fix: working model for gesture complete project

app.py

@@ -9,59 +9,26 @@ import sys
 from tensorflow import keras
 from tensorflow.keras import layers
 
-EPOCHS = 10
-IMG_WIDTH = 30
-IMG_HEIGHT = 30
-NUM_CATEGORIES = 43
-TEST_SIZE = 0.4
 
 def get_model():
-    """
-    Returns a compiled convolutional neural network model. Assume that the
-    `input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
-    The output layer should have `NUM_CATEGORIES` units, one for each category.
-    """
-    # Experimenting with different architectures of a Sequential model and this is so far best for me
-    # Accepts input with shape of (30, 30, 3)
-    # Convolutional layer with 65 filters and relu activation function
-    # Maxpooled by (2, 2) kernel
-    # Second convolutional layer with higher number of filter i.e 256 and relu activation function
-    # Flattering nd shape and Dense layer with 450 nuerons and relu activation funtion
-    # Output layer with 43 nuerons and softmax activation function
-
-    model = tf.keras.Sequential([
-        layers.Conv2D(64, 5, input_shape = (30, 30, 3), name = "conv1", activation="relu"),
-        layers.MaxPool2D((2, 2), name = "pool1"),
-        layers.Conv2D(256, 3, name = "conv2", activation="relu"),
-        layers.MaxPool2D((2, 2), name = "pool2"),
-        layers.Flatten(),
-        layers.Dense(450, activation = "relu", name = "dense1"),
-        layers.Dense(NUM_CATEGORIES-1, activation = "softmax", name = "output")
-    ])
-
-    # Printing model summary and compiling with adam algorithm, categorical_crossentropy as 43 output neurons
+    model = load_model("model_1.h5")
+    model.compile(optimizer="adam", loss="categorical_crossentropy")
     model.summary()
-
-    model.compile(
-        optimizer = "adam",
-        loss = "categorical_crossentropy",
-        metrics=["accuracy"]
-    )
-
     return model
 
-# model = load_model("best_traffic_model.h5")
-# model.compile(optimizer="adam", loss="categorical_crossentropy")
 
 model = get_model()
 
 labels = ["zero","one","two","three","four","five","six","seven","eight","nine","ten","eleven","twelve","thrteen","fourteen","fifteen","sixteen","seventeen","eightteen","nineteen"]
 
 def predict(img):
-    img = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT), interpolation=cv2.INTER_AREA)
+    img = cv2.resize(img, (64, 64), interpolation=cv2.INTER_AREA)
     img = img.astype("float32")
-    img = img.reshape(1, IMG_HEIGHT, IMG_WIDTH, 3)    
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = img.reshape(1, 64, 64, 1)
+    print(img.shape)
     out = model.predict(img)
+    print(out.shape)
     cls = out.argmax()
     return cls