image classification to file without training

imageClasification/Classificator.py

@@ -1,136 +0,0 @@
-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
-
-
-class Classificator():
-
-    def __init__(self, data_dir: str) -> None:
-        super().__init__()
-        self.data = self.train_model(data_dir)
-
-    def train_model(self, data_dir):
-        batch_size = 32
-        img_height = 180
-        img_width = 180
-
-        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()
-        return model, class_names, img_width, img_height
-
-    def image_clasification(self,image_path):
-        # ścieżka do sprawdzanego obrazu
-        # image_path = "./th-367101945.jpg"
-
-        img = tf.keras.utils.load_img(
-            image_path, target_size=(180, 180)
-        )
-        img_array = tf.keras.utils.img_to_array(img)
-        img_array = tf.expand_dims(img_array, 0)  # Create a batch
-
-        predictions = self.data[0].predict(img_array)
-        score = tf.nn.softmax(predictions[0])
-
-        print(
-            "This image most likely belongs to {} with a {:.2f} percent confidence."
-                .format(self.data[1][np.argmax(score)], 100 * np.max(score))
-        )
-        return self.data[1][np.argmax(score)]

imageClasification/usedAssets/ClassificatorInside.py

@@ -0,0 +1,136 @@
+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
+
+
+class Classificator():
+
+    def __init__(self, data_dir: str) -> None:
+        super().__init__()
+        self.data = self.train_model(data_dir)
+
+    def train_model(self, data_dir):
+        batch_size = 32
+        img_height = 180
+        img_width = 180
+
+        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()
+        return model, class_names, img_width, img_height
+
+    def image_clasification(self,image_path):
+        # ścieżka do sprawdzanego obrazu
+        # image_path = "./th-367101945.jpg"
+
+        img = tf.keras.utils.load_img(
+            image_path, target_size=(180, 180)
+        )
+        img_array = tf.keras.utils.img_to_array(img)
+        img_array = tf.expand_dims(img_array, 0)  # Create a batch
+
+        predictions = self.data[0].predict(img_array)
+        score = tf.nn.softmax(predictions[0])
+
+        print(
+            "This image most likely belongs to {} with a {:.2f} percent confidence."
+                .format(self.data[1][np.argmax(score)], 100 * np.max(score))
+        )
+        return self.data[1][np.argmax(score)]