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* add tests * add `PATIENT_ID` as a variable * add masked `deepFCD` segmentations for tests * re-organize * add nvm version * update image tag
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env bash | ||
| set -e | ||
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| pushd "$(dirname "$0")" | ||
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| echo "Running all tests" | ||
| python3 test_deepFCD.py $@ | ||
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| popd |
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Test deepFCD.py | ||
| nptest.assert_allclose | ||
| self.assertEqual | ||
| self.assertTrue | ||
| """ | ||
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| import os | ||
| import unittest | ||
| from tempfile import mktemp | ||
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| import ants | ||
| import numpy as np | ||
| import numpy.testing as nptest | ||
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| from utils import compare_images | ||
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| params = {} | ||
| if os.environ.get("CI_TESTING") is not None: | ||
| params["CI_TESTING_PRED_DIR"] = os.environ.get("CI_TESTING_PRED_DIR") | ||
| params["CI_TESTING_PATIENT_ID"] = os.environ.get("CI_TESTING_PATIENT_ID") | ||
| else: | ||
| params["CI_TESTING_PRED_DIR"] = "/host/hamlet/local_raid/data/ravnoor/sandbox/pytests" | ||
| params["CI_TESTING_PATIENT_ID"] = "sub-00055" | ||
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| class TestModule_deepFCD(unittest.TestCase): | ||
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| def setUp(self): | ||
| # load predictions from a previous validated run (known as ground-truth labels in this context) | ||
| self.gt_deepMask = ants.image_read('segmentations/sub-00055/sub-00055_brain_mask_final.nii.gz').clone('unsigned int') | ||
| self.gt_deepFCD_mean = ants.image_read('segmentations/sub-00055/noel_deepFCD_dropoutMC/sub-00055_noel_deepFCD_dropoutMC_prob_mean_1.nii.gz').clone('float') | ||
| self.gt_deepFCD_var = ants.image_read('segmentations/sub-00055/noel_deepFCD_dropoutMC/sub-00055_noel_deepFCD_dropoutMC_prob_var_1.nii.gz').clone('float') | ||
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| pred_path = os.path.join(params["CI_TESTING_PRED_DIR"], params["CI_TESTING_PATIENT_ID"]) | ||
| # load predicitions from the most recent run | ||
| self.pred_deepMask = ants.image_read(pred_path + '/' + params["CI_TESTING_PATIENT_ID"] + '_brain_mask_final.nii.gz').clone('unsigned int') | ||
| self.pred_deepFCD_mean = ants.image_read(pred_path + '/noel_deepFCD_dropoutMC/' + params["CI_TESTING_PATIENT_ID"] + '_noel_deepFCD_dropoutMC_prob_mean_1.nii.gz').clone('float') | ||
| self.pred_deepFCD_var = ants.image_read(pred_path + '/noel_deepFCD_dropoutMC/' + params["CI_TESTING_PATIENT_ID"] + '_noel_deepFCD_dropoutMC_prob_var_1.nii.gz').clone('float') | ||
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| self.imgs = [self.pred_deepMask, self.pred_deepFCD_mean, self.pred_deepFCD_var] | ||
| self.pixeltypes = ['unsigned char', 'unsigned int', 'float'] | ||
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| def tearDown(self): | ||
| pass | ||
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| def test_image_header_info(self): | ||
| # def image_header_info(filename): | ||
| for img in self.imgs: | ||
| img.set_spacing([6.9]*img.dimension) | ||
| img.set_origin([3.6]*img.dimension) | ||
| tmpfile = mktemp(suffix='.nii.gz') | ||
| ants.image_write(img, tmpfile) | ||
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| info = ants.image_header_info(tmpfile) | ||
| self.assertEqual(info['dimensions'], img.shape) | ||
| nptest.assert_allclose(info['direction'], img.direction) | ||
| self.assertEqual(info['nComponents'], img.components) | ||
| self.assertEqual(info['nDimensions'], img.dimension) | ||
| self.assertEqual(info['origin'], img.origin) | ||
| self.assertEqual(info['pixeltype'], img.pixeltype) | ||
| self.assertEqual(info['pixelclass'], 'vector' if img.has_components else 'scalar') | ||
| self.assertEqual(info['spacing'], img.spacing) | ||
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| try: | ||
| os.remove(tmpfile) | ||
| except: | ||
| pass | ||
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| # non-existent file | ||
| with self.assertRaises(Exception): | ||
| tmpfile = mktemp(suffix='.nii.gz') | ||
| ants.image_header_info(tmpfile) | ||
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| def test_image_read_write(self): | ||
| # def image_read(filename, dimension=None, pixeltype='float'): | ||
| # def image_write(image, filename): | ||
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| # test scalar images | ||
| for img in self.imgs: | ||
| img = (img - img.min()) / (img.max() - img.min()) | ||
| img = img * 255. | ||
| img = img.clone('unsigned char') | ||
| for ptype in self.pixeltypes: | ||
| img = img.clone(ptype) | ||
| tmpfile = mktemp(suffix='.nii.gz') | ||
| ants.image_write(img, tmpfile) | ||
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| img2 = ants.image_read(tmpfile) | ||
| self.assertTrue(ants.image_physical_space_consistency(img,img2)) | ||
| self.assertEqual(img2.components, img.components) | ||
| nptest.assert_allclose(img.numpy(), img2.numpy()) | ||
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| # unsupported ptype | ||
| with self.assertRaises(Exception): | ||
| ants.image_read(tmpfile, pixeltype='not-suppoted-ptype') | ||
