110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
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import pandas as pd
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import tensorflow as tf
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import numpy as np
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import warnings
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import os
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from keras.utils import load_img
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import keras
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from sklearn.model_selection import train_test_split
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from keras.preprocessing.image import ImageDataGenerator
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from keras import Sequential
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from keras.layers import Conv2D, MaxPool2D, Flatten, Dense
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warnings.filterwarnings('ignore')
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create_model = False
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learning_sets_path = "data/learning_sets"
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save_model_path = "data/models/true_mine_recognizer.model"
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load_model_path = "data/models/mine_recognizer.model"
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image_size = 128
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class NeuralNetwork():
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def __init__(self):
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if create_model:
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input_path = []
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label = []
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for class_name in os.listdir(learning_sets_path):
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for path in os.listdir(learning_sets_path+ "/" +class_name):
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if class_name == 'mine':
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label.append(0)
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else:
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label.append(1)
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input_path.append(os.path.join(learning_sets_path, class_name, path))
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print(input_path[0], label[0])
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df = pd.DataFrame()
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df['images'] = input_path
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df['label'] = label
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df = df.sample(frac=1).reset_index(drop=True)
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df.head()
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df['label'] = df['label'].astype('str')
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df.head()
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train, test = train_test_split(df, test_size=0.2, random_state=42)
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train_generator = ImageDataGenerator(
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rescale = 1./255,
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rotation_range = 40,
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shear_range = 0.2,
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zoom_range = 0.2,
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horizontal_flip = True,
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fill_mode = 'nearest'
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)
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val_generator = ImageDataGenerator(rescale = 1./255)
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train_iterator = train_generator.flow_from_dataframe(
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train,
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x_col='images',
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y_col='label',
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target_size=(image_size,image_size),
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batch_size=512,
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class_mode='binary'
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)
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val_iterator = val_generator.flow_from_dataframe(
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test,
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x_col='images',
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y_col='label',
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target_size=(image_size,image_size),
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batch_size=512,
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class_mode='binary'
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)
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self.model = Sequential([
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Conv2D(16, (3,3), activation='relu', input_shape=(image_size,image_size,3)),
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MaxPool2D((2,2)),
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Conv2D(32, (3,3), activation='relu'),
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MaxPool2D((2,2)),
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Conv2D(64, (3,3), activation='relu'),
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MaxPool2D((2,2)),
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Flatten(),
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Dense(512, activation='relu'),
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Dense(1, activation='sigmoid')
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])
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self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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self.model.summary()
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self.model.fit(train_iterator, epochs=10, validation_data=val_iterator)
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self.model.save(save_model_path)
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else:
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self.model = keras.models.load_model(load_model_path,
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compile=True
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)
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def recognize(self, image_path):
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image = keras.utils.load_img(image_path, target_size=(image_size, image_size))
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image_array = keras.utils.img_to_array(image)
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image_array = keras.backend.expand_dims(image_array, 0)
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prediction = self.model.predict(image_array)
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if prediction[0] > 0.5:
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predict = "notmine"
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accuracy = prediction[0] * 100
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elif prediction[0] <= 0.5:
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predict = "mine"
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accuracy = (1 - prediction[0]) * 100
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print("Image: ",image_path," is classified as: ", predict," with: ", accuracy, " accuracy")
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if predict == "mine":
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return True
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else:
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return False
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