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)))