#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: lancezhange
# @Date:   2015-08-18 11:28:02
# @Last Modified by:   root
# @Last Modified time: 2015-08-25 14:48:57

from __future__ import division
import cv2
import numpy as np
from skimage.feature import hog


def getFeature(image, blocks=5):
    '''
    Given an cv2 Image object it returns its feature vector.

    Args:
      image (ndarray): image to process.
      blocks (int, optional): number of block to subdivide the RGB space into.

    Returns:
      numpy array.
    '''
    image_resized = cv2.resize(image, (64, 64))
    imgray = cv2.cvtColor(image_resized, cv2.COLOR_RGB2GRAY)
    feature = np.zeros(blocks * blocks * blocks + 144)  # 144 for hog
    width, height, channel = image_resized.shape
    pixel_count = width * height
    r = ((image[:, :, 2]) / (256 / blocks)).astype("int")
    g = ((image[:, :, 1]) / (256 / blocks)).astype("int")
    b = ((image[:, :, 0]) / (256 / blocks)).astype("int")
    result = r + g * blocks + b * blocks * blocks
    unique, counts = np.unique(result, return_counts=True)
    feature[unique] = counts
    feature = feature / (pixel_count)
    feature_hog = hog(
        imgray, orientations=9,
        pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=False)
    feature[blocks**3:] = feature_hog
    return feature