#!/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)

http://www.robinhewitt.com/research/track/camshift.html

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
import video


class App(object):
    def __init__(self, video_src):
        self.cam = video.create_capture(video_src)
        ret, self.frame = self.cam.read()
        cv2.namedWindow('camshift')
        cv2.setMouseCallback('camshift', self.onmouse)

        self.selection = None
        self.drag_start = None
        self.tracking_state = 0
        self.show_backproj = False

    def onmouse(self, event, x, y, flags, param):
        x, y = np.int16([x, y]) # BUG
        if event == cv2.EVENT_LBUTTONDOWN:
            self.drag_start = (x, y)
            self.tracking_state = 0
            return
        if self.drag_start:
            if flags & cv2.EVENT_FLAG_LBUTTON:
                h, w = self.frame.shape[:2]
                xo, yo = self.drag_start
                x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y]))
                x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y]))
                self.selection = None
                if x1-x0 > 0 and y1-y0 > 0:
                    self.selection = (x0, y0, x1, y1)
            else:
                self.drag_start = None
                if self.selection is not None:
                    self.tracking_state = 1

    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 xrange(bin_count):
            h = int(self.hist[i])
            cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
        img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
        cv2.imshow('hist', img)

    def run(self):
        while True:
            ret, self.frame = self.cam.read()
            vis = self.frame.copy()
            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
            mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

            if self.selection:
                x0, y0, x1, y1 = self.selection
                self.track_window = (x0, y0, x1-x0, y1-y0)
                hsv_roi = hsv[y0:y1, x0:x1]
                mask_roi = mask[y0:y1, x0:x1]
                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
                self.hist = hist.reshape(-1)
                self.show_hist()

                vis_roi = vis[y0:y1, x0:x1]
                cv2.bitwise_not(vis_roi, vis_roi)
                vis[mask == 0] = 0

            if self.tracking_state == 1:
                self.selection = None
                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
                prob &= mask
                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)

                if self.show_backproj:
                    vis[:] = prob[...,np.newaxis]
                try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
                except: print track_box

            cv2.imshow('camshift', vis)

            ch = 0xFF & cv2.waitKey(5)
            if ch == 27:
                break
            if ch == ord('b'):
                self.show_backproj = not self.show_backproj
        cv2.destroyAllWindows()


if __name__ == '__main__':
    import sys
    try: video_src = sys.argv[1]
    except: video_src = 0
    print __doc__
    App(video_src).run()
