Пример #1
0
    def _read_pkgconfig_changes(self):
        pkgconfig = {}

        files = FileUtils()
        files.open_datafile(PKGCONFIG_CONVERSIONS)
        for line in files.f:
            # the values are split by  ': '
            pair = line.split(': ')
            pkgconfig[pair[0]] = pair[1][:-1]
        files.close()
        return pkgconfig
Пример #2
0
    def _read_licenses_changes(self):
        licenses = {}

        files = FileUtils()
        files.open_datafile(LICENSES_CHANGES)
        for line in files.f:
            # strip newline
            line = line.rstrip('\n')
            # file has format
            # correct license string<tab>known bad license string
            # tab is used as separator
            pair = line.split('\t')
            licenses[pair[1]] = pair[0]
        files.close()
        return licenses
Пример #3
0
    def _find_macros_with_arg(self, spec):
        """
        Load argumented macros from specfile
        """

        macrofuncs = []

        files = FileUtils()
        files.open(spec, 'r')
        for line in files.f:
            line = line.rstrip('\n')
            found_macro = self.re_spec_macrofunc.sub(r'\1', line)
            if found_macro != line:
                macrofuncs += [ found_macro ]
        files.close()
        return macrofuncs
Пример #4
0
    def _load_keywords_whitelist(self):
        """
        Create regexp for the unbrace keywords based on
        rpm showrc and whitelist.
        """

        BRACKETING_EXCLUDES = 'excludes-bracketing.txt'

        # load the keywords
        files = FileUtils()
        files.open_datafile(BRACKETING_EXCLUDES)
        keywords= []
        for line in files.f:
            keywords.append(line.rstrip('\n'))
        files.close()

        return keywords
import os, sys
import numpy as np
from fileutils import FileUtils
from metric import ConfusionMatrix
from sklearn.metrics import accuracy_score
from utils.ply import read_ply, write_ply
import ipdb

utils = FileUtils()

num_classes = 9

label_names = [
    'unclassified', 'ground', 'veg', 'cars', 'trucks', 'powerlines',
    'fences/hedges', 'poles', 'buildings'
]

data_dir = '/home/vlab/Nina/SemanticSegmentation/KPConv/Data/NPM3D/test_points/'


def get_rgb_color_codes(preds):

    rgb = np.zeros((preds.shape[0], 3))
    rgb[np.where(preds == 0)[0], :] = [0, 0, 100]
    rgb[np.where(preds == 1)[0], :] = [0, 0, 255]
    rgb[np.where(preds == 2)[0], :] = [0, 153, 0]
    rgb[np.where(preds == 3)[0], :] = [255, 0, 255]
    rgb[np.where(preds == 4)[0], :] = [255, 0, 255]
    rgb[np.where(preds == 5)[0], :] = [255, 255, 0]
    rgb[np.where(preds == 6)[0], :] = [255, 128, 0]
    rgb[np.where(preds == 7)[0], :] = [0, 255, 255]