From b165ab4633326ce876952b1da07ed3f18feba5e1 Mon Sep 17 00:00:00 2001
From: "michael.divia" <michael.divia@etu.hesge.ch>
Date: Wed, 9 Apr 2025 17:58:58 +0200
Subject: [PATCH] Added Dynamic size
---
python/convert_onnx.py | 8 ++++++--
python/pokedex_rpi.py | 4 +++-
python/pokedex_test.py | 4 +++-
3 files changed, 12 insertions(+), 4 deletions(-)
diff --git a/python/convert_onnx.py b/python/convert_onnx.py
index 5cc6061..846a0b8 100644
--- a/python/convert_onnx.py
+++ b/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,
diff --git a/python/pokedex_rpi.py b/python/pokedex_rpi.py
index 3f197ca..258de2e 100644
--- a/python/pokedex_rpi.py
+++ b/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)
diff --git a/python/pokedex_test.py b/python/pokedex_test.py
index 4d3e1b8..bccf30f 100644
--- a/python/pokedex_test.py
+++ b/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)
--
GitLab