)
if tang < 0:
tang = 360 + tang
'''if dx[y,x] < 0 and dy[y,x] >= 0:
tang += 180
elif dx[y,x] < 0 and dy[y,x] < 0:
tang -= 180'''
else:
tang = 0
if tangent[y, x] != tang:
print 'DX ->column: ', x, ' ,Row: ', y, ' ,Value: ', dx[y,
x]
print 'DY ->column: ', x, ' ,Row: ', y, ' ,Value: ', dy[y,
x]
print 'Real tangent:', tang
print 'The tangent from function', tangent[y, x]
self.fail("The tangents do not match")
if canny[y, x] > 0 and not (
(tangent[y, x] >= 180 and tangent[y, x] <= 270)):
print 'The tangent from function', tangent[
y, x], "calculated here is:", tang
self.fail(
"The tangent is not in the range of 225 and 270. (45 degrees)"
)
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
def processaImagem(self):
"""
Crio uma imagem cinza a partir da atual para o programa ficar mais rapido, crio uma imagem com a
diferenca da imagem anterior e a imagem atual, e binarizo a imagem cinza para filtrar pixels pequenos.
"""
# Remove os falsos positivos.
cv.Smooth(self.imagem_atual, self.imagem_atual)
# Aqui eu coloco um tempo de execucao entre as imagens.
cv.RunningAvg(self.imagem_atual, self.imagem_auxiliar, 0.05)
# Covertendo de volta a imagem para poder trabalhar.
cv.Convert(self.imagem_auxiliar, self.imagem_anterior)
# Cria uma nova imagem com a diferenca entre a imagem anterior e a atual.
cv.AbsDiff(self.imagem_atual, self.imagem_anterior, self.imagem_diferenca)
# Converte a imagem atual em escala de cinza.
cv.CvtColor(self.imagem_diferenca, self.imagem_cinza, cv.CV_RGB2GRAY)
# Binariza a imagem. Para poder filtrar pixels pequenos.
cv.Threshold(self.imagem_cinza, self.imagem_cinza, 50, 255, cv.CV_THRESH_BINARY)
def ImagePro(self, capture, orig, processed, storage, grid):
orig = cv.QueryFrame(capture)
#cv.Normalize(orig)
# filter for all yellow and blue - everything else is black
processed = processor.colorFilterCombine(orig, "yellow", "blue", s)
# Some processing and smoothing for easier circle detection
cv.Canny(processed, processed, 5, 70, 3)
cv.Smooth(processed, processed, cv.CV_GAUSSIAN, 7, 7)
#cv.ShowImage('processed2', processed)
# Find&Draw circles
processor.find_circles(processed, storage, 100)
#if it is in the range of 1 to 9, we can try and recalibrate our filter
#if 1 <= storage.rows < 10:
# s = autocalibrate(orig, storage)
processor.draw_circles(storage, orig)
#warp = processor.update_grid(storage, orig, grid)
# Delete and recreate the storage so it has the correct width
#del(storage)
#storage = cv.CreateMat(orig.width, 1, cv.CV_32FC3)
#cv.ShowImage('output', orig)
#return processed
#cv.ShowImage('grid', warp)
#warp = perspective_transform(orig)
#cv.ShowImage('warped', warp)
mask = cv.CreateImage((640, 480), cv.IPL_DEPTH_8U, 3)
cv.Resize(orig, mask)
return mask
# blank lists to store coordinates of blue blob blue = [] 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:
