02_Cats And Dogs Classifier ( 안드로이드 )

프로젝트명 : 카메라를 이용해서 개와 고양이를 구분하는 안드로이드 앱

  

 

APK : https://drive.google.com/file/d/1mT7PBNToRTvC2yj6GKB00djyNFnycMW0/view?usp=sharing

훈련 시간에 비해 실제 고양이와 모니터에 출력된 강아지사진 모두 잘 작동 합니다.

PS.안드로이드 프로젝트 파일이 용량이 커서 업로드가 안되네요. 혹시 코드 필요하신분은 댓글 달아주세요.

< 모델 작성 >

# -*- coding: utf-8 -*-
"""cats_dogs_android.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1gPyCFufUR4cGkEH95om6w6Pl1pf7uDmn
"""
 
import numpy as np
import pandas as pd 
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator, load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
import os
 
FAST_RUN = False
IMAGE_WIDTH=128
IMAGE_HEIGHT=128
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3
 
filenames = os.listdir("./dogs-vs-cats/train")
categories = []
for filename in filenames:
    category = filename.split('.')[0]
    if category == 'dog':
        categories.append(1)
    else:
        categories.append(0)
 
df = pd.DataFrame({
    'filename': filenames,
    'category': categories
})
 
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
 
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
import keras.backend as K
 
# with tf.device("device:XLA_GPU:0"):
model = Sequential()
 
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
 
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
 
model.summary()
 
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
earlystop = EarlyStopping(patience=10)
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', 
                                            patience=2, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=0.00001)
callbacks = [earlystop, learning_rate_reduction]
 
df["category"] = df["category"].replace({0: 'cat', 1: 'dog'})
 
train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)
train_df = train_df.reset_index(drop=True)
validate_df = validate_df.reset_index(drop=True)
 
total_train = train_df.shape[0]
total_validate = validate_df.shape[0]
batch_size=16
 
train_datagen = ImageDataGenerator(
    rotation_range=15,
    rescale=1./255,
    shear_range=0.1,
    zoom_range=0.2,
    horizontal_flip=True,
    width_shift_range=0.1,
    height_shift_range=0.1
)
 
train_generator = train_datagen.flow_from_dataframe(
    train_df, 
    "./dogs-vs-cats/train/", 
    x_col='filename',
    y_col='category',
    target_size=IMAGE_SIZE,
    class_mode='categorical',
    batch_size=batch_size
)
 
validation_datagen = ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_dataframe(
    validate_df, 
    "./dogs-vs-cats/train/", 
    x_col='filename',
    y_col='category',
    target_size=IMAGE_SIZE,
    class_mode='categorical',
    batch_size=batch_size
)
 
example_df = train_df.sample(n=1).reset_index(drop=True)
example_generator = train_datagen.flow_from_dataframe(
    example_df, 
    "./dogs-vs-cats/train/", 
    x_col='filename',
    y_col='category',
    target_size=IMAGE_SIZE,
    class_mode='categorical'
)
 
plt.figure(figsize=(12, 12))
for i in range(0, 15):
    plt.subplot(5, 3, i+1)
    for X_batch, Y_batch in example_generator:
        image = X_batch[0]
        plt.imshow(image)
        break
plt.tight_layout()
plt.show()
 
epochs=3 if FAST_RUN else 20
history = model.fit_generator(
    train_generator, 
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=total_validate//batch_size,
    steps_per_epoch=total_train//batch_size,
    callbacks=callbacks
)
model.save("./my_model")
 
def convert_model(model_path, tflite_path):
    converter = tf.lite.TFLiteConverter.from_keras_model_file(model_path)
    flat_data = converter.convert()
 
    with open(tflite_path, 'wb') as f:
        f.write(flat_data)
 
 
convert_model('./my_model', './cats_and_dogs.tflite')