Updates

* dlib sorta miss a lot

.gitignore

@@ -0,0 +1,4 @@
+*__pycache__*
+*.mp4
+
+.DS_Store

.vscode/settings.json

@@ -1,4 +1,7 @@
 {
     "python.pythonPath": "~/.virtualenvs/obj-tracking/bin/python",
-    "python.formatting.provider": "black"
+    "python.formatting.provider": "black",
+    "python.linting.pylintArgs": [
+        "--extension-pkg-whitelist=cv2"
+    ]
 }

README.md

@@ -1,7 +1,7 @@
 
 # Handai aerial detector
 
-We need to analyze road users in mid-block crossing area between Handai monorail station and handai hospital.
+We need to analyze road users in mid-block crossing area between Handai monorail station and Handai hospital.
 
 ## Output we need
 

examples/dlib_objs_tracking.py

@@ -14,21 +14,21 @@ 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")
+    help="path to input video file")
 ap.add_argument("-t", "--tracker", type=str, default="kcf",
-	help="OpenCV object tracker type")
+    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
+    "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
@@ -37,92 +37,92 @@ 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)
+    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"])
+    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
+    # 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()
+    vs.stop()
 
 # otherwise, release the file pointer
 else:
-	vs.release()
+    vs.release()
 
 # close all windows
 cv2.destroyAllWindows()

examples/dlib_objs_tracking_queue.py

@@ -12,47 +12,47 @@ import dlib
 import cv2
 
 def start_tracker(box, label, rgb, inputQueue, outputQueue):
-	# construct a dlib rectangle object from the bounding box
-	# coordinates and then start the correlation tracker
-	t = dlib.correlation_tracker()
-	rect = dlib.rectangle(box[0], box[1], box[2], box[3])
-	t.start_track(rgb, rect)
-
-	# loop indefinitely -- this function will be called as a daemon
-	# process so we don't need to worry about joining it
-	while True:
-		# attempt to grab the next frame from the input queue
-		rgb = inputQueue.get()
-
-		# if there was an entry in our queue, process it
-		if rgb is not None:
-			# update the tracker and grab the position of the tracked
-			# object
-			t.update(rgb)
-			pos = t.get_position()
-
-			# unpack the position object
-			startX = int(pos.left())
-			startY = int(pos.top())
-			endX = int(pos.right())
-			endY = int(pos.bottom())
-
-			# add the label + bounding box coordinates to the output
-			# queue
-			outputQueue.put((label, (startX, startY, endX, endY)))
+    # construct a dlib rectangle object from the bounding box
+    # coordinates and then start the correlation tracker
+    t = dlib.correlation_tracker()
+    rect = dlib.rectangle(box[0], box[1], box[2], box[3])
+    t.start_track(rgb, rect)
+
+    # loop indefinitely -- this function will be called as a daemon
+    # process so we don't need to worry about joining it
+    while True:
+        # attempt to grab the next frame from the input queue
+        rgb = inputQueue.get()
+
+        # if there was an entry in our queue, process it
+        if rgb is not None:
+            # update the tracker and grab the position of the tracked
+            # object
+            t.update(rgb)
+            pos = t.get_position()
+
+            # unpack the position object
+            startX = int(pos.left())
+            startY = int(pos.top())
+            endX = int(pos.right())
+            endY = int(pos.bottom())
+
+            # add the label + bounding box coordinates to the output
+            # queue
+            outputQueue.put((label, (startX, startY, endX, endY)))
 
 # construct the argument parser and parse the arguments
 ap = argparse.ArgumentParser()
 ap.add_argument("-p", "--prototxt", required=True,
-	help="path to Caffe 'deploy' prototxt file")
+    help="path to Caffe 'deploy' prototxt file")
 ap.add_argument("-m", "--model", required=True,
-	help="path to Caffe pre-trained model")
+    help="path to Caffe pre-trained model")
 ap.add_argument("-v", "--video", required=True,
-	help="path to input video file")
+    help="path to input video file")
 ap.add_argument("-o", "--output", type=str,
-	help="path to optional output video file")
+    help="path to optional output video file")
 ap.add_argument("-c", "--confidence", type=float, default=0.2,
-	help="minimum probability to filter weak detections")
+    help="minimum probability to filter weak detections")
 args = vars(ap.parse_args())
 
 # initialize our list of queues -- both input queue and output queue
@@ -63,9 +63,9 @@ outputQueues = []
 # initialize the list of class labels MobileNet SSD was trained to
 # detect
 CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
-	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
-	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
-	"sofa", "train", "tvmonitor"]
+    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+    "sofa", "train", "tvmonitor"]
 
