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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 = "your_image_path" # replace with your image
detect_anomaly(test_image_path)
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