File size: 3,414 Bytes
62a0e4e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import cv2
import random
from pathlib import Path
from openslide import OpenSlide
import numpy as np
data_root = Path("/data/TCGA")
output_filename = "sample_dataset_30.txt"
patch_size = 224
max_tries = 1000
def hsv(tile_rgb):
"""
Checks if a given tile has a high concentration of tissue based on an HSV mask.
"""
tile = np.array(tile_rgb)
# Convert from RGB to HSV color space
tile = cv2.cvtColor(tile, cv2.COLOR_RGB2HSV)
min_ratio = .6
# Define the color range for tissue in HSV
lower_bound = np.array([90, 8, 103])
upper_bound = np.array([180, 255, 255])
# Create a mask for the specified color range
mask = cv2.inRange(tile, lower_bound, upper_bound)
# Calculate the ratio of tissue pixels
ratio = np.count_nonzero(mask) / mask.size
if ratio > min_ratio:
return tile_rgb
else:
return None
finish = 3072 * 1000000
svs_files = sorted(str(path) for path in data_root.rglob("*.svs"))
if not svs_files:
raise RuntimeError(f"No SVS files found under {data_root}")
# Open the output file in write mode ('w')
# This will create the file if it doesn't exist or overwrite it if it does.
with open(output_filename, 'w') as f:
print(f"Starting patch sampling. Output will be saved to {output_filename}")
print("\nFor our OpenMidnight checkpoint we ran this script until we reached 29 million patches and then manually force-quit the script. You can adjust the 'finish' variable as needed.")
for e in range(0, finish):
for path in svs_files:
image = OpenSlide(path)
# Iterate through each level of the slide
for level in range(0, image.level_count):
# Get dimensions for the current level being processed
height = image.level_dimensions[0][1]
width = image.level_dimensions[0][0]
# Ensure dimensions are valid for patch extraction
if width < patch_size or height < patch_size:
continue
tries = 0
while True:
tries += 1
# Randomly select a top-left coordinate for the patch
x = random.randint(0, width - patch_size)
y = random.randint(0, height - patch_size)
# Read the region from the slