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Refactor tests to extract duplicate logic into helper method

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Co-authored-by: Teagan42 <2989925+Teagan42@users.noreply.github.com>
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copilot-swe-agent[bot] 2026-01-31 11:34:29 +00:00 committed by Teagan glenn
parent a2ad45e357
commit 12de5011fe
1 changed files with 53 additions and 48 deletions
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101
frigate/test/test_custom_classification.py
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@ -6,6 +6,38 @@ from unittest.mock import MagicMock
class TestCustomObjectClassificationZones(unittest.TestCase):
"""Test that zone information is correctly added to custom classification MQTT messages"""
def _build_classification_data(
self, obj_data, classification_type="sub_label", label="person_walking"
):
"""Helper method to build classification data with conditional zones.
Args:
obj_data: Object data dictionary containing id, camera, and optionally current_zones
classification_type: Either "sub_label" or "attribute"
label: The classification label
Returns:
Dictionary with classification data, including zones if applicable
"""
classification_data = {
"type": "classification",
"id": obj_data["id"],
"camera": obj_data["camera"],
"timestamp": 1234567890.0,
"model": "test_classifier",
"score": 0.89,
}
if classification_type == "sub_label":
classification_data["sub_label"] = label
else:
classification_data["attribute"] = label
if obj_data.get("current_zones"):
classification_data["zones"] = obj_data["current_zones"]
return classification_data
def test_sub_label_message_includes_zones_when_present(self):
"""Test that zones are included in sub_label classification messages when object is in zones"""
# Create a simple mock requestor
@ -18,18 +50,10 @@ class TestCustomObjectClassificationZones(unittest.TestCase):
"current_zones": ["driveway", "front_yard"],
}
# Simulate what the processor does when publishing sub_label classification
classification_data = {
"type": "classification",
"id": obj_data["id"],
"camera": obj_data["camera"],
"timestamp": 1234567890.0,
"model": "test_classifier",
"sub_label": "person_walking",
"score": 0.89,
}
if obj_data.get("current_zones"):
classification_data["zones"] = obj_data["current_zones"]
# Build classification data using helper
classification_data = self._build_classification_data(
obj_data, "sub_label", "person_walking"
)
requestor.send_data("tracked_object_update", json.dumps(classification_data))
@ -65,18 +89,11 @@ class TestCustomObjectClassificationZones(unittest.TestCase):
"current_zones": [],
}
# Simulate what the processor does when publishing sub_label classification
classification_data = {
"type": "classification",
"id": obj_data["id"],
"camera": obj_data["camera"],
"timestamp": 1234567890.0,
"model": "test_classifier",
"sub_label": "person_running",
"score": 0.87,
}
if obj_data.get("current_zones"):
classification_data["zones"] = obj_data["current_zones"]
# Build classification data using helper
classification_data = self._build_classification_data(
obj_data, "sub_label", "person_running"
)
classification_data["score"] = 0.87
requestor.send_data("tracked_object_update", json.dumps(classification_data))
@ -99,18 +116,12 @@ class TestCustomObjectClassificationZones(unittest.TestCase):
"current_zones": ["site_entrance"],
}
# Simulate what the processor does when publishing attribute classification
classification_data = {
"type": "classification",
"id": obj_data["id"],
"camera": obj_data["camera"],
"timestamp": 1234567890.0,
"model": "helmet_detector",
"attribute": "wearing_helmet",
"score": 0.92,
}
if obj_data.get("current_zones"):
classification_data["zones"] = obj_data["current_zones"]
# Build classification data using helper
classification_data = self._build_classification_data(
obj_data, "attribute", "wearing_helmet"
)
classification_data["score"] = 0.92
classification_data["model"] = "helmet_detector"
requestor.send_data("tracked_object_update", json.dumps(classification_data))
@ -138,18 +149,12 @@ class TestCustomObjectClassificationZones(unittest.TestCase):
"camera": "parking_lot",
}
# Simulate what the processor does when publishing attribute classification
classification_data = {
"type": "classification",
"id": obj_data["id"],
"camera": obj_data["camera"],
"timestamp": 1234567890.0,
"model": "vehicle_type",
"attribute": "sedan",
"score": 0.95,
}
if obj_data.get("current_zones"):
classification_data["zones"] = obj_data["current_zones"]
# Build classification data using helper
classification_data = self._build_classification_data(
obj_data, "attribute", "sedan"
)
classification_data["score"] = 0.95
classification_data["model"] = "vehicle_type"
requestor.send_data("tracked_object_update", json.dumps(classification_data))