Simple object tracking with OpenCV

centroidtracker.py

# https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/

# import the necessary packages
from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as np

class CentroidTracker():
	def __init__(self, maxDisappeared=50):
		# initialize the next unique object ID along with two ordered
		# dictionaries used to keep track of mapping a given object
		# ID to its centroid and number of consecutive frames it has
		# been marked as "disappeared", respectively
		self.nextObjectID = 0
		self.objects = OrderedDict()
		self.disappeared = OrderedDict()

		# store the number of maximum consecutive frames a given
		# object is allowed to be marked as "disappeared" until we
		# need to deregister the object from tracking
		self.maxDisappeared = maxDisappeared

	def register(self, centroid):
		# when registering an object we use the next available object
		# ID to store the centroid
		self.objects[self.nextObjectID] = centroid
		self.disappeared[self.nextObjectID] = 0
		self.nextObjectID += 1

	def deregister(self, objectID):
		# to deregister an object ID we delete the object ID from
		# both of our respective dictionaries
		del self.objects[objectID]
		del self.disappeared[objectID]

	def update(self, rects):
		# check to see if the list of input bounding box rectangles
		# is empty
		if len(rects) == 0:
			# loop over any existing tracked objects and mark them
			# as disappeared
			for objectID in self.disappeared.keys():
				self.disappeared[objectID] += 1

				# if we have reached a maximum number of consecutive
				# frames where a given object has been marked as
				# missing, deregister it
				if self.disappeared[objectID] > self.maxDisappeared:
					self.deregister(objectID)

			# return early as there are no centroids or tracking info
			# to update
			return self.objects

		# initialize an array of input centroids for the current frame
		inputCentroids = np.zeros((len(rects), 2), dtype="int")

		# loop over the bounding box rectangles
		for (i, (startX, startY, endX, endY)) in enumerate(rects):
			# use the bounding box coordinates to derive the centroid
			cX = int((startX + endX) / 2.0)
			cY = int((startY + endY) / 2.0)
			inputCentroids[i] = (cX, cY)

		# if we are currently not tracking any objects take the input
		# centroids and register each of them
		if len(self.objects) == 0:
			for i in range(0, len(inputCentroids)):
				self.register(inputCentroids[i])

		# otherwise, are are currently tracking objects so we need to
		# try to match the input centroids to existing object
		# centroids
		else:
			# grab the set of object IDs and corresponding centroids
			objectIDs = list(self.objects.keys())
			objectCentroids = list(self.objects.values())

			# compute the distance between each pair of object
			# centroids and input centroids, respectively -- our
			# goal will be to match an input centroid to an existing
			# object centroid
			D = dist.cdist(np.array(objectCentroids), inputCentroids)

			# in order to perform this matching we must (1) find the
			# smallest value in each row and then (2) sort the row
			# indexes based on their minimum values so that the row
			# with the smallest value as at the *front* of the index
			# list
			rows = D.min(axis=1).argsort()

			# next, we perform a similar process on the columns by
			# finding the smallest value in each column and then
			# sorting using the previously computed row index list
			cols = D.argmin(axis=1)[rows]

			# in order to determine if we need to update, register,
			# or deregister an object we need to keep track of which
			# of the rows and column indexes we have already examined
			usedRows = set()
			usedCols = set()

			# loop over the combination of the (row, column) index
			# tuples
			for (row, col) in zip(rows, cols):
				# if we have already examined either the row or
				# column value before, ignore it
				# val
				if row in usedRows or col in usedCols:
					continue

				# otherwise, grab the object ID for the current row,
				# set its new centroid, and reset the disappeared
				# counter
				objectID = objectIDs[row]
				self.objects[objectID] = inputCentroids[col]
				self.disappeared[objectID] = 0

				# indicate that we have examined each of the row and
				# column indexes, respectively
				usedRows.add(row)
				usedCols.add(col)

			# compute both the row and column index we have NOT yet
			# examined
			unusedRows = set(range(0, D.shape[0])).difference(usedRows)
			unusedCols = set(range(0, D.shape[1])).difference(usedCols)

			# in the event that the number of object centroids is
			# equal or greater than the number of input centroids
			# we need to check and see if some of these objects have
			# potentially disappeared
			if D.shape[0] >= D.shape[1]:
				# loop over the unused row indexes
				for row in unusedRows:
					# grab the object ID for the corresponding row
					# index and increment the disappeared counter
					objectID = objectIDs[row]
					self.disappeared[objectID] += 1

					# check to see if the number of consecutive
					# frames the object has been marked "disappeared"
					# for warrants deregistering the object
					if self.disappeared[objectID] > self.maxDisappeared:
						self.deregister(objectID)

			# otherwise, if the number of input centroids is greater
			# than the number of existing object centroids we need to
			# register each new input centroid as a trackable object
			else:
				for col in unusedCols:
					self.register(inputCentroids[col])

		# return the set of trackable objects
		return self.objects

object_tracker.py

# https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/

# USAGE
# python object_tracker.py --prototxt deploy.prototxt --model res10_300x300_ssd_iter_140000.caffemodel

# import the necessary packages
from pyimagesearch.centroidtracker import CentroidTracker
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
	help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
	help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# initialize our centroid tracker and frame dimensions
ct = CentroidTracker()
(H, W) = (None, None)

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# initialize the video stream and allow the camera sensor to warmup
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# loop over the frames from the video stream
while True:
	# read the next frame from the video stream and resize it
	frame = vs.read()
	frame = imutils.resize(frame, width=400)

	# if the frame dimensions are None, grab them
	if W is None or H is None:
		(H, W) = frame.shape[:2]

	# construct a blob from the frame, pass it through the network,
	# obtain our output predictions, and initialize the list of
	# bounding box rectangles
	blob = cv2.dnn.blobFromImage(frame, 1.0, (W, H),
		(104.0, 177.0, 123.0))
	net.setInput(blob)
	detections = net.forward()
	rects = []

	# loop over the detections
	for i in range(0, detections.shape[2]):
		# filter out weak detections by ensuring the predicted
		# probability is greater than a minimum threshold
		if detections[0, 0, i, 2] > args["confidence"]:
			# compute the (x, y)-coordinates of the bounding box for
			# the object, then update the bounding box rectangles list
			box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
			rects.append(box.astype("int"))

			# draw a bounding box surrounding the object so we can
			# visualize it
			(startX, startY, endX, endY) = box.astype("int")
			cv2.rectangle(frame, (startX, startY), (endX, endY),
				(0, 255, 0), 2)

	# update our centroid tracker using the computed set of bounding
	# box rectangles
	objects = ct.update(rects)

	# loop over the tracked objects
	for (objectID, centroid) in objects.items():
		# draw both the ID of the object and the centroid of the
		# object on the output frame
		text = "ID {}".format(objectID)
		cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
		cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)

	# 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

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

Thank you for providing this. @adioshun

For those looking for 2 files needed to run the following command:

python object_tracker.py --prototxt deploy.prototxt --model res10_300x300_ssd_iter_140000.caffemodel

Run the commands below to download the files:

# deploy.prototxt
wget https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
# res10_300x300_ssd_iter_140000.caffemodel
wget https://github.com/gopinath-balu/computer_vision/raw/master/CAFFE_DNN/res10_300x300_ssd_iter_140000.caffemodel

To detect the object from the image from scratch using python; Click here I found the best article https://debuggingsolution.blogspot.com/2022/02/object-detection-from-scratch-in-python.html