functioning preprocessing

Author: ryanccarelli

pathml/datasets/peso.py

@@ -13,6 +13,8 @@
 from pathml.datasets.utils import download_from_url
 from pathml.preprocessing.transforms import TissueDetectionHE
 from pathml.preprocessing.pipeline import Pipeline
+from pathml.core.slide_classes import HESlide
+from pathml.core.masks import Masks
 
 class PesoDataModule(BaseDataModule):
     def __init__(self,
@@ -39,10 +41,8 @@ def __repr__(self):
         return f"repr=(DataModule for PESO segmentation dataset)"
 
     def _download_peso(self, download_dir):
-        # throw exception if download directory exists
         # TODO: check hash
         if not os.path.isdir(download_dir):
-            x.
             print("Downloading Peso Dataset. Total file size is ~100GB, please wait.")
             files = ['peso_testset_mapping.csv','peso_testset_png.zip','peso_testset_png_padded.zip','peso_testset_regions.zip','peso_testset_wsi_1.zip','peso_testset_wsi_2.zip','peso_testset_wsi_3.zip','peso_testset_wsi_4.zip','peso_training_colordeconvolution.zip','peso_training_masks.zip','peso_training_masks_corrected.zip','peso_training_wsi_1.zip','peso_training_wsi_2.zip','peso_training_wsi_3.zip','peso_training_wsi_4.zip','peso_training_wsi_5.zip','peso_training_wsi_6.zip']
             url = f'https://zenodo.org/record/1485967/files/'
@@ -56,36 +56,39 @@ def _download_peso(self, download_dir):
                         with zipfile.ZipFile(f"{root}/{file}",'r') as zip_ref:
                             zip_ref.extractall(f"{root}/{Path(file).stem}")
                         os.remove(f"{root}/{file}")
-            trainingwsifolders = [
-                    'peso_training_wsi_1',
-                    'peso_training_wsi_2',
-                    'peso_training_wsi_3',
-                    'peso_training_wsi_4',
-                    'peso_training_wsi_5',
-                    'peso_training_wsi_6'
-            ]
-            for trainingwsifolder in trainingwsifolders:
-                for file in os.listdir(Path(download_dir)/Path(trainingwsifolder)):
-                    if file.endswith('.tif'):
-                        name = '_'.join(file.split('_')[:-1])
-                        maskpath = Path(name+'_HE_training_mask.tif') 
-                        mask = HESlide(filepath = Path(download_dir)/Path('peso_training_masks')/maskpath, name = name)
-                        shape1, shape2 = mask.slide.get_image_shape()
-                        shape = (shape2, shape1)
-                        mask = mask.slide.slide.read_region(((0,0)), 0, shape)
-                        mask, _, _, _ = mask.split()
-                        # mask.point(lambda x: x * 255) # optionally convert to 255 for img
-                        masks = {'stroma': mask}
-                        wsi = HESlide(file , masks = masks)
-                        pipeline = Pipeline([
-                            TissueDetectionHE(mask_name = 'tissue', min_region_size = 500,
-                                threshold = 30, outer_contours_only = True)
-                            ])
-                        # TODO: choose tile size
-                        slide.run(pipeline, tile_size=224)
-                        wsi.write(Path(download_dir)/Path('h5')/Path(name+'.h5'))
-                        os.remove(Path(download_dir)/Path(traininwsifolder)/Path(file))
-                        os.remove(Path(download_dir)/Path('peso_training_masks')/maskpath)
+        trainingwsifolders = [
+                'peso_training_wsi_1',
+                'peso_training_wsi_2',
+                'peso_training_wsi_3',
+                'peso_training_wsi_4',
+                'peso_training_wsi_5',
+                'peso_training_wsi_6'
+        ]
+        for trainingwsifolder in trainingwsifolders:
+            for file in os.listdir(Path(download_dir)/Path(trainingwsifolder)):
+                if file.endswith('.tif'):
+                    name = '_'.join(file.split('_')[:-1])
+                    maskpath = Path(name+'_HE_training_mask.tif') 
+                    mask = HESlide(filepath = str(Path(download_dir)/Path('peso_training_masks')/maskpath), name = name)
+                    shape1, shape2 = mask.slide.get_image_shape()
+                    shape = (shape2, shape1)
+                    print(f"image shape is {shape}")
+                    mask = mask.slide.slide.read_region(((0,0)), 0, shape)
+                    mask, _, _, _ = mask.split()
+                    mask = np.array(mask)
+                    # mask.point(lambda x: x * 255) # optionally convert to dynamic range 255 for img
+                    masks = {'stroma': mask}
+                    masks = Masks(masks)
+                    wsi = HESlide(str(Path(download_dir)/Path(trainingwsifolder)/Path(file)) , masks = masks)
+                    pipeline = Pipeline([
+                        TissueDetectionHE(mask_name = 'tissue', min_region_size = 500,
+                            threshold = 30, outer_contours_only = True)
+                        ])
+                    # TODO: choose tile size
+                    wsi.run(pipeline, tile_size=3000)
+                    wsi.write(str(Path(download_dir)/Path('h5')/Path(name+'.h5')))
+                    os.remove(str(Path(download_dir)/Path(traininwsifolder)/Path(file)))
+                    os.remove(str(Path(download_dir)/Path('peso_training_masks')/maskpath))
 
         else:
             warn(f'download_dir exists, download canceled')
@@ -184,4 +187,3 @@ def __getitem__(self, ix):
             self.wsi = read(self.data_dir / Path('h5') / Path(wsiname + '.h5'))
         tile = self.wsi.tiles[index] 
         return tile
-