forestfire_classification.py

# -*- coding: utf-8 -*-
"""ForestFire Classification.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1GfE55sq2vm1AMVgKzuAGFrWknMmuKerG
"""

# Commented out IPython magic to ensure Python compatibility.
from google.colab import drive
drive.mount('/gdrive/')
# %cd /gdrive

ls

cd/gdrive/MyDrive/Forestfire classification/

ls

import tensorflow as tf
from tensorflow.keras import models, layers
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.preprocessing.image import load_img

import os
import PIL
import pathlib
import pandas as pd
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import preprocessing
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.python.ops.numpy_ops import np_utils

BATCH_SIZE = 62
IMAGE_SIZE = 256
EPOCHS=20
CHANNELS=3

dataset = tf.keras.preprocessing.image_dataset_from_directory(
    "/gdrive/MyDrive/Forestfire classification",
    seed=123,
    shuffle=True,
    image_size=(IMAGE_SIZE,IMAGE_SIZE),
    batch_size=BATCH_SIZE
)

class_names = dataset.class_names
class_names

len(dataset)

for image_batch, label_batch in dataset.take(1):
  print(image_batch.shape)
  print(image_batch[1])
  print(label_batch.numpy())

plt.figure(figsize=(15, 15))
for image_batch, labels_batch in dataset.take(1):
    for i in range(BATCH_SIZE):
        ax = plt.subplot(8, 8, i + 1)
        plt.imshow(image_batch[i].numpy().astype("uint8"))
        plt.title(class_names[labels_batch[i]])
        plt.axis("off")

def get_dataset_partitions_tf(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
    assert (train_split + test_split + val_split) == 1
    ds_size = len(ds)
    if shuffle:
        ds = ds.shuffle(shuffle_size, seed=12)
    train_size = int(train_split * ds_size)
    val_size = int(val_split * ds_size)
    train_ds = ds.take(train_size)    
    val_ds = ds.skip(train_size).take(val_size)
    test_ds = ds.skip(train_size).skip(val_size)
    # Autotune all the 3 datasets 
    train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
    val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
    test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
    return train_ds, val_ds, test_ds

train_ds, val_ds, test_ds = get_dataset_partitions_tf(dataset)

resize_and_rescale = tf.keras.Sequential([
  layers.experimental.preprocessing.Resizing(IMAGE_SIZE, IMAGE_SIZE),
  layers.experimental.preprocessing.Rescaling(1./255),
])

data_augmentation = tf.keras.Sequential([
  layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
  layers.experimental.preprocessing.RandomRotation(0.2),
])

input_shape = (BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, CHANNELS)
n_classes = 9

model = models.Sequential([
    resize_and_rescale,
    # data_augmentation,
    layers.Conv2D(32, kernel_size = (3,3), activation='relu', input_shape=input_shape),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64,  kernel_size = (3,3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64,  kernel_size = (3,3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(n_classes, activation='softmax'),
])
model.build(input_shape=input_shape)

model.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
    metrics=['accuracy']
)

model.summary()

history = model.fit(
    train_ds,
    batch_size=BATCH_SIZE,
    validation_data=val_ds,
    verbose=1,
    epochs=EPOCHS,
)

model.evaluate(test_ds)

acc = history.history['accuracy']
loss = history.history['loss']

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(range(EPOCHS), acc, label=' Accuracy')
plt.legend(loc='lower right')
plt.title('Accuracy')
plt.subplot(1, 2, 2)
plt.plot(range(EPOCHS), loss, label=' Loss')
plt.legend(loc='upper right')
plt.title('Loss')
plt.show()

image_path = "/gdrive/MyDrive/Forestfire classification/nofire/abc192.jpg"
image = preprocessing.image.load_img(image_path)
image_array = preprocessing.image.img_to_array(image)
scaled_img = np.expand_dims(image_array, axis=0)
image

pred = model.predict(scaled_img)

output = class_names[np.argmax(pred)]

output

model.save("Forestfire classification.h5")