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70 lines
2.1 KiB
70 lines
2.1 KiB
# import pickle
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# # Load the pickle file
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# with open("face_store.pkl", "rb") as f:
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# data = pickle.load(f)
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# # Print the data for inspection
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# print(data)
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# import json
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# import numpy as np
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# def convert_ndarray(obj):
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# """Recursively convert numpy.ndarray to list in a data structure."""
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# if isinstance(obj, np.ndarray):
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# return obj.tolist()
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# elif isinstance(obj, list):
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# return [convert_ndarray(item) for item in obj]
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# elif isinstance(obj, dict):
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# return {key: convert_ndarray(value) for key, value in obj.items()}
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# else:
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# return obj
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# # Assuming `data` contains numpy arrays
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# # data = {
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# # "name": "example",
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# # "embedding": np.array([1.0, 2.0, 3.0]),
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# # "nested": {
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# # "another_array": np.array([4.0, 5.0])
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# # }
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# # }
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# # Convert `data` to JSON-serializable format
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# data_serializable = convert_ndarray(data)
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# # Save as JSON
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# with open("data.json", "w") as json_file:
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# json.dump(data_serializable, json_file, indent=4)
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# print("JSON file saved successfully!")
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import pickle
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import json
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import numpy as np
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# Function to convert non-serializable objects (like numpy arrays) to a serializable format
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def convert_to_serializable(data):
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if isinstance(data, np.ndarray):
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return data.tolist() # Convert ndarray to list
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elif isinstance(data, dict): # If data is a dictionary, recursively convert values
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return {key: convert_to_serializable(value) for key, value in data.items()}
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elif isinstance(data, list): # If data is a list, recursively convert items
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return [convert_to_serializable(item) for item in data]
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else:
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return data # For other types, return as is
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# Load the pickle file
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with open("face_store.pkl", "rb") as f:
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data = pickle.load(f)
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# Convert the data to a JSON-serializable format
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data_serializable = convert_to_serializable(data)
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# Save to a JSON file
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with open("face_store.json", "w") as json_file:
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json.dump(data_serializable, json_file, indent=4)
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print("Data has been saved to face_store.json")
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