done

Model/Model_ResNet-50_Tomato-leaf-disease.py

@@ -0,0 +1,418 @@
+# -*- coding: utf-8 -*-
+"""Skripsi ResNet-50_Tomato Leaf Disease Classification_FINAL.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DCYce_7Aik3nvrZQM-_YWCoyeyZBu4KH
+
+# Libraries
+"""
+
+!nvidia-smi
+
+!pip install tensorflow
+!pip install tensorflowjs
+
+!pip list
+
+import tensorflow as tf
+import numpy as np
+# import tensorflowjs as tfjs
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+import matplotlib.pyplot as plt
+from tensorflow.keras import layers
+from tensorflow.keras.utils import plot_model
+from tensorflow.keras.utils import image_dataset_from_directory
+from tensorflow.keras import mixed_precision
+
+print("TensorFlow version:", tf.__version__)
+print("GPU detected:", tf.config.list_physical_devices('GPU'))
+mixed_precision.set_global_policy('mixed_float16')
+
+!nvcc --version
+
+# from google.colab import drive
+# drive.mount('/content/drive')
+
+# !unzip "/content/drive/MyDrive/daun tomat/tomato_leaf_disease_dataset.zip" -d "/content"
+# !unzip "/content/drive/MyDrive/daun tomat/tomato_leaf_disease_dataset_nobg.zip" -d "/content"
+
+!unzip 'tomato_leaf_disease_dataset.zip'
+
+"""# Read dataset"""
+
+dataset_dir = '/content/tomato_leaf_disease_dataset'
+
+image_size = (224, 224)
+
+train_dataset = image_dataset_from_directory(
+    dataset_dir,
+    batch_size=32,
+    image_size=image_size,
+    seed=42,
+    validation_split=0.2,
+    subset="training",
+)
+
+val_dataset = image_dataset_from_directory(
+    dataset_dir,
+    batch_size=32,
+    image_size=image_size,
+    seed=42,
+    validation_split=0.2,
+    subset="validation",
+)
+
+classes = train_dataset.class_names
+
+"""# Show some of dataset"""
+
+plt.figure(figsize=(15, 20))
+for images, labels in train_dataset.take(1):
+  for i in range(16):
+    plt.subplot(8, 4, i + 1)
+    plt.imshow(images[i]/255)
+    plt.title(f"{classes[labels[i].numpy()]}")
+    plt.axis(False);
+
+"""# Cache"""
+
+train_ds = train_dataset.cache().prefetch(tf.data.AUTOTUNE)
+val_ds = val_dataset.cache().prefetch(tf.data.AUTOTUNE)
+
+"""#Model"""
+
+resnet = ResNet50(include_top=False, weights='imagenet')
+resnet.trainable = False
+
+inputs = layers.Input(shape=(224, 224, 3))
+x = preprocess_input(inputs)
+x = resnet(inputs, training=False)
+x = layers.GlobalAveragePooling2D()(x)
+x = layers.Dropout(0.25)(x)
+outputs = layers.Dense(
+    len(classes),
+    activation='softmax'
+)(x)
+
+
+model = tf.keras.Model(inputs=inputs, outputs=outputs)
+
+model.summary()
+
+plot_model(model, show_shapes=True, show_layer_activations=True,  dpi=70)
+
+"""# Set Loss Function and Optimizer"""
+
+model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy')])
+
+"""# Callback
+
+"""
+
+import tensorflow as tf
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
+
+early_stop_callback = EarlyStopping(
+    monitor='val_loss',
+    patience=3,
+    restore_best_weights=True,
+    verbose=0
+)
+
+reduce_lr_callback = ReduceLROnPlateau(
+    monitor='val_loss',
+    factor=0.5,
+    patience=2,
+    min_lr=1e-6,
+    verbose=0
+)
+
+checkpoint_callback = ModelCheckpoint(
+    filepath='best_model.weights.h5',
+    monitor='val_loss',
+    save_weights_only=True,
+    save_best_only=True,
+    mode='min',
+    verbose=0
+)
+
+callbacks = [early_stop_callback, reduce_lr_callback, checkpoint_callback]
+
+"""# Train Model"""
+
+hist = model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=100,
+    callbacks=callbacks,
+)
+
+"""# Visualize the train and validation accuracy&loss"""
+
+import matplotlib.pyplot as plt
+def plot_curves(history):
+  acc = history.history['accuracy']
+  val_acc = history.history['val_accuracy']
+  loss = history.history['loss']
+  val_loss = history.history['val_loss']
+  epochs = range(len(history.history['accuracy']))
