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119
library.py
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import numpy as np | ||
import ps_utils as ps | ||
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from skimage.io import imshow | ||
from skimage import img_as_float | ||
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from itertools import combinations | ||
from numpy.linalg import det | ||
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def back_to_the_matrix(n, mask): | ||
n_list=list(n) | ||
matrix = np.zeros(mask.shape) | ||
for (i,j), val in np.ndenumerate(mask): | ||
if val: | ||
matrix[i,j] = n_list.pop(0) | ||
return matrix | ||
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def get_j(img, mask): | ||
mask_list = ps.tolist(mask) | ||
img_list = ps.tolist(img) | ||
j = [] | ||
for m_val, i_val in zip(mask_list, img_list): | ||
if m_val: | ||
j.append(i_val) | ||
return j | ||
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def get_independent(vectors): | ||
#probably should be normalized before but they're not | ||
combs = combinations(vectors, 3) | ||
#0 89 | ||
best_vecs = [] | ||
best_det = 0 | ||
for vecs3 in combs: | ||
print(det(vecs3)) | ||
if det(vecs3) > best_det: | ||
best_vecs = vecs3 | ||
best_det = det(vecs3) | ||
print('best det', best_det) | ||
return best_vecs | ||
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def show(img): | ||
imshow(img, cmap='gray') | ||
return | ||
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def get_albedo(M, m, I): | ||
#Creating list of M arrays | ||
hodl = [M[0], M[1], M[2]] | ||
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#Creating temporary matrix to hold x,y,z values | ||
MM = np.zeros(I.shape) | ||
indel = 0 | ||
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#Iterating thorugh temp matrix and adding values from hodl list | ||
for (i, j, c), _ in np.ndenumerate(MM): | ||
if m[i, j]: | ||
hodl[c][indel] | ||
MM[i, j, c] = hodl[c][indel] | ||
if c == 2: | ||
indel += 1 | ||
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#Creating Albedo matrix to length of vector values | ||
Albedo = np.zeros(m.shape) | ||
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#Iterating through Albedo matrix to calculate and add length of vector | ||
for (i, j), _ in np.ndenumerate(Albedo): | ||
if m[i, j]: | ||
x = MM[i, j, 0] | ||
y = MM[i, j, 1] | ||
z = MM[i, j, 2] | ||
Albedo[i, j] = np.sqrt(x**2 + y**2 + z**2) | ||
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# normalization | ||
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#Extracting and normalizing the values in Albedo matrix | ||
Albedo_nlist = normalize(ps.tolist(Albedo), (0,1)) | ||
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#Creating matrix to hold normalized values | ||
Albedo_normalized = np.zeros(Albedo.shape) | ||
indel = 0 | ||
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#Iterating through normalized albedo matrix to add normalized values | ||
for (i, j), _ in np.ndenumerate(Albedo_normalized): | ||
if m[i, j] and Albedo_nlist[indel] != 0: | ||
Albedo_normalized[i, j] = Albedo_nlist[indel] | ||
indel += 1 | ||
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return Albedo_normalized | ||
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def normalize(differences, range=(0,1.0)): | ||
#linear rescaling | ||
max_val = max(differences) | ||
min_val = min(differences) | ||
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return np.multiply(np.subtract(range[1], range[0]), np.divide( np.subtract(differences, min_val), np.subtract(max_val, min_val))) | ||
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def display_3d(N): | ||
# the code commented below is taken from here: https://plot.ly/python/3d-surface-plots/ | ||
# and it works only in jupyter notebook | ||
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import pandas as pd | ||
import plotly.plotly as py | ||
import plotly.graph_objs as go | ||
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot | ||
init_notebook_mode(connected=True) | ||
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data = [go.Surface(z=N)] | ||
layout = go.Layout(title='3D plot', autosize=True) | ||
fig = go.Figure(data=data, layout=layout) | ||
iplot(fig) | ||
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