Initial (and hopefully last) commit

app.py

@@ -0,0 +1,113 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models, Sequential
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from PyQt6.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QPushButton, QLabel
+from PyQt6.QtGui import QPainter, QPen, QImage
+from PyQt6.QtCore import Qt, QPoint
+import sys
+
+data_dir = "/home/mia/Schule/KISY/schrifterkennung/" # Ignore full path, had some weird problem otherwise
+
+model_file = "model.keras" # Model save file. No Idea if this is the correct extention but nobody cares, right?
+
+print("We have done training already so we load this to not waste very precious cpu :)")
+model = tf.keras.models.load_model(model_file)
+
+#for images, labels in val_ds.take(10):
+#    preds = model.predict(images)
+#    print(f"Prediction: {class_names[np.argmax(preds[0])]}")
+#    print(f"Label: {class_names[labels[0].numpy().astype(int)]}")
+#    plt.imshow(images[0].numpy().squeeze(), cmap='gray')
+#    plt.title(f"Pred: {class_names[np.argmax(preds[0])]}")
+#    plt.show()
+
+#### DISCLAIMER: This was written by AI; I hate GUI stuff ####
+
+class DrawingCanvas(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setFixedSize(320, 320)  # 10x the model input size for easier drawing
+        self.image = QImage(self.size(), QImage.Format.Format_Grayscale8)
+        self.image.fill(Qt.GlobalColor.white)
+        self.drawing = False
+        self.last_point = QPoint()
+
+    def paintEvent(self, event):
+        painter = QPainter(self)
+        painter.drawImage(0, 0, self.image)
+
+    def mousePressEvent(self, event):
+        if event.button() == Qt.MouseButton.LeftButton:
+            self.drawing = True
+            self.last_point = event.position().toPoint()
+
+    def mouseMoveEvent(self, event):
+        if (event.buttons() & Qt.MouseButton.LeftButton) and self.drawing:
+            painter = QPainter(self.image)
+            # We use a thick white pen because the model was trained on grayscale images
+            painter.setPen(QPen(Qt.GlobalColor.black, 18, Qt.PenStyle.SolidLine, Qt.PenCapStyle.RoundCap))
+            painter.drawLine(self.last_point, event.position().toPoint())
+            self.last_point = event.position().toPoint()
+            self.update()
+
+    def clear(self):
+        self.image.fill(Qt.GlobalColor.white)
+        self.update()
+
+
+class MainWindow(QMainWindow):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("Handwriting Recognition")
+
+        main_layout = QVBoxLayout()
+        self.canvas = DrawingCanvas()
+        self.result_label = QLabel("Draw something and click Predict. (Need to fill entire space or model has stroke")
+        self.result_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
+
+        predict_btn = QPushButton("Predict")
+        predict_btn.clicked.connect(self.predict_image)
+
+        clear_btn = QPushButton("Clear Canvas")
+        clear_btn.clicked.connect(self.canvas.clear)
+
+        main_layout.addWidget(self.canvas)
+        main_layout.addWidget(self.result_label)
+        main_layout.addWidget(predict_btn)
+        main_layout.addWidget(clear_btn)
+
+        container = QWidget()
+        container.setLayout(main_layout)
+        self.setCentralWidget(container)
+
+    def predict_image(self):
+        # 1. Resize the drawing to 32x32 to match the model input
+        scaled_img = self.canvas.image.scaled(32, 32, Qt.AspectRatioMode.IgnoreAspectRatio,
+                                              Qt.TransformationMode.SmoothTransformation)
+
+        # 2. Convert QImage to Numpy Array
+        ptr = scaled_img.bits()
+        ptr.setsize(32 * 32)
+        arr = np.frombuffer(ptr, np.uint8).reshape(32, 32, 1)
+
+        # 3. Add batch dimension and predict
+        # Note: We don't manually rescale by 1/255 here because your model has a Rescaling layer built-in!
+        img_batch = np.expand_dims(arr, axis=0)
+        prediction = model.predict(img_batch, verbose=0)
+        print(type(prediction), prediction)
+
+        class_names = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')
+        print([x for x in zip(class_names, prediction[0])])
+        result = class_names[np.argmax(prediction)]
+        confidence = np.max(prediction) * 100
+        self.result_label.setText(f"Prediction: {result} ({confidence:.1f}%)")
+
+
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    app.setStyle("Breeze") # Use system theme so it looks nice on linux
+    window = MainWindow()
+    window.show()
+    sys.exit(app.exec())