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import unittest
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from unittest.mock import patch
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from app import batch_runner
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from app.config import settings
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class BatchRunnerCliTests(unittest.TestCase):
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def test_readme_options_are_parsed(self):
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args = batch_runner.parse_args(
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[
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"--input",
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"/tmp/input",
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"--output",
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"/tmp/output",
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"--sample-interval",
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"5",
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"--no-recursive",
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"--disable-detection",
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"--disable-classification",
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"--disable-density",
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"--det-conf-thres",
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"0.15",
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"--det-iou-thres",
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"0.40",
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"--det-second-conf-thres",
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"0.45",
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"--min-box-area-ratio",
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"0.0001",
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"--max-box-area-ratio",
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"0.10",
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"--max-box-aspect-ratio",
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"4.0",
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"--detection-roi-y-min-ratio",
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"0.08",
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"--detection-roi-y-max-ratio",
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"0.82",
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"--cls-show-conf-thres",
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"0.60",
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"--large-box-area-ratio",
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"0.003",
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"--large-box-cls-show-conf-thres",
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"0.30",
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"--show-large-box-name-always",
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"false",
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"--dense-scene-count-thres",
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"30",
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"--hide-cls-name-in-dense-scene",
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"false",
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"--crop-padding-ratio",
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"0.10",
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"--max-detections",
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"75",
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"--uniform-species-color",
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"true",
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"--species-name-remap-min-confidence",
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"0.80",
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"--binary-dark-species-name",
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"东方白鹳",
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"--binary-light-species-name",
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"白琵鹭",
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"--binary-dark-pixel-threshold",
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"85",
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"--binary-dark-ratio-threshold",
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"0.12",
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"--dark-override-species-name",
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"雁鸭类",
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"--dark-override-species-class-id",
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"60",
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"--dark-override-pixel-threshold",
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"75",
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"--dark-override-ratio-threshold",
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"0.30",
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"--dark-override-min-box-area-ratio",
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"0.00001",
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"--dark-override-max-box-area-ratio",
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"0.002",
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"--dark-override-roi-y-min-ratio",
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"0.60",
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"--dark-override-roi-y-max-ratio",
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"0.95",
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"--region-override-species-name",
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"白头鹤",
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"--region-override-species-class-id",
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"28",
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"--region-override-source-species-names",
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"白鹤,白头鹤,白枕鹤",
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"--region-override-roi-y-min-ratio",
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"0.24",
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"--region-override-roi-y-max-ratio",
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"0.45",
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"--enable-crane-appearance-correction",
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"--crane-white-ratio-threshold",
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"0.33",
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"--crane-dark-ratio-threshold",
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"0.29",
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"--crane-tail-white-ratio-threshold",
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"0.14",
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"--crane-uncertain-hide-label",
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"false",
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]
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)
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self.assertEqual(args.input, "/tmp/input")
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self.assertEqual(args.output, "/tmp/output")
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self.assertEqual(args.sample_interval, 5)
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self.assertFalse(args.recursive)
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self.assertFalse(args.enable_detection)
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self.assertFalse(args.enable_classification)
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self.assertFalse(args.enable_density)
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self.assertEqual(args.det_conf_thres, 0.15)
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self.assertEqual(args.det_iou_thres, 0.40)
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self.assertEqual(args.det_second_conf_thres, 0.45)
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self.assertEqual(args.min_box_area_ratio, 0.0001)
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self.assertEqual(args.max_box_area_ratio, 0.10)
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self.assertEqual(args.max_box_aspect_ratio, 4.0)
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self.assertEqual(args.detection_roi_y_min_ratio, 0.08)
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self.assertEqual(args.detection_roi_y_max_ratio, 0.82)
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self.assertEqual(args.cls_show_conf_thres, 0.60)
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self.assertEqual(args.large_box_area_ratio, 0.003)
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self.assertEqual(args.large_box_cls_show_conf_thres, 0.30)
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self.assertFalse(args.show_large_box_name_always)
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self.assertEqual(args.dense_scene_count_thres, 30)
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self.assertFalse(args.hide_cls_name_in_dense_scene)
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self.assertEqual(args.crop_padding_ratio, 0.10)
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self.assertEqual(args.max_detections, 75)
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self.assertTrue(args.uniform_species_color)
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self.assertEqual(args.species_name_remap_min_confidence, 0.80)
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self.assertEqual(args.binary_dark_species_name, "东方白鹳")
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self.assertEqual(args.binary_light_species_name, "白琵鹭")
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self.assertEqual(args.binary_dark_pixel_threshold, 85)
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self.assertEqual(args.binary_dark_ratio_threshold, 0.12)
