| """
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| Inference script
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| Version combining baseline structure with enhanced features
|
| """
|
|
|
| import os
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| import pickle
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| import cv2
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| import pandas as pd
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| import numpy as np
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| from utils.utils import extract_features_from_image, apply_pca_transform
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|
|
|
|
| def run_inference(TEST_IMAGE_PATH, svm_model, pca_params, SUBMISSION_CSV_SAVE_PATH):
|
| """
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| Run inference on test images
|
|
|
| Args:
|
| TEST_IMAGE_PATH: Path to test images (/tmp/data/test_images)
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| svm_model: Trained SVM model
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| pca_params: Dictionary containing PCA transformation parameters
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| SUBMISSION_CSV_SAVE_PATH: Path to save submission.csv
|
| """
|
|
|
|
|
| test_images = os.listdir(TEST_IMAGE_PATH)
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| test_images.sort()
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|
|
|
|
| image_feature_list = []
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|
|
| for test_image in test_images:
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| path_to_image = os.path.join(TEST_IMAGE_PATH, test_image)
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|
|
| image = cv2.imread(path_to_image)
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|
|
|
|
| image_features = extract_features_from_image(image)
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|
|
| image_feature_list.append(image_features)
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|
|
| features_array = np.array(image_feature_list)
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|
|
|
|
| features_reduced = apply_pca_transform(features_array, pca_params)
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|
|
|
|
| predictions = svm_model.predict(features_reduced)
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|
|
|
|
| df_predictions = pd.DataFrame({
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| "file_name": test_images,
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| "category_id": predictions
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| })
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|
|
| df_predictions.to_csv(SUBMISSION_CSV_SAVE_PATH, index=False)
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|
|
|
|
| if __name__ == "__main__":
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|
|
|
|
| current_directory = os.path.dirname(os.path.abspath(__file__))
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| TEST_IMAGE_PATH = "/tmp/data/test_images"
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|
|
| MODEL_NAME = "multiclass_model.pkl"
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| MODEL_PATH = os.path.join(current_directory, MODEL_NAME)
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|
|
| PCA_PARAMS_NAME = "pca_params.pkl"
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| PCA_PARAMS_PATH = os.path.join(current_directory, PCA_PARAMS_NAME)
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|
|
| SUBMISSION_CSV_SAVE_PATH = os.path.join(current_directory, "submission.csv")
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|
|
|
|
| with open(MODEL_PATH, 'rb') as file:
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| svm_model = pickle.load(file)
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|
|
|
|
| with open(PCA_PARAMS_PATH, 'rb') as file:
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| pca_params = pickle.load(file)
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|
|
|
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| run_inference(TEST_IMAGE_PATH, svm_model, pca_params, SUBMISSION_CSV_SAVE_PATH) |
