InteligentnySaper/classes/neuralNetwork.py

import pandas as pd
import tensorflow as tf
import numpy as np
import warnings
import os
from keras.utils import load_img
import keras
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import ImageDataGenerator
from keras import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense
warnings.filterwarnings('ignore')

create_model = False
learning_sets_path = "data/learning_sets"
save_model_path = "data/models/true_mine_recognizer2.h5"
load_model_path = "data/models/true_mine_recognizer2.h5"
image_size = 128

class NeuralNetwork():
    def __init__(self):
        if create_model:
            input_path = []
            label = []

            for class_name in os.listdir(learning_sets_path):
                for path in os.listdir(learning_sets_path+ "/" +class_name):
                    if class_name == 'mine':
                        label.append(0)
                    else:
                        label.append(1)
                    input_path.append(os.path.join(learning_sets_path, class_name, path))
            print(input_path[0], label[0])
            df = pd.DataFrame()
            df['images'] = input_path
            df['label'] = label
            df = df.sample(frac=1).reset_index(drop=True)
            df.head()
            df['label'] = df['label'].astype('str')
            df.head()

            train, test = train_test_split(df, test_size=0.2, random_state=42)


            train_generator = ImageDataGenerator(
                rescale = 1./255,  
                rotation_range = 40, 
                shear_range = 0.2,
                zoom_range = 0.2,
                horizontal_flip = True,
                fill_mode = 'nearest'
            )

            val_generator = ImageDataGenerator(rescale = 1./255)

            train_iterator = train_generator.flow_from_dataframe(
                train, 
                x_col='images', 
                y_col='label', 
                target_size=(image_size,image_size), 
                batch_size=512, 
                class_mode='binary'
            )

            val_iterator = val_generator.flow_from_dataframe(
                test, 
                x_col='images', 
                y_col='label', 
                target_size=(image_size,image_size), 
                batch_size=512, 
                class_mode='binary'
            )

            self.model = Sequential([
                                Conv2D(16, (3,3), activation='relu', input_shape=(image_size,image_size,3)),
                                MaxPool2D((2,2)),
                                Conv2D(32, (3,3), activation='relu'),
                                MaxPool2D((2,2)),
                                Conv2D(64, (3,3), activation='relu'),
                                MaxPool2D((2,2)),
                                Flatten(),
                                Dense(512, activation='relu'),
                                Dense(1, activation='sigmoid')
            ])
            self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
            self.model.summary()
            self.model.fit(train_iterator, epochs=10, validation_data=val_iterator)
            self.model.save(save_model_path)
        else:
            self.model = keras.models.load_model(load_model_path,
                                                compile=True
                                                )
    def recognize(self, image_path):
            image = keras.utils.load_img(image_path, target_size=(image_size, image_size))
            image_array = keras.utils.img_to_array(image)
            image_array = keras.backend.expand_dims(image_array, 0)

            prediction = self.model.predict(image_array)
            if prediction[0] > 0.5:
                predict = "notmine"
            elif prediction[0] <= 0.5:
                predict = "mine"

            print("Image: ",image_path," is classified as: ", predict) 
            if predict == "mine":
                return True
            else:
                return False