Input image test

.gitignore

@@ -178,4 +178,7 @@ pyrightconfig.json
 # End of https://www.toptal.com/developers/gitignore/api/python
 
 
-Combined_Dataset
\ No newline at end of file
+Combined_Dataset
+SSD_Dataset
+YOLOv8_Data
+pokedex-env
\ No newline at end of file

python/pokedex_test.py

@@ -10,6 +10,7 @@ import argparse
 # --- Parse CLI arguments ---
 parser = argparse.ArgumentParser(description="Test trained Pokémon model.")
 parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
+parser.add_argument("--image", type=str, help="Optional image path for single inference")
 args = parser.parse_args()
 
 # --- Paths ---
@@ -29,51 +30,80 @@ with open(json_path, "r") as f:
     class_names = json.load(f)
     class_names = [class_names[i] for i in range(len(class_names))]
 
-# --- Load model (NO COMPILE to avoid 'reduction=auto' bug) ---
+# --- Load model ---
 model = keras.models.load_model(h5_path, compile=False)
 
-# --- 2x2 Random Image Test ---
-plt.figure(figsize=(10, 10))
-
-for i in range(4):
-    # Pick random Pokémon class & image
-    true_class = random.choice(class_names)
-    class_folder = os.path.join(base_path, true_class)
-    img_file = random.choice([
-        f for f in os.listdir(class_folder)
-        if f.lower().endswith(('.png', '.jpg', '.jpeg'))
-    ])
-    img_path = os.path.join(class_folder, img_file)
-
-    # Load and preprocess image
-    img = keras.utils.load_img(img_path, target_size=size)
+def preprocess_image(image_path, target_size):
+    img = keras.utils.load_img(image_path, target_size=target_size)
     img_array = keras.utils.img_to_array(img)
     img_array = tf.expand_dims(img_array, 0)  # [1, height, width, 3]
+    return img_array, img
 
-    # Predict
+def predict_and_display(img_array, img, true_label=None):
     predictions = model.predict(img_array, verbose=0)
     probabilities = tf.nn.softmax(predictions[0]).numpy()
     predicted_index = np.argmax(probabilities)
     predicted_label = class_names[predicted_index]
     confidence = 100 * probabilities[predicted_index]
-    is_correct = predicted_label == true_class
 
     # Show top 5
-    print(f"\n Image: {img_file} | True: {true_class}")
-    print("-- Top 5 predictions:")
+    print("\n-- Top 5 predictions:")
     for idx in np.argsort(probabilities)[-5:][::-1]:
         print(f"{class_names[idx]:<20}: {probabilities[idx]*100:.2f}%")
 
-    # Plot
-    ax = plt.subplot(2, 2, i + 1)
+    # Display image
     plt.imshow(img)
     plt.axis("off")
-    plt.title(
-        f"Pred: {predicted_label}\n"
-        f"True: {true_class}\n"
-        f"{'YES' if is_correct else 'NO'} | {confidence:.1f}%",
-        fontsize=10
-    )
-
-plt.tight_layout()
-plt.show()
+    title = f"Pred: {predicted_label} | {confidence:.1f}%"
+    if true_label:
+        correctness = "YES" if predicted_label == true_label else "NO"
+        title += f"\nTrue: {true_label} | {correctness}"
+    plt.title(title, fontsize=12)
+    plt.show()
+
+# --- Single image prediction ---
+if args.image:
+    if not os.path.isfile(args.image):
+        print(f"Error: Image '{args.image}' not found.")
+        exit(1)
+
+    img_array, img = preprocess_image(args.image, size)
+    predict_and_display(img_array, img)
+
+# --- Else: Random 2x2 Test Grid ---
+else:
+    plt.figure(figsize=(10, 10))
+    for i in range(4):
+        true_class = random.choice(class_names)
+        class_folder = os.path.join(base_path, true_class)
+        img_file = random.choice([
+            f for f in os.listdir(class_folder)
+            if f.lower().endswith(('.png', '.jpg', '.jpeg'))
+        ])
+        img_path = os.path.join(class_folder, img_file)
+
+        img_array, img = preprocess_image(img_path, size)
+
+        predictions = model.predict(img_array, verbose=0)
+        probabilities = tf.nn.softmax(predictions[0]).numpy()
+        predicted_index = np.argmax(probabilities)
+        predicted_label = class_names[predicted_index]
+        confidence = 100 * probabilities[predicted_index]
+        is_correct = predicted_label == true_class
+
+        print(f"\nImage: {img_file} | True: {true_class}")
+        print("-- Top 5 predictions:")
+        for idx in np.argsort(probabilities)[-5:][::-1]:
+            print(f"{class_names[idx]:<20}: {probabilities[idx]*100:.2f}%")
+
+        ax = plt.subplot(2, 2, i + 1)
+        plt.imshow(img)
+        plt.axis("off")
+        plt.title(
+            f"Pred: {predicted_label}\n"
+            f"True: {true_class}\n"
+            f"{'YES' if is_correct else 'NO'} | {confidence:.1f}%",
+            fontsize=10
+        )
+    plt.tight_layout()
+    plt.show()