Regorganized

.gitignore

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+# Directories
+documentation
+*.tif
+*.jpg
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/

autopanaroma/README.txt

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+## Author
+Krishna Parashar
+
+## Project
+CS194-26 Computational Photography Project 7
+
+
+## Contents of This Folder
+
+- README.txt
+This file.
+
+- manual.py
+All the code needed for Part A
+
+- automatic.py 
+All the code needed to run the additional Part B stuff. 
+
+- main.py
+This files hosts all the calls to the different function stored in the other files for each part. All configurable are in this file.
+
+
+- /data
+All images used in for this project are sourced from this directory. Configurable in main.py.
+
+- /results
+All resulting images from all parts of this project are put in this directory. Configurable in main.py,
+
+
+## Notes

autopanaroma/data/campanile/1_2_points.txt

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+3.120000000000000000e+02 5.750000000000000000e+02
+1.130000000000000000e+02 5.730000000000000000e+02
+3.410000000000000000e+02 4.800000000000000000e+02
+3.720000000000000000e+02 4.820000000000000000e+02
+4.430000000000000000e+02 3.670000000000000000e+02
+4.670000000000000000e+02 3.670000000000000000e+02

autopanaroma/data/campanile/1_points.txt

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+5.300000000000000000e+02 5.730000000000000000e+02
+3.530000000000000000e+02 5.350000000000000000e+02
+5.590000000000000000e+02 4.800000000000000000e+02
+5.960000000000000000e+02 4.830000000000000000e+02
+6.800000000000000000e+02 3.560000000000000000e+02
+7.140000000000000000e+02 3.560000000000000000e+02

autopanaroma/data/london/1_2_points.txt

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+8.800000000000000000e+01 4.100000000000000000e+02
+1.120000000000000000e+02 4.140000000000000000e+02
+6.600000000000000000e+01 3.370000000000000000e+02
+8.600000000000000000e+01 3.390000000000000000e+02
+1.970000000000000000e+02 2.910000000000000000e+02
+3.220000000000000000e+02 3.230000000000000000e+02
+4.210000000000000000e+02 3.250000000000000000e+02
+3.720000000000000000e+02 1.980000000000000000e+02

autopanaroma/data/london/1_points.txt

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+5.720000000000000000e+02 4.200000000000000000e+02
+5.920000000000000000e+02 4.220000000000000000e+02
+5.500000000000000000e+02 3.510000000000000000e+02
+5.680000000000000000e+02 3.470000000000000000e+02
+6.750000000000000000e+02 2.990000000000000000e+02
+8.020000000000000000e+02 3.310000000000000000e+02
+9.030000000000000000e+02 3.290000000000000000e+02
+8.480000000000000000e+02 2.020000000000000000e+02

autopanaroma/data/rectify/graph_points.txt

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+8.800000000000000000e+01 2.080000000000000000e+02
+3.360000000000000000e+02 1.200000000000000000e+01
+2.950000000000000000e+02 3.560000000000000000e+02
+5.250000000000000000e+02 9.300000000000000000e+01

autopanaroma/data/rectify/graph_rectified_points.txt

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+1.560000000000000000e+02 3.300000000000000000e+02
+1.520000000000000000e+02 5.300000000000000000e+01
+3.970000000000000000e+02 3.240000000000000000e+02
+3.850000000000000000e+02 4.700000000000000000e+01

autopanaroma/data/rectify/holstee_points.txt

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+7.700000000000000000e+01 6.070000000000000000e+02
+1.710000000000000000e+02 3.500000000000000000e+01
+5.030000000000000000e+02 7.520000000000000000e+02
+4.640000000000000000e+02 2.640000000000000000e+02

autopanaroma/data/rectify/holstee_rectified_points.txt

@@ -0,0 +1,4 @@
+1.170000000000000000e+02 7.680000000000000000e+02
+1.030000000000000000e+02 2.100000000000000000e+02
+4.870000000000000000e+02 7.800000000000000000e+02
+4.660000000000000000e+02 2.150000000000000000e+02

autopanaroma/data/sunset/1_2_points.txt

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+2.640000000000000000e+02 4.860000000000000000e+02
+2.940000000000000000e+02 5.530000000000000000e+02
+3.340000000000000000e+02 5.290000000000000000e+02
+3.340000000000000000e+02 5.870000000000000000e+02
+1.790000000000000000e+02 2.890000000000000000e+02
+3.160000000000000000e+02 3.210000000000000000e+02
+4.270000000000000000e+02 3.610000000000000000e+02
+4.930000000000000000e+02 3.630000000000000000e+02
+4.410000000000000000e+02 3.910000000000000000e+02
+4.950000000000000000e+02 4.080000000000000000e+02
+5.340000000000000000e+02 4.080000000000000000e+02
+5.660000000000000000e+02 4.120000000000000000e+02
+5.920000000000000000e+02 4.120000000000000000e+02
+4.550000000000000000e+02 4.020000000000000000e+02

autopanaroma/data/sunset/1_points.txt

@@ -0,0 +1,14 @@
+6.100000000000000000e+02 5.250000000000000000e+02
+6.510000000000000000e+02 5.590000000000000000e+02
+6.890000000000000000e+02 5.370000000000000000e+02
+6.890000000000000000e+02 5.950000000000000000e+02
+5.360000000000000000e+02 2.990000000000000000e+02
+6.690000000000000000e+02 3.270000000000000000e+02
+7.820000000000000000e+02 3.690000000000000000e+02
+8.750000000000000000e+02 3.750000000000000000e+02
+7.960000000000000000e+02 4.000000000000000000e+02
+8.680000000000000000e+02 4.180000000000000000e+02
+9.090000000000000000e+02 4.220000000000000000e+02
+9.590000000000000000e+02 4.280000000000000000e+02
+9.870000000000000000e+02 4.280000000000000000e+02
+8.080000000000000000e+02 4.120000000000000000e+02

autopanaroma/harris.py

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+import numpy as np
+from skimage.feature import corner_harris, peak_local_max
+
+
+def get_harris_corners(im, edge_discard=20):
+    """
+    This function takes a b&w image and an optional amount to discard
+    on the edge (default is 5 pixels), and finds all harris corners
+    in the image. Harris corners near the edge are discarded and the
+    coordinates of the remaining corners are returned. A 2d array (h)
+    containing the h value of every pixel is also returned.
+
+    h is the same shape as the original image, im.
+    coords is 2 x n (ys, xs).
+    """
+
+    assert edge_discard >= 20
+
+    # find harris corners
+    h = corner_harris(im, method='eps', sigma=1)
+    coords = peak_local_max(h, min_distance=1, indices=True)
+
+    # discard points on edge
+    edge = edge_discard  # pixels
+    mask = (coords[:, 0] > edge) & \
+           (coords[:, 0] < im.shape[0] - edge) & \
+           (coords[:, 1] > edge) & \
+           (coords[:, 1] < im.shape[1] - edge)
+    coords = coords[mask].T
+    return h, coords
+
+
+def dist2(x, c):
+    """
+    dist2  Calculates squared distance between two sets of points.
+
+    Description
+    D = DIST2(X, C) takes two matrices of vectors and calculates the
+    squared Euclidean distance between them.  Both matrices must be of
+    the same column dimension.  If X has M rows and N columns, and C has
+    L rows and N columns, then the result has M rows and L columns.  The
+    I, Jth entry is the  squared distance from the Ith row of X to the
+    Jth row of C.
+
+    Adapted from code by Christopher M Bishop and Ian T Nabney.
+    """
+
+    ndata, dimx = x.shape
+    ncenters, dimc = c.shape
+    assert(dimx == dimc, 'Data dimension does not match dimension of centers')
+
+    return (np.ones((ncenters, 1)) * np.sum((x**2).T, axis=0)).T + \
+            np.ones((   ndata, 1)) * np.sum((c**2).T, axis=0)    - \
+            2 * np.inner(x, c)
+