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self.assertEqual(args.dark_override_species_name, "雁鸭类")
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self.assertEqual(args.dark_override_species_class_id, 60)
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self.assertEqual(args.dark_override_pixel_threshold, 75)
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self.assertEqual(args.dark_override_ratio_threshold, 0.30)
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self.assertEqual(args.dark_override_min_box_area_ratio, 0.00001)
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self.assertEqual(args.dark_override_max_box_area_ratio, 0.002)
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self.assertEqual(args.dark_override_roi_y_min_ratio, 0.60)
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self.assertEqual(args.dark_override_roi_y_max_ratio, 0.95)
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self.assertEqual(args.region_override_species_name, "白头鹤")
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self.assertEqual(args.region_override_species_class_id, 28)
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self.assertEqual(
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args.region_override_source_species_names,
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"白鹤,白头鹤,白枕鹤",
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)
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self.assertEqual(args.region_override_roi_y_min_ratio, 0.24)
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self.assertEqual(args.region_override_roi_y_max_ratio, 0.45)
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self.assertTrue(args.enable_crane_appearance_correction)
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self.assertEqual(args.crane_white_ratio_threshold, 0.33)
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self.assertEqual(args.crane_dark_ratio_threshold, 0.29)
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self.assertEqual(args.crane_tail_white_ratio_threshold, 0.14)
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self.assertFalse(args.crane_uncertain_hide_label)
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def test_readme_defaults_enable_recursive_and_all_models(self):
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args = batch_runner.parse_args(["--input", "/tmp/input"])
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self.assertTrue(args.recursive)
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self.assertEqual(args.enable_detection, settings.enable_detection)
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self.assertEqual(
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args.enable_classification,
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settings.enable_classification,
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)
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self.assertEqual(args.enable_density, settings.enable_density)
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self.assertEqual(args.sample_interval, settings.video_sample_interval)
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self.assertEqual(args.max_box_area_ratio, 1.0)
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self.assertEqual(
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args.uniform_species_color,
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settings.uniform_species_color,
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)
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def test_main_applies_cli_values_before_running_batch(self):
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setting_names = (
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"enable_detection",
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"enable_classification",
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"enable_density",
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"det_conf_thres",
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"hide_cls_name_in_dense_scene",
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"max_detections",
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"uniform_species_color",
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)
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original_values = {
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name: getattr(settings, name)
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for name in setting_names
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}
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try:
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with patch.object(
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batch_runner,
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"run_batch",
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return_value={"success": True},
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) as run_batch_mock:
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result = batch_runner.main(
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[
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"--input",
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"/tmp/input",
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"--output",
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"/tmp/output",
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"--disable-detection",
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"--disable-classification",
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"--disable-density",
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"--det-conf-thres",
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"0.15",
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"--hide-cls-name-in-dense-scene",
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"false",
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"--max-detections",
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"75",
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"--uniform-species-color",
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"true",
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]
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)
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self.assertEqual(result, {"success": True})
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self.assertFalse(settings.enable_detection)
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self.assertFalse(settings.enable_classification)
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self.assertFalse(settings.enable_density)
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self.assertEqual(settings.det_conf_thres, 0.15)
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self.assertFalse(settings.hide_cls_name_in_dense_scene)
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self.assertEqual(settings.max_detections, 75)
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self.assertTrue(settings.uniform_species_color)
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run_batch_mock.assert_called_once_with(
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input_path="/tmp/input",
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output_base="/tmp/output",
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recursive=True,
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sample_interval=2,
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)
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finally:
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for name, value in original_values.items():
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object.__setattr__(settings, name, value)
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if __name__ == "__main__":
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unittest.main()
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@@ -0,0 +1,24 @@
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import unittest
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import numpy as np
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from app.inference.convnext_onnx import softmax, to_probabilities
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class ClassifierProbabilityTests(unittest.TestCase):
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def test_existing_probabilities_are_not_softmaxed_again(self):
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output = np.array([0.01, 0.09, 0.90], dtype=np.float32)
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probabilities = to_probabilities(output)
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np.testing.assert_allclose(probabilities, output, atol=1e-6)
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def test_logits_are_converted_with_softmax(self):
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logits = np.array([-1.0, 0.0, 3.0], dtype=np.float32)
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np.testing.assert_allclose(
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to_probabilities(logits),
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softmax(logits),
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atol=1e-6,
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)
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if __name__ == "__main__":
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unittest.main()
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@@ -0,0 +1,114 @@
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import unittest
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from unittest.mock import patch
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import numpy as np
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from app.config import settings
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from app.pipeline import BirdCascadePipeline
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class PipelineSwitchTests(unittest.TestCase):
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setting_names = (
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"enable_detection",
