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'))])
for cpb in range(2, 11):
ppc = int(100 / cpb)
print("hog_8_", ppc, "_", cpb, " features")
hog = util.loading_map(
import image_operations as operations
import data_loading as loader
import util
import numpy
from skimage import color
import csv_output
from sklearn import lda
import time
#preloading
print("loading train data...")
#images, classes = loader.loadUniqueTrainingAndClasses()
images, classes = loader.loadTrainingAndClasses()
amount = len(images)
print("resizing...")
resized = util.loading_map(lambda x: operations.cropAndResize(x, 0, 50),
images)
print("normalizing...")
normalized = util.loading_map(operations.normalizeImage, resized)
print("grayscaling...")
grayscale = util.loading_map(color.rgb2gray, resized)
print("mean channels features...")
split_color_features = util.loading_map(
lambda x: extraction.split_image_features(extraction.color_features, 3, x),
resized)
print("weighted angle features")
weighted_angle_features = util.loading_map(
lambda x: extraction.weightedAngleFeatures(x, 7), resized)
print("Perceived brightness features")
from sklearn.ensemble import ExtraTreesClassifier
from sklearn import lda
import image_operations
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import feature_validation as validation
print("Loading images")
images, labels, classes = loader.loadTrainingImagesPoleNumbersAndClasses()
amount = len(images)
print("Making thumbnails")
size = 50
thumbs = [image_operations.cropAndResize(img, 0.1, size) for img in images]
print("Extract features")
#the method needs square single channel images
thumbsGray = [color.rgb2gray(img) for img in thumbs]
HOGGray = [
feature_extraction.calcHOGWrapper(img,
orient=8,
pixel_per_cell=5,
nr_of_cells_per_block=2)
for img in thumbsGray
]
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 4 15:03:01 2015
@author: Rian
"""
import image_transformation_testing as plotter
import image_operations as op
#regular images
plotter.plotTransformation(lambda x: x)
#resized and cropped images
plotter.plotTransformation(lambda x: op.cropAndResize(x, 0.10, 50))
#normalized images
plotter.plotTransformation(
lambda x: op.normalizeImage(op.cropAndResize(x, 0.10, 50)))