Merge pull request 'master' (#1) from s473554/SztIn_gr.234798:master into master

Reviewed-on: #1

recognition_v1/main.py

@@ -0,0 +1,51 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+
+def normalize(image, label):
+    return image / 255, label
+
+
+directoryTRAIN = "C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/train"
+directoryVALIDATION = "C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/validation"
+
+train_ds = tf.keras.utils.image_dataset_from_directory(directoryTRAIN,
+                                                       seed=123, batch_size=32,
+                                                       image_size=(224, 224), color_mode='rgb')
+
+val_ds = tf.keras.utils.image_dataset_from_directory(directoryVALIDATION,
+                                                     seed=123, batch_size=32,
+                                                     image_size=(224, 224), color_mode='rgb')
+
+train_ds = train_ds.map(normalize)
+val_ds = val_ds.map(normalize)
+
+model = keras.Sequential([
+    keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)),
+    keras.layers.MaxPool2D((2, 2)),
+
+    keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    keras.layers.MaxPool2D((2, 2)),
+
+    keras.layers.Conv2D(256, (3, 3), activation='relu'),
+    keras.layers.MaxPool2D((2, 2)),
+
+    keras.layers.Flatten(),
+    keras.layers.Dense(1024, activation='relu'),
+    keras.layers.Dense(9, activation='softmax')
+])
+
+print(model.summary())
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+trainHistory = model.fit(train_ds, epochs=4, validation_data=val_ds)
+
+model = keras.models.load_model("C:/Users/KimD/PycharmProjects/Traktor_V1/mode2.h5")
+
+(loss, accuracy) = model.evaluate(val_ds)
+print(loss)
+print(accuracy)
+
+# model.save("mode2.h5")

recognition_v1/proverka.py

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+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import cv2
+
+directory = "C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/test"
+
+
+class VegebatlesRecognizer:
+
+    def recognize(self, image_path) -> str:
+        model = keras.models.load_model("C:/Users/KimD/PycharmProjects/Traktor_V1/mode2.h5")
+        class_names = ['Bean', 'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cucumber', 'Potato', 'Pumpkin', 'Tomato']
+
+        img = cv2.imread(image_path)
+        # cv2.imshow("lala", img)
+        # cv2.waitKey(0)
+        img = (np.expand_dims(img, 0))
+
+        predictions = model.predict(img)[0].tolist()
+
+        print(class_names)
+        print(predictions)
+        print(max(predictions))
+        print(predictions.index(max(predictions)))
+
+        return class_names[predictions.index(max(predictions))]
+
+
+# image_path = 'C:/Users/KimD/PycharmProjects/Traktor_V1/Vegetable Images/test/Carrot/1001.jpg'
+# uio = VegebatlesRecognizer()
+# print(uio.recognize(image_path))