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R6289/code/compute_motion.py
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compute_motion.py
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import os.path
from skimage import io
from subprocess import call
import numpy as np
from code import segment
def compute_motion(I1, I2, I1Match, I2Match, fnDeepMatching, param, t):
sz0 = I1.shape
I1 = np.pad(I1, [15,15], 'edge')
I2 = np.pad(I2, [15,15], 'edge')
if not os.path.exists('tmp'):
os.mkdir('tmp')
fnI1 = 'tmp/I1_'+str(t)+'.png'
fnI2 = 'tmp/I2_'+str(t)+'.png'
fnMatch = 'tmp/match_'+str(t)+'.png'
io.imsave(fnI1, I1Match.astype(np.uint8))
io.imsave(fnI2, I2Match.astype(np.uint8))
call(['./'+fnDeepMatching+'/deepmatching', fnI1, fnI2, '-out', fnMatch])
call(['rm', fnI1])
call(['rm', fnI2])
corresp = np.loadtxt(fnMatch)
scores = corresp[4,:]
corresp = corresp[:4,:].astype(np.int_)
call(['rm', fnMatch])
thresh = param['threshMatch']
Iseg = segment.segment(I1Match, 'variance', thresh)
matches = np.zeros((5,1))
zeros_column = matches
k=0
for i in range(0,corresp.shape[1]):
if corresp[0,i]>0 and corresp[0,i]<=sz0[1] and corresp[1,i]>0 and corresp[1,i]<=sz0[0]:
if Iseg[corresp[1,i],corresp[0,i]] == 1:
matches = np.concatenate((matches, zeros_column), axis=1)
matches[0,k] = corresp[0,i]
matches[1,k] = corresp[1,i]
matches[2,k] = corresp[2,i]-corresp[0,i]
matches[3,k] = corresp[3,i]-corresp[1,i]
k += 1
idx = np.nonzero(scores)
scores = scores[idx]
scores = (scores-min(scores))/(max(scores)-min(scores))
for j in range(len(idx)):
matches[4,idx[j]] = scores[j]
#Optical flow
v = 0 #if v=1 verbose mode
disp = 0 # if disp=1 displays the results at each iteration
w = of_l1_l2_fm_admm(I1, I2, sz0, matches, param, v, disp)
w = crop_fit_size_center(w, [sz0[0],sz0[1],2])
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