And now ?

python/pokedex_rpi.py

 import json
 import argparse
-import os
 import numpy as np
 from picamera2 import Picamera2
 from picamera2.devices.hailo import Hailo
 import cv2
+import os
 
 # --- Argparse ---
 parser = argparse.ArgumentParser(description="Pokémon Classifier Inference with Hailo-8")
 parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
 args = parser.parse_args()
 
-# --- Paths ---
+# --- Paths & Input Sizes ---
 if args.model == "1":
     hef_path = "../models/ResNet50/pokedex_ResNet50.hef"
     json_path = "../models/ResNet50/class_names.json"
-    size=(224, 224)
+    input_shape = (224, 224)
 elif args.model == "2":
     hef_path = "../models/Xception/pokedex_Xception.hef"
     json_path = "../models/Xception/class_names.json"
-    size = (256, 256)
+    input_shape = (256, 256)
 else:
     raise ValueError("Invalid model selection")
 
@@ -27,15 +27,13 @@ else:
 with open(json_path, "r") as f:
     class_names = json.load(f)
 
-# --- Inference ---
+# --- Hailo Inference with PiCamera2 ---
 with Hailo(hef_path) as hailo:
-    print(f"-- Hailo model input size: {size}")
+    print(f"-- Model input shape: {input_shape}")
 
     picam2 = Picamera2()
-
-    # Use *raw* capture configuration with correct size
     config = picam2.create_still_configuration(
-        main={"size": size, "format": "RGB888"},
+        main={"size": input_shape, "format": "RGB888"},
         lores=None,
         display=None
     )
@@ -46,7 +44,7 @@ with Hailo(hef_path) as hailo:
     frame = picam2.capture_array()
     print(f"-- Captured frame shape: {frame.shape}")
 
-    # Optional: display image
+    # Optional: show image
     try:
         cv2.imshow("Captured Image", frame)
         print("-- Press any key to continue...")
@@ -58,8 +56,14 @@ with Hailo(hef_path) as hailo:
         cv2.imwrite(output_path, frame)
         os.system(f"feh --fullscreen {output_path}")
 
+    # --- Preprocess (normalize + layout + batch) ---
+    image = frame.astype(np.float32)  # Convert to float32
+    image -= [123.68, 116.779, 103.939]  # Subtract ImageNet mean
+    image = np.transpose(image, (2, 0, 1))  # HWC → CHW
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
     print("-- Running inference...")
-    inference_results = hailo.run(frame)
+    inference_results = hailo.run(image)
 
     predicted_idx = int(np.argmax(inference_results))
     predicted_name = class_names[predicted_idx]