lipase/tests/test0001.py

"""This script is supposed to be launched from fiji's jython interpreter
"""
# # note: fiji's jython doesn't support encoding keyword

# https://imagej.net/Scripting_Headless
#@ String raw_images_root_path
#@ String tests_output_data_path


import unittest  # unittest2 doesn't exist in fiji
import sys
from lipase.imageengine import IImageEngine, PixelType, Aabb, NullDebugger, FileBasedDebugger
from lipase.imagej.ijimageengine import IJImageEngine, IJImage
from lipase.telemos import WhiteEstimator, correct_non_uniform_lighting
from lipase.maxima_finder import MaximaFinder
from lipase.template_matcher import TemplateMatcher
from lipase.traps_detector import TrapsDetector
from lipase.catalog import ImageCatalog, Sequence
from lipase.lipase import Lipase, ImageLogger
from lipase.lipase import GlobulesAreaEstimator, EmptyFrameBackgroundEstimator
from lipase.circsymdetector import CircularSymmetryDetector
from lipase.imagej.hdf5serializer import save_hdf5_file


class TestLipase(unittest.TestCase):

    RAW_IMAGES_ROOT_PATH = raw_images_root_path  # eg '/Users/graffy/ownCloud/ipr/lipase/raw-images' pylint: disable=undefined-variable
    TESTS_OUTPUT_DATA_PATH = tests_output_data_path  # eg '/tmp/lipase/tests-output-data' pylint: disable=undefined-variable

    # we need to know if the test succeeded or not https://stackoverflow.com/questions/4414234/getting-pythons-unittest-results-in-a-teardown-method
    # CURRENT_RESULT = None  # holds last result object passed to run method

    def setUp(self):
        print("initializing TestLipase instance")
        IImageEngine.set_instance(IJImageEngine(debugger=FileBasedDebugger('%s/debug-images' % self.TESTS_OUTPUT_DATA_PATH)))
        self.catalog = ImageCatalog(self.RAW_IMAGES_ROOT_PATH)

    def tearDown(self):
        print("uninitializing TestLipase instance")
        self.catalog = None

    def test_estimate_white(self):
        sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos2']
        white_estimator = WhiteEstimator(open_size=75, close_size=75, average_size=75)
        white_estimate = white_estimator.estimate_white([sequence], ['DM300_327-353_fluo'])
        # find_white_reference_image(white_estimate, sequence.get_white())
        print(white_estimate)
        IImageEngine.get_instance().debugger.on_image(white_estimate, 'white_estimate')
        # assert False, "hellooooo"
        print('end of test_estimate_white')

    def test_uniform_lighting_correction(self):
        non_uniform_sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0']
        uniform_sequence = correct_non_uniform_lighting(non_uniform_sequence, 'DM300_nofilter_vis', white_estimator=WhiteEstimator(open_size=75, close_size=75, average_size=75))  # pylint: disable=unused-variable

    def test_template_matcher(self):
        sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0']
        stack = sequence.as_hyperstack(['DM300_nofilter_vis'], selected_frames=[0])
        first_image = stack.get_image(frame_index=0)
        x_min = 423
        x_max = 553
        y_min = 419
        y_max = 533
        template_trap_aabb = Aabb(x_min, y_min, x_max, y_max)
        template_trap_image = first_image.get_subimage(template_trap_aabb)
        for image in [first_image, template_trap_image]:
            print(image.get_pixel_type(), image.get_width(), image.get_height())
        # the typical value of peaks is -2.e10 and the value between peaks is below -8.0e10
        threshold = -3.0e10
        tolerance = 1.0e10
        maxima_finder = MaximaFinder(threshold, tolerance)
        template_matcher = TemplateMatcher(maxima_finder)
        matches = template_matcher.match_template(first_image, template_trap_image)
        num_traps = len(matches)
        print("number of traps found : %d" % num_traps)
        num_expected_traps = 13  # 13 traps are completely visible in the first image
        self.assertAlmostEqual(len(matches), num_expected_traps, delta=1.0)


    def test_traps_detector(self):
        # the typical value of peaks is -500 and the value between peaks is below -2500
        threshold = -1500.0
        tolerance = 1500
        maxima_finder = MaximaFinder(threshold, tolerance)
        template_matcher = TemplateMatcher(maxima_finder)
        traps_detector = TrapsDetector(template_matcher)
        sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0']
        x_min = 423
        x_max = 553
        y_min = 419
        y_max = 533
        trap_aabb = Aabb(x_min, y_min, x_max, y_max)
        traps_mask = traps_detector.compute_traps_mask(sequence, 'DM300_nofilter_vis', trap_aabb)
        measured_mean_value = traps_mask.get_mean_value()
        expected_traps_coverage = 0.07909
        traps_pixel_value = 255.0
        expected_mean_value = expected_traps_coverage * traps_pixel_value
        print("expected_mean_value: %f" % expected_mean_value)
        print("measured_mean_value: %f" % measured_mean_value)
        self.assertAlmostEqual(measured_mean_value, expected_mean_value, delta=0.01)

    def test_visible_traps_sequence_processing(self):
        traps_sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0']
        visible_traps_sequence = traps_sequence.as_hyperstack(['DM300_nofilter_vis'])
        background_estimator = EmptyFrameBackgroundEstimator(empty_frame_index=39)
        processor = GlobulesAreaEstimator(background_estimator=background_estimator, particle_threshold=2000.0)
        results = processor.detect_particles(visible_traps_sequence)
        save_hdf5_file('%s/results.h5' % self.TESTS_OUTPUT_DATA_PATH, results)
        # results file could be checked with "h5dump --xml ./lipase.git/results.h5"
        first_frame_measured_ratio = results['globules_area_ratio'][(0,)]
        first_frame_expected_ratio = 0.008
        self.assertAlmostEqual(first_frame_measured_ratio, first_frame_expected_ratio, delta=0.01)

    def test_circle_detector(self):
        traps_sequence = self.catalog.sequences['res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0']
        visible_traps_sequence = traps_sequence.as_hyperstack(['DM300_nofilter_vis'])
        src_image = visible_traps_sequence.get_image(frame_index=0)
        # ie = IImageEngine.get_instance()
        detector = CircularSymmetryDetector(max_radius=32.0, num_angular_sectors=4, num_radial_sectors=8)
        radial_profiles, angular_variance_avg_image = detector.compute_radial_profiles(src_image)  #  pylint: disable=unused-variable

    # def test_lipase_process(self):
    #     lipase = Lipase(self.catalog, debugger=NullDebugger())



def run_script():

    # unittest.main() # this would result in : ImportError: No module named __main__
    # solution from : https://discourse.mcneel.com/t/using-unittest-in-rhino-python-not-possible/15364
    suite = unittest.TestLoader().loadTestsFromTestCase(TestLipase)
    stream = sys.stdout  # by default it's sys.stderr, which doesn't appear in imagej's output
    test_result = unittest.TextTestRunner(stream=stream, verbosity=2).run(suite)
    print('test_result : %s' % test_result)
    # store summary of the result in a file so that the caller of imagej can detect that this python script failed (imagej seems to always return error code 0, regardless the error returned by the python script it executes : even sys.exit(1) doesn't change this)
    with open('/tmp/test_result.txt', 'w') as f:
        f.write('%d' % {True: 0, False: 1}[test_result.wasSuccessful()])
    print('end of run_script')

# note : when launched from fiji, __name__ doesn't have the value "__main__", as when launched from python
run_script()