Delete classes/slide_processor.py

classes/slide_processor.py

@@ -1,175 +0,0 @@
-import numpy as np
-from concurrent.futures import ThreadPoolExecutor
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-=======
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-import os
-import openslide
-from PIL import Image
-from openslide import OpenSlideError
-from openslide.deepzoom import DeepZoomGenerator
-import math
-import random
-from scipy.ndimage.morphology import binary_fill_holes
-from skimage.color import rgb2gray
-from skimage.feature import canny
-from skimage.morphology import binary_closing, binary_dilation, disk
-from concurrent.futures import ProcessPoolExecutor
-
-class SlideProcessor:
-    def __init__(self,img_processor=None, tile_size=1024, overlap=0, tissue_threshold=0.65, max_workers=30):
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-#import pandas as pd
-import matplotlib.pyplot as plt
-import os
-import openslide
-from openslide import OpenSlideError
-from openslide.deepzoom import DeepZoomGenerator
-from concurrent.futures import ProcessPoolExecutor
-import math
-import tqdm
-
-class SlideProcessor:
-    def __init__(self, tile_size=1024, overlap=0, tissue_threshold=0.65, max_workers=60,CINFO=None):
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->>>>>>> Initial commit for reconnecting to Hugging Face
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-        self.tile_size = tile_size
-        self.overlap = overlap
-        self.tissue_threshold = tissue_threshold
-        self.max_workers = max_workers
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-=======
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-        self.img_processor = img_processor
-
-    def optical_density(self, tile):
-        tile = tile.astype(np.float64)
-        od = -np.log((tile+1)/240)
-        return od
-
-    def keep_tile(self, tile, tissue_threshold=None):
-        if tissue_threshold is None:
-            tissue_threshold = self.tissue_threshold
-            
-        if tile.shape[0:2] == (self.tile_size, self.tile_size):
-            tile_orig = tile
-            tile = rgb2gray(tile)
-            tile = 1 - tile
-            tile = canny(tile)
-            tile = binary_closing(tile, disk(10))
-            tile = binary_dilation(tile, disk(10))
-            tile = binary_fill_holes(tile)
-            percentage = tile.mean()
-
-            check1 = percentage >= tissue_threshold
-
-            tile = self.optical_density(tile_orig)
-            beta = 0.15
-            tile = np.min(tile, axis=2) >= beta
-            tile = binary_closing(tile, disk(2))
-            tile = binary_dilation(tile, disk(2))
-            tile = binary_fill_holes(tile)
-            percentage = tile.mean()
-
-            check2 = percentage >= tissue_threshold
-
-            return check1 and check2
-        else:
-            return False
-        
-    def filter_tiles(self, tile_indices, generator):
-        def process_tile(tile_index):
-            tile_size, overlap, zoom_level, col, row = tile_index
-            tile = np.asarray(generator.get_tile(zoom_level, (col, row)))
-            if self.keep_tile(tile, self.tissue_threshold):
-                return col, row
-            return None
-
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            results = executor.map(process_tile, tile_indices)
-        
-        return [result for result in results if result is not None]
-
-
-    def get_tiles(self, filtered_tiles, tile_indices, generator):
-        tiles = {}
-        for col, row in filtered_tiles:
-            # Find the tile_index with matching col and row
-            tile_index = next((ti for ti in tile_indices if ti[3] == col and ti[4] == row), None)
-            if tile_index:
-                tile_size, overlap, zoom_level, col, row = tile_index
-                tile = np.asarray(generator.get_tile(zoom_level, (col, row)))
-                tiles[(col,row)] = tile
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-
-
-    def fetch_tile(self, tile_index, generator):
-        """ Fetch a single tile given a tile index and the tile generator. """
-        tile_size, overlap, zoom_level, col, row = tile_index
-        tile = np.asarray(generator.get_tile(zoom_level, (col, row)))
-        return (col, row), tile
-
-    def get_tiles(self, filtered_tiles, tile_indices, generator):
-        """ Retrieve tiles in parallel and return them as a dictionary with (col, row) keys. """
-        tiles = {}
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            futures = [executor.submit(self.fetch_tile, ti, generator) for ti in tile_indices if (ti[3], ti[4]) in filtered_tiles]
-            
-            for future in futures:
-                key, tile = future.result()
-                if tile.shape == (self.tile_size,self.tile_size,3):
-                    tiles[key] = tile        
-        
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->>>>>>> Initial commit for reconnecting to Hugging Face
->>>>>>> new-branch
-        return tiles
-
-    def process_one_slide(self, file_loc):
-        f2p = file_loc
-        
-        img1 = openslide.open_slide(f2p) 
-        generator = DeepZoomGenerator(img1, tile_size=self.tile_size, overlap=self.overlap, limit_bounds=True)
-        highest_zoom_level = generator.level_count - 1
-
-        try:
-            mag = int(img1.properties[openslide.PROPERTY_NAME_OBJECTIVE_POWER])
-            offset = math.floor((mag / 20) / 2)
-            level = highest_zoom_level - offset
-        except (ValueError, KeyError):
-            level = highest_zoom_level
-
-        zoom_level = level
-        cols, rows = generator.level_tiles[zoom_level]
-        tile_indices = [(self.tile_size, self.overlap, zoom_level, col, row) for col in range(cols) for row in range(rows)]
-        
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-=======
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-        filtered_tiles = self.filter_tiles(tile_indices, generator)
-
-        if file_loc.endswith('.svs'):
-            file = file_loc[-16:-4]
-            print(file)
-        
-        tiles = self.get_tiles(filtered_tiles, tile_indices, generator)
-
-        return tiles
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->>>>>>> new-branch
-        x_info = tile_indices.copy()
-
-        all_x_info = [x[-2:] for x in x_info ]
-
-        tiles = self.get_tiles(all_x_info, tile_indices, generator)
-        
-        return tiles
-<<<<<<< HEAD
-=======
->>>>>>> Initial commit for reconnecting to Hugging Face
->>>>>>> new-branch