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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') |
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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') |
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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() |
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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) |
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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)) |
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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) |
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# 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) | |
# |
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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) |
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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) |
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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) |
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