I've read https://www.pyimagesearch.com/2015/11/02/watershed-opencv/ tutorial which has opened my eyes to this amazing possibility and I am now trying to implement it into my current object tracking program. I am struggling to implement this algorithm into my project because it requires a video stream and it also creates a mask to only see red objects. My main issue with the program is that overlapping objects are counted as one whereas after reading this tutorial I realized theres an algorithm for this but I cannot figure out how to implement it into my project.
Could anyone share some insight and hopefully call me an idiot and open my eyes to the possibility of this.
I appreciate any type of comment. thank you very much
tutorials/research I have followed https://www.pyimagesearch.com/2015/11/02/watershed-opencv/ Image Segmentation using Mean Shift explained https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_watershed/py_watershed.html
# This is my main functionality in my code. And I have no idea where I can implement
# watershed succesfully because of the color filtering and constant background change
while True:
frame = camera.read() # read camera
if frame is None:
print('fail with camera. Is it being used? src # correct?')
break
frame = imutils.resize(frame, width=400) # resize frame
height = np.size(frame, 0) # calculates the height of frame
width = np.size(frame, 1) # calculates the width of frame
blurred = cv2.GaussianBlur(frame, (21, 21), 0) # blurring image before hsv applied (less noise)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) # creating hsv from blurred frame and converting the bgr to hsv
mask = cv2.inRange(hsv, np.array(args["a"]), np.array(args["b"])) # mask is setting hsv in range of the lower and upper blue ranges
mask = cv2.erode(mask, None, iterations=2) # erode for less noise / more white
mask = cv2.dilate(mask, None, iterations=2) # dilate does similar but makes whiteness thicker
res = cv2.bitwise_and(frame, frame, mask=mask)
contours = cv2.findContours(mask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # find contours of mask
contours = imutils.grab_contours(contours) # get them
middleLine = (height / 2) # calculate the middleLine
cv2.line(frame, (0, height // 2), (width, height // 2), (100, 200, 100), 2) # // = int division | draw the line
rects = []
if len(contours) > 0: # don't pass in empty contour!!!
for c in contours: # loop through them
if cv2.contourArea(c) < args["e"]: # not big enough to be considered an object
continue # go next
(x, y, w, h) = cv2.boundingRect(c) # create rect for the object
I expect to be able to calculate a watershed algorithm in order to be able to uniquely identify objects that overlap on a webcam stream that also has color filtering but tutorials I've followed always leave me on a "cliffhanger" if you will because they use methods that work for images but not video and they dont have color filtering so I can't seem to get the picture of what I need to do to make it work for a videostream and color filtering project.
