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)
cvblob.FilterByLabel(bigblobs, bigblob)
#print str(len(bigblobs.keys())) + " blobs with label " + str(bigblob)
# find centroid of largest blob
centroid = cvblob.Centroid(bigblobs[bigblob])
print "centroid of blob " + str(bigblob) + " is " + str(centroid)
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)
print "mean color for blob " + str(bigblob) + " is " + str(
cvblob.BlobMeanColor(blobs[bigblob], labelImg, img))
#cv.ShowImage("test_filtered", filtered)
cv.ShowImage("Original", img)
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