Changing model to cnn

app.py

@@ -5,16 +5,16 @@ import cv2
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 
-IMG_HEIGHT = 96
-IMG_WIDTH = 96
+IMG_HEIGHT = 64
+IMG_WIDTH = 64
 
 # Load the saved Keras model
-model = load_model("model_01.keras")
+model = load_model("cnn_1.keras")
 
 # Define the labels for ASL classes
 labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
          'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
-          'U', 'V', 'W', 'X', 'Y']  
+          'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']  
 
 def preprocess_frame(frame):
     """Preprocess the video frame for the ASL model."""
@@ -44,10 +44,15 @@ def preprocess_frame(frame):
 
     return frame
 
+def preprocess_frame_cnn(frame):
+    img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    img = np.expand_dims(cv2.resize(img, (64, 64)), axis = 0)
+    return img
+
 
 def predict_asl(frame):
     """Predict the ASL sign and return the label and probabilities."""
-    processed_frame = preprocess_frame(frame)
+    processed_frame = preprocess_frame_cnn(frame)
     predictions = model.predict(processed_frame)  # Predict probabilities
     predicted_label = labels[np.argmax(predictions)]  # Get the class with the highest probability