import tensorflow as tf
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
import random
from sklearn.model_selection import train_test_split

training_data =[]

#This function separates both features & labels from training data
def seggregate_data(training_data):
    X = []
    Y = []
    for features, lables in training_data:
        X.append(features)
        Y.append(lables)
    X = np.array(X)
    Y = np.array(Y)
    return X,Y

#This function reads each folder and each image and converts into array
#This function internally calls seggregate_data to separate features & labels
def create_training_data(CATEGORIES,DATADIR, IMG_SIZE = 100):
    for category in CATEGORIES:
        class_num = CATEGORIES.index(category)
        path = os.path.join(DATADIR,category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE) 
                new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
                training_data.append([new_array,class_num])
            except Exception as e:
                pass
    random.shuffle(training_data)
    features,labels = seggregate_data(training_data)
    return features,labels

#This function splits the data into test and train
#This function normalizes the features values in both train and test
#This function converts label values to binary matrix
def split_normalize_data(X, Y, category_count, TEST_SIZE = 0.20,IMG_SIZE=100 ):
    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = TEST_SIZE)
    x_train = x_train.reshape(x_train.shape[0], IMG_SIZE, IMG_SIZE, 1)
    x_test = x_test.reshape(x_test.shape[0], IMG_SIZE, IMG_SIZE, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')

    x_train/=255
    x_test/=255

    y_train = tf.keras.utils.to_categorical(y_train, category_count)
    y_test = tf.keras.utils.to_categorical(y_test, category_count)
    
    return x_train, x_test, y_train, y_test

#This function creates a model compiling input, Hidden and output layers
def image_classifier(category_count, IMG_SIZE = 100):
    
    model = tf.keras.models.Sequential()

    model.add(tf.keras.layers.Conv2D(32,(3,3),input_shape=(IMG_SIZE,IMG_SIZE,1)))
    model.add(tf.keras.layers.Dense(32,activation=tf.nn.relu))
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))

    model.add(tf.keras.layers.Conv2D(64, (3,3)))
    model.add(tf.keras.layers.Dense(64,activation=tf.nn.relu))
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))

    model.add(tf.keras.layers.Flatten())

    model.add(tf.keras.layers.Dropout(0.2))

    model.add(tf.keras.layers.Dense(category_count,activation=tf.nn.softmax))

    model.compile(optimizer='adam',
                     loss='categorical_crossentropy',
                     metrics=['accuracy'])
    return model

import tensorflow as tf
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
import random
from sklearn.model_selection import train_test_split

training_data =[]

#This function separates both features & labels from training data
def seggregate_data(training_data):
    X = []
    Y = []
    for features, lables in training_data:
        X.append(features)
        Y.append(lables)
    X = np.array(X)
    Y = np.array(Y)
    return X,Y

#This function reads each folder and each image and converts into array
#This function internally calls seggregate_data to separate features & labels
def create_training_data(CATEGORIES,DATADIR, IMG_SIZE = 100):
    for category in CATEGORIES:
        class_num = CATEGORIES.index(category)
        path = os.path.join(DATADIR,category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE) 
                new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
                training_data.append([new_array,class_num])
            except Exception as e:
                pass
    random.shuffle(training_data)
    features,labels = seggregate_data(training_data)
    return features,labels

#This function splits the data into test and train
#This function normalizes the features values in both train and test
#This function converts label values to binary matrix
def split_normalize_data(X, Y, category_count, TEST_SIZE = 0.20,IMG_SIZE=100 ):
    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = TEST_SIZE)
    x_train = x_train.reshape(x_train.shape[0], IMG_SIZE, IMG_SIZE, 1)
    x_test = x_test.reshape(x_test.shape[0], IMG_SIZE, IMG_SIZE, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')

    x_train/=255
    x_test/=255

    y_train = tf.keras.utils.to_categorical(y_train, category_count)
    y_test = tf.keras.utils.to_categorical(y_test, category_count)
    
    return x_train, x_test, y_train, y_test

#This function creates a model compiling input, Hidden and output layers
def image_classifier(category_count, IMG_SIZE = 100):
    
    model = tf.keras.models.Sequential()

    model.add(tf.keras.layers.Conv2D(32,(3,3),input_shape=(IMG_SIZE,IMG_SIZE,1)))
    model.add(tf.keras.layers.Dense(32,activation=tf.nn.relu))
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))

    model.add(tf.keras.layers.Conv2D(64, (3,3)))
    model.add(tf.keras.layers.Dense(64,activation=tf.nn.relu))
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))

    model.add(tf.keras.layers.Flatten())

    model.add(tf.keras.layers.Dropout(0.2))

    model.add(tf.keras.layers.Dense(category_count,activation=tf.nn.softmax))

    model.compile(optimizer='adam',
                     loss='categorical_crossentropy',
                     metrics=['accuracy'])
    return model

#This function separates both features & labels from training data #This function reads each folder and each image and converts into array #This function internally calls seggregate_data to separate features & labels #This function splits the data into test and train #This function normalizes the features values in both train and test #This function converts label values to binary matrix #This function creates a model compiling input, Hidden and output layers