84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
"""Unit tests for validating stack classification logic in AutofocusThing.
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This module tests whether the `check_stack_result` method correctly classifies
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z-stack sharpness profiles into "success", "continue", or "restart" categories
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based on predefined test cases.
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Sharpness profiles are loaded from a JSON file and converted into mock capture
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objects to simulate real camera captures.
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"""
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import json
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import os
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from labthings_fastapi.testing import create_thing_without_server
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from openflexure_microscope_server.things.autofocus import AutofocusThing
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THIS_DIR = os.path.dirname(__file__)
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DATA_PATH = os.path.join(THIS_DIR, "data", "sharpness_test_cases.json")
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class MockCapture:
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"""Simple mock object representing a captured image.
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This class mimics the minimal interface required by
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`check_stack_result` by adding the `sharpness` and `buffer_id`
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attributes.
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:param sharpness: The sharpness value associated with the image.
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:param buffer_id: A unique identifier for the image buffer.
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"""
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def __init__(self, sharpness, buffer_id):
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"""Give each capture a sharpness and buffer_id."""
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self.sharpness = sharpness
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self.buffer_id = buffer_id
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def make_captures(sharpness_list):
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"""Convert a list of sharpness values into mock capture objects.
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:param sharpness_list: A list of numeric sharpness values.
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:returns: A list of MockCapture instances with sequential buffer IDs.
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"""
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return [MockCapture(s, i) for i, s in enumerate(sharpness_list)]
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def test_stack_labelling():
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"""Test stack classification accuracy against labelled sharpness cases.
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This test loads predefined sharpness profiles and their expected labels
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from a JSON file, converts them into mock capture objects, and evaluates
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the classification returned by `check_stack_result`.
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The test asserts that at least 90% of cases are correctly classified.
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Expected labels may be a single value or a list of acceptable values.
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"""
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autofocus_thing = create_thing_without_server(AutofocusThing, mock_all_slots=True)
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with open(DATA_PATH) as f:
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data = json.load(f)
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success = 0
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total = len(data)
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for _i, case in enumerate(data):
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sharpnesses = case["sharpnesses"]
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expected = case["label"]
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# Allow multiple acceptable labels
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if not isinstance(expected, list):
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expected = [expected]
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# Convert to capture objects
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captures = make_captures(sharpnesses)
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# Call the method under test
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result, _ = autofocus_thing.check_stack_result(
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captures, check_turning_points=False
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)
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if result in expected:
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success += 1
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assert success > 0.8 * total
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