 # load our serialized model from disk
 print("[INFO] loading model...")
@@ -81,120 +81,120 @@ fps = FPS().start()
 
 # loop over frames from the video file stream
 while True:
-	# grab the next frame from the video file
-	(grabbed, frame) = vs.read()
-
-	# check to see if we have reached the end of the video file
-	if frame is None:
-		break
-
-	# resize the frame for faster processing and then convert the
-	# frame from BGR to RGB ordering (dlib needs RGB ordering)
-	frame = imutils.resize(frame, width=600)
-	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-	# if we are supposed to be writing a video to disk, initialize
-	# the writer
-	if args["output"] is not None and writer is None:
-		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
-		writer = cv2.VideoWriter(args["output"], fourcc, 30,
-			(frame.shape[1], frame.shape[0]), True)
-
-	# if our list of queues is empty then we know we have yet to
-	# create our first object tracker
-	if len(inputQueues) == 0:
-		# grab the frame dimensions and convert the frame to a blob
-		(h, w) = frame.shape[:2]
-		blob = cv2.dnn.blobFromImage(frame, 0.007843, (w, h), 127.5)
-
-		# pass the blob through the network and obtain the detections
-		# and predictions
-		net.setInput(blob)
-		detections = net.forward()
-
-		# loop over the detections
-		for i in np.arange(0, detections.shape[2]):
-			# extract the confidence (i.e., probability) associated
-			# with the prediction
-			confidence = detections[0, 0, i, 2]
-
-			# filter out weak detections by requiring a minimum
-			# confidence
-			if confidence > args["confidence"]:
-				# extract the index of the class label from the
-				# detections list
-				idx = int(detections[0, 0, i, 1])
-				label = CLASSES[idx]
-
-				# if the class label is not a person, ignore it
-				if CLASSES[idx] != "person":
-					continue
-
-				# compute the (x, y)-coordinates of the bounding box
-				# for the object
-				box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
-				(startX, startY, endX, endY) = box.astype("int")
-				bb = (startX, startY, endX, endY)
-
-				# create two brand new input and output queues,
-				# respectively
-				iq = multiprocessing.Queue()
-				oq = multiprocessing.Queue()
-				inputQueues.append(iq)
-				outputQueues.append(oq)
-
-				# spawn a daemon process for a new object tracker
-				p = multiprocessing.Process(
-					target=start_tracker,
-					args=(bb, label, rgb, iq, oq))
-				p.daemon = True
-				p.start()
-
-				# grab the corresponding class label for the detection
-				# and draw the bounding box
-				cv2.rectangle(frame, (startX, startY), (endX, endY),
-					(0, 255, 0), 2)
-				cv2.putText(frame, label, (startX, startY - 15),
-					cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
-
-	# otherwise, we've already performed detection so let's track
-	# multiple objects
-	else:
-		# loop over each of our input ques and add the input RGB
-		# frame to it, enabling us to update each of the respective
-		# object trackers running in separate processes
-		for iq in inputQueues:
-			iq.put(rgb)
-
-		# loop over each of the output queues
-		for oq in outputQueues:
-			# grab the updated bounding box coordinates for the
-			# object -- the .get method is a blocking operation so
-			# this will pause our execution until the respective
-			# process finishes the tracking update
-			(label, (startX, startY, endX, endY)) = oq.get()
-
-			# draw the bounding box from the correlation object
-			# tracker
-			cv2.rectangle(frame, (startX, startY), (endX, endY),
-				(0, 255, 0), 2)
-			cv2.putText(frame, label, (startX, startY - 15),
-				cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
-
-	# check to see if we should write the frame to disk
-	if writer is not None:
-		writer.write(frame)
-
-	# show the output frame
-	cv2.imshow("Frame", frame)
-	key = cv2.waitKey(1) & 0xFF
-
-	# if the `q` key was pressed, break from the loop
-	if key == ord("q"):
-		break
-
-	# update the FPS counter
-	fps.update()
+    # grab the next frame from the video file
+    (grabbed, frame) = vs.read()
+
+    # check to see if we have reached the end of the video file
+    if frame is None:
+        break
+
+    # resize the frame for faster processing and then convert the
+    # frame from BGR to RGB ordering (dlib needs RGB ordering)
+    frame = imutils.resize(frame, width=600)
+    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    # if we are supposed to be writing a video to disk, initialize
+    # the writer
+    if args["output"] is not None and writer is None:
+        fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+        writer = cv2.VideoWriter(args["output"], fourcc, 30,
+            (frame.shape[1], frame.shape[0]), True)
+
+    # if our list of queues is empty then we know we have yet to
+    # create our first object tracker
+    if len(inputQueues) == 0:
+        # grab the frame dimensions and convert the frame to a blob
+        (h, w) = frame.shape[:2]
+        blob = cv2.dnn.blobFromImage(frame, 0.007843, (w, h), 127.5)
+
+        # pass the blob through the network and obtain the detections
+        # and predictions
+        net.setInput(blob)
+        detections = net.forward()
+
+        # loop over the detections
+        for i in np.arange(0, detections.shape[2]):
+            # extract the confidence (i.e., probability) associated
+            # with the prediction
+            confidence = detections[0, 0, i, 2]
+
+            # filter out weak detections by requiring a minimum
+            # confidence
+            if confidence > args["confidence"]:
+                # extract the index of the class label from the
+                # detections list
+                idx = int(detections[0, 0, i, 1])
+                label = CLASSES[idx]
+
+                # if the class label is not a person, ignore it
+                if CLASSES[idx] != "person":
+                    continue
+
+                # compute the (x, y)-coordinates of the bounding box
+                # for the object
+                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+                (startX, startY, endX, endY) = box.astype("int")
+                bb = (startX, startY, endX, endY)
+
+                # create two brand new input and output queues,
+                # respectively
+                iq = multiprocessing.Queue()
+                oq = multiprocessing.Queue()
+                inputQueues.append(iq)
+                outputQueues.append(oq)
+
+                # spawn a daemon process for a new object tracker
+                p = multiprocessing.Process(
+                    target=start_tracker,
+                    args=(bb, label, rgb, iq, oq))
+                p.daemon = True
+                p.start()
+
+                # grab the corresponding class label for the detection
+                # and draw the bounding box
+                cv2.rectangle(frame, (startX, startY), (endX, endY),
+                    (0, 255, 0), 2)
+                cv2.putText(frame, label, (startX, startY - 15),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
+
+    # otherwise, we've already performed detection so let's track
+    # multiple objects
+    else:
+        # loop over each of our input ques and add the input RGB
+        # frame to it, enabling us to update each of the respective
+        # object trackers running in separate processes
+        for iq in inputQueues:
+            iq.put(rgb)
+
+        # loop over each of the output queues
+        for oq in outputQueues:
+            # grab the updated bounding box coordinates for the
+            # object -- the .get method is a blocking operation so
+            # this will pause our execution until the respective
+            # process finishes the tracking update
+            (label, (startX, startY, endX, endY)) = oq.get()
+
+            # draw the bounding box from the correlation object
+            # tracker
+            cv2.rectangle(frame, (startX, startY), (endX, endY),
+                (0, 255, 0), 2)
+            cv2.putText(frame, label, (startX, startY - 15),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
+
+    # check to see if we should write the frame to disk
+    if writer is not None:
+        writer.write(frame)
+
+    # show the output frame
+    cv2.imshow("Frame", frame)
+    key = cv2.waitKey(1) & 0xFF
+
+    # if the `q` key was pressed, break from the loop
+    if key == ord("q"):
+        break
+
+    # update the FPS counter
+    fps.update()
 
 # stop the timer and display FPS information
 fps.stop()
@@ -203,7 +203,7 @@ print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
 
 # check to see if we need to release the video writer pointer
 if writer is not None:
-	writer.release()
+    writer.release()
 
 # do a bit of cleanup
 cv2.destroyAllWindows()

examples/opencv_objs_tracking.py

@@ -11,21 +11,21 @@ import cv2
 # construct the argument parser and parse the arguments
 ap = argparse.ArgumentParser()
 ap.add_argument("-v", "--video", type=str,
-	help="path to input video file")
+    help="path to input video file")
 ap.add_argument("-t", "--tracker", type=str, default="kcf",
-	help="OpenCV object tracker type")
+    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
+    "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
@@ -33,68 +33,72 @@ trackers = cv2.MultiTracker_create()
 
 # 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)
+    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"])
+    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)
-
-	# 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)
-	trackers.d
-
-	# 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)
-
-		# 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)
-
-	# if the `q` key was pressed, break from the loop
-	elif key == ord("q"):
-		break
+    # 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)
+
+    # 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)
+
+    # 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)
+
+    # objs = trackers.getObjects()
+    # for o in objs:
+    #     print(type(o), o)
+
+    # 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)
+
+        # create a new object tracker for the bounding box and add it
+        # to our multi-object tracker
+        tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
+        # print('handpick box', type(box), box)
+        n = trackers.add(tracker, frame, box)
+        # print(type(n), n)
+    # 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()
+    vs.stop()
 
