Test of new rpi script

python/pokedex_rpi.py

 import cv2
 import numpy as np
 import json
-from hailo_platform.pyhailort import HailoRT
 import argparse
+from hailo_platform.pyhailort import HailoRT
 
-# --- WHAT ? ---
-parser = argparse.ArgumentParser(description="WHAT ?!")
+# --- 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 ---
 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")
 
-# Load class names
+# --- Load class names ---
 with open(json_path, "r") as f:
     class_names = json.load(f)
 
+# --- Setup device and network ---
 device = HailoRT.Device()
 hef = HailoRT.Hef(hef_path)
-configured_network_group = device.create_hef_group(hef)
-input_vstream_info = configured_network_group.get_input_vstream_infos()[0]
-output_vstream_info = configured_network_group.get_output_vstream_infos()[0]
+network_group = device.create_hef_group(hef)
+
+input_info = network_group.get_input_vstream_infos()[0]
+output_info = network_group.get_output_vstream_infos()[0]
 
-# --- Open webcam ---
+# --- Open webcam and capture image ---
 cap = cv2.VideoCapture(0)
 if not cap.isOpened():
     print("-- Unable to open webcam")
-    exit()
+    exit(1)
 
-print("-- Taking picture...")
+print("-- Capturing image...")
 ret, frame = cap.read()
 cap.release()
 
 if not ret:
     print("-- Failed to capture image")
-    exit()
+    exit(1)
 
 # --- Preprocess image ---
-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)
+image = cv2.resize(frame, input_shape)
+image = image.astype(np.float32)
+
+# Standard Hailo normalization (if your model expects ImageNet style)
+image -= [123.68, 116.779, 103.939]
+# image /= 255.0  # only if your model was trained with [0,1] normalization
+
+# NHWC → NCHW
+image = np.transpose(image, (2, 0, 1))   # (H, W, C) → (C, H, W)
+image = np.expand_dims(image, axis=0)    # Add batch dimension → (1, C, H, W)
 
 # --- Inference ---
-with HailoRT.VirtualStreams(input_vstream_info, output_vstream_info, configured_network_group) as (input_vstreams, output_vstreams):
+with HailoRT.VirtualStreams(input_info, output_info, network_group) as (input_vstreams, output_vstreams):
     input_vstreams[0].send(image)
     output_data = output_vstreams[0].recv()
 
 # --- Postprocess ---
 predicted_idx = int(np.argmax(output_data))
 predicted_name = class_names[predicted_idx]
-print(f"-- Predicted Pokémon: {predicted_name}")
+print(f"🎯 Predicted Pokémon: {predicted_name}")