def compute_input_features(input_paths): result = [] for image_path in input_paths: image = cv2.imread(image_path, 1) image_features = fe.hue_histogram_zone(image, 32) result.append(image_features) return result
from os.path import isfile, join from os.path import join, basename, splitext import cv2 import numpy def calc_distance(v1, v2): return numpy.linalg.norm(v1-v2) caras_path = "D:\\Mis Documentos\\MaterialU\\Memoria\\CartoonRecognizer\\Data\\DetectorTrainingPositive" all_caras = [ join(caras_path, data) for data in listdir(caras_path) if isfile(join(caras_path, data)) ] kashima_train_path = "D:\\Mis Documentos\\MaterialU\\Memoria\\CartoonRecognizer\\Data\\input\\mikorin2.png" kashima_train = cv2.imread(kashima_train_path) d = feature_extraction.hue_histogram_zone(kashima_train, 32) min_distance = sys.maxint best_match = None for cara in all_caras: imagen = cv2.imread(cara) a =feature_extraction.hue_histogram_zone(imagen, 32) c = calc_distance(a, d) if min_distance > c: min_distance = c best_match = imagen print min_distance cv2.imshow('frame',best_match)
def test_zone_hue_histogram(self): histogram = feature_extraction.hue_histogram_zone(self.color_space_image, 16) self.assertTrue(len(histogram) == 80)