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| # test saving/loading as npy | ||
| for img in self.imgs: | ||
| tmpfile = mktemp(suffix='.npy') | ||
| ants.image_write(img, tmpfile) | ||
| img2 = ants.image_read(tmpfile) | ||
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| self.assertTrue(ants.image_physical_space_consistency(img,img2)) | ||
| self.assertEqual(img2.components, img.components) | ||
| nptest.assert_allclose(img.numpy(), img2.numpy()) | ||
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| # with no json header | ||
| arr = img.numpy() | ||
| tmpfile = mktemp(suffix='.npy') | ||
| np.save(tmpfile, arr) | ||
| img2 = ants.image_read(tmpfile) | ||
| nptest.assert_allclose(img.numpy(), img2.numpy()) | ||
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| # non-existant file | ||
| with self.assertRaises(Exception): | ||
| tmpfile = mktemp(suffix='.nii.gz') | ||
| ants.image_read(tmpfile) | ||
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| def test_brain_mask_segmentation(self): | ||
| metric = compare_images(self.gt_deepMask, self.pred_deepMask) | ||
| print("overlap of the brain mask with the label: {}".format(metric)) | ||
| # set relative tolerance to 0.05 | ||
| # predicted image is expected to have overlap within 0.05 | ||
| nptest.assert_allclose(1., metric, rtol=0.05, atol=0) | ||
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| def test_deepFCD_segmentation_mean(self): | ||
| metric = compare_images(self.gt_deepFCD_mean, self.pred_deepFCD_mean, metric_type='correlation') | ||
| print("correlation of the mean probability map with the the label: {}".format(metric)) | ||
| # set relative tolerance to 0.05 | ||
| # predicted image is expected to have correlation within 0.05 | ||
| nptest.assert_allclose(1., metric, rtol=0.05, atol=0) | ||
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| def test_deepFCD_segmentation_var(self): | ||
| metric = compare_images(self.gt_deepFCD_var, self.pred_deepFCD_var, metric_type='correlation') | ||
| print("correlation of the mean uncertainty map with the the label: {}".format(metric)) | ||
| # set relative tolerance to 0.05 | ||
| # predicted image is expected to have correlation within 0.05 | ||
| nptest.assert_allclose(1., metric, rtol=0.05, atol=0) | ||
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| if __name__ == '__main__': | ||
| unittest.main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| import ants | ||
| import numpy as np | ||
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| def dilate_labels(label, dilated_label_fname): | ||
| """ | ||
| Apply morphological operations to an image | ||
| ANTsR function: `morphology` | ||
| Arguments | ||
| --------- | ||
| input : ANTsImage | ||
| input image | ||
| operation : string | ||
| operation to apply | ||
| "close" Morpholgical closing | ||
| "dilate" Morpholgical dilation | ||
| "erode" Morpholgical erosion | ||
| "open" Morpholgical opening | ||
| radius : scalar | ||
| radius of structuring element | ||
| mtype : string | ||
| type of morphology | ||
| "binary" Binary operation on a single value | ||
| "grayscale" Grayscale operations | ||
| value : scalar | ||
| value to operation on (type='binary' only) | ||
| shape : string | ||
| shape of the structuring element ( type='binary' only ) | ||
| "ball" spherical structuring element | ||
| "box" box shaped structuring element | ||
| "cross" cross shaped structuring element | ||
| "annulus" annulus shaped structuring element | ||
| "polygon" polygon structuring element | ||
| radius_is_parametric : boolean | ||
| used parametric radius boolean (shape='ball' and shape='annulus' only) | ||
| thickness : scalar | ||
| thickness (shape='annulus' only) | ||
| lines : integer | ||
| number of lines in polygon (shape='polygon' only) | ||
| include_center : boolean | ||
| include center of annulus boolean (shape='annulus' only) | ||
| Returns | ||
| ------- | ||
| ANTsImage | ||
| Example | ||
| ------- | ||
| >>> import ants | ||
| >>> fi = ants.image_read( ants.get_ants_data('r16') , 2 ) | ||
| >>> mask = ants.get_mask( fi ) | ||
| >>> dilated_ball = ants.morphology( mask, operation='dilate', radius=3, mtype='binary', shape='ball') | ||
| >>> eroded_box = ants.morphology( mask, operation='erode', radius=3, mtype='binary', shape='box') | ||
| >>> opened_annulus = ants.morphology( mask, operation='open', radius=5, mtype='binary', shape='annulus', thickness=2) | ||
| """ | ||
| label = ants.image_read(label) | ||
| ants.morphology(label, operation='dilate', radius=30, mtype='binary', shape='ball').to_filename(dilated_label_fname) | ||
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| def compare_images(predicted_image, ground_truth_image, metric_type='correlation'): | ||
| """ | ||
| Measure similarity between two images. | ||
| NOTE: Similarity is actually returned as distance (i.e. dissimilarity) | ||
| per ITK/ANTs convention. E.g. using Correlation metric, the similarity | ||
| of an image with itself returns -1. | ||
| """ | ||
| # predicted_image = ants.image_read(predicted_image) | ||
| # ground_truth_image = ants.image_read(ground_truth_image) | ||
| if metric_type == 'correlation': | ||
| metric = ants.image_similarity(predicted_image, ground_truth_image, metric_type='ANTSNeighborhoodCorrelation') | ||
| metric = np.abs(metric) | ||
| else: | ||
| metric = ants.label_overlap_measures(predicted_image, ground_truth_image).TotalOrTargetOverlap[1] | ||
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| return metric |