added obj tracking using hsv

Author: saimj7

Object tracking using HSV color spaces/README.md

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+# Object detection and tracking
+### Using HSV color spaces
+
+> Benefits: Simple code; Fast object tracking in real-time, easily obtaining 32+ FPS on modern hardware systems.
+
+- To test on a video: ```python run.py --video video.mp4```.
+
+- To test on webcam: ```python run.py```.
+
+> Where video.mp4 should be a video file in your disk. Note that the script is intended to find balls (green and blue) based only on their colors.
+
+- One example to define such a color space (line 12-14 in run.py) is by using the HSV chart:
+
+<div align="center">
+<img src= misc/hsv.png?raw=true width=500>
+</div>
+
+- Notice how values fall in the range [0, 360]. Simply divide by 2 to bring values into the range [0, 180] which is what OpenCV expects.
+
+> As we know, colors can appear dramatically different depending on our lighting conditions. Instead, an approach of gathering data, performing experiments, and validating is better.
+
+## References
+- For solid theory: https://www.pyimagesearch.com/pyimagesearch-gurus/
+
+---
+
+saimj7/ 26-05-2021 © <a href="http://saimj7.github.io" target="_blank">Sai_Mj</a>.

Object tracking using HSV color spaces/misc/hsv.png

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+# import the necessary packages
+import argparse
+import imutils
+import cv2
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-v", "--video", help="path to the (optional) video file")
+args = vars(ap.parse_args())
+
+# define the color ranges
+colorRanges = [
+	((29, 86, 6), (64, 255, 255), "green"),
+	((57, 68, 0), (151, 255, 255), "blue")]
+
+# if a video path was not supplied, grab the reference to the webcam
+if not args.get("video", False):
+	camera = cv2.VideoCapture(0)
+
+# otherwise, grab a reference to the video file
+else:
+	camera = cv2.VideoCapture(args["video"])
+
+# keep looping
+while True:
+	# grab the current frame
+	(grabbed, frame) = camera.read()
+
+	# if we are viewing a video and we did not grab a frame, then we have
+	# reached the end of the video
+	if args.get("video") and not grabbed:
+		break
+
+	# resize the frame, blur it, and convert it to the HSV color space
+	frame = imutils.resize(frame, width=600)
+	blurred = cv2.GaussianBlur(frame, (11, 11), 0)
+	hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+	# loop over the color ranges
+	for (lower, upper, colorName) in colorRanges:
+		# construct a mask for all colors in the current HSV range, then
+		# perform a series of dilations and erosions to remove any small
+		# blobs left in the mask
+		mask = cv2.inRange(hsv, lower, upper)
+		mask = cv2.erode(mask, None, iterations=2)
+		mask = cv2.dilate(mask, None, iterations=2)
+
+		# find contours in the mask
+		cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
+			cv2.CHAIN_APPROX_SIMPLE)
+		cnts = imutils.grab_contours(cnts)
+
+		# only proceed if at least one contour was found
+		if len(cnts) > 0:
+			# find the largest contour in the mask, then use it to compute
+			# the minimum enclosing circle and centroid
+			c = max(cnts, key=cv2.contourArea)
+			((x, y), radius) = cv2.minEnclosingCircle(c)
+			M = cv2.moments(c)
+			(cX, cY) = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
+
+			# only draw the enclosing circle and text if the radious meets
+			# a minimum size
+			if radius > 10:
+				cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)
+				cv2.putText(frame, colorName, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
+					1.0, (0, 255, 255), 2)
+
+	# show the frame to our screen
+	cv2.imshow("Frame", frame)
+	key = cv2.waitKey(1) & 0xFF
+
+	# if the 'q' key is pressed, stop the loop
+	if key == ord("q"):
+		break
+
+# cleanup the camera and close any open windows
+camera.release()
+cv2.destroyAllWindows()