Added Dynamic size

python/convert_onnx.py

@@ -11,15 +11,19 @@ args = parser.parse_args()
 if args.model == "1":
     h5_path = "../models/ResNet50/pokedex_ResNet50.h5"
     onnx_path = "../models/ResNet50/pokedex_ResNet50.onnx"
+    size=(224, 224, 3)
+    size_2=(1, 224, 224, 3)
 elif args.model == "2":
     h5_path = "../models/Xception/pokedex_Xception.h5"
     onnx_path = "../models/ResNet50/pokedex_Xception.onnx"
+    size=(256, 256, 3)
+    size_2=(1, 256, 256, 3)
 
 # --- Load Sequential model ---
 seq_model = tf.keras.models.load_model(h5_path, compile=False)
 
 # --- Create input layer with same shape ---
-inputs = tf.keras.Input(shape=(224, 224, 3), name="input")
+inputs = tf.keras.Input(shape=size, name="input")
 
 # --- Call the Sequential model as a function ---
 outputs = seq_model(inputs)
@@ -28,7 +32,7 @@ outputs = seq_model(inputs)
 model = tf.keras.Model(inputs=inputs, outputs=outputs)
 
 # --- Convert to ONNX ---
-spec = (tf.TensorSpec((1, 224, 224, 3), tf.float32, name="input"),)
+spec = (tf.TensorSpec(size_2, tf.float32, name="input"),)
 onnx_model, _ = tf2onnx.convert.from_keras(
     model,
     input_signature=spec,

python/pokedex_rpi.py

@@ -13,9 +13,11 @@ args = parser.parse_args()
 if args.model == "1":
     hef_path = "../models/ResNet50/pokedex_ResNet50.hef"
     json_path = "../models/ResNet50/class_names.json"
+    size=(224,224)
 elif args.model == "2":
     hef_path = "../models/Xception/pokedex_Xception.hef"
     json_path = "../models/Xception/class_names.json"
+    size=(256,256)
 
 # Load class names
 with open(json_path, "r") as f:
@@ -42,7 +44,7 @@ if not ret:
     exit()
 
 # --- Preprocess image ---
-image = cv2.resize(frame, (224, 224))
+image = cv2.resize(frame, size)
 image = image.astype(np.float32) / 255.0  # Normalize to [0, 1]
 image = np.expand_dims(image, axis=0)     # Add batch dimension
 image = np.transpose(image, (0, 3, 1, 2))  # NHWC ? NCHW if required (check your model)

python/pokedex_test.py

@@ -16,9 +16,11 @@ args = parser.parse_args()
 if args.model == "1":
     h5_path = "../models/ResNet50/pokedex_ResNet50.h5"
     json_path = "../models/ResNet50/class_names.json"
+    size = (224,224)
 elif args.model == "2":
     h5_path = "../models/Xception/pokedex_Xception.h5"
     json_path = "../models/Xception/class_names.json"
+    size = (256,256)
 
 # --- Load class names from JSON ---
 with open(json_path, "r") as f:
@@ -45,7 +47,7 @@ for i in range(4):
     img_path = os.path.join(class_folder, random_image)
 
     # --- Load & Preprocess Image ---
-    img = keras.utils.load_img(img_path, target_size=(224, 224))  # resize to match model input
+    img = keras.utils.load_img(img_path, target_size=size)  # resize to match model input
     img_array = keras.utils.img_to_array(img)
     img_array = img_array / 255.0  # normalize if your model expects it
     img_array = tf.expand_dims(img_array, 0)