Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/env python
'''
Camshift tracker
================
This is a demo that shows mean-shift based tracking
You select a color objects such as your face and it tracks it.
This reads from video camera (0 by default, or the camera number the user enters)
[1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.7673
Usage:
------
camshift.py [<video source>]
To initialize tracking, select the object with mouse
Keys:
-----
ESC - exit
b - toggle back-projected probability visualization
'''
import numpy as np
import cv2 as cv
# local module
import video
from video import presets
class App(object):
def __init__(self, video_src):
self.cam = video.create_capture(video_src, presets['cube'])
_ret, self.frame = self.cam.read()
cv.namedWindow('camshift')
cv.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.show_backproj = False
self.track_window = None
def onmouse(self, event, x, y, flags, param):
if event == cv.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
self.track_window = None
if self.drag_start:
xmin = min(x, self.drag_start[0])
ymin = min(y, self.drag_start[1])
xmax = max(x, self.drag_start[0])
ymax = max(y, self.drag_start[1])
self.selection = (xmin, ymin, xmax, ymax)
if event == cv.EVENT_LBUTTONUP:
self.drag_start = None
self.track_window = (xmin, ymin, xmax - xmin, ymax - ymin)
def show_hist(self):
bin_count = self.hist.shape[0]
bin_w = 24
img = np.zeros((256, bin_count*bin_w, 3), np.uint8)
for i in range(bin_count):
h = int(self.hist[i])
cv.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
img = cv.cvtColor(img, cv.COLOR_HSV2BGR)
cv.imshow('hist', img)
def run(self):
while True:
_ret, self.frame = self.cam.read()
vis = self.frame.copy()
hsv = cv.cvtColor(self.frame, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.selection
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX)
self.hist = hist.reshape(-1)
self.show_hist()
vis_roi = vis[y0:y1, x0:x1]
cv.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
if self.track_window and self.track_window[2] > 0 and self.track_window[3] > 0:
self.selection = None
prob = cv.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
track_box, self.track_window = cv.CamShift(prob, self.track_window, term_crit)
if self.show_backproj:
vis[:] = prob[...,np.newaxis]
try:
cv.ellipse(vis, track_box, (0, 0, 255), 2)
except:
print(track_box)
cv.imshow('camshift', vis)
ch = cv.waitKey(5)
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
cv.destroyAllWindows()
if __name__ == '__main__':
print(__doc__)
import sys
try:
video_src = sys.argv[1]
except:
video_src = 0
App(video_src).run()