Creating a Control Network

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

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'])

cg.edge[0][1].plot(clean_keys=['fundamental'], line_kwargs={'alpha':0.35}, downsampling=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7f2904231400>

# Apply the subpixel matcher
import cv2
# Scale the keypoints coordinates back to full resolution.
for i, n in cg.nodes_iter(data=True):
    n.keypoints.x *= n['downsample_amount']
    n.keypoints.y *= n['downsample_amount']

cg.subpixel_register(clean_keys=['fundamental'], tiled=True, func=cv2.TM_CCORR_NORMED, max_x_shift=7, max_y_shift=7)

# Scale the keypoint coordinates back to the reduced resolution
for i, n in cg.nodes_iter(data=True):
    n.keypoints.x /= n['downsample_amount']
    n.keypoints.y /= n['downsample_amount']

# Create a control network object
cg.generate_cnet(clean_keys=['subpixel'])

# Write the (invalid) control network to disk - this net is not valid because it does not have isis3 serial numbers...use cubes
from plio.io.io_controlnetwork import to_isis
to_isis('sample_pan.net', cg.cn, mode='wb', targetname='Moon')

!ls -alh *.net

-rw-r--r-- 1 jlaura flagstaf 294K May 17 13:20 sample_pan.net