It’s pretty darn slow. I don’t think it’s optimizing the Nvidia Tesla v100
power. It uses some openCv , and it just screams for paral/acceleration. I’d
also love to learn and see how it’s done
import cv2
import numpy as np
import os
import pickle
import sys
from cgls import cgls
from filterplot import filterplot
from gaussian2d import gaussian2d
from gettrainargs import gettrainargs
from hashkey import hashkey
from math import floor
from matplotlib import pyplot as plt
from scipy import interpolate
from skimage import transform
args = gettrainargs()
# Define parameters
R = 2
patchsize = 11
gradientsize = 9
Qangle = 24
Qstrength = 3
Qcoherence = 3
trainpath = 'train'
# Calculate the margin
maxblocksize = max(patchsize, gradientsize)
margin = floor(maxblocksize/2)
patchmargin = floor(patchsize/2)
gradientmargin = floor(gradientsize/2)
Q = np.zeros((Qangle, Qstrength, Qcoherence, R*R, patchsize*patchsize,
patchsize*patchsize))
V = np.zeros((Qangle, Qstrength, Qcoherence, R*R, patchsize*patchsize))
h = np.zeros((Qangle, Qstrength, Qcoherence, R*R, patchsize*patchsize))
# Read Q,V from file
if args.qmatrix:
with open(args.qmatrix, "rb") as fp:
Q = pickle.load(fp)
if args.vmatrix:
with open(args.vmatrix, "rb") as fp:
V = pickle.load(fp)
# Matrix preprocessing
# Preprocessing normalized Gaussian matrix W for hashkey calculation
weighting = gaussian2d([gradientsize, gradientsize], 2)
weighting = np.diag(weighting.ravel())
# Get image list
imagelist = []
for parent, dirnames, filenames in os.walk(trainpath):
for filename in filenames:
if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg',
'.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
imagelist.append(os.path.join(parent, filename))
# Compute Q and V
imagecount = 1
for image in imagelist:
print('\r', end='')
print(' ' * 60, end='')
print('\rProcessing image ' + str(imagecount) + ' of ' +
str(len(imagelist)) + ' (' + image + ')')
origin = cv2.imread(image)
# Extract only the luminance in YCbCr
grayorigin = cv2.cvtColor(origin, cv2.COLOR_BGR2YCrCb)[:,:,0]
# Normalized to [0,1]
grayorigin = cv2.normalize(grayorigin.astype('float'), None,
grayorigin.min()/255, grayorigin.max()/255, cv2.NORM_MINMAX)
# Downscale (bicubic interpolation)
height, width = grayorigin.shape
LR = transform.resize(grayorigin, (floor((height+1)/2),floor((width+1)/2)),
mode='reflect', anti_aliasing=False)
# Upscale (bilinear interpolation)
height, width = LR.shape
heightgrid = np.linspace(0, height-1, height)
widthgrid = np.linspace(0, width-1, width)
bilinearinterp = interpolate.interp2d(widthgrid, heightgrid, LR,
kind='linear')
heightgrid = np.linspace(0, height-1, height*2-1)
widthgrid = np.linspace(0, width-1, width*2-1)
upscaledLR = bilinearinterp(widthgrid, heightgrid)
# Calculate A'A, A'b and push them into Q, V
height, width = upscaledLR.shape
operationcount = 0
totaloperations = (height-2*margin) * (width-2*margin)
for row in range(margin, height-margin):
for col in range(margin, width-margin):
if round(operationcount*100/totaloperations) !=
round((operationcount+1)*100/totaloperations):
print('\r|', end='')
print('#' * round((operationcount+1)*100/totaloperations/2),
end='')
print(' ' * (50 -
round((operationcount+1)*100/totaloperations/2)), end='')
print('| ' +
str(round((operationcount+1)*100/totaloperations)) + '%', end='')
sys.stdout.flush()
operationcount += 1
# Get patch
patch = upscaledLR[row-patchmargin:row+patchmargin+1,
col-patchmargin:col+patchmargin+1]
patch = np.matrix(patch.ravel())
# Get gradient block
gradientblock = upscaledLR[row-gradientmargin:row+gradientmargin+1,
col-gradientmargin:col+gradientmargin+1]
# Calculate hashkey
angle, strength, coherence = hashkey(gradientblock, Qangle,
weighting)
# Get pixel type
pixeltype = ((row-margin) % R) * R + ((col-margin) % R)
# Get corresponding HR pixel
pixelHR = grayorigin[row,col]
# Compute A'A and A'b
ATA = np.dot(patch.T, patch)
ATb = np.dot(patch.T, pixelHR)
ATb = np.array(ATb).ravel()
# Compute Q and V
Q[angle,strength,coherence,pixeltype] += ATA
V[angle,strength,coherence,pixeltype] += ATb
imagecount += 1
# Write Q,V to file
with open("q.p", "wb") as fp:
pickle.dump(Q, fp)
with open("v.p", "wb") as fp:
pickle.dump(V, fp)
# Preprocessing permutation matrices P for nearly-free 8x more learning examples
print('\r', end='')
print(' ' * 60, end='')
print('\rPreprocessing permutation matrices P for nearly-free 8x more learning
examples ...')
