build image clasification and update requirements

imageClasification/imageClasification.py

@@ -0,0 +1,128 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import PIL
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
+batch_size = 32
+img_height = 180
+img_width = 180
+data_dir = "../SI_InteligentnyWozekWidlowy/imageClasification/images"
+
+train_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="training",
+    seed=123,
+    image_size=(img_height, img_width),
+    batch_size=batch_size)
+
+val_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=(img_height, img_width),
+    batch_size=batch_size)
+
+class_names = train_ds.class_names
+print(class_names)
+
+plt.figure(figsize=(10, 10))
+for images, labels in train_ds.take(1):
+    for i in range(9):
+        ax = plt.subplot(3, 3, i + 1)
+        plt.imshow(images[i].numpy().astype("uint8"))
+        plt.title(class_names[labels[i]])
+        plt.axis("off")
+
+for image_batch, labels_batch in train_ds:
+    print(image_batch.shape)
+    print(labels_batch.shape)
+    break
+
+AUTOTUNE = tf.data.AUTOTUNE
+
+train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
+normalization_layer = layers.Rescaling(1. / 255)
+
+normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+first_image = image_batch[0]
+# Notice the pixel values are now in `[0,1]`.
+print(np.min(first_image), np.max(first_image))
+
+num_classes = len(class_names)
+
+model = Sequential([
+    layers.Rescaling(1. / 255, input_shape=(img_height, img_width, 3)),
+    layers.Conv2D(16, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(32, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(num_classes)
+])
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+# model.summary()
+
+
+epochs = 10
+history = model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs
+)
+
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs_range = range(epochs)
+
+plt.figure(figsize=(8, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+
+
+# ścieżka do sprawdzanego obrazu
+image_path = "./th-367101945.jpg"
+
+img = tf.keras.utils.load_img(
+    image_path, target_size=(img_height, img_width)
+)
+img_array = tf.keras.utils.img_to_array(img)
+img_array = tf.expand_dims(img_array, 0)  # Create a batch
+
+predictions = model.predict(img_array)
+score = tf.nn.softmax(predictions[0])
+
+print(
+    "This image most likely belongs to {} with a {:.2f} percent confidence."
+        .format(class_names[np.argmax(score)], 100 * np.max(score))
+)

requirements.txt

@@ -1,5 +1,10 @@
 jupyter
-matplotlib
+matplotlib~=3.5.2
-mesa
+mesa~=0.9.0
-numpy
+numpy~=1.22.3
-sklearn
+sklearn~=0.0
+pandas~=1.4.2
+scikit-learn~=1.1.1
+Pillow~=9.1.1
+requests~=2.27.1
+tensorflow~=2.9.1