import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.filters import prewitt_h,prewitt_v

from skimage.transform import resize
from skimage.feature import hog
from skimage import exposure

import os
import pickle


path = '/home/zzz/Desktop/DS250 - Data Analytics/Assignment1b/fruits-360_dataset/fruits-360/Training'
classes = os.listdir(path)

prototypes = []

class_count = 0
for clas in classes:
    prototype = np.zeros((12996))
    count = 0
    for image in os.listdir(f'{path}/{clas}'):
        fruit_img = imread(f'{path}/{clas}/{image}')
        fv = hog(fruit_img, orientations=9, pixels_per_cell=(5, 5), cells_per_block=(2, 2), multichannel=True)
        prototype += fv
        count += 1

    prototype = prototype / count
    prototypes.append(prototypes)
    class_count += 1
    print(f'Class #{class_count} {clas} is complete')

pickle.dump(prototypes, open('prototypes.pkl', 'wb'))