 # otherwise, release the file pointer
 else:
-	vs.release()
+    vs.release()
 
 # close all windows
 cv2.destroyAllWindows()

examples/yolo_img_obj_detector.py

@@ -0,0 +1,109 @@
+"""USAGE
+python examples/yolo_obj_detector.py \
+    -c ~/dev/obj-tracking/yolov3.cfg \
+    -w ~/dev/obj-tracking/yolov3.weights \
+    -cl ~/dev/obj-tracking/yolo/darknet/data/coco.names \
+    -i ~/dev/obj-tracking/person.jpg
+
+python examples/yolo_obj_detector.py \
+    -c ~/syncthing/dropbox/tracking-obj/mytrain.cfg \
+    -w ~/syncthing/dropbox/tracking-obj/mytrain_final.weights \
+    -cl ~/syncthing/dropbox/tracking-obj/mytrain.names \
+    -i /media/sipp11/500BUP/handai_photos/test/6294.jpg
+"""
+import cv2
+import argparse
+import numpy as np
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True, help="path to input image")
+ap.add_argument("-c", "--config", required=True, help="path to yolo config file")
+ap.add_argument(
+    "-w", "--weights", required=True, help="path to yolo pre-trained weights"
+)
+ap.add_argument(
+    "-cl", "--classes", required=True, help="path to text file containing class names"
+)
+args = ap.parse_args()
+
+
+def get_output_layers(net):
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+    return output_layers
+
+
+def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
+    label = str(classes[class_id])
+    color = COLORS[class_id]
+    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+image = cv2.imread(args.image)
+
+Width = image.shape[1]
+Height = image.shape[0]
+scale = 0.00392
+
+classes = None
+
+with open(args.classes, "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+
+COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+
+net = cv2.dnn.readNet(args.weights, args.config)
+blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
+
+net.setInput(blob)
+
+outs = net.forward(get_output_layers(net))
+
+class_ids = []
+confidences = []
+boxes = []
+conf_threshold = 0.5
+nms_threshold = 0.4
+
+
+for out in outs:
+    for detection in out:
+        scores = detection[5:]
+        class_id = np.argmax(scores)
+        confidence = scores[class_id]
+        if confidence > 0.5:
+            center_x = int(detection[0] * Width)
+            center_y = int(detection[1] * Height)
+            w = int(detection[2] * Width)
+            h = int(detection[3] * Height)
+            x = center_x - w / 2
+            y = center_y - h / 2
+            class_ids.append(class_id)
+            confidences.append(float(confidence))
+            boxes.append([x, y, w, h])
+
+
+indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
+
+for i in indices:
+    i = i[0]
+    box = boxes[i]
+    x = box[0]
+    y = box[1]
+    w = box[2]
+    h = box[3]
+    draw_prediction(
+        image,
+        class_ids[i],
+        confidences[i],
+        round(x),
+        round(y),
+        round(x + w),
+        round(y + h),
+    )
+
+cv2.imshow("object detection", image)
+cv2.waitKey()
+
+cv2.imwrite("object-detection.jpg", image)
+cv2.destroyAllWindows()

examples/yolo_obj_detector.py

@@ -1,103 +1,198 @@
-"""USAGE
-python examples/yolo_obj_detector.py \
-    -c ~/dev/obj-tracking/yolov3.cfg \
-    -w ~/dev/obj-tracking/yolov3.weights \
-    -cl ~/dev/obj-tracking/yolo/darknet/data/coco.names \
-    -i ~/dev/obj-tracking/person.jpg
+"""USAGE:
+
+time python examples/test.py --input ~/Desktop/5min.mp4 -o output.mp4
+time python examples/test.py --input ~/Desktop/5min.mp4 -l
+
 """
-import cv2
-import argparse
+# import the necessary packages
 import numpy as np
+import argparse
+import imutils
+import time
+import cv2
+import os
 
+# construct the argument parse and parse the arguments
 ap = argparse.ArgumentParser()
-ap.add_argument("-i", "--image", required=True, help="path to input image")
-ap.add_argument("-c", "--config", required=True, help="path to yolo config file")
+ap.add_argument("-i", "--input", required=True, help="path to input video")
+ap.add_argument("-o", "--output", required=False, help="path to output video")
+ap.add_argument("-l", "--live", action='store_true', help="Show live detection")
+# ap.add_argument("-y", "--yolo", required=True,
+# 	help="base path to YOLO directory")
 ap.add_argument(
-    "-w", "--weights", required=True, help="path to yolo pre-trained weights"
+    "-c",
+    "--confidence",
+    type=float,
+    default=0.5,
+    help="minimum probability to filter weak detections",
 )
 ap.add_argument(
-    "-cl", "--classes", required=True, help="path to text file containing class names"
+    "-t",
+    "--threshold",
+    type=float,
+    default=0.3,
+    help="threshold when applyong non-maxima suppression",
 )
-args = ap.parse_args()
-
-
-def get_output_layers(net):
-    layer_names = net.getLayerNames()
-    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
-    return output_layers
-
-
-def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
-    label = str(classes[class_id])
-    color = COLORS[class_id]
-    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
-    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-image = cv2.imread(args.image)
-
-Width = image.shape[1]
-Height = image.shape[0]
-scale = 0.00392
-
-classes = None
-
-with open(args.classes, "r") as f:
-    classes = [line.strip() for line in f.readlines()]
-
-COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
-
-net = cv2.dnn.readNet(args.weights, args.config)
-blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
-
-net.setInput(blob)
-
-outs = net.forward(get_output_layers(net))
-
-class_ids = []
-confidences = []
-boxes = []
-conf_threshold = 0.5
-nms_threshold = 0.4
-
-
-for out in outs:
-    for detection in out:
-        scores = detection[5:]
-        class_id = np.argmax(scores)
-        confidence = scores[class_id]
-        if confidence > 0.5:
-            center_x = int(detection[0] * Width)
-            center_y = int(detection[1] * Height)
-            w = int(detection[2] * Width)
-            h = int(detection[3] * Height)
-            x = center_x - w / 2
-            y = center_y - h / 2
-            class_ids.append(class_id)
-            confidences.append(float(confidence))
-            boxes.append([x, y, w, h])
-
-
-indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
-
-for i in indices:
-    i = i[0]
-    box = boxes[i]
-    x = box[0]
-    y = box[1]
-    w = box[2]
-    h = box[3]
-    draw_prediction(
-        image,
-        class_ids[i],
-        confidences[i],
-        round(x),
-        round(y),
-        round(x + w),
-        round(y + h),
+args = vars(ap.parse_args())
+
+# load the COCO class labels our YOLO model was trained on
+# labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
+labelsPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain.names"
+LABELS = open(labelsPath).read().strip().split("\n")
+
+# initialize a list of colors to represent each possible class label
+np.random.seed(42)
+COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
+
+# derive the paths to the YOLO weights and model configuration
+# weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
+# configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
+
+weightsPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain_final.weights"
+configPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain.cfg"
+
+# load our YOLO object detector trained on COCO dataset (80 classes)
+# and determine only the *output* layer names that we need from YOLO
+print("[INFO] loading YOLO from disk...")
+net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
+ln = net.getLayerNames()
+ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+
+
+# initialize the video stream, pointer to output video file, and
+# frame dimensions
+vs = cv2.VideoCapture(args["input"])
+writer = None
+(W, H) = (None, None)
+
+# try to determine the total number of frames in the video file
+try:
+    prop = (
+        cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() else cv2.CAP_PROP_FRAME_COUNT
     )
-
-cv2.imshow("object detection", image)
-cv2.waitKey()
-
-cv2.imwrite("object-detection.jpg", image)
-cv2.destroyAllWindows()
+    total = int(vs.get(prop))
+    print("[INFO] {} total frames in video".format(total))
+
+# an error occurred while trying to determine the total
+# number of frames in the video file
+except:
+    print("[INFO] could not determine # of frames in video")
+    print("[INFO] no approx. completion time can be provided")
+    total = -1
+
+
+# loop over frames from the video file stream
+while True:
+    # read the next frame from the file
+    (grabbed, frame) = vs.read()
+
+    # if the frame was not grabbed, then we have reached the end
+    # of the stream
+    if not grabbed:
+        break
+
+    # if the frame dimensions are empty, grab them
+    if W is None or H is None:
+        (H, W) = frame.shape[:2]
+
+    # construct a blob from the input frame and then perform a forward
+    # pass of the YOLO object detector, giving us our bounding boxes
+    # and associated probabilities
+    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
+    net.setInput(blob)
+    start = time.time()
+    layerOutputs = net.forward(ln)
+    end = time.time()
+
+    # initialize our lists of detected bounding boxes, confidences,
+    # and class IDs, respectively
+    boxes = []
+    confidences = []
+    classIDs = []
+
+    # loop over each of the layer outputs
+    for output in layerOutputs:
+        # loop over each of the detections
+        for detection in output:
+            # extract the class ID and confidence (i.e., probability)
+            # of the current object detection
+            scores = detection[5:]
+            classID = np.argmax(scores)
+            confidence = scores[classID]
+
+            # filter out weak predictions by ensuring the detected
+            # probability is greater than the minimum probability
+            if confidence > args["confidence"]:
+                # scale the bounding box coordinates back relative to
+                # the size of the image, keeping in mind that YOLO
+                # actually returns the center (x, y)-coordinates of
+                # the bounding box followed by the boxes' width and
+                # height
+                box = detection[0:4] * np.array([W, H, W, H])
+                (centerX, centerY, width, height) = box.astype("int")
+
+                # use the center (x, y)-coordinates to derive the top
+                # and and left corner of the bounding box
+                x = int(centerX - (width / 2))
+                y = int(centerY - (height / 2))
+
+                # update our list of bounding box coordinates,
+                # confidences, and class IDs
+                boxes.append([x, y, int(width), int(height)])
+                confidences.append(float(confidence))
+                classIDs.append(classID)
+
+        # apply non-maxima suppression to suppress weak, overlapping
+        # bounding boxes
+        idxs = cv2.dnn.NMSBoxes(
+            boxes, confidences, args["confidence"], args["threshold"]
+        )
+
+        # ensure at least one detection exists
+        if len(idxs) > 0:
+            # loop over the indexes we are keeping
+            for i in idxs.flatten():
+                # extract the bounding box coordinates
+                (x, y) = (boxes[i][0], boxes[i][1])
+                (w, h) = (boxes[i][2], boxes[i][3])
+
+                # draw a bounding box rectangle and label on the frame
+                color = [int(c) for c in COLORS[classIDs[i]]]
+                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
+                text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
+                cv2.putText(
+                    frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2
+                )
+
+    if args["live"]:
+        cv2.imshow("Frame", frame)
+        key = cv2.waitKey(1) & 0xFF
+
+        # if the `q` key was pressed, break from the loop
+        if key == ord("q"):
+            break
+
+    if args["output"]:
+        # check if the video writer is None
+        if writer is None:
+            # initialize our video writer
+            fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+            writer = cv2.VideoWriter(
+                args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True
+            )
+
+            # some information on processing single frame
+            if total > 0:
+                elap = end - start
+                print("[INFO] single frame took {:.4f} seconds".format(elap))
+                print(
+                    "[INFO] estimated total time to finish: {:.4f}".format(elap * total)
+                )
+
+        # write the output frame to disk
+        writer.write(frame)
+
+# release the file pointers
+print("[INFO] cleaning up...")
+writer.release()
+vs.release()

