import unittest from unittest.mock import Mock, patch import numpy as np from pydantic import parse_obj_as import frigate.detectors as detectors import frigate.object_detection from frigate.config import DetectorConfig, ModelConfig from frigate.detectors import DetectorTypeEnum from frigate.detectors.detector_config import InputTensorEnum class TestLocalObjectDetector(unittest.TestCase): def test_localdetectorprocess_should_only_create_specified_detector_type(self): for det_type in detectors.api_types: with self.subTest(det_type=det_type): with patch.dict( "frigate.detectors.api_types", {det_type: Mock() for det_type in DetectorTypeEnum}, ): test_cfg = parse_obj_as( DetectorConfig, ({"type": det_type, "model": {}}) ) test_cfg.model.path = "/test/modelpath" test_obj = frigate.object_detection.LocalObjectDetector( detector_config=test_cfg ) assert test_obj is not None for api_key, mock_detector in detectors.api_types.items(): if test_cfg.type == api_key: mock_detector.assert_called_once_with(test_cfg) else: mock_detector.assert_not_called() @patch.dict( "frigate.detectors.api_types", {det_type: Mock() for det_type in DetectorTypeEnum}, ) def test_detect_raw_given_tensor_input_should_return_api_detect_raw_result(self): mock_cputfl = detectors.api_types[DetectorTypeEnum.cpu] TEST_DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32]) test_obj_detect = frigate.object_detection.LocalObjectDetector( detector_config=parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}}) ) mock_det_api = mock_cputfl.return_value mock_det_api.detect_raw.return_value = TEST_DETECT_RESULT test_result = test_obj_detect.detect_raw(TEST_DATA) mock_det_api.detect_raw.assert_called_once_with(tensor_input=TEST_DATA) assert test_result is mock_det_api.detect_raw.return_value @patch.dict( "frigate.detectors.api_types", {det_type: Mock() for det_type in DetectorTypeEnum}, ) def test_detect_raw_given_tensor_input_should_call_api_detect_raw_with_transposed_tensor( self, ): mock_cputfl = detectors.api_types[DetectorTypeEnum.cpu] TEST_DATA = np.zeros((1, 32, 32, 3), np.uint8) TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32]) test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}}) test_cfg.model.input_tensor = InputTensorEnum.nchw test_obj_detect = frigate.object_detection.LocalObjectDetector( detector_config=test_cfg ) mock_det_api = mock_cputfl.return_value mock_det_api.detect_raw.return_value = TEST_DETECT_RESULT test_result = test_obj_detect.detect_raw(TEST_DATA) mock_det_api.detect_raw.assert_called_once() assert ( mock_det_api.detect_raw.call_args.kwargs["tensor_input"].shape == np.zeros((1, 3, 32, 32)).shape ) assert test_result is mock_det_api.detect_raw.return_value @patch.dict( "frigate.detectors.api_types", {det_type: Mock() for det_type in DetectorTypeEnum}, ) @patch("frigate.object_detection.load_labels") def test_detect_given_tensor_input_should_return_lfiltered_detections( self, mock_load_labels ): mock_cputfl = detectors.api_types[DetectorTypeEnum.cpu] TEST_DATA = np.zeros((1, 32, 32, 3), np.uint8) TEST_DETECT_RAW = [ [2, 0.9, 5, 4, 3, 2], [1, 0.5, 8, 7, 6, 5], [0, 0.4, 2, 4, 8, 16], ] TEST_DETECT_RESULT = [ ("label-3", 0.9, (5, 4, 3, 2)), ("label-2", 0.5, (8, 7, 6, 5)), ] TEST_LABEL_FILE = "/test_labels.txt" mock_load_labels.return_value = [ "label-1", "label-2", "label-3", "label-4", "label-5", ] test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}}) test_cfg.model = ModelConfig() test_obj_detect = frigate.object_detection.LocalObjectDetector( detector_config=test_cfg, labels=TEST_LABEL_FILE, ) mock_load_labels.assert_called_once_with(TEST_LABEL_FILE) mock_det_api = mock_cputfl.return_value mock_det_api.detect_raw.return_value = TEST_DETECT_RAW test_result = test_obj_detect.detect(tensor_input=TEST_DATA, threshold=0.5) mock_det_api.detect_raw.assert_called_once() assert ( mock_det_api.detect_raw.call_args.kwargs["tensor_input"].shape == np.zeros((1, 32, 32, 3)).shape ) assert test_result == TEST_DETECT_RESULT