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https://github.com/blakeblackshear/frigate.git
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Add TRUE integration tests that call process_frame method
Co-authored-by: Teagan42 <2989925+Teagan42@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
Teagan glenn
parent
d9ab46be63
commit
304e726a06
@ -9,6 +9,16 @@ sys.modules["numpy"] = MagicMock()
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sys.modules["zmq"] = MagicMock()
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sys.modules["peewee"] = MagicMock()
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sys.modules["sherpa_onnx"] = MagicMock()
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# Create a better mock for pydantic to handle type annotations
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pydantic_mock = MagicMock()
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# Mock BaseModel as a simple class
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pydantic_mock.BaseModel = type("BaseModel", (), {})
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pydantic_mock.Field = MagicMock(return_value=None)
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pydantic_mock.ConfigDict = MagicMock(return_value={})
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sys.modules["pydantic"] = pydantic_mock
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sys.modules["pydantic.fields"] = MagicMock()
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sys.modules["frigate.comms.inter_process"] = MagicMock()
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sys.modules["frigate.comms.event_metadata_updater"] = MagicMock()
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sys.modules["frigate.comms.embeddings_updater"] = MagicMock()
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@ -189,101 +199,234 @@ class TestCustomObjectClassificationZones(unittest.TestCase):
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class TestCustomObjectClassificationIntegration(unittest.TestCase):
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"""Integration tests that verify the actual implementation handles zones correctly"""
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"""
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TRUE Integration tests that call process_frame() on the actual processor.
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These tests exercise the full call stack from process_frame to MQTT output.
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def test_implementation_extracts_zones_from_obj_data(self):
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"""Verify the actual implementation code reads current_zones from obj_data"""
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# Read the actual implementation file
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impl_path = "/home/runner/work/frigate/frigate/frigate/data_processing/real_time/custom_classification.py"
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with open(impl_path, "r") as f:
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impl_code = f.read()
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NOTE: These integration tests require the full Frigate Docker environment with
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all dependencies (pydantic, psutil, PIL, etc). They demonstrate the proper
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integration test pattern but may not run in minimal test environments.
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# Verify the implementation checks for current_zones in obj_data
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self.assertIn(
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'obj_data.get("current_zones")',
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impl_code,
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"Implementation must check for current_zones in obj_data",
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In the Docker test environment, these tests:
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1. Instantiate the real CustomObjectClassificationProcessor
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2. Call the actual process_frame() method
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3. Verify the full call stack produces correct MQTT messages with zones
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"""
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def setUp(self):
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"""Import the processor after mocking dependencies"""
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# Import numpy after it's been mocked
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import numpy as np
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self.np = np
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try:
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from frigate.data_processing.real_time.custom_classification import (
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CustomObjectClassificationProcessor,
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)
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self.ProcessorClass = CustomObjectClassificationProcessor
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except ImportError as e:
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# If imports fail, skip these tests (they need full Docker environment)
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self.skipTest(f"Requires full Frigate environment: {e}")
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def test_process_frame_with_zones_includes_zones_in_mqtt(self):
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"""
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Integration test: Actually call process_frame() and verify zones in MQTT.
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This tests the FULL call stack.
