Upload inference.py

inference.py

@@ -0,0 +1,69 @@
+import cv2
+import numpy as np
+from openvino.runtime import Core
+import matplotlib.pyplot as plt
+import os
+
+# --- Configuration ---
+MODEL_PATH = "casting_ir/model.xml"
+THRESHOLD = 0.0004  
+IMG_SIZE = 304
+
+# --- Initialize OpenVINO ---
+ie = Core()
+model = ie.read_model(model=MODEL_PATH)
+compiled_model = ie.compile_model(model=model, device_name="CPU")
+infer_request = compiled_model.create_infer_request()
+
+# --- Preprocessing ---
+def preprocess_image(image_path):
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    if img is None:
+        raise ValueError(f"Image not found: {image_path}")
+    
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)).astype(np.float32) / 255.0
+    img = np.stack([img]*3, axis=0)  # Shape: [3, 304, 304]
+    img = np.expand_dims(img, 0)     # Shape: [1, 3, 304, 304]
+    return img
+
+# --- Reconstruction error ---
+def reconstruction_error(original, reconstructed):
+    return np.mean((original - reconstructed)**2)
+
+# --- Save reconstruction ---
+def save_reconstruction(img, reconstructed, output_path="reconstruction.png"):
+    original = img[0].transpose(1,2,0)
+    recon = reconstructed[0].transpose(1,2,0)
+    
+    plt.figure(figsize=(10,5))
+    plt.subplot(1,2,1)
+    plt.title("Original")
+    plt.imshow(original, cmap='gray')
+    plt.subplot(1,2,2)
+    plt.title("Reconstructed")
+    plt.imshow(recon, cmap='gray')
+    plt.savefig(output_path)
+    plt.close()
+    print(f"[INFO] Reconstruction saved to {output_path}")
+
+# --- Detect anomaly ---
+def detect_anomaly(image_path, threshold=THRESHOLD):
+    img = preprocess_image(image_path)
+    result = infer_request.infer(inputs={compiled_model.inputs[0]: img})
+    reconstructed = result[compiled_model.outputs[0]]
+    
+    error = reconstruction_error(img, reconstructed)
+    print(f"[INFO] Reconstruction error: {error:.6f}")
+    
+    save_reconstruction(img, reconstructed)
+    
+    if error > threshold:
+        print("Defective Casting Detected ✅")
+    else:
+        print("Casting OK ✅")
+
+# --- Run ---
+if __name__ == "__main__":
+    test_image_path = "cast_def_0_174.jpeg"  # replace with your image
+    detect_anomaly(test_image_path)
+