Tempat_VisualStudioCode/imagerecognition.py at master · Nando-suka/Tempat_VisualStudioCode · GitHub

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

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1bg2H_JuJjnRSGDcitTpiElaxOx8BKHc1
"""

import tensorflow as tf
print(tf.__version__)

!wget --no-check-certificate \
      https://dicoding-academy-assets.sgp1.cdn.digitaloceanspaces.com/184/messy-vs-clean-room.zip \
      -O /tmp/messy_vs_clean_room.zip

# melakukan ekstraksi pada file zip
    import zipfile,os
    local_zip = '/tmp/messy_vs_clean_room.zip'
    zip_ref = zipfile.ZipFile(local_zip, 'r')
    zip_ref.extractall('/tmp')
    zip_ref.close()

    base_dir = '/tmp/images'
    train_dir = os.path.join(base_dir, 'train')
    validation_dir = os.path.join(base_dir, 'val')

os.listdir('/tmp/images/train')

os.listdir('/tmp/images/val')

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
      rescale = 1./255,
      rotation_range = 20,
      horizontal_flip =True,
      shear_range = 0.2,
      fill_mode = 'nearest'
)

test_datagen = ImageDataGenerator(
    rescale = 1./255
)

train_generator = train_datagen.flow_from_directory(
    train_dir, # direktori data latih
    target_size = (150,150), # Mengubah resolusi gambar menjadi 150x150 piksel
    batch_size = 4,
    # Karena ini merupakan masalah klasifikasi 2 kelas, gunakan class_mode = 'binary'
    class_mode = 'binary'
)
validation_generator = test_datagen.flow_from_directory(
        validation_dir, # direktori data validasi
        target_size=(150, 150), # mengubah resolusi seluruh gambar menjadi 150x150 piksel
        batch_size=4, # karena ini merupakan masalah klasifikasi 2 kelas gunakan class_mode = 'binary'
        class_mode='binary')

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(512, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(1, activation=  'sigmoid')
])

# Untuk melihat model summar dari arsitektur yang sudah kita bangun
model.summary()

# compile model dengan 'adam' optimizer loss function 'binary_crossentropy'
model.compile(loss='binary_crossentropy',
              optimizer=tf.optimizers.Adam(),
              metrics=['accuracy'])

# Latih model dengan fit
model.fit(
    train_generator,
    steps_per_epoch=25, # Berapa batch yang akan dieksekusi tiap epoch
    epochs= 20, # tambahkan epochs jika akurasi belum optimal
    validation_data = validation_generator, # Menampilkan akurasi pengujian data validasi
    validation_steps = 5, # Berapa batch yang akan dieksekusi tiap epoch
    verbose=5

)

# Commented out IPython magic to ensure Python compatibility.
    import numpy as np
    from google.colab import files
    from tensorflow.keras.preprocessing import image
    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
#     %matplotlib inline

    uploaded = files.upload()

    for fn in uploaded.keys():

      # predicting images
      path = fn
      img = image.load_img(path, target_size=(150,150))

      imgplot = plt.imshow(img)
      x = image.img_to_array(img)
      x = np.expand_dims(x, axis=0)
      images = np.vstack([x])

      classes = model.predict(images, batch_size=10)
      print(fn)
      if classes==0:
       print('clean')
      else:
       print('messy')