src/draw_area.py

@@ -0,0 +1,38 @@
+import argparse
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from PIL import Image
+import numpy as np
+from utils import AREAS
+from matplotlib.cm import get_cmap
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--input", required=True, help="path to input iamge")
+args = vars(ap.parse_args())
+
+im = np.array(Image.open(args['input']), dtype=np.uint8)
+
+# Create figure and axes
+fig, ax = plt.subplots(1)
+
+# Display the image
+ax.imshow(im)
+
+for _area in AREAS:
+    a = dict(_area)
+    color = get_cmap('tab20')(a['id'])
+    x1, y1 = a['area'][0]
+    x2, y2 = a['area'][1]
+    w, h = x2 - x1, y2- y1
+    # Create a Rectangle patch
+    rect = patches.Rectangle(
+        a['area'][0], w, h, linewidth=1, edgecolor=color, facecolor="none"
+    )
+    # Add the patch to the Axes
+    ax.add_patch(rect)
+    ax.text(x1, y1 - 5, f"AREA: {a['id']}", fontsize=5, color=color)
+
+
+plt.savefig("test.jpg", dpi=300)
+

src/log.py

@@ -0,0 +1,19 @@
+import logging
+
+
+def init_logger():
+    logger = logging.getLogger()  # get the root logger
+    logger.setLevel(logging.DEBUG)
+
+    handler = logging.StreamHandler()
+    handler.setLevel(logging.DEBUG)
+    formatter = logging.Formatter("[%(levelname)s] %(message)s")
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+    trace_handler = logging.FileHandler("trace.log", "w")
+    trace_handler.setLevel(logging.INFO)
+    # trace_formatter = logging.Formatter("%(asctime)s[%(levelname)s],%(message)s")
+    trace_formatter = logging.Formatter("%(message)s")
+    trace_handler.setFormatter(trace_formatter)
+    logger.addHandler(trace_handler)

