Create inference.py

inference.py

+
+import os
+import argparse
+import numpy as np
+import tensorflow as tf
+from keras.models import load_model
+from unet_util import dice_coef, dice_coef_loss, UNET_224
+
+def load_and_preprocess_img(mapPath, jsonPath, featureType):
+    # TODO: Implement the function to load and preprocess the map based on the provided paths and feature type.
+    # This is a placeholder and may need to be adjusted based on the actual data and preprocessing steps.
+    map_img = tf.io.read_file(mapPath)
+    map_img = tf.cast(tf.io.decode_png(map_img), dtype=tf.float32) / 255.0
+    map_img = tf.image.resize(map_img, [256, 256])
+    
+    # Additional preprocessing based on jsonPath and featureType can be added here.
+    
+    return map_img
+
+def perform_inference(map_img, model):
+    prediction = model.predict(map_img)
+    return prediction
+
+def save_results(prediction, outputPath):
+    # TODO: Implement the function to save the prediction results to the specified output path.
+    # This is a placeholder and may need to be adjusted based on the desired output format.
+    with open(outputPath, 'w') as f:
+        f.write(str(prediction))
+
+def main(args):
+    # Load and preprocess the map
+    map_img = load_and_preprocess_img(args.mapPath, args.jsonPath, args.featureType)
+    
+    # Load the trained model
+    model = load_model(args.modelPath, custom_objects={'dice_coef_loss': dice_coef_loss, 'dice_coef': dice_coef})
+    
+    # Perform inference
+    prediction = perform_inference(map_img, model)
+    
+    # Save the results
+    save_results(prediction, args.outputPath)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Perform inference on a given map.")
+    parser.add_argument("--mapPath", required=True, help="Path to the map image.")
+    parser.add_argument("--jsonPath", required=True, help="Path to the JSON file.")
+    parser.add_argument("--featureType", choices=["Polygon"], default="Polygon", help="Type of feature to detect. Currently supports only 'Polygon'.")
+    parser.add_argument("--outputPath", required=True, help="Path to save the inference results.")
+    parser.add_argument("--modelPath", default="./models_Unet-attentionUnet/Unet-attentionUnet.h5", help="Path to the trained model. Default is './models_Unet-attentionUnet/Unet-attentionUnet.h5'.")
+    
+    args = parser.parse_args()
+    main(args)