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"enable_classification",
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"enable_density",
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"uniform_species_color",
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)
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def setUp(self):
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self.original_values = {
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name: getattr(settings, name)
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for name in self.setting_names
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}
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def tearDown(self):
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for name, value in self.original_values.items():
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object.__setattr__(settings, name, value)
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def test_all_models_can_be_disabled_without_loading_onnx(self):
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object.__setattr__(settings, "enable_detection", False)
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object.__setattr__(settings, "enable_classification", False)
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object.__setattr__(settings, "enable_density", False)
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with (
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patch("app.pipeline.YOLOv5ONNXDetector") as detector_mock,
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patch("app.pipeline.ConvNeXtONNXClassifier") as classifier_mock,
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patch("app.pipeline.DensityEstimatorONNX") as density_mock,
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):
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pipeline = BirdCascadePipeline()
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self.assertIsNone(pipeline.detector)
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self.assertIsNone(pipeline.classifier)
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self.assertIsNone(pipeline.density_estimator)
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detector_mock.assert_not_called()
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classifier_mock.assert_not_called()
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density_mock.assert_not_called()
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output = pipeline.predict(
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np.zeros((32, 32, 3), dtype=np.uint8),
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)
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self.assertEqual(output["results"], [])
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self.assertIsNone(output["density"])
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def test_detection_without_classification_keeps_detection_fields(self):
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object.__setattr__(settings, "enable_detection", True)
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object.__setattr__(settings, "enable_classification", False)
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object.__setattr__(settings, "enable_density", False)
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fake_detector_result = [
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{
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"bbox_xyxy": [4.0, 4.0, 20.0, 20.0],
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"det_confidence": 0.9,
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"det_class_id": 0,
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"det_class_name": "bird",
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}
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]
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with (
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patch(
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"app.pipeline.read_classes",
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return_value=["bird"],
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),
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patch(
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"app.pipeline.os.path.exists",
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return_value=True,
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),
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patch("app.pipeline.YOLOv5ONNXDetector") as detector_class,
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patch("app.pipeline.ConvNeXtONNXClassifier") as classifier_class,
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):
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detector_class.return_value.infer.return_value = fake_detector_result
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pipeline = BirdCascadePipeline()
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output = pipeline.predict(
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np.zeros((32, 32, 3), dtype=np.uint8),
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)
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classifier_class.assert_not_called()
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self.assertEqual(len(output["results"]), 1)
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result = output["results"][0]
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self.assertEqual(result["det_class_name"], "bird")
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self.assertEqual(result["cls_class_id"], -1)
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self.assertEqual(result["cls_class_name"], "")
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self.assertEqual(result["cls_confidence"], 0.0)
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self.assertFalse(result["show_cls_name"])
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def test_uniform_species_color_uses_one_color_for_every_class(self):
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pipeline = object.__new__(BirdCascadePipeline)
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object.__setattr__(settings, "uniform_species_color", False)
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first_color = pipeline._get_color_by_class_id(1)
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second_color = pipeline._get_color_by_class_id(2)
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self.assertNotEqual(first_color, second_color)
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object.__setattr__(settings, "uniform_species_color", True)
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self.assertEqual(
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pipeline._get_color_by_class_id(1),
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pipeline._get_color_by_class_id(2),
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)
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self.assertEqual(
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pipeline._get_color_by_class_id(999),
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(0, 190, 0),
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)
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if __name__ == "__main__":
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unittest.main()
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@@ -0,0 +1,38 @@
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import unittest
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from app.video_pipeline import VideoResultSmoother
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def result(class_id, class_name, confidence, box):
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return {
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"bbox_xyxy": box,
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"cls_class_id": class_id,
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"cls_class_name": class_name,
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"cls_confidence": confidence,
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"show_cls_name": True,
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}
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class VideoResultSmootherTests(unittest.TestCase):
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def test_one_low_confidence_label_does_not_flip_track(self):
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smoother = VideoResultSmoother(hold_frames=4, alpha=0.5)
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smoother.update([result(1, "白鹤", 0.95, [0, 0, 20, 20])], 0)
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second = smoother.update(
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[result(2, "白枕鹤", 0.20, [1, 1, 21, 21])],
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1,
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)
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self.assertEqual(second[0]["cls_class_name"], "白鹤")
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self.assertNotIn("_smoother_class_votes", second[0])
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def test_matching_does_not_require_same_predicted_class(self):
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smoother = VideoResultSmoother(hold_frames=4, alpha=0.5)
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smoother.update([result(1, "白鹤", 0.9, [0, 0, 20, 20])], 0)
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second = smoother.update(
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[result(2, "白枕鹤", 0.9, [2, 2, 22, 22])],
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1,
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)
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self.assertEqual(second[0]["bbox_xyxy"], [1.0, 1.0, 21.0, 21.0])
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if __name__ == "__main__":
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unittest.main()
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