src/main.py

@@ -0,0 +1,566 @@
+"""USAGE:
+
+time python src/_detector.py --input ~/Desktop/5min.mp4 -o output.mp4
+time python src/_detector.py --input ~/Desktop/5min.mp4 -l
+
+"""
+import sys
+import time
+import argparse
+import pprint
+import numpy as np
+import imutils
+import cv2
+from utils import (
+    check_if_inside_the_boxes,
+    is_it_the_same_obj,
+    box_distance,
+    get_heading,
+    get_avg_heading,
+    OBJ_LEAVING_COND,
+    DONTCARE,
+)
+from log import init_logger, logging
+
+init_logger()
+
+pp = pprint.PrettyPrinter(indent=2)
+# tracking
+OPENCV_OBJECT_TRACKERS = {"csrt": cv2.TrackerCSRT_create}
+# initialize OpenCV's special multi-object tracker
+cv_trackers = cv2.MultiTracker_create()
+trackers = []
+finished = []
+W4A = {}  # this stands for "wait for arrival [at ...]"
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--input", required=True, help="path to input video")
+ap.add_argument("-o", "--output", required=False, help="path to output video")
+ap.add_argument("-l", "--live", action="store_true", help="Show live detection")
+# ap.add_argument("-y", "--yolo", required=True,
+# 	help="base path to YOLO directory")
+ap.add_argument(
+    "-c",
+    "--confidence",
+    type=float,
+    default=0.95,
+    help="minimum probability to filter weak detections",
+)
+ap.add_argument(
+    "-t",
+    "--threshold",
+    type=float,
+    default=0.3,
+    help="threshold when applyong non-maxima suppression",
+)
+args = vars(ap.parse_args())
+
+# load the COCO class labels our YOLO model was trained on
+# labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
+labelsPath = "../../syncthing/dropbox/tracking-obj/mytrain.names"
+LABELS = open(labelsPath).read().strip().split("\n")
+# 0 person, 1 wheelchair, 2 bicycle, 3 motorbike, 4 car, 5 bus, 6 truck
+
+# initialize a list of colors to represent each possible class label
+np.random.seed(42)
+COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
+
+# derive the paths to the YOLO weights and model configuration
+weightsPath = "../../syncthing/dropbox/tracking-obj/mytrain_final.weights"
+configPath = "../../syncthing/dropbox/tracking-obj/mytrain.cfg"
+
+
+# load our YOLO object detector trained on COCO dataset (80 classes)
+# and determine only the *output* layer names that we need from YOLO
+logging.debug("[INFO] loading YOLO from disk...")
+net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
+ln = net.getLayerNames()
+ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+start = end = 0
+
+
+def detect_stuffs(_frame):
+    # construct a blob from the input frame and then perform a forward
+    # pass of the YOLO object detector, giving us our bounding boxes
+    # and associated probabilities
+    blob = cv2.dnn.blobFromImage(_frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
+    net.setInput(blob)
+    start = time.time()
+    layerOutputs = net.forward(ln)
+    end = time.time()
+
+    # initialize our lists of detected bounding boxes, confidences,
+    # and class IDs, respectively
+    boxes = []
+    confidences = []
+    classIDs = []
+
+    # loop over each of the layer outputs
+    for output in layerOutputs:
+        # print(f'[{_frame_count:08d}] output -> ', len(output))
+        # loop over each of the detections
+        for detection in output:
+            # extract the class ID and confidence (i.e., probability)
+            # of the current object detection
+            scores = detection[5:]
+            classID = np.argmax(scores)
+            confidence = scores[classID]
+
+            # filter out weak predictions by ensuring the detected
+            # probability is greater than the minimum probability
+            if confidence <= args["confidence"]:
+                continue
+
+            # scale the bounding box coordinates back relative to
+            # the size of the image, keeping in mind that YOLO
+            # actually returns the center (x, y)-coordinates of
+            # the bounding box followed by the boxes' width and
+            # height
+            box = detection[0:4] * np.array([W, H, W, H])
+            (centerX, centerY, width, height) = box.astype("int")
+
+            # use the center (x, y)-coordinates to derive the top
+            # and and left corner of the bounding box
+            x = int(centerX - (width / 2))
+            y = int(centerY - (height / 2))
+
+            # update our list of bounding box coordinates,
+            # confidences, and class IDs
+            boxes.append([x, y, int(width), int(height)])
+            confidences.append(float(confidence))
+            classIDs.append(classID)
+
+        # apply non-maxima suppression to suppress weak, overlapping
+        # bounding boxes
+        idxs = cv2.dnn.NMSBoxes(
+            boxes, confidences, args["confidence"], args["threshold"]
+        )
+
+        # ensure at least one detection exists
+        if len(idxs) == 0:
+            continue
+
+    return idxs, boxes, confidences, classIDs, start, end
+
+
+# initialize the video stream, pointer to output video file, and
+# frame dimensions
+vs = cv2.VideoCapture(args["input"])
+writer = None
+(W, H) = (None, None)
+
+# try to determine the total number of frames in the video file
+try:
+    prop = (
+        cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() else cv2.CAP_PROP_FRAME_COUNT
+    )
+    total = int(vs.get(prop))
+    print("[INFO] {} total frames in video".format(total))
+
+# an error occurred while trying to determine the total
+# number of frames in the video file
+except:
+    print("[INFO] could not determine # of frames in video")
+    print("[INFO] no approx. completion time can be provided")
+    total = -1
+
+_frame_count = 0
+tracker_counter = 1
+
+logging.debug("INIT")
+
+# for xx in range(3996):
+#     (grabbed, frame) = vs.read()
+#     _frame_count += 1
+
+# loop over frames from the video file stream
+while True:
+    # read the next frame from the file
+    (grabbed, frame) = vs.read()
+
+    # if the frame was not grabbed, then we have reached the end
+    # of the stream
+    if not grabbed:
+        break
+
+    # if the frame dimensions are empty, grab them
+    if W is None or H is None:
+        (H, W) = frame.shape[:2]
+
+    _frame_count += 1
+
+    # grab the updated bounding box coordinates (if any) for each
+    # object that is being tracked
+    (success, boxes) = cv_trackers.update(frame)
+    # print("success", success)
+    # print("boxes", boxes)
+    print(f"[{_frame_count:08d}] ::")
+
+    # quit if unable to read the video file
+    if not success:
+        print("Failed to read video", grabbed, frame)
+
+        cv_trackers.clear()
+        cv_trackers = cv2.MultiTracker_create()
+        # print("  INIT AGAIN cv_tracker", type(cv_trackers), cv_trackers)
+        # get rid of them in trackers too
+        trackers = [_ for _ in trackers if _["id"] not in ut_ids]
+        # add tracker again
+        for _trckr in trackers:
+            __tkr = OPENCV_OBJECT_TRACKERS["csrt"]()
+            cv_trackers.add(__tkr, frame, tuple(_trckr["curr_position"]))
+        (success, boxes) = cv_trackers.update(frame)
