import os
import cv2
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.pyplot import imshow

path_potatoes = 'neural_network\\images\\potatoes'
path_beetroot = 'neural_network\\images\\beetroot'
size = 100

#POTATOES
image_data = []
label_data = []
for img in os.listdir(path_potatoes):
    pic = cv2.imread(os.path.join(path_potatoes,img))
    pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
    pic = cv2.resize(pic,(size,size))
    image_data.append([pic])
    label_data.append(1)

#np.save(os.path.join('neural_network','potatoes-dataset'),np.array(training_data_potatoes))

#saved_potatoes = np.load(os.path.join('neural_network','potatoes-dataset.npy'))

#BEETROOT
for img in os.listdir(path_beetroot):
    pic = cv2.imread(os.path.join(path_beetroot,img))
    pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
    pic = cv2.resize(pic,(size,size))
    image_data.append([pic])
    label_data.append(0)

#np.save(os.path.join('neural_network','beetroot-dataset'),np.array(training_data_beetroot))

#saved_potatoes = np.load(os.path.join('neural_network','beetroot-dataset.npy'))

np.save(os.path.join('neural_network','image-dataset'),np.array(image_data))
np.save(os.path.join('neural_network','label-dataset'),np.array(label_data))

saved_images = np.load(os.path.join('neural_network','image-dataset.npy'))

print(saved_images.shape)

plt.imshow(saved_images[0].reshape(size,size,3))
plt.imshow(np.array(image_data[0]).reshape(size,size,3))
plt.show()