generated from dopt-python/py311
181 lines
6.4 KiB
Python
181 lines
6.4 KiB
Python
import shutil
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from unittest.mock import patch
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import numpy as np
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import pytest
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from dopt_basics import result_pattern
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import dopt_sensor_anomalies._find_paths
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import dopt_sensor_anomalies.detection as detect
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import dopt_sensor_anomalies.types as t
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from dopt_sensor_anomalies import constants, errors
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# TODO remove
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# @pytest.fixture(scope="module")
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# def img_paths() -> tuple[Path, ...]:
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# img_folder = Path(__file__).parent / "_img"
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# if not img_folder.exists():
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# raise FileNotFoundError("Img path not existing")
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# img_paths = tuple(img_folder.glob("*.bmp"))
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# if not img_paths:
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# raise ValueError("No images found")
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# return img_paths
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# @pytest.fixture(scope="module")
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# def single_img_path() -> Path:
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# img_folder = Path(__file__).parent / "_img"
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# if not img_folder.exists():
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# raise FileNotFoundError("Img path not existing")
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# img_paths = tuple(img_folder.glob("*_12.bmp"))
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# if not img_paths:
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# raise ValueError("No images found")
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# return img_paths[0]
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def test_midpoint():
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ptA = np.array([1.0, 2.0])
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ptB = np.array([3.0, 4.0])
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mid_x, mid_y = detect.midpoint(ptA, ptB)
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assert mid_x == 2.0
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assert mid_y == 3.0
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def test_check_box_redundancy_SameBox():
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box_1: t.Box = (
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(64.70450592041016, 505.09185791015625),
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(32.305301666259766, 162.31748962402344),
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2.385944128036499,
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)
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box_2: t.Box = (
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(64.5450592041016, 504.59185791015625),
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(32.305301666259766, 163.01748962402344),
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2.415944128036499,
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)
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assert detect.check_box_redundancy(box_1, box_2)
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def test_check_box_redundancy_DifferentBox():
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box_1: t.Box = (
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(64.70450592041016, 505.09185791015625),
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(32.305301666259766, 162.31748962402344),
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2.385944128036499,
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)
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box_2: t.Box = (
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(84.5450592041016, 104.59185791015625),
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(31.305301666259766, 150.01748962402344),
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2.35944128036499,
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)
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assert not detect.check_box_redundancy(box_1, box_2)
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def test_measure_length(single_img_path):
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pixels_per_metric_X: float = 0.251
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pixels_per_metric_Y: float = 0.251
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data, imgs = detect.measure_length(
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single_img_path,
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pixels_per_metric_X,
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pixels_per_metric_Y,
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)
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assert len(data) == 18
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assert isinstance(data[0], str)
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assert float(data[0].replace(",", ".")) == pytest.approx(1266.932)
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img_left = imgs["left"]
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assert 235 < img_left.shape[0] < 260
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assert 910 < img_left.shape[1] < 960
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assert img_left.shape[2] == 3
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img_right = imgs["right"]
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assert 235 < img_right.shape[0] < 260
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assert 910 < img_right.shape[1] < 960
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assert img_right.shape[2] == 3
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@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
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def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
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pixels_per_metric_X: float = 0.251
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pixels_per_metric_Y: float = 0.251
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MODEL_FOLDER = dopt_sensor_anomalies._find_paths.get_model_folder()
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assert MODEL_FOLDER.exists(), "model folder not existing"
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DETECTION_MODELS = dopt_sensor_anomalies._find_paths.get_detection_models(MODEL_FOLDER)
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assert DETECTION_MODELS["left"].exists()
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assert DETECTION_MODELS["right"].exists()
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data_csv, sensor_images = detect.measure_length(
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path_img_with_failure_TrainedModel,
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pixels_per_metric_X,
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pixels_per_metric_Y,
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)
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print(">>>>>>> Data: ", data_csv)
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# measured sizes
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assert len(data_csv) == 18
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assert sensor_images["left"] is not None
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assert sensor_images["right"] is not None
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detect.anomaly_detection(
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img_path=path_img_with_failure_TrainedModel,
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detection_models=DETECTION_MODELS,
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data_csv=data_csv,
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sensor_images=sensor_images,
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)
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# check files for existence
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root_img = path_img_with_failure_TrainedModel.parent
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file_stem = path_img_with_failure_TrainedModel.stem
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csv_file = root_img / f"{file_stem}.csv"
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heatmap_file = root_img / f"{file_stem}{constants.HEATMAP_FILENAME_SUFFIX}.png"
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assert csv_file.exists()
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assert heatmap_file.exists()
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shutil.copy(csv_file, (results_folder / csv_file.name))
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shutil.copy(heatmap_file, (results_folder / heatmap_file.name))
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@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
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def test_full_pipeline_wrapped_FailImagePath(setup_temp_dir):
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img_path = str(setup_temp_dir / "not-existing.bmp")
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MESSAGE = "The provided path seems not to exist"
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pixels_per_metric_X: float = 0.251
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pixels_per_metric_Y: float = 0.251
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ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
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assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
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assert ret.status.ExceptionType is FileNotFoundError
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assert ret.status.message == MESSAGE
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with pytest.raises(FileNotFoundError, match=MESSAGE):
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_ = ret.unwrap()
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@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
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def test_full_pipeline_wrapped_FailElectrodeCount(path_img_with_failure_ElectrodeCount):
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img_path = str(path_img_with_failure_ElectrodeCount)
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MESSAGE = "Number of counted electrodes does not match the"
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pixels_per_metric_X: float = 0.251
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pixels_per_metric_Y: float = 0.251
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ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
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assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
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assert ret.status.ExceptionType is errors.InvalidElectrodeCount
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assert MESSAGE in ret.status.message
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with pytest.raises(errors.InvalidElectrodeCount, match=MESSAGE):
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_ = ret.unwrap()
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@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
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def test_full_pipeline_wrapped_Success(results_folder, path_img_with_failure_TrainedModel):
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img_path = str(path_img_with_failure_TrainedModel)
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pixels_per_metric_X: float = 0.251
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pixels_per_metric_Y: float = 0.251
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ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
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assert ret.status == result_pattern.STATUS_HANDLER.SUCCESS
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assert ret.status.code == 0
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assert ret.status.ExceptionType is None
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# check files for existence
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root_img = path_img_with_failure_TrainedModel.parent
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file_stem = path_img_with_failure_TrainedModel.stem
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csv_file = root_img / f"{file_stem}.csv"
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heatmap_file = root_img / f"{file_stem}{constants.HEATMAP_FILENAME_SUFFIX}.png"
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assert csv_file.exists()
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assert heatmap_file.exists()
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shutil.copy(csv_file, (results_folder / csv_file.name))
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shutil.copy(heatmap_file, (results_folder / heatmap_file.name))
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