"""
Extract HOG feature of images.But we decide to use C instead of Python considering the 10-times efficiency gap.

@author: chunk
chunkplus@gmail.com
2014 Dec
"""
__author__ = 'hadoop'

from . import *
from ..common import *

import numpy as np
from PIL import Image
from skimage.feature import hog
from skimage import io, color, transform, exposure


class FeatHOG(FeatureBase):
    def __init__(self, size=(64, 64)):
        FeatureBase.__init__(self)
        self.size = size

    def _feat_sk(self, image, size=(64, 64)):
        """
        N.B. image[x][y][z] where x for line and y for column,z(0,1,2) for (R,G,B)
        """
        if size is None:
            size = (64, 64)
        img = io.imread(image, as_grey=True)  # 0.02s
        img = transform.resize(img, size)
        # img = color.rgb2gray(img)

        fd = hog(img, orientations=9, pixels_per_cell=(8, 8),
                 cells_per_block=(2, 2), visualise=False, normalise=False)  # 0.18s
        # hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
        #
        # cv2.imshow('processed', ski2cv(hog_image_rescaled))
        # cv2.waitKey(0)

        return fd



    def set_size(self, size):
        self.size = size

    def feat(self, image):
        return self._feat_sk(image, self.size)