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camshift and meanshift and webcam drivers scripts added
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‎CAMShiftTrack.py

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'''
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Created on Sep 9, 2017
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@author: inayat
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'''
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# import the required packages
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from imutils.video import WebcamVideoStream
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#from imutils.video import FPS
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import numpy as np
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import argparse
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import imutils
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import time
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import cv2
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from utils.fps2 import FPS2
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import dlib
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from trackers.camshifttracker import CAMShiftTracker
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if __name__ == '__main__':
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print("[info] starting to read a webcam ...")
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capWebCam = WebcamVideoStream(0).start()
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time.sleep(1.0)
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# initialize dlib face detector
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frontFaceDetector = dlib.get_frontal_face_detector()
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# meanShift tracker
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camShifTracker = None
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curWindow = None
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# start the frame per second (FPS) counter
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#fps = FPS2().start()
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boolDetectFaceinfirsFrameOnly = True
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# loop over the frames obtained from the webcam
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while True:
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# grab each frame from the threaded stream,
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# resize
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# it, and convert it to grayscale (while still retaining 3
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# channels)
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frame1 = capWebCam.read()
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frame = cv2.flip(frame1,1)
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#frame = imutils.resize(frame, width=450)
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#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#frame = np.dstack([frame, frame, frame])
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# display the size of the queue on the frame
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#cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
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# (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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if boolDetectFaceinfirsFrameOnly:
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faceRect = frontFaceDetector(frame, 0)
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if(not len(faceRect) ):
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print("[info] Face not found")
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continue
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# start the frame per second (FPS) counter
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fps = FPS2().start()
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bbox = faceRect[0]
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# convert dlib rect to opencv rect
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curWindow = (int(bbox.left()), int(bbox.top()), int(bbox.right() - bbox.left()),
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int(bbox.bottom() - bbox.top()) )
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# intialize the CAMShift Tracker
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camShifTracker = CAMShiftTracker(curWindow, frame)
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boolDetectFaceinfirsFrameOnly = False
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continue
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camShifTracker.computeNewWindow(frame)
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x,y, w, h = camShifTracker.getCurWindow()
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bkprojectImage = camShifTracker.getBackProjectedImage(frame)
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cv2.imshow("CAMShift Face in Back Project Image", bkprojectImage)
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# display the current window
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cv2.rectangle(frame, (x,y), (x+w, y+h), (255, 0, 0), 2, cv2.LINE_AA)
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rotatedWindow = camShifTracker.getRotatedWindow()
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#display rotated window
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cv2.polylines(frame, [rotatedWindow], True, (0,255,0), 2, cv2.LINE_AA)
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fps.update()
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cv2.putText(frame, "FPS: {:.2f}".format(fps.fps()),
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(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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# show the frame and update the FPS counter
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cv2.imshow("CAMShift Face Tracking", frame)
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k = cv2.waitKey(10) & 0xff
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if k == 27:
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break
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# stop the timer and display FPS information
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fps.stop()
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print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
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print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
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# do a bit of cleanup
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cv2.destroyAllWindows()
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capWebCam.stop()
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‎Readme.md

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# Tracking Using OpenCV and Python-3.5
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# Tracking Using OpenCV >= 3.2 and Python-3.5
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In this repository I will give some implementation of tracking algorithms.
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---
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---
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## Meanshift and Camshift
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## Meanshift and CAMshift
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The MeanShift algorithm looks to object tracking as mode-seeking problem. Mean-shift was first presented by [Fukunaga et al in 1975](http://ieeexplore.ieee.org/document/1055330/). It is a non-parametric approach for finding the maxima of a density function. The process is an iterative approach that involves calculating and shifting the mean of a set of data points, which fall in a circle, in the direction of the mean shift vector and thus it is called Meanshift. The radius of the circle is also called window size.
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The value of the radius does matter. Very small value will generate local maxima while veray large value of the radius will let the algo to find true maxima. If there are more than one mode then they will be merged. To handle this problem radius of the circle needs to be changed adaptively. This is done by CAMshift algorithm ( Continuously Adaptive Meanshift)
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For the widow located at \\(x\\), the center of the mass \\(m(x)\\)of the neighboring points \\(x_i\\) is calculated as
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$$m(x) = \frac{\sum_i{K(x - x_i)}x_i}{\sum_i{K(x - x_i)}}$$
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where \\( K\\) is the kernel used to decide the widow size and how the weights of different points will be accumulated
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### Tracking using MeanShift in OpenCV
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We will use the following steps for live tracking of face using web cam:
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1. Detect the face, which we want to track, using dlib face detector. Compute the histogram of the face region using Hue Channel of the HSV color space. However, both H and S can be used. Its worthy to note that color information is very sensitive to lighting variations.
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use calcHist() function of OpenCV for computing the histogram and normalize the values in the range [0,255]
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2. Find the back projected image for every new frame using calcBackPorject() function
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3. Use meanShift() function to find the maxima in the backprojected image in the neigborhood of the old position. This alog finds the mode of the back projected image which obviously a confidence map of similarity between the color distribution of the face and the new image.
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#### Remarks
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The MeanShift trackers sometimes fails when the scale of the object of interest changes, because the tracker is initialized to the scale of the object in the first frame. Later when scale of the object is changed then the tracker window size does not match the actual size of the object. This problem is handeled by the CAMshift tracker.
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#### usage:
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$ python meanShiftTrack.py
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### Tracking using CAMshift in OpenCV
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CAMshift tries to tackle the scale problem by using varying window size for applying meanshift. CamShift was developed by [Gary Bradski in 1998](http://dl.acm.org/citation.cfm?id=836819)
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## *References*
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1. [Pyimagesearch Adrian Rosebrock](http://www.pyimagesearch.com/)
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2. [Learn OpenCV, Satya Mallick](http://www.learnopencv.com)

‎fastframeratewebcam.py

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'''
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Created on Sep 9, 2017
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@author: inayat
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'''
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# import the required packages
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from imutils.video import WebcamVideoStream
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#from imutils.video import FPS
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import numpy as np
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import argparse
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import imutils
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import time
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import cv2
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from utils.fps2 import FPS2
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if __name__ == '__main__':
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print("[info] starting to read a webcam ...")
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capWebCam = WebcamVideoStream(0).start()
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time.sleep(1.0)
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# start the frame per second (FPS) counter
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fps = FPS2().start()
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# loop over the frames obtained from the webcam
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while True:
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# grab each frame from the threaded stream,
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# resize
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# it, and convert it to grayscale (while still retaining 3
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# channels)
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frame1 = capWebCam.read()
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frame = cv2.flip(frame1,1)
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#frame = imutils.resize(frame, width=450)
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#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#frame = np.dstack([frame, frame, frame])
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# display the size of the queue on the frame
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#cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
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# (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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fps.update()
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cv2.putText(frame, "FPS: {:.2f}".format(fps.fps()),
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(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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# show the frame and update the FPS counter
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cv2.imshow("Face Tracking", frame)
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k = cv2.waitKey(10) & 0xff
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if k == 27:
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break
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# stop the timer and display FPS information
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fps.stop()
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print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
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print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
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# do a bit of cleanup
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cv2.destroyAllWindows()
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capWebCam.stop()
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