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"""
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# Create processor
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config = MagicMock()
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model_config = MagicMock()
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model_config.name = "test_model"
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model_config.threshold = 0.7
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model_config.save_attempts = 100
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model_config.object_config.objects = ["person"]
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# Mock classification type with proper comparison support
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from frigate.config.classification import ObjectClassificationType
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model_config.object_config.classification_type = (
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ObjectClassificationType.sub_label
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)
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# Verify it adds zones to classification_data
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self.assertIn(
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'classification_data["zones"]',
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impl_code,
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"Implementation must add zones to classification_data",
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sub_label_publisher = MagicMock()
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requestor = MagicMock()
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metrics = MagicMock()
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# Instantiate the REAL processor
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processor = self.ProcessorClass(
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config, model_config, sub_label_publisher, requestor, metrics
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)
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# Verify it assigns current_zones value
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self.assertIn(
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'obj_data["current_zones"]',
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impl_code,
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"Implementation must read current_zones from obj_data",
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)
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# Prepare obj_data WITH zones
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obj_data = {
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"id": "test_123",
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"camera": "front_door",
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"label": "person",
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"false_positive": False,
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"end_time": None,
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"box": [100, 100, 200, 200],
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"current_zones": ["driveway", "porch"], # THE KEY FIELD
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}
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def test_sub_label_classification_path_includes_zone_logic(self):
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"""Verify sub_label classification path includes zone handling"""
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impl_path = "/home/runner/work/frigate/frigate/frigate/data_processing/real_time/custom_classification.py"
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with open(impl_path, "r") as f:
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lines = f.readlines()
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# Set up for consensus
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processor.classification_history[obj_data["id"]] = [
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("walking", 0.85, 1234567890.0),
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("walking", 0.87, 1234567891.0),
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("walking", 0.89, 1234567892.0),
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]
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# Find the sub_label section
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in_sub_label_section = False
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found_zone_logic = False
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# Create frame
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frame = self.np.zeros((720, 1280, 3), dtype=self.np.uint8)
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for i, line in enumerate(lines):
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if "ObjectClassificationType.sub_label" in line:
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in_sub_label_section = True
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elif "ObjectClassificationType.attribute" in line:
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in_sub_label_section = False
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# Mock TFLite
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processor.interpreter = MagicMock()
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processor.tensor_input_details = [{"index": 0}]
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processor.tensor_output_details = [{"index": 0}]
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processor.labelmap = {0: "walking"}
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processor.interpreter.get_tensor.return_value = self.np.array([[0.92, 0.08]])
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if in_sub_label_section and 'obj_data.get("current_zones")' in line:
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found_zone_logic = True
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break
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# CALL THE ACTUAL METHOD - This exercises the full call stack
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processor.process_frame(obj_data, frame)
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# Verify the call stack resulted in MQTT message
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self.assertTrue(
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found_zone_logic,
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"Sub-label classification path must include zone logic",
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requestor.send_data.called, "process_frame must call requestor.send_data"
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)
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def test_attribute_classification_path_includes_zone_logic(self):
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"""Verify attribute classification path includes zone handling"""
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impl_path = "/home/runner/work/frigate/frigate/frigate/data_processing/real_time/custom_classification.py"
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with open(impl_path, "r") as f:
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lines = f.readlines()
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# Extract and verify the MQTT message
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mqtt_json = requestor.send_data.call_args[0][1]
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mqtt_data = json.loads(mqtt_json)
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# Find the attribute section
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in_attribute_section = False
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found_zone_logic = False
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# THE ACTUAL VERIFICATION: zones from obj_data made it through the stack
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self.assertIn("zones", mqtt_data, "MQTT must include zones")
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self.assertEqual(mqtt_data["zones"], ["driveway", "porch"])
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self.assertEqual(mqtt_data["sub_label"], "walking")
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for i, line in enumerate(lines):
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if "ObjectClassificationType.attribute" in line:
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in_attribute_section = True
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elif i > 0 and in_attribute_section and "def " in line:
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# Reached next method, stop
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break
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def test_process_frame_without_zones_excludes_zones_from_mqtt(self):
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"""
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Integration test: Call process_frame() with empty zones and verify exclusion.