sys.stdout.flush()
P = np.zeros((patchsize*patchsize, patchsize*patchsize, 7))
rotate = np.zeros((patchsize*patchsize, patchsize*patchsize))
flip = np.zeros((patchsize*patchsize, patchsize*patchsize))
for i in range(0, patchsize*patchsize):
i1 = i % patchsize
i2 = floor(i / patchsize)
j = patchsize * patchsize - patchsize + i2 - patchsize * i1
rotate[j,i] = 1
k = patchsize * (i2 + 1) - i1 - 1
flip[k,i] = 1
for i in range(1, 8):
i1 = i % 4
i2 = floor(i / 4)
P[:,:,i-1] =
np.linalg.matrix_power(flip,i2).dot(np.linalg.matrix_power(rotate,i1))
Qextended = np.zeros((Qangle, Qstrength, Qcoherence, R*R, patchsize*patchsize,
patchsize*patchsize))
Vextended = np.zeros((Qangle, Qstrength, Qcoherence, R*R, patchsize*patchsize))
for pixeltype in range(0, R*R):
for angle in range(0, Qangle):
for strength in range(0, Qstrength):
for coherence in range(0, Qcoherence):
for m in range(1, 8):
m1 = m % 4
m2 = floor(m / 4)
newangleslot = angle
if m2 == 1:
newangleslot = Qangle-angle-1
newangleslot = int(newangleslot-Qangle/2*m1)
while newangleslot < 0:
newangleslot += Qangle
newQ =
P[:,:,m-1].T.dot(Q[angle,strength,coherence,pixeltype]).dot(P[:,:,m-1])
newV =
P[:,:,m-1].T.dot(V[angle,strength,coherence,pixeltype])
Qextended[newangleslot,strength,coherence,pixeltype] += newQ
Vextended[newangleslot,strength,coherence,pixeltype] += newV
Q += Qextended
V += Vextended
# Compute filter h
print('Computing h ...')
sys.stdout.flush()
operationcount = 0
totaloperations = R * R * Qangle * Qstrength * Qcoherence
for pixeltype in range(0, R*R):
for angle in range(0, Qangle):
for strength in range(0, Qstrength):
for coherence in range(0, Qcoherence):
if round(operationcount*100/totaloperations) !=
round((operationcount+1)*100/totaloperations):
print('\r|', end='')
print('#' *
round((operationcount+1)*100/totaloperations/2), end='')
print(' ' * (50 -
round((operationcount+1)*100/totaloperations/2)), end='')
print('| ' +
str(round((operationcount+1)*100/totaloperations)) + '%', end='')
sys.stdout.flush()
operationcount += 1
h[angle,strength,coherence,pixeltype] =
cgls(Q[angle,strength,coherence,pixeltype],
V[angle,strength,coherence,pixeltype])
# Write filter to file
with open("filter.p", "wb") as fp:
pickle.dump(h, fp)
# Plot the learned filters
if args.plot:
filterplot(h, R, Qangle, Qstrength, Qcoherence, patchsize)
print('\r', end='')
print(' ' * 60, end='')
print('\rFinished.')
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