readicculus

colmap exhaustive_matcher \
        --database_path fl09.db \
        --SiftMatching.num_threads=-1 \
        --SiftMatching.use_gpu=1 \
        --SiftMatching.gpu_index=0 \
        --SiftMatching.max_ratio=0.80000000000000004 \
        --SiftMatching.max_distance=0.69999999999999996 \
        --SiftMatching.cross_check=1 \
        --SiftMatching.max_error=4 \
        --SiftMatching.max_num_matches=32768 \

readicculus

## Instructions
# 1. first generate a list in a text file of all IR .tif images, one filename per line, it is ok to include files from different
# cameras and flights but if a flight_camera is included in the list you should include all images from that flight_camera.
# 2. Preprocess(extract features).  Run the script with the preprocess target and list from step 1
#   ex 'python nuc.py preprocess --image_list images.txt'
#   this will take some time and in the end will save an output.csv in the same directory that the script was run from
#   you can override the output default name by specifying the flag --csv_out custom_name.csv
#
# Once you have this output.csv there are two commands available for getting the NUCs.
# 1. To list the NUCs use the list command and pass in the csv that step 2 generated.

readicculus

import argparse
import os

import cv2
import numpy as np
from sklearn import svm
from sklearn.base import BaseEstimator
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline