import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
import BasicFunctions as bf
import Sketching as sketch
# Parameters.
IMAGE_PATH = "../../data/"
IMAGE_NAME = "lenna.png"
SIZE = (50, 50)
ALPHA = 2
BASIS_OVERSAMPLING = 1.0
# Import the image.
img = misc.imresize(bf.rgb2gray(bf.imread(IMAGE_PATH + IMAGE_NAME)), SIZE)
# Obtain Fourier basis.
basis, coefficients = sketch.basisSketchL1(img, ALPHA, BASIS_OVERSAMPLING)
# Compute reconstruction.
reconstruction = (basis * coefficients).reshape(img.shape)
# Plot.
plt.figure(1)
plt.subplot(121)
plt.imshow(reconstruction, cmap="gray")
max_value = np.absolute(coefficients).max()
plt.title(
"Reconstruction using random basis \n in image domain \n %.2f%% sparsity" %
from functools import partial
import sys
import cvxpy as cvx
# Parameters.
IMAGE_PATH = "../../data/"
IMAGE_NAME = "lenna.png"
BLOCK_SIZE = 30
RHO = 1.0
ALPHA = 1.0
BASIS_OVERSAMPLING = 1.0
if __name__ == "__main__":
# Import the image.
img = misc.imresize(bf.rgb2gray(bf.imread(IMAGE_PATH + IMAGE_NAME)),
(60, 60)).astype(np.float32)
# Get blocks.
blocks = sketch.getBlocks(img, BLOCK_SIZE)
print "Got %d blocks." % len(blocks)
# Compress each block.
print "Running CS on each block..."
basis, block_coefficients = sketch.basisCompressedSenseDCTHuber(blocks,
RHO,
ALPHA,
BASIS_OVERSAMPLING)
# Get sparsity.
"""
import numpy as np
import BasicFunctions as bf
from Sharpening import sharpen
from Blurring import blur
from FindEyes import searchForEyesSVM, createSVM
import time, os
import cPickle as pickle
from TrackEyes import trackEyes
# import image
#img = bf.imread("lotr.JPG")
#img = bf.imread("eye.png")
#img = bf.imread("obama.jpg")
img = bf.imread("me.jpg")
# test blurring
#blurred = blur(img, mode="gaussian", k=5)
#bf.imshow(blurred)
# test sharpening
#sharpened = sharpen(img, k=21, lo_pass=True, min_diff=0.01, alpha=3.0)
#bf.imshow(sharpened)
# test exposure adjustment
# darker = bf.adjustExposure(img, gamma=1.5)
# bf.imshow(darker)
# lighter = bf.adjustExposure(img, gamma=0.5)
# bf.imshow(lighter)
import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
import BasicFunctions as bf
import Sketching as sketch
# Parameters.
IMAGE_PATH = "../../data/"
IMAGE_NAME = "lenna.png"
SIZE = (50, 50)
ALPHA = 100.0
BASIS_OVERSAMPLING = 1.0
# Import the image.
img = misc.imresize(bf.rgb2gray(bf.imread(IMAGE_PATH + IMAGE_NAME)), SIZE).astype(np.float32)
# Obtain Fourier basis.
basis, coefficients = sketch.basisCompressedSenseImgL1(img, ALPHA, BASIS_OVERSAMPLING)
# Compute reconstruction.
reconstruction = (basis * coefficients).reshape(img.shape)
# print estimate of sparsity
print np.median(np.asarray(coefficients.T))
# Plot.
max_value = np.absolute(coefficients).max()
plt.figure(1)
plt.subplot(121)
plt.imshow(reconstruction, cmap="gray")
import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
import BasicFunctions as bf
import Sketching as sketch
# Parameters.
IMAGE_PATH = "../../data/"
IMAGE_NAME = "lenna.png"
SIZE = (200, 200)
ALPHA = 0.1
BASIS_OVERSAMPLING = 0.5
# Import the image.
img = misc.imresize(bf.rgb2gray(bf.imread(IMAGE_PATH + IMAGE_NAME)), SIZE).astype(np.float32)
# Obtain Fourier basis.
basis, coefficients = sketch.basisCompressedSenseDCTL1(img, ALPHA, BASIS_OVERSAMPLING)
# Compute reconstruction.
reconstruction = (basis * coefficients).reshape(img.shape)
# print estimate of sparsity
print np.median(np.asarray(coefficients.T))
# Plot.
max_value = np.absolute(coefficients).max()
plt.figure(1)
plt.subplot(121)
plt.imshow(reconstruction, cmap="gray")
