lizecillie

from __future__ import division
import numpy as np
from skimage import io, color
import matplotlib.pyplot as plt
import vlfeat as vlf
import os
import math

# Reduced matches
l1,d1 = vlf.read_features_from_file('bust_matches.txt')

lizecillie

from __future__ import division
import numpy as np
from skimage import io, color
import matplotlib.pyplot as plt
import vlfeat as vlf
import os
import math

# Reduced matches
l1,d1 = vlf.read_features_from_file('bust_matches.txt')

lizecillie

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, img_as_float

A = img_as_float(io.imread('lego1.jpg', plugin='pil'))

# noise
n = np.random.random_sample()

lizecillie

image1 = io.imread('perold1.jpg')
image2 = io.imread('perold2.jpg')

image = np.zeros((image1.shape[0], image1.shape[1]*2, 3))
image[:, 0:image1.shape[1], :] = image1

image[:, image1.shape[1]:, :] = image2 
  
imshow(image)

lizecillie

from __future__ import division
import numpy as np
from _growcut import growcut
from orientation import orientation
from skimage import io
from skimage.morphology.convex_hull import grid_points_inside_poly


def segmentation(image):
    image = np.resize(image, (256, 256))

lizecillie

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, color
from skimage.io import ImageCollection

#Training recognition system
ic = ImageCollection('*.pgm')
for image in enumerate(ic):
    image = color.rgb2gray(image)

lizecillie

# On branch master
# Changes to be committed:
#
#	new file:   experiments/fin_detect_shortest_path.py
#	new file:   sharkfin/__init__.py
#	new file:   sharkfin/data.py
#
# Unmerged paths:
#   (use "git add/rm <file>..." as appropriate to mark resolution)
#

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, color, img_as_float

def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A.flatten(), range=(0, 1), bins=256)
    histogram = histogram/np.max(histogram)

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, color, img_as_float

def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A.flatten(), range=(0, 1), bins=256)
    histogram = histogram/np.max(histogram)

lizecillie

def histogram_equalization_point_transformation(A):
    B = A.copy()
    P1 = B[:, :, 0]
    P2 = B[:, :, 1]
    P3 = B[:, :, 2]
    G = (np.sum(B * (1/3, 1/3, 1/3), axis=2))/255

    rows = G.shape[0]
    columns = G.shape[1]
    histogram, bin_edges = np.histogram(G.flatten(), range=(0, 1), bins=256)