handai-detector/examples/dlib_objs_tracking.py

# USAGE
# python multi_object_tracking.py --video videos/soccer_01.mp4 --tracker csrt
# python multi_object_tracking.py --video ~/Desktop/5min.mp4 --tracker csrt


# import the necessary packages
from imutils.video import VideoStream
import argparse
import imutils
import time
import cv2
import dlib

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str,
	help="path to input video file")
ap.add_argument("-t", "--tracker", type=str, default="kcf",
	help="OpenCV object tracker type")
args = vars(ap.parse_args())

# initialize a dictionary that maps strings to their corresponding
# OpenCV object tracker implementations
OPENCV_OBJECT_TRACKERS = {
	"csrt": cv2.TrackerCSRT_create,
	"kcf": cv2.TrackerKCF_create,
	"boosting": cv2.TrackerBoosting_create,
	"mil": cv2.TrackerMIL_create,
	"tld": cv2.TrackerTLD_create,
	"medianflow": cv2.TrackerMedianFlow_create,
	"mosse": cv2.TrackerMOSSE_create
}

# initialize OpenCV's special multi-object tracker
# trackers = cv2.MultiTracker_create()
trackers = []

# if a video path was not supplied, grab the reference to the web cam
if not args.get("video", False):
	print("[INFO] starting video stream...")
	vs = VideoStream(src=0).start()
	time.sleep(1.0)

# otherwise, grab a reference to the video file
else:
	vs = cv2.VideoCapture(args["video"])

# loop over frames from the video stream
while True:
	# grab the current frame, then handle if we are using a
	# VideoStream or VideoCapture object
	frame = vs.read()
	frame = frame[1] if args.get("video", False) else frame

	# check to see if we have reached the end of the stream
	if frame is None:
		break

	# resize the frame (so we can process it faster)
	# frame = imutils.resize(frame, width=600)
	frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	# grab the updated bounding box coordinates (if any) for each
	# object that is being tracked
	# (success, boxes) = trackers.update(frame)
	# print('success', success)
	# print('boxes', boxes)

	for tk in trackers:
		tk.update(frame_rgb)
		pos = tk.get_position()
		# unpack the position object
		startX = int(pos.left())
		startY = int(pos.top())
		endX = int(pos.right())
		endY = int(pos.bottom())

		cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)

	# loop over the bounding boxes and draw then on the frame
	# for box in boxes:
	# 	(x, y, w, h) = [int(v) for v in box]
	# 	cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

	# show the output frame
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF

	# if the 's' key is selected, we are going to "select" a bounding
	# box to track
	if key == ord("s"):
		# select the bounding box of the object we want to track (make
		# sure you press ENTER or SPACE after selecting the ROI)
		box = cv2.selectROI("Frame", frame, fromCenter=False,
			showCrosshair=True)
		print('select box: ', box)
		(x,y,w,h) = box
		startX = x
		startY = y
		endX = x + w
		endY = y + h
		print(startX, startY, endX, endY)

		# create a new object tracker for the bounding box and add it
		# to our multi-object tracker
		# tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
		# trackers.add(tracker, frame, box)
		tracker = dlib.correlation_tracker()
		rect = dlib.rectangle(startX, startY, endX, endY)
		print('rect', rect)

		tracker.start_track(frame_rgb, rect)
		trackers.append(tracker)

	# if the `q` key was pressed, break from the loop
	elif key == ord("q"):
		break

# if we are using a webcam, release the pointer
if not args.get("video", False):
	vs.stop()

# otherwise, release the file pointer
else:
	vs.release()

# close all windows
cv2.destroyAllWindows()