import cv2
import numpy as np
from os.path import join as pjoin
import glob
from tqdm import tqdm
from Config import Config

cfg = Config()


class Data:
    def __init__(self):
        self.data_num = 0
        self.images = []
        self.labels = []
        self.X_train, self.Y_train = None, None
        self.X_test, self.Y_test = None, None

        self.image_shape = cfg.image_shape
        self.class_number = cfg.class_number
        self.class_map = cfg.class_map
        self.DATA_PATH = cfg.DATA_PATH

    def load_data(self, resize=True, shape=None, max_number=1000000):
        # if customize shape
        if shape is not None:
            self.image_shape = shape
        else:
            shape = self.image_shape

        # load data
        for p in glob.glob(pjoin(self.DATA_PATH, '*')):
            print("*** Loading components of %s: %d ***" %(p.split('\\')[-1], int(len(glob.glob(pjoin(p, '*.png'))))))
            label = self.class_map.index(p.split('\\')[-1])  # map to index of classes
            for i, image_path in enumerate(tqdm(glob.glob(pjoin(p, '*.png'))[:max_number])):
                image = cv2.imread(image_path)
                if resize:
                    image = cv2.resize(image, shape[:2])
                self.images.append(image)
                self.labels.append(label)

        assert len(self.images) == len(self.labels)
        self.data_num = len(self.images)
        print('%d Data Loaded' % self.data_num)

    def generate_training_data(self, train_data_ratio=0.8):
        # transfer int into c dimensions one-hot array
        def expand(label, class_number):
            # return y : (num_class, num_samples)
            y = np.eye(class_number)[label]
            y = np.squeeze(y)
            return y

        # reshuffle
        np.random.seed(0)
        self.images = np.random.permutation(self.images)
        np.random.seed(0)
        self.labels = np.random.permutation(self.labels)
        Y = expand(self.labels, self.class_number)

        # separate dataset
        cut = int(train_data_ratio * self.data_num)
        self.X_train = (self.images[:cut] / 255).astype('float32')
        self.X_test = (self.images[cut:] / 255).astype('float32')
        self.Y_train = Y[:cut]
        self.Y_test = Y[cut:]

        print('X_train:%d, Y_train:%d' % (len(self.X_train), len(self.Y_train)))
        print('X_test:%d, Y_test:%d' % (len(self.X_test), len(self.Y_test)))

import cv2
import numpy as np
from os.path import join as pjoin
import glob
from tqdm import tqdm
from Config import Config

cfg = Config()


class Data:
    def __init__(self):
        self.data_num = 0
        self.images = []
        self.labels = []
        self.X_train, self.Y_train = None, None
        self.X_test, self.Y_test = None, None

        self.image_shape = cfg.image_shape
        self.class_number = cfg.class_number
        self.class_map = cfg.class_map
        self.DATA_PATH = cfg.DATA_PATH

    def load_data(self, resize=True, shape=None, max_number=1000000):
        # if customize shape
        if shape is not None:
            self.image_shape = shape
        else:
            shape = self.image_shape

        # load data
        for p in glob.glob(pjoin(self.DATA_PATH, '*')):
            print("*** Loading components of %s: %d ***" %(p.split('\\')[-1], int(len(glob.glob(pjoin(p, '*.png'))))))
            label = self.class_map.index(p.split('\\')[-1])  # map to index of classes
            for i, image_path in enumerate(tqdm(glob.glob(pjoin(p, '*.png'))[:max_number])):
                image = cv2.imread(image_path)
                if resize:
                    image = cv2.resize(image, shape[:2])
                self.images.append(image)
                self.labels.append(label)

        assert len(self.images) == len(self.labels)
        self.data_num = len(self.images)
        print('%d Data Loaded' % self.data_num)

    def generate_training_data(self, train_data_ratio=0.8):
        # transfer int into c dimensions one-hot array
        def expand(label, class_number):
            # return y : (num_class, num_samples)
            y = np.eye(class_number)[label]
            y = np.squeeze(y)
            return y

        # reshuffle
        np.random.seed(0)
        self.images = np.random.permutation(self.images)
        np.random.seed(0)
        self.labels = np.random.permutation(self.labels)
        Y = expand(self.labels, self.class_number)

        # separate dataset
        cut = int(train_data_ratio * self.data_num)
        self.X_train = (self.images[:cut] / 255).astype('float32')
        self.X_test = (self.images[cut:] / 255).astype('float32')
        self.Y_train = Y[:cut]
        self.Y_test = Y[cut:]

        print('X_train:%d, Y_train:%d' % (len(self.X_train), len(self.Y_train)))
        print('X_test:%d, Y_test:%d' % (len(self.X_test), len(self.Y_test)))

# if customize shape # load data # map to index of classes # transfer int into c dimensions one-hot array # return y : (num_class, num_samples) # reshuffle # separate dataset