我正在修改以下代码以跟踪对象并保存被跟踪对象的图像：
#!/usr/bin/env python
'''
MOSSE tracking sample
This sample implements correlation-based tracking approach, described in [1].
Usage:
mosse.py [--pause] [<video source>]
--pause - Start with playback paused at the first video frame.
Useful for tracking target selection.
Draw rectangles around objects with a mouse to track them.
Keys:
SPACE - pause video
c - clear targets
[1] David S. Bolme et al. "Visual Object Tracking using Adaptive Correlation Filters"
http://www.cs.colostate.edu/~bolme/publications/Bolme2010Tracking.pdf
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
import cv2
from common import draw_str, RectSelector
import video
def rnd_warp(a):
h, w = a.shape[:2]
T = np.zeros((2, 3))
coef = 0.2
ang = (np.random.rand()-0.5)*coef
c, s = np.cos(ang), np.sin(ang)
T[:2, :2] = [[c,-s], [s, c]]
T[:2, :2] += (np.random.rand(2, 2) - 0.5)*coef
c = (w/2, h/2)
T[:,2] = c - np.dot(T[:2, :2], c)
return cv2.warpAffine(a, T, (w, h), borderMode = cv2.BORDER_REFLECT)
def divSpec(A, B):
Ar, Ai = A[...,0], A[...,1]
Br, Bi = B[...,0], B[...,1]
C = (Ar+1j*Ai)/(Br+1j*Bi)
C = np.dstack([np.real(C), np.imag(C)]).copy()
return C
eps = 1e-5
class MOSSE:
def __init__(self, frame, rect):
x1, y1, x2, y2 = rect
w, h = map(cv2.getOptimalDFTSize, [x2-x1, y2-y1])
x1, y1 = (x1+x2-w)//2, (y1+y2-h)//2
self.pos = x, y = x1+0.5*(w-1), y1+0.5*(h-1)
self.size = w, h
img = cv2.getRectSubPix(frame, (w, h), (x, y))
self.win = cv2.createHanningWindow((w, h), cv2.CV_32F)
g = np.zeros((h, w), np.float32)
g[h//2, w//2] = 1
g = cv2.GaussianBlur(g, (-1, -1), 2.0)
g /= g.max()
self.G = cv2.dft(g, flags=cv2.DFT_COMPLEX_OUTPUT)
self.H1 = np.zeros_like(self.G)
self.H2 = np.zeros_like(self.G)
for i in xrange(128):
a = self.preprocess(rnd_warp(img))
A = cv2.dft(a, flags=cv2.DFT_COMPLEX_OUTPUT)
self.H1 += cv2.mulSpectrums(self.G, A, 0, conjB=True)
self.H2 += cv2.mulSpectrums( A, A, 0, conjB=True)
self.update_kernel()
self.update(frame)
def update(self, frame, rate = 0.125):
(x, y), (w, h) = self.pos, self.size
self.last_img = img = cv2.getRectSubPix(frame, (w, h), (x, y))
img = self.preprocess(img)
self.last_resp, (dx, dy), self.psr = self.correlate(img)
self.good = self.psr > 8.0
if not self.good:
return
self.pos = x+dx, y+dy
self.last_img = img = cv2.getRectSubPix(frame, (w, h), self.pos)
img = self.preprocess(img)
A = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
H1 = cv2.mulSpectrums(self.G, A, 0, conjB=True)
H2 = cv2.mulSpectrums( A, A, 0, conjB=True)
self.H1 = self.H1 * (1.0-rate) + H1 * rate
self.H2 = self.H2 * (1.0-rate) + H2 * rate
self.update_kernel()
@property
def state_vis(self):
f = cv2.idft(self.H, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
h, w = f.shape
f = np.roll(f, -h//2, 0)
f = np.roll(f, -w//2, 1)
kernel = np.uint8( (f-f.min()) / f.ptp()*255 )
resp = self.last_resp
resp = np.uint8(np.clip(resp/resp.max(), 0, 1)*255)
vis = np.hstack([self.last_img, kernel, resp])
return vis
def draw_state(self, vis):
(x, y), (w, h) = self.pos, self.size
x1, y1, x2, y2 = int(x-0.5*w), int(y-0.5*h), int(x+0.5*w), int(y+0.5*h)
cv2.rectangle(vis, (x1, y1), (x2, y2), (0, 0, 255))
if self.good:
cv2.circle(vis, (int(x), int(y)), 2, (0, 0, 255), -1)
else:
cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255))
cv2.line(vis, (x2, y1), (x1, y2), (0, 0, 255))
draw_str(vis, (x1, y2+16), 'PSR: %.2f' % self.psr)
def preprocess(self, img):
img = np.log(np.float32(img)+1.0)
img = (img-img.mean()) / (img.std()+eps)
return img*self.win
def correlate(self, img):
C = cv2.mulSpectrums(cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT), self.H, 0, conjB=True)
resp = cv2.idft(C, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT)
h, w = resp.shape
_, mval, _, (mx, my) = cv2.minMaxLoc(resp)
side_resp = resp.copy()
cv2.rectangle(side_resp, (mx-5, my-5), (mx+5, my+5), 0, -1)
smean, sstd = side_resp.mean(), side_resp.std()
psr = (mval-smean) / (sstd+eps)
return resp, (mx-w//2, my-h//2), psr
def update_kernel(self):
self.H = divSpec(self.H1, self.H2)
self.H[...,1] *= -1
class App:
def __init__(self, video_src, paused = False):
self.cap = video.create_capture(video_src)
_, self.frame = self.cap.read()
cv2.imshow('frame', self.frame)
self.rect_sel = RectSelector('frame', self.onrect)
self.trackers = []
self.paused = paused
def onrect(self, rect):
frame_gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
tracker = MOSSE(frame_gray, rect)
self.trackers.append(tracker)
def run(self):
while True:
if not self.paused:
ret, self.frame = self.cap.read()
if not ret:
break
frame_gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
for tracker in self.trackers:
tracker.update(frame_gray)
vis = self.frame.copy()
for tracker in self.trackers:
tracker.draw_state(vis)
if len(self.trackers) > 0:
cv2.imshow('tracker state', self.trackers[-1].state_vis)
cv2.imwrite('object.jpg', self.trackers[-1].state_vis)
self.rect_sel.draw(vis)
cv2.imshow('frame', vis)
ch = cv2.waitKey(10) & 0xFF
if ch == 27:
break
if ch == ord(' '):
self.paused = not self.paused
if ch == ord('c'):
self.trackers = []
if __name__ == '__main__':
print (__doc__)
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['pause'])
opts = dict(opts)
try:
video_src = args[0]
except:
video_src = '0'
App(video_src, paused = '--pause' in opts).run()
但是在跟踪器状态窗口中，它会捕获跟踪对象（我不需要）的轮廓和浮雕版本以及对象。我只需要对象的正常图像（第一部分）跟踪器状态窗口。）
请注意，跟踪器状态窗口中有三个部分。 有人可以帮忙吗？
谢谢！
答案 0 :(得分：1)
基于Mosse跟踪的这个video，我假设物体的轮廓是矩形的，这在看代码时很明显。
我没有直截了当的回答（不喜欢在这里发布作业的人）但是，我还有一个提示：
cv2.rectangle()（link to doc）的行，因为它负责绘制矩形（正如您可能已经猜到的那样）。 这不应该太难解决！