import numpy
import csv_output
from sklearn import lda, svm
from skimage import feature, color, exposure
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.decomposition import PCA
from sklearn import random_projection
from sklearn.ensemble import RandomForestClassifier
from sklearn import feature_selection
#preloading
print("loading data...")
size = 100
images, labels, classes = loader.loadTrainingImagesPoleNumbersAndClasses()
amount = len(images)
print("resizing...")
resized = util.loading_map(lambda x: operations.cropAndResize(x, 0, size),
images)
print("grayscaling...")
grayscaled = util.loading_map(color.rgb2gray, resized)
n_folds = 10
model = Pipeline([("standard scaler", StandardScaler()),
("logistic regression",
LogisticRegression(solver='lbfgs',
multi_class='multinomial'))])
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 28 15:43:03 2015
@author: Rian
"""
from distance_model import DistanceModel
import data_loading
import util
import image_operations as operations
import feature_extraction as extraction
from skimage import feature, color, exposure
import feature_validation as validation
training_images, training_labels, training_classes = data_loading.loadTrainingImagesPoleNumbersAndClasses(
)
size = 100
print("resizing...")
resized = util.loading_map(lambda x: operations.cropAndResize(x, 0, size),
training_images)
print("hsv...")
hsv = util.loading_map(color.rgb2hsv, resized)
print("grayscaling...")
grayscaled = util.loading_map(color.rgb2gray, resized)
print("colors")
colors = util.loading_map(
lambda x: extraction.split_image_features(
extraction.calculateColorFeatures, 7, x), hsv)