+        # print('SUCCESS? ', success)
+
+    untracking = []
+    # loop over the bounding boxes and draw then on the frame
+
+    obj_cnt = len(boxes)
+    # print(f"obj_cnt: ", obj_cnt, ' | tracker #', len(trackers))
+    for idx in range(obj_cnt):
+        GONE = False
+
+        box = boxes[idx]
+        (x, y, w, h) = [int(v) for v in box]
+
+        logging.info(f"{_frame_count},{trackers[idx]['id']},{trackers[idx]['type']},POSITION,{x},{y},{w},{h}")
+
+        # check if size is growing? if more than twice then, untrack it
+        # TODO: make it static bound should be better
+        ow, oh = trackers[idx]["size"]
+        if ow / w > 2 or oh / h > 2:
+            print(f"      {tk['id']} GROW_TOO_BIG")
+            trackers[idx]["status"] = "grow-too-big"
+            logging.info(
+                f"{_frame_count},{tk['id']},{tk['type']},GROW_TOO_BIG,{x},{y},{w},{h}"
+            )
+            untracking.append(trackers[idx])
+            continue
+
+        _last_pos = trackers[idx]["history"][0]
+        curr_distance = box_distance(box, _last_pos)
+        last_distance = trackers[idx]["distance"]
+        trackers[idx]["distance"] = curr_distance
+        trackers[idx]["curr_position"] = box  # [int(v) for v in box]
+        trackers[idx]["history"].insert(0, box)
+
+        _last_idx = 2 if len(trackers[idx]["history"]) > 2 else 1
+        _x, _y, _w, _h = trackers[idx]["history"][_last_idx]
+        _heading = get_heading(_x, _y, x, y)
+        tk = trackers[idx]
+
+        STILL_DIST_PX = 1
+        if last_distance < STILL_DIST_PX and curr_distance < STILL_DIST_PX:
+            trackers[idx]["still"] += 1
+        else:
+            trackers[idx]["still"] = 0
+
+        if trackers[idx]["still"] == 0:
+            trackers[idx]["heading"] = [_heading] + trackers[idx]["heading"]
+
+        # trackers[idx]["heading"] = trackers[idx]["heading"][:20]
+
+        if trackers[idx]["still"] > 30 or x < 5 or x > 1250:
+            logging.info(
+                f"{_frame_count},{tk['id']},{tk['type']},LEFT,{x},{y},{w},{h},STILL"
+            )
+            untracking.append(trackers[idx])
+
+        if trackers[idx]["still"] > 10 and len(trackers[idx]["heading"]) < 5:
+            print(f"      {tk['id']} LEFT - short-life")
+            logging.info(
+                f"{_frame_count},{tk['id']},{tk['type']},LEFT,{x},{y},{w},{h},SHORT_LIFE"
+            )
+            trackers[idx]["status"] = "short-life"
+            untracking.append(trackers[idx])
+
+        # check if it's hit the first
+        avg_heading = get_avg_heading(trackers[idx]["heading"])
+
+        h_count = len(tk["heading"])
+        if 5 < h_count and h_count < 10:
+            # enough to validate, but never an established one
+            DONTCARE_IDS = [_[0][1] for _ in DONTCARE]
+            if tk["origin"]["id"] in DONTCARE_IDS:
+                dc_id = DONTCARE_IDS.index(tk["origin"]["id"])
+                dc_dict = dict(DONTCARE[dc_id])
+                print(DONTCARE_IDS, tk["origin"]["id"], dc_id, dc_dict["heading"])
+                if avg_heading in dc_dict["heading"]:
+                    print(f"      {tk['id']} DONT-CARE condition")
+                    logging.info(
+                        f"{_frame_count},{tk['id']},{tk['type']},LEFT,{x},{y},{w},{h},WRONG_DETECTION"
+                    )
+                    trackers[idx]["status"] = "dont-care"
+                    untracking.append(trackers[idx])
+
+        # TODO: assign id here! for anything that is -1 id
+        gid = None
+        if tk["id"] == -1 and 5 < h_count and h_count < 15:
+            _area_id = f"id_{tk['origin']['id']}"
+            if _area_id in W4A:
+                # check candidates which has "matched" opportunity too
+                _po = W4A[_area_id]["objects"]
+                _po = [_ for _ in _po if _frame_count > _[2]]  # opportunity
+                _po = sorted(_po, key=lambda kk: kk[2])  # first one first
+                if _po:
+                    trackers[idx]["id"] = gid = _po[0][0]["id"]
+                    # remove this id out of next W4A
+                    W4A[_area_id]["objects"] = [
+                        _ for _ in W4A[_area_id]["objects"] if _[0]["id"] != gid
+                    ]
+                    # print(f" --- {len(_po)} candicate: picked id={gid}")
+                # else:
+                #     print(f" --- no candidate")
+                print(
+                    f"         {gid} RE-ENTERS (origin: {trackers[idx]['origin']['id']})"
+                )
+                logging.info(
+                    f"{_frame_count},{tk['id']},{tk['type']},RE-ENTER,{x},{y},{w},{h}"
+                )
+        elif tk["id"] == -1 and h_count >= 15:
+            # assign an id
+            trackers[idx]["id"] = tracker_counter
+            tracker_counter += 1
+            logging.info(
+                f"{_frame_count},{tk['id']},{tk['type']},ENTER,{x},{y},{w},{h}"
+            )
+            print(f"         {trackers[idx]['id']} ENTERS (& ID assigned)")
+
+        for lvng_cnd in OBJ_LEAVING_COND:
+            _origin = tk["origin"]["id"]
+            cond = dict(lvng_cnd)
+            if cond["origin_id"] != _origin:
+                continue
+            if cond["heading"] != "" and avg_heading != cond["heading"]:
+                continue
+            REACH_condition = False
+            if cond["x"] != -1:
+                # print("REACH condition: X", x, cond["x"])
+                if cond["heading"] in "NE":
+                    _cond = cond["x"](y + w) if callable(cond["x"]) else cond["x"]
+                    if x + w > _cond:
+                        REACH_condition = True
+                elif cond["heading"] in "SW":
+                    _cond = cond["x"](y + h) if callable(cond["x"]) else cond["x"]
+                    if x < _cond:
+                        REACH_condition = True
+                elif cond["heading"] == "":
+                    _cond = cond["x"](y) if callable(cond["x"]) else cond["x"]
+                    if x < _cond:
+                        REACH_condition = True
+            elif cond["y"] != -1:
+                _cond = cond["y"](x + h) if callable(cond["y"]) else cond["y"]
+                # Don't have one yet
+                if cond["heading"] in "SW" and y + h > _cond:
+                    print("REACH condition: Y", y + h, _cond)
+                    REACH_condition = True
+                # pass
+            if not REACH_condition:
+                continue
+
+            # print("MATCH COND")
+            # pp.pprint(cond)
+            if not cond["next_area"]:
+                # left the frame
+                print(f"         {tk['id']} LEFT from frame")
+                logging.info(
+                    f"{_frame_count},{tk['id']},{tk['type']},LEFT,{x},{y},{w},{h}"
+                )
+                untracking.append(tk)
+                continue
+
+            _nid = f"id_{cond['next_area'][0]}"
+            # print(f"#{tk['id']} origin:#{_origin} to#{_nid}", end="")
+            print(f"         {tk['id']} LEFT from {_origin} -> {_nid}")
+
+            logging.info(
+                f"{_frame_count},{tk['id']},{tk['type']},WAIT,{x},{y},{w},{h},SWITCH_ZONE:{_origin}:{_nid}"
+            )
+
+            if _nid not in W4A:
+                # print(f">>add AREA {_nid} to W4A", end="")
+                W4A[_nid] = {"objects": []}
+            # put this object to W4A for next area if doesn't exist
+            has_this = [_ for _ in W4A[_nid]["objects"] if _[0]["id"] == tk["id"]]
+            if not has_this:
+                GONE = True
+                # unit in frame
+                _expected_frame = _frame_count + cond["duration_to_next"]
+                W4A[_nid]["objects"].append((tk, _frame_count, _expected_frame))
+                untracking.append(tk)
+                # print(f'>>GONE - W#{len(W4A[_nid]["objects"])}')
+                # print(f'     {_nid} objs: ')
+                # pp.pprint(W4A[_nid]["objects"])
+                # print(f'     {_nid} untracking: ', [_['id'] for _ in untracking])
+
+        if GONE:
+            continue
+
+        print(
+            f"[{tk['id']}{tk['type'][:1]}] o#{tk['origin']['id']} (x,y)=({x},{y},{w},{h})"
+            f" | still #{tk['still']} | distance:  "
+            f"{last_distance:.3f} -> {curr_distance:.3f}",
+            end="",
+        )
+        _htxt = ",".join(trackers[idx]["heading"][:18])
+        print(f"|heading:{_htxt}", end="")
+        print(f"|avg:{avg_heading}", end="")
+        print("")
+
+        # DRAW on FRAME
+        color = [int(c) for c in COLORS[0]]
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(
+            frame,
+            f"{tk['id'] if tk['id'] > 0 else '?'} - {tk['type']}",
+            (x, y - 5),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            color,
+            2,
+        )
+
+    # Cleanup Tracker
+    if untracking:
+        ut_ids = [_["id"] for _ in untracking]  # untracking ids
+        # cv_trackers.clear()
+        cv_trackers.clear()
+        cv_trackers = None
+        time.sleep(1)
+        cv_trackers = cv2.MultiTracker_create()
+        # print("  cv_tracker", type(cv_trackers), cv_trackers)
+        # get rid of them in trackers too
+        trackers = [_ for _ in trackers if _["id"] not in ut_ids]
+        # add tracker again
+        for _trckr in trackers:
+            __tkr = OPENCV_OBJECT_TRACKERS["csrt"]()
+            cv_trackers.add(__tkr, frame, tuple(_trckr["curr_position"]))
+        # print(f"=== AFTER CLEANUP ---- UNTRACKING ===")
+        # print(f"      cv #{len(cv_trackers.getObjects())} trackers #{len(trackers)}")
+        print(f"         Total #{len(cv_trackers.getObjects())}")
+
+    # only detect once a sec
+    if _frame_count % 15 == 1:
+        idxs, boxes, confidences, classIDs, start, end = detect_stuffs(frame)
+        # loop over the indexes we are keeping
+        if len(idxs) == 0:
+            print("CAN NOT DETECT STUFFS??")
+            continue
+        if isinstance(idxs, tuple):
+            pp.pprint(idxs)
+
+        for i in idxs.flatten():
+            # extract the bounding box coordinates
+            (x, y) = (boxes[i][0], boxes[i][1])
+            (w, h) = (boxes[i][2], boxes[i][3])
+
+            _class = LABELS[classIDs[i]]
+            found_at = check_if_inside_the_boxes(x, y, w, h, _class)
+            if not found_at:
+                continue
+
+            # only do for person/wheelchair
+            if _class not in ["person", "wheelchair", "bicycle"]:
+                continue
+
+            # (1) check whether it's the same object as one in trackers
+            is_same = False
+            for t in trackers:
+                if _class != t["type"]:
+                    continue
+
+                _x, _y, _w, _h = t["curr_position"]
+                # print(f"[{t['id']}] - {t['type']}")
+                is_same = is_it_the_same_obj(x, y, w, h, _x, _y, _w, _h, id=t["id"])
+                if is_same:
+                    break
+
+            if not is_same:
+                # create tracker and add to multi-object tracker
+                _tracker = OPENCV_OBJECT_TRACKERS["csrt"]()
+                bbox = (x, y, w, h)
+                cv_trackers.add(_tracker, frame, bbox)
+                t = {
+                    "id": -1,  # NOTE: assign later; tracker_counter if gid is None else gid,
+                    "type": _class,
+                    "status": "",
+                    "curr_position": bbox,
+                    "size": (w, h),
+                    "heading": [],
+                    "origin": found_at,
+                    "distance": -1,
+                    "first_position": bbox,
+                    "still": 0,
+                    "history": [bbox],
+                }
+                trackers.append(t)
+                print(f"         total #{len(trackers)}")
+                pp.pprint(t)
+                # print(f" i -> {i} ({x},{y}), {w},{h} ({x + w},{y + h})")
+
+        # _what = ",".join([LABELS[c] for c in classIDs])
+        # print(f"[{_frame_count:08d}] :: {_what}")
+
+    # CLEANUP
+    cleanups = []
+    for k in W4A:
+        print(f"   CLEANUP[area {k}] ", end="")
+        for _o in W4A[k]["objects"]:
+            print(f"   {_o[0]['id']} - {_o[1]} - {_o[2]}", end="")
+            if _o[2] < _frame_count:
+                continue
+            wf = _o[2] - _o[1]
+            wf = wf if wf < 30 * 6 else 180
+            if _o[2] - wf > _frame_count:
+                # get rid of this
+                cleanups.append(_o)
+                # remove this id out of next W4A
+                W4A[k]["objects"] = [
+                    _ for _ in W4A[_area_id]["objects"] if _[0]["id"] != _o[0]["id"]
+                ]
+        print(f" | tracked #{len(trackers)}")
+    for obj, _s, _e in cleanups:
+        print(
+            f"         {obj['id']} CLEANED UP should found at {_e - _frame_count} ago"
+        )
+
+    if args["live"]:
+        cv2.imshow("Frame", frame)
+        key = cv2.waitKey(1) & 0xFF
+
+        if key == ord("w"):
+            while cv2.waitKey(1) & 0xFF != ord("w"):
+                pass
+        # if the `q` key was pressed, break from the loop
+        elif key == ord("q"):
+            break
+
+    if args["output"]:
+        # check if the video writer is None
+        if writer is None:
+            # initialize our video writer
+            fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+            writer = cv2.VideoWriter(
+                args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True
+            )
+
+            # some information on processing single frame
+            if total > 0:
+                elap = end - start
+                print("[INFO] single frame took {:.4f} seconds".format(elap))
+                print(
+                    "[INFO] estimated total time to finish: {:.4f}".format(elap * total)
+                )
+
+        # write the output frame to disk
+        writer.write(frame)
+
+    # TODO: find a way to get rid of IMPOSSIBLE obj for example
+    #       suddenly appear on area 7 then move up NORTH... -- this is incorrectly detected indeed.
+
+# release the file pointers
+print("[INFO] cleaning up...")
+if writer:
+    writer.release()
+vs.release()

src/utils.py

@@ -0,0 +1,291 @@
+import collections
+import math
+from collections import namedtuple
+
+Rectangle = namedtuple("Rectangle", "xmin ymin xmax ymax")
+
+
+def area(a, b):
+    # returns None if rectangles don't intersect
+    dx = min(a.xmax, b.xmax) - max(a.xmin, b.xmin)
+    dy = min(a.ymax, b.ymax) - max(a.ymin, b.ymin)
+    if (dx >= 0) and (dy >= 0):
+        return dx * dy
+
+
+# detecting area
+AREAS = [
+    [
+        ("id", 1),
+        ("area", ((0, 40), (120, 129))),
+        ("target", ["car", "bus", "motorbike"]),
+        ("next", [6]),
+    ],
+    [
+        ("id", 2),
+        ("area", ((85, 0), (222, 74))),
+        ("target", ["person", "bicycle"]),
+        ("next", [7]),
+    ],
+    [
+        ("id", 3),
+        ("area", ((38, 340), (130, 482))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [5]),
+    ],
+    [
+        ("id", 4),
+        ("area", ((96, 310), (145, 461))),
+        ("target", ["person", "wheelchair"]),
+    ],
+    [
+        ("id", 5),
+        ("area", ((286, 230), (441, 346))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [8]),
+    ],
+    [
+        ("id", 6),
+        ("area", ((421, 190), (555, 304))),
+        ("target", ["car", "bus", "motorbike"]),
+        ("next", []),
+    ],
+    [
+        ("id", 7),
+        ("area", ((555, 170), (700, 295))),
+        ("target", ["person", "wheelchair", "bicycle"]),
+        ("next", [4]),
+    ],
+    [
+        ("id", 11),
+        ("area", ((875, 179), (945, 278))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [9]),
+    ],
+    [
+        ("id", 8),
+        ("area", ((901, 223), (976, 342))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [7, 5]),
+    ],
+    [
+        ("id", 9),
+        ("area", ((1047, 229), (1120, 338))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [8]),
+    ],
+    [
+        ("id", 10),
+        ("area", ((1158, 200), (1230, 307))),
+        ("target", ["person", "wheelchair"]),
+        ("next", [9]),
+    ],
+]
+
+OBJ_LEAVING_COND = [
+    # [
+    #     ('origin_id', ),
+    #     ('heading', ''),
+    #     ('x', ),
+    #     ('y', ),
+    #     ('next_area', []),
+    #     ('duration_to_next', )
+    # ],
+    [
+        ("origin_id", 2),
+        ("heading", ""),
+        ("x", lambda y: (y - 112.6) / -0.2270),
+        ("y", -1),
+        ("next_area", [5, 7]),
+        ("duration_to_next", 30 * 5.5),
+    ],
+    [
+        ("origin_id", 3),
+        ("heading", "N"),
+        ("x", lambda y: (y + 1993) / 15),
+        ("y", -1),
+        ("next_area", [5]),
+        ("duration_to_next", 30 * 4),
+    ],
+    [
+        ("origin_id", 4),
+        ("heading", "S"),
+        ("x", 34),
+        ("y", -1),
+        ("next_area", []),
+        ("duration_to_next", 30 * 0),
+    ],
+    [
+        ("origin_id", 5),
+        ("heading", "N"),
+        ("x", 715),
+        ("y", -1),
+        ("next_area", [11]),
+        ("duration_to_next", 30 * 3),
+    ],
+    [
+        ("origin_id", 5),
+        ("heading", "S"),
+        ("x", lambda y: (y - 165.3) / 0.4143),
+        ("y", -1),
+        ("next_area", [4]),
+        ("duration_to_next", 30 * 4),
+    ],
+    [
+        ("origin_id", 7),
+        ("heading", "S"),
+        ("x", -1),
+        ("y", lambda x: 0.4 * x + 152),
+        ("next_area", [4]),
+        ("duration_to_next", 30 * 4),
+    ],
+    [
+        ("origin_id", 11),
+        ("heading", "S"),
+        ("x", 870),
+        ("y", -1),
+        ("next_area", [7]),
+        ("duration_to_next", 30 * 2),
+    ],
+    # [
+    #     ('origin_id', ),
+    #     ('heading', ''),
+    #     ('x', ),
+    #     ('y', ),
+    #     ('next_area', []),
+    #     ('duration_to_next', )
+    # ],
+]
+
+DONTCARE = (
+    (("origin_id", 5), ("heading", ["S", "W"])),
+    (("origin_id", 4), ("heading", ["S", "W"])),
+    (("origin_id", 7), ("heading", ["N", "E"])),
+    (("origin_id", 10), ("heading", ["N", "E"])),
+)
+
+
+def get_linear_equation(pnt1, pnt2):
+    if not (isinstance(pnt1, tuple) or isinstance(pnt2, tuple)):
+        return None
+    a = (pnt2[1] - pnt1[1]) / (pnt2[0] - pnt1[0])
+    b = pnt1[1] - a * pnt1[0]
+    return f"y = {a:.4f}x + {b:.1f}"
+
+
+def get_avg_heading(headings):
+    latest = headings[:15]
+    _h = "".join(latest).replace("W", "").replace("E", "")
+    chars = collections.Counter(_h).most_common(10)
+    if chars:
+        return chars[0][0]
+    return None
+
+
+def get_heading(x1, y1, x2, y2):
+    diff_x, diff_y = x2 - x1, y2 - y1
+    if diff_x > 0 and diff_y > 0:
+        return "NE"
+    if diff_x > 0 and diff_y == 0:
+        return "N"
+    if diff_x > 0:
+        return "NW"
+    if diff_y > 0:
+        return "SE"
+    if diff_y == 0:
+        return "S"
+    return "SW"
+
+
+def box_distance(pos1, pos2):
+    x1, y1, w1, h1 = pos1
+    x2, y2, w2, h2 = pos2
+    tx1, tx2 = x1 + w1 / 2, x2 + w2 / 2
+    ty1, ty2 = y1 + h1 / 2, y2 + h2 / 2
+    return math.sqrt(math.pow(tx2 - tx1, 2) + math.pow(ty2 - ty1, 2))
+
+
+def distance(x2, y2, x1, y1):
+    return math.sqrt(math.pow(x2 - x1, 2) + math.pow(y2 - y1, 2))
+
+
+def check_if_inside_the_boxes(x, y, w, h, _type):
+    cx, cy = x + w / 2, y + h / 2
+    # print(cx, cy)
+    is_inside = False
+    for _box in AREAS:
+        if is_inside:
+            break
+        box = dict(_box)
+        ((x1, y1), (x2, y2)) = box["area"]
+        # print(x1, cx, x2, ' -- ', y1, cy, y2, _type, box['target'])
+        if x1 < cx and cx < x2 and y1 < cy and cy < y2 and _type in box["target"]:
+            # print('inside --> ', _type, cx, cy, box['id'])
+            is_inside = True
+        # if diff_x < box_w
+    if is_inside:
+        # print("INSIDE!! this -> ", box)
+        return box
+    return False
+
+
+def is_it_the_same_obj(x1, y1, w1, h1, i1, j1, w2, h2, **kwargs):
+    """We would use the centroid location to check whether they are the same
+    object and of course, dimension too.
+    """
+    _id = kwargs.get("id", None)
+    # if _id:
+    #     print(" :: check against id:", _id, end="")
+
+    # if first coords are pretty much the same spot, then they are the same
+    if abs(x1 - i1) / x1 < 0.05 and abs(y1 - j1) / y1 < 0.05:
+        # print(" same 1st coords")
+        return True
+
+    DIMENSION_SHIFT = 0.15
+    # we have to use centroid !! from the experience
+    cx1, cy1, cx2, cy2 = x1 + w1 / 2, y1 + h1 / 2, i1 + w2 / 2, j1 + h2 / 2
+
+    c_dff_x, c_dff_y = abs(cx2 - cx1), abs(cy2 - cy1)
+    w_shift, h_shift = w1 * DIMENSION_SHIFT, h1 * DIMENSION_SHIFT
+    # print(" ::SAME:: shift", end="")
+    # print(f" | SHIFT w:{w_shift},h:{h_shift}", end="")
+    # print(f" | centroid  {c_dff_x}, {c_dff_y}", end="")
+    if c_dff_x > w_shift and c_dff_y > h_shift:
+        # print(" ::SAME:: shift too much already -- NOT THE SAME")
+        return False
+
+    # if one inside the other
+    if i1 > x1 and (w1 - w2) > (i1 - x1) and j1 > y1 and h1 - h2 > j1 - y1:
+        # one is inside the other
+        # print(" ::SAME:: new one inside existing tracker")
+        return True
+    if x1 > i1 and (w2 - w1) > x1 - i1 and y1 > j1 and h2 - h1 > y1 - j1:
+        # one is inside the other
+        # print(" ::SAME:: existing tracker inside new tracker")
+        return True
+
+    # if it's 90% overlapped then, assumed it's the same
+    # ra = Rectangle(x1, y1, x1 + w1, y1 + h1)
+    # rb = Rectangle(i1, j1, i1 + w2, j1 + w2)
+    # print(f'overlapped area: {area(ra, rb)} 1:{w1*h1:.1f} 1:{w2*h2:.1f}')
+    # print(f'||one {x1},{y1},{w1},{h1} two {i1},{j1},{w2},{h2}||')
+
+    # if it's not inside the other, then we can use "size" if it's different
+    size1, size2 = w1 * h1, w2 * h2
+    # if size is larger than 20%, then it's not the same thing
+    # print(" ::SAME:: size")
+    if abs(size2 - size1) / size1 > 0.45:
+        # print(f" sz {size1}, {size2}, diff%{abs(size2 - size1)/size1}", end="")
+        # print(" ^^ too diff in size -- NOT THE SAME", end="")
+        return False
+
+    # print(" ::SAME:: last")
+    # print("")
+    return True
+
+
+if __name__ == "__main__":
+    check_if_inside_the_boxes(461, 263, 24, 65, "person")
+    check_if_inside_the_boxes(8, 45, 172, 193, "bus")
+    check_if_inside_the_boxes(300, 300, 24, 65, "person")