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Open Source Computer Vision Library
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119 lines
3.9 KiB
119 lines
3.9 KiB
#!/usr/bin/env python |
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''' |
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Camshift tracker |
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================ |
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This is a demo that shows mean-shift based tracking |
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You select a color objects such as your face and it tracks it. |
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This reads from video camera (0 by default, or the camera number the user enters) |
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http://www.robinhewitt.com/research/track/camshift.html |
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Usage: |
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------ |
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camshift.py [<video source>] |
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To initialize tracking, select the object with mouse |
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Keys: |
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----- |
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ESC - exit |
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b - toggle back-projected probability visualization |
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''' |
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import numpy as np |
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import cv2 |
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import video |
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class App(object): |
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def __init__(self, video_src): |
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self.cam = video.create_capture(video_src) |
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ret, self.frame = self.cam.read() |
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cv2.namedWindow('camshift') |
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cv2.setMouseCallback('camshift', self.onmouse) |
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self.selection = None |
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self.drag_start = None |
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self.tracking_state = 0 |
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self.show_backproj = False |
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def onmouse(self, event, x, y, flags, param): |
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x, y = np.int16([x, y]) # BUG |
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if event == cv2.EVENT_LBUTTONDOWN: |
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self.drag_start = (x, y) |
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self.tracking_state = 0 |
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if self.drag_start: |
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if flags & cv2.EVENT_FLAG_LBUTTON: |
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h, w = self.frame.shape[:2] |
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xo, yo = self.drag_start |
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x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y])) |
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x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y])) |
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self.selection = None |
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if x1-x0 > 0 and y1-y0 > 0: |
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self.selection = (x0, y0, x1, y1) |
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else: |
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self.drag_start = None |
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if self.selection is not None: |
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self.tracking_state = 1 |
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def show_hist(self): |
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bin_count = self.hist.shape[0] |
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bin_w = 24 |
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img = np.zeros((256, bin_count*bin_w, 3), np.uint8) |
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for i in xrange(bin_count): |
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h = int(self.hist[i]) |
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cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1) |
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img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR) |
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cv2.imshow('hist', img) |
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def run(self): |
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while True: |
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ret, self.frame = self.cam.read() |
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vis = self.frame.copy() |
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hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV) |
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mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.))) |
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if self.selection: |
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x0, y0, x1, y1 = self.selection |
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self.track_window = (x0, y0, x1-x0, y1-y0) |
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hsv_roi = hsv[y0:y1, x0:x1] |
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mask_roi = mask[y0:y1, x0:x1] |
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hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] ) |
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cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX); |
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self.hist = hist.reshape(-1) |
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self.show_hist() |
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vis_roi = vis[y0:y1, x0:x1] |
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cv2.bitwise_not(vis_roi, vis_roi) |
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vis[mask == 0] = 0 |
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if self.tracking_state == 1: |
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self.selection = None |
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prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1) |
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prob &= mask |
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term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 ) |
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track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit) |
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if self.show_backproj: |
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vis[:] = prob[...,np.newaxis] |
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try: cv2.ellipse(vis, track_box, (0, 0, 255), 2) |
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except: print track_box |
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cv2.imshow('camshift', vis) |
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ch = 0xFF & cv2.waitKey(5) |
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if ch == 27: |
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break |
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if ch == ord('b'): |
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self.show_backproj = not self.show_backproj |
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cv2.destroyAllWindows() |
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if __name__ == '__main__': |
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import sys |
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try: video_src = sys.argv[1] |
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except: video_src = 0 |
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print __doc__ |
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App(video_src).run()
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