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#!/usr/bin/env python
import os
import sys
import numpy as np
import cv2
import struct
import argparse
from math import sqrt
argparser = argparse.ArgumentParser(
description='''Use this script to generate prior for using with PCAFlow.
Basis size here must match corresponding parameter in the PCAFlow.
Gamma should be selected experimentally.''')
argparser.add_argument('-f',
'--files',
nargs='+',
help='List of optical flow .flo files for learning. You can pass a directory here and it will be scanned recursively for .flo files.',
required=True)
argparser.add_argument('-o',
'--output',
help='Output file for prior',
required=True)
argparser.add_argument('--width',
type=int,
help='Size of the basis first dimension',
required=True,
default=18)
argparser.add_argument('--height',
type=int,
help='Size of the basis second dimension',
required=True,
default=14)
argparser.add_argument(
'-g',
'--gamma',
type=float,
help='Amount of regularization. The greater this parameter, the bigger will be an impact of the regularization.',
required=True)
args = argparser.parse_args()
basis_size = (args.height, args.width)
gamma = args.gamma
def find_flo(pp):
f = []
for p in pp:
if os.path.isfile(p):
f.append(p)
else:
for root, subdirs, files in os.walk(p):
f += map(lambda x: os.path.join(root, x),
filter(lambda x: x.split('.')[-1] == 'flo', files))
return list(set(f))
def load_flo(flo):
with open(flo, 'rb') as f:
magic = np.fromfile(f, np.float32, count=1)[0]
if 202021.25 != magic:
print('Magic number incorrect. Invalid .flo file')
else:
w = np.fromfile(f, np.int32, count=1)[0]
h = np.fromfile(f, np.int32, count=1)[0]
print('Reading %dx%d flo file %s' % (w, h, flo))
data = np.fromfile(f, np.float32, count=2 * w * h)
# Reshape data into 3D array (columns, rows, bands)
flow = np.reshape(data, (h, w, 2))
return flow[:, :, 0], flow[:, :, 1]
def get_w(m):
s = m.shape
w = cv2.dct(m)
w *= 2.0 / sqrt(s[0] * s[1])
#w[0,0] *= 0.5
w[:, 0] *= sqrt(0.5)
w[0, :] *= sqrt(0.5)
w = w[0:basis_size[0], 0:basis_size[1]].transpose().flatten()
return w
w1 = []
w2 = []
for flo in find_flo(args.files):
x, y = load_flo(flo)
w1.append(get_w(x))
w2.append(get_w(y))
w1mean = sum(w1) / len(w1)
w2mean = sum(w2) / len(w2)
for i in xrange(len(w1)):
w1[i] -= w1mean
for i in xrange(len(w2)):
w2[i] -= w2mean
Q1 = sum([w1[i].reshape(-1, 1).dot(w1[i].reshape(1, -1))
for i in xrange(len(w1))]) / len(w1)
Q2 = sum([w2[i].reshape(-1, 1).dot(w2[i].reshape(1, -1))
for i in xrange(len(w2))]) / len(w2)
Q1 = np.matrix(Q1)
Q2 = np.matrix(Q2)
if len(w1) > 1:
while True:
try:
L1 = np.linalg.cholesky(Q1)
break
except np.linalg.linalg.LinAlgError:
mev = min(np.linalg.eig(Q1)[0]).real
assert (mev < 0)
print('Q1', mev)
if -mev < 1e-6:
mev = -1e-6
Q1 += (-mev * 1.000001) * np.identity(Q1.shape[0])
while True:
try:
L2 = np.linalg.cholesky(Q2)
break
except np.linalg.linalg.LinAlgError:
mev = min(np.linalg.eig(Q2)[0]).real
assert (mev < 0)
print('Q2', mev)
if -mev < 1e-6:
mev = -1e-6
Q2 += (-mev * 1.000001) * np.identity(Q2.shape[0])
else:
L1 = np.identity(Q1.shape[0])
L2 = np.identity(Q2.shape[0])
L1 = np.linalg.inv(L1) * gamma
L2 = np.linalg.inv(L2) * gamma
assert (L1.shape == L2.shape)
assert (L1.shape[0] == L1.shape[1])
f = open(args.output, 'wb')
f.write(struct.pack('I', L1.shape[0]))
f.write(struct.pack('I', L1.shape[1]))
for i in xrange(L1.shape[0]):
for j in xrange(L1.shape[1]):
f.write(struct.pack('f', L1[i, j]))
for i in xrange(L2.shape[0]):
for j in xrange(L2.shape[1]):
f.write(struct.pack('f', L2[i, j]))
b1 = L1.dot(w1mean.reshape(-1, 1))
b2 = L2.dot(w2mean.reshape(-1, 1))
assert (L1.shape[0] == b1.shape[0])
for i in xrange(b1.shape[0]):
f.write(struct.pack('f', b1[i, 0]))
for i in xrange(b2.shape[0]):
f.write(struct.pack('f', b2[i, 0]))
f.close()