+
+  plt.title("Train and Val Accuracy")
+  plt.plot(epochs, acc, label='training_acc')
+  plt.plot(epochs, val_acc, label='val_acc')
+  plt.ylabel('Accuracy')
+  plt.xlabel('Epoch')
+  plt.legend(loc="lower right")
+
+  plt.figure()
+  plt.title("Train and Val Loss")
+  plt.plot(epochs, loss, label='training_loss')
+  plt.plot(epochs, val_loss, label='val_loss')
+  plt.ylabel('Loss')
+  plt.xlabel('Epoch')
+  plt.legend()
+
+plot_curves(hist)
+
+from sklearn.metrics import classification_report
+import numpy as np
+
+class_names = train_dataset.class_names
+
+y_pred = []
+y_true = []
+
+for images, labels in val_dataset:
+    predictions = model.predict(images)
+    y_pred.extend(np.argmax(predictions, axis=1))
+    y_true.extend(labels.numpy())
+
+# Hasilkan laporan klasifikasi
+report = classification_report(y_true, y_pred, target_names=class_names)
+print(report)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+import random
+
+# Buat confusion matrix
+cm = confusion_matrix(y_true, y_pred)
+plt.figure(figsize=(10, 8))
+sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=class_names, yticklabels=class_names)
+plt.xlabel("Predicted Label")
+plt.ylabel("True Label")
+plt.title("Confusion Matrix")
+plt.show()
+
+
+all_images = []
+all_labels = []
+
+for images, labels in val_dataset:
+    all_images.extend(images)
+    all_labels.extend(labels)
+
+# Ambil indeks acak
+num_samples = 9
+indices = random.sample(range(len(all_images)), num_samples)
+
+plt.figure(figsize=(15, 10))
+for i, idx in enumerate(indices):
+    img = all_images[idx]
+    true_label = all_labels[idx].numpy()
+
+    pred = model.predict(np.expand_dims(img, axis=0))
+    pred_label = np.argmax(pred, axis=1)[0]
+
+    plt.subplot(3, 3, i+1)
+    plt.imshow(img.numpy().astype("uint8"))
+    plt.title(f"True: {class_names[true_label]}\nPred: {class_names[pred_label]}")
+    plt.axis('off')
+
+plt.tight_layout()
+plt.show()
+
+"""# Save model"""
+
+model.save('ResNet-50_tomato-leaf-disease.keras');
+
+import tensorflow as tf
+
+model = tf.keras.models.load_model('/content/ResNet-50_tomato-leaf-disease.keras')
+
+model.summary()
+model.save('/content/drive/MyDrive/model_pak_ridwan_custom.h5')
+print("Model telah disimpan dalam format .h5")
+
+model.export('saved_model/')
+
+tfjs.converters.save_keras_model(model, 'ResNet-50_tomato-leaf-disease-tfjs/')
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+converter.target_spec.supported_ops = [
+    tf.lite.OpsSet.TFLITE_BUILTINS,
+    tf.lite.OpsSet.SELECT_TF_OPS
+]
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+
+tflite_model = converter.convert()
+
+with open('ResNet-50_tomato-leaf-disease_v8.tflite', 'wb') as f:
+    f.write(tflite_model)
+
+"""# Predict an image"""
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load Model
+model = tf.keras.models.load_model('ResNet-50_tomato-leaf-disease.keras', compile=False)
+
+
+# Load dan praproses gambar
+img_path = '/content/fd3a25ef-50f2-4ca2-a20d-d0a99bcbae13___GCREC_Bact-Sp-6394_JPG.rf.1e9be7991e041d414a175bc85e0e505b.jpg'
+image = tf.keras.utils.load_img(img_path, target_size=(224, 224))
+image_array = tf.keras.utils.img_to_array(image)
+image_array = tf.expand_dims(image_array, axis=0)
+
+# Prediksi
+predict = model.predict(image_array)
+predicted_index = np.argmax(predict, axis=1)[0]
+predicted_class = classes[predicted_index]
+predicted_prob = predict[0][predicted_index]
+
+# Tampilkan hasil
+plt.imshow(image)
+plt.title(f"Prediksi: {predicted_class}; Probability: {predicted_prob:.2f}")
+plt.axis('off')
+plt.show()
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Fungsi untuk load model TFLite
+def load_tflite_model(model_path):
+    interpreter = tf.lite.Interpreter(model_path=model_path)
+    interpreter.allocate_tensors()
+    return interpreter
+
+# Load model TFLite
+model_tflite = load_tflite_model('ResNet-50_tomato-leaf-disease.tflite')
+
+# Load dan praproses gambar
+img_path = '/content/fd3a25ef-50f2-4ca2-a20d-d0a99bcbae13___GCREC_Bact-Sp-6394_JPG.rf.1e9be7991e041d414a175bc85e0e505b.jpg'
+image = tf.keras.utils.load_img(img_path, target_size=(224, 224))
+image_array = tf.keras.utils.img_to_array(image)
+image_array = tf.expand_dims(image_array, axis=0)
+
+# Mendapatkan input dan output tensor dari model TFLite
+input_details = model_tflite.get_input_details()
+output_details = model_tflite.get_output_details()
+
+# Menyiapkan input data untuk TFLite
+input_data = np.array(image_array, dtype=np.float32)
+model_tflite.set_tensor(input_details[0]['index'], input_data)
+
+# Menjalankan inferensi
+model_tflite.invoke()
+
+# Mengambil hasil output dari inferensi
+output_data = model_tflite.get_tensor(output_details[0]['index'])
+
+# Prediksi
+predicted_index = np.argmax(output_data, axis=1)[0]
+predicted_class = classes[predicted_index]  # Pastikan 'classes' sudah didefinisikan
+predicted_prob = output_data[0][predicted_index]
+
+# Tampilkan hasil
+plt.imshow(image)
+plt.title(f"Prediksi: {predicted_class}; Probability: {predicted_prob:.2f}")
+plt.axis('off')
+plt.show()
+
+# !pip install rembg
+# !pip install onnxruntime
+
+# import tensorflow as tf
+# import numpy as np
+# import matplotlib.pyplot as plt
+# from rembg import remove
+# from PIL import Image
+# import io
+
+# # Load Model
+# model = tf.keras.models.load_model('ResNet-50_tomato-leaf-disease_nobg.keras')
+
+# # Kelas penyakit daun tomat
+# classes = [
+#     'bacterial_spot',
+#     'healthy',
+#     'late_blight',
+#     'leaf_curl_virus',
+#     'leaf_mold',
+#     'mosaic_virus',
+#     'septoria_leaf_spot'
+# ]
+
+# # Load gambar dan hilangkan background
+# img_path = 'jamurdaun.jpeg'
+# with open(img_path, 'rb') as f:
+#     input_image_bytes = f.read()
+
+# output_image_bytes = remove(input_image_bytes)
+# image_no_bg = Image.open(io.BytesIO(output_image_bytes)).convert("RGB")
+# image_no_bg = image_no_bg.resize((224, 224))
+
+# # Konversi gambar ke array untuk prediksi
+# image_array = tf.keras.utils.img_to_array(image_no_bg)
+# image_array = tf.expand_dims(image_array, axis=0)
+
+# # Prediksi
+# predict = model.predict(image_array)
+# predicted_index = np.argmax(predict, axis=1)[0]
+# predicted_class = classes[predicted_index]
+# predicted_prob = predict[0][predicted_index]
+
+# # Tampilkan hasil prediksi
+# plt.imshow(image_no_bg)
+# plt.title(f"Prediksi: {predicted_class}; Probability: {predicted_prob:.2f}")
+# plt.axis('off')
+# plt.show()
+
+# !pip install tensorrt
+# !pip install tensorflowjs
+# !pip install TensorFlow==2.15.0
+# !pip install tensorflow-decision-forests==1.8.1
+
+!pip uninstall -y tensorflow tensorflowjs
+!pip install tensorflow==2.15.0 tensorflowjs==4.9.0
+!pip install --upgrade jax==0.4.30
+
+!tensorflowjs_converter --input_format=keras ResNet-50_tomato-leaf-disease.h5 ResNet-50_tomato-leaf-disease-tfjs
+
+# import zipfile
+# import os
+
+# def zip_folder(folder_path, output_zip_path):
+#     """
+#     Kompres sebuah folder menjadi file ZIP.
+
+#     Args:
+#         folder_path (str): Path ke folder yang ingin dikompres.
+#         output_zip_path (str): Path untuk output file ZIP.
+#     """
+#     with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+#         for root, dirs, files in os.walk(folder_path):
+#             for file in files:
+#                 file_path = os.path.join(root, file)
+#                 zipf.write(file_path, os.path.relpath(file_path, folder_path))
+
+# folder_to_zip = "ResNet-50_tomato-leaf-disease-tfjs_nobg"
+# output_zip_file = "ResNet-50_tomato-leaf-disease-tfjs_nobg.zip"
+# zip_folder(folder_to_zip, output_zip_file)
+# print(f"Folder '{folder_to_zip}' berhasil dikompres menjadi '{output_zip_file}'")