Saving and Loading

import math
import os
import sys

sys.path.insert(0, os.path.abspath('/data/autocnet'))

import autocnet
# Enable the GPU
autocnet.cuda(enable=True)

from autocnet import CandidateGraph

# The GPU based extraction library that contains SIFT extraction and matching
import cudasift as cs
# A method to resize the images on the fly.
from scipy.misc import imresize

%pylab inline
figsize(16,4)

Populating the interactive namespace from numpy and matplotlib

/home/jlaura/anaconda3/envs/autocnet/lib/python3.5/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['e', 'load']
`%matplotlib` prevents importing * from pylab and numpy
  "\n`%matplotlib` prevents importing * from pylab and numpy"

Create the CandidateGraph

Just like the other notebooks this cell creates the candidate graph. This also patches in functionality to the object on the fly. To get a handle on what is going on, checkout the Advanced 1. Extending the CandidateGraph notebook.

# Create the candidate graph and enable a GPU
a = 'AS15-P-0111_CENTER_LRG_CROPPED.png'
b = 'AS15-P-0112_CENTER_LRG_CROPPED.png'

adj = {a:[b],
       b:[a]}

cg = CandidateGraph.from_adjacency(adj)

# Define a function to do the feature extraction.
def extract_features(self, arr, downsample_amount=None, **kwargs):
    total_size = arr.shape[0] * arr.shape[1]
    if not downsample_amount:
        downsample_amount = math.ceil(total_size / 12500**2)
    shape = (int(arr.shape[0] / downsample_amount), int(arr.shape[1] / downsample_amount))
    # Downsample
    arr = imresize(arr, shape, interp='lanczos')

    npts = max(arr.shape) / 3.5
    sd = cs.PySiftData(npts)
    cs.ExtractKeypoints(arr, sd, **kwargs)
    kp, des = sd.to_data_frame()
    kp = kp[['x', 'y', 'scale', 'sharpness', 'edgeness', 'orientation', 'score', 'ambiguity']]
    kp['score'] = 0.0
    kp['ambiguity'] = 0.0

    # Match the interface defined in the edge.
    self.keypoints = kp
    self.descriptors = des

    self['downsample_amount'] = downsample_amount

# Import the class and update it.  This updates the current instance of the CandidateGraph
from autocnet.graph.node import Node
Node.extract_features = extract_features

# Extract the features
cg.extract_features(thresh=1)

# Match
cg.match()

# Apply outlier detection - see the above linked notebook if you are curious about what is going on here.
for s,d,e in cg.edges_iter(data=True):
    e.masks['ratio'] = e.matches.ambiguity <= e.matches.ambiguity.quantile(0.015)
    e.masks['score'] = e.matches.score >= e.matches.score.quantile(0.85)

# Compute the F Matrix
cg.compute_fundamental_matrices(clean_keys=['ratio', 'score'])

Saving and Loading

Below, we save an AutoCNet project to disk. This serializes all of th correspondences, matches, and attributes into a number of different files (JSON metadata and binary/compressed arrays). These are then packed into a zip file with a user definable file ending, e.g. .proj.

cg.save('ApolloPan.proj')

!ls *.proj

ApolloPan.proj

from autocnet.io.network import load

cg2 = load('ApolloPan.proj')

cg2.edge[0][1].plot(clean_keys=['fundamental'], downsampling=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7f21efdd4eb8>