Ejemplo n.º 1
0
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)
Ejemplo n.º 3
0
	def test_zone_hue_histogram(self):
		histogram = feature_extraction.hue_histogram_zone(self.color_space_image, 16)
		self.assertTrue(len(histogram) == 80)