Frame differencing gives us some useful information, but we cannot use it to build anything meaningful. In order to build a good object tracker, we need to understand what characteristics can be used to make our tracking robust and accurate. So, let's take a step in that direction and see how we can use colorspaces to come up with a good tracker. As we have discussed in previous chapters, HSVcolorspace is very informative when it comes to human perception. We can convert an image to the HSV space, and then use colorspacethresholding to track a given object.
Consider the following frame in the video:
If you run it through the colorspace filter and track the object, you will see something like this:
As we can see here, our tracker recognizes a particular object in the video, based on the color characteristics. In order to use this tracker, we need to know the color distribution of our target object. Following is the code:
import cv2
import numpy as np
# Capture the input frame from webcam
def get_frame(cap, scaling_factor):
# Capture the frame from video capture object
ret, frame = cap.read()
# Resize the input frame
frame = cv2.resize(frame, None, fx=scaling_factor,
fy=scaling_factor, interpolation=cv2.INTER_AREA)
return frame
if __name__=='__main__':
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
# Iterate until the user presses ESC key
while True:
frame = get_frame(cap, scaling_factor)
# Convert the HSV colorspace
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define 'blue' range in HSV colorspace
lower = np.array([60,100,100])
upper = np.array([180,255,255])
# Threshold the HSV image to get only blue color
mask = cv2.inRange(hsv, lower, upper)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame, frame, mask=mask)
res = cv2.medianBlur(res, 5)
cv2.imshow('Original image', frame)
cv2.imshow('Color Detector', res)
# Check if the user pressed ESC key
c = cv2.waitKey(5)
if c == 27:
break
cv2.destroyAllWindows()