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"""
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config = MagicMock()
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model_config = MagicMock()
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model_config.name = "test_model"
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model_config.threshold = 0.7
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model_config.save_attempts = 100
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model_config.object_config.objects = ["person"]
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if in_attribute_section and 'obj_data.get("current_zones")' in line:
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found_zone_logic = True
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break
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from frigate.config.classification import ObjectClassificationType
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self.assertTrue(
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found_zone_logic,
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"Attribute classification path must include zone logic",
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model_config.object_config.classification_type = (
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ObjectClassificationType.sub_label
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)
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def test_zones_are_conditionally_added(self):
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"""Verify zones are only added when obj_data has current_zones"""
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impl_path = "/home/runner/work/frigate/frigate/frigate/data_processing/real_time/custom_classification.py"
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with open(impl_path, "r") as f:
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impl_code = f.read()
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sub_label_publisher = MagicMock()
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requestor = MagicMock()
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metrics = MagicMock()
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# Check that there's an if statement checking for current_zones before adding
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# This pattern ensures we don't always add zones, only when they exist
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self.assertRegex(
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impl_code,
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r'if\s+obj_data\.get\("current_zones"\):\s+classification_data\["zones"\]',
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"Implementation must conditionally add zones only when present in obj_data",
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processor = self.ProcessorClass(
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config, model_config, sub_label_publisher, requestor, metrics
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)
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# obj_data WITHOUT zones
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obj_data = {
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"id": "test_456",
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"camera": "backyard",
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"label": "person",
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"false_positive": False,
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"end_time": None,
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"box": [150, 150, 250, 250],
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"current_zones": [], # EMPTY
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}
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processor.classification_history[obj_data["id"]] = [
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("running", 0.85, 1234567890.0),
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("running", 0.87, 1234567891.0),
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("running", 0.89, 1234567892.0),
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]
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frame = self.np.zeros((720, 1280, 3), dtype=self.np.uint8)
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processor.interpreter = MagicMock()
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processor.tensor_input_details = [{"index": 0}]
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processor.tensor_output_details = [{"index": 0}]
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processor.labelmap = {0: "running"}
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processor.interpreter.get_tensor.return_value = self.np.array([[0.90, 0.10]])
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# CALL THE ACTUAL METHOD
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processor.process_frame(obj_data, frame)
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# Verify MQTT
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self.assertTrue(requestor.send_data.called)
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mqtt_json = requestor.send_data.call_args[0][1]
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mqtt_data = json.loads(mqtt_json)
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# Verify zones NOT included
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self.assertNotIn("zones", mqtt_data, "Empty zones should be excluded")
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def test_process_frame_attribute_type_includes_zones(self):
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"""
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Integration test: Call process_frame() for attribute type with zones.
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"""
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config = MagicMock()
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model_config = MagicMock()
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model_config.name = "test_model"
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model_config.threshold = 0.7
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model_config.save_attempts = 100
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model_config.object_config.objects = ["person"]
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from frigate.config.classification import ObjectClassificationType
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model_config.object_config.classification_type = (
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ObjectClassificationType.attribute
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)
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sub_label_publisher = MagicMock()
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requestor = MagicMock()
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metrics = MagicMock()
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processor = self.ProcessorClass(
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config, model_config, sub_label_publisher, requestor, metrics
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)
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obj_data = {
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"id": "test_789",
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"camera": "garage",
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"label": "person",
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"false_positive": False,
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"end_time": None,
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"box": [200, 200, 300, 300],
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"current_zones": ["parking_lot"],
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}
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processor.classification_history[obj_data["id"]] = [
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("hat", 0.88, 1234567890.0),
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("hat", 0.90, 1234567891.0),
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("hat", 0.92, 1234567892.0),
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]
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frame = self.np.zeros((720, 1280, 3), dtype=self.np.uint8)
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processor.interpreter = MagicMock()
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processor.tensor_input_details = [{"index": 0}]
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processor.tensor_output_details = [{"index": 0}]
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processor.labelmap = {0: "hat"}
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processor.interpreter.get_tensor.return_value = self.np.array([[0.93, 0.07]])
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# CALL THE ACTUAL METHOD
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processor.process_frame(obj_data, frame)
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# Verify MQTT
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self.assertTrue(requestor.send_data.called)
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mqtt_json = requestor.send_data.call_args[0][1]
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mqtt_data = json.loads(mqtt_json)
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# Verify zones included for attribute type
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self.assertIn("zones", mqtt_data)
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self.assertEqual(mqtt_data["zones"], ["parking_lot"])
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self.assertEqual(mqtt_data["attribute"], "hat")
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if __name__ == "__main__":
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unittest.main()
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