initial commit

library.py

@@ -0,0 +1,119 @@
+import numpy as np
+import ps_utils as ps
+
+from skimage.io import imshow
+from skimage import img_as_float
+
+from itertools import combinations
+from numpy.linalg import det
+
+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
+
+
+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
+
+
+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
+
+
+def show(img):
+    imshow(img, cmap='gray')
+    return
+
+
+def get_albedo(M, m, I):
+    #Creating list of M arrays
+    hodl = [M[0], M[1], M[2]]
+
+    #Creating temporary matrix to hold x,y,z values
+    MM = np.zeros(I.shape)
+    indel = 0
+
+    #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
+
+    #Creating Albedo matrix to length of vector values
+    Albedo = np.zeros(m.shape)
+
+    #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)
+
+
+
+    # normalization
+
+    #Extracting and normalizing the values in Albedo matrix
+    Albedo_nlist = normalize(ps.tolist(Albedo), (0,1))
+
+    #Creating matrix to hold normalized values
+    Albedo_normalized = np.zeros(Albedo.shape)
+    indel = 0
+
+    #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
+
+    return Albedo_normalized
+
+
+def normalize(differences, range=(0,1.0)):
+    #linear rescaling
+    max_val = max(differences)
+    min_val = min(differences)
+
+    return np.multiply(np.subtract(range[1], range[0]), np.divide( np.subtract(differences, min_val), np.subtract(max_val, min_val)))
+
+
+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
+
+    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)
+
+
+    data = [go.Surface(z=N)]
+    layout = go.Layout(title='3D plot', autosize=True)
+    fig = go.Figure(data=data, layout=layout)
+    iplot(fig)
+