def TrackFile(anim):
tracks = cvb.Tracks()
capture = cv.CreateFileCapture(anim)
cv.GrabFrame(capture)
img = cv.RetrieveFrame(capture)
frame = cv.CreateImage(cv.GetSize(img), img.depth, img.nChannels)
cnt = 1
while cv.GrabFrame(capture):
# Capture Frames
img = cv.RetrieveFrame(capture)
cv.ResetImageROI(frame)
cv.ConvertScale(img, frame, 1, 0)
cv.Threshold(frame, frame, 100, 200, cv.CV_THRESH_BINARY)
#rct=cv.Rectangle(0, 25, 383, 287)
cv.SetImageROI(frame, (0, 25, 383, 287))
chB = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.Split(frame, chB, None, None, None)
labelImg = cv.CreateImage(cv.GetSize(frame), cvb.IPL_DEPTH_LABEL, 1)
# Get Blobs and try Update Tracks
blobs = cvb.Blobs()
result = cvb.Label(chB, labelImg, blobs)
cvb.FilterByArea(blobs, 500, 1000)
# Trys are implemented here just to ensure crashes don't happen when blobs are not present
try:
print type(blobs.items()[0][1])
except:
pass
cvb.UpdateTracks(blobs, tracks, 5., 10, 0)
try:
print type(blobs.items()[0][1])
except:
pass
try:
print type(tracks.items()[0][1])
except:
pass
imgOut = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 3)
cv.Zero(imgOut)
cvb.RenderBlobs(
labelImg, blobs, frame, imgOut, cvb.CV_BLOB_RENDER_COLOR
| cvb.CV_BLOB_RENDER_CENTROID | cvb.CV_BLOB_RENDER_BOUNDING_BOX)
# Save images to see what's blobs are getting out.
cnt = cnt + 1
print cnt #
cv.SaveImage('blobs' + str(cnt) + '.png', imgOut)
return tracks, blobs
def printBlobs(img, min_color, max_color):
blobs = cvblob.Blobs() #Starts the blob class
size = cv.GetSize(img) #gets the size of the img
hsv = cv.CreateImage(
size, cv.IPL_DEPTH_8U,
3) #creates a new image for when the colored image is converted to hsv
thresh = cv.CreateImage(
size, cv.IPL_DEPTH_8U,
1) #creates a new image that is 1 channeled for the thresholding
labelImg = cv.CreateImage(
size, cvblob.IPL_DEPTH_LABEL,
1) #creates a image for later use for showing blobs
cv.CvtColor(img, hsv,
cv.CV_BGR2HSV) #converts the color image to an hsv image
cv.InRangeS(
hsv, min_color, max_color, thresh
) #finds colors between a min range and a max range with a source img and a destination image
#these are for corrections to thresholding to remove as many false positives
cv.Smooth(thresh, thresh, cv.CV_BLUR) #smooths out the thresholded image
cv.Dilate(thresh, thresh) #Dilates the thresholded image
cv.Erode(thresh, thresh) #Erodes the thresholded image
result = cvblob.Label(
thresh, labelImg, blobs
) #Don't know what this does exactly but it is used later to print the total number of pixels found
numblobs = len(
blobs.keys()
) #Number of blobs found. blobs is a dictionary with the keys as the number of the blob found and the value as the pointer to the location of the blob
avgSize = int(result /
numblobs) #Average size of the blobs casted as an int
print "average size: " + str(avgSize)
arr = [] #empty array to keep track fo what blobs to remove
for x in blobs:
if (
blobs[x].area < avgSize
): #if the size of the blob is less than half of the mean size, the blob is added to the array
arr.append(x)
for x in arr:
del blobs[x] # the blob is then removed from the dictionary of blobs
for x in blobs:
print str(blobs[x]) + "," + str(
blobs[x].area) #prints the blob number and the area of the blob
hmin = 50
hrange = 50
min_green = cv.Scalar(hmin, 100, 100, 0)
max_green = cv.Scalar(hmin + hrange, 256, 256, 0)
#cv.Smooth(img, img, cv.CV_GAUSSIAN)
#cv.Smooth(img, img, cv.CV_GAUSSIAN)
cv.CvtColor(img, hsv, cv.CV_BGR2HSV)
cv.InRangeS(hsv, min_green, max_green, green)
#cv.InRangeS(img, cv.RGB(200,230,200), cv.RGB(250,256,250),green);
''' Smooth out the HSV_image and dilate/erode'''
cv.Smooth(green, green, cv.CV_BLUR)
cv.Dilate(green, green)
cv.Erode(green, green)
result = cvblob.Label(green, labelImg, blobs)
numblobs = len(blobs.keys())
if (numblobs > 0):
# Average Size of Blobs
avgsize = int(result / numblobs)
print str(numblobs) + " blobs found covering " + str(result) + "px"
filtered = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_8U, 1)
cvblob.FilterLabels(labelImg, filtered, blobs)
# Largest Blob size
bigblob = cvblob.GreaterBlob(blobs)
print "largest blob is " + str(bigblob) + " which is " + str(
blobs[bigblob].area) + " px"
bigblobs = copy.copy(blobs)
blobs = cvblob.Blobs() size = cv.GetSize(img) hsv = cv.CreateImage(size, cv.IPL_DEPTH_8U, 3) h = cv.CreateImage(size, 8, 1) grey = cv.CreateImage(size, 8, 1) yuv = cv.CreateImage(size, 8, 3) red = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1) labelImg = cv.CreateImage(size,cvblob.IPL_DEPTH_LABEL, 1) hsv_min = cv.Scalar(0,10,10,0) hsv_max = cv.Scalar(180,255,255,0) cv.InRangeS(img, cv.RGB(200,0,0), cv.RGB(255,225,225),red); result = cvblob.Label(red,labelImg,blobs) numblobs = len(blobs.keys()) avgsize = int(result/numblobs) print str(numblobs) + " Blobs found covering " + str(result) + "px" filtered = cv.CreateImage(cv.GetSize(img),cv.IPL_DEPTH_8U,1) cvblob.FilterLabels(labelImg,filtered,blobs) bigblob = cvblob.GreaterBlob(blobs) print "largest blob is " + str(bigblob) + " which is " + str(blobs[bigblob].area + "px" bigblobs = copy.copy(blobs) cvblob.FilterByLabel(bigblobs,bigblob) print str(len(bigblobs.keys())) + " blobs with label " + str(bigblob) centroid = cvblob.Centroid(bigblobs[bigblob])
def findBlobs(img, min_color, max_color, window, renderedwindow, location,
area):
blobs = cvblob.Blobs() #initializes the blobs class
size = cv.GetSize(img) #gets size of image
hsv = cv.CreateImage(size, cv.IPL_DEPTH_8U, 3) #New HSV image for alter
thresh = cv.CreateImage(size, cv.IPL_DEPTH_8U,
1) #New Gray Image for later
labelImg = cv.CreateImage(size, cvblob.IPL_DEPTH_LABEL,
1) #New Blob image for later
cv.CvtColor(img, hsv, cv.CV_BGR2HSV) #converts image to hsv image
cv.InRangeS(hsv, min_color, max_color, thresh) #thresholds it
#Corrections to remove false positives
cv.Smooth(thresh, thresh, cv.CV_BLUR)
cv.Dilate(thresh, thresh)
cv.Erode(thresh, thresh)
result = cvblob.Label(thresh, labelImg,
blobs) #extracts blobs from a greyscale image
numblobs = len(
blobs.keys()) #number of blobs based off of length of blobs dictionary
#if there are blobs found
if (numblobs > 0):
avgsize = int(result / numblobs)
print str(numblobs) + " blobs found covering " + str(result) + "px"
#Removes blobs that are smaller than a certain size based off of average size
remv = []
for x in blobs:
if (blobs[x].area < avgsize / 3):
remv.append(x)
for x in remv:
del blobs[x]
numblobs = len(
blobs.keys()) #gets the number of blobs again after removing some
print str(numblobs) + " blobs remaining"
filtered = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_8U, 1)
cvblob.FilterLabels(
labelImg, filtered, blobs
) #Creates a binary image with the blobs formed (imgIn, imgOut, blobs)
#Centroid, area, and circle for all blobs
for blob in blobs:
location.append(cvblob.Centroid(blobs[blob]))
area.append(blobs[blob].area)
cv.Circle(img, (int(cvblob.Centroid(
blobs[blob])[0]), int(cvblob.Centroid(blobs[blob])[1])),
int(math.sqrt(int(blobs[blob].area) / 3.14)) + 25,
cv.Scalar(0, 0, 0))
imgOut = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_8U, 3)
cv.Zero(imgOut)
cvblob.RenderBlobs(
labelImg, blobs, img, imgOut,
cvblob.CV_BLOB_RENDER_COLOR | cvblob.CV_BLOB_RENDER_CENTROID
| cvblob.CV_BLOB_RENDER_BOUNDING_BOX | cvblob.CV_BLOB_RENDER_ANGLE,
1.0) #Marks up the blobs image to put bounding boxes, etc on it
cv.ShowImage("Window", img) #shows the orininalimage
cv.ShowImage("Rendered", imgOut) #shows the blobs image
return blobs #returns the list of blobs
else:
print " ...Zero blobs found. \nRedifine color range for better results" #if no blobs were found print an error message
cv.ShowImage("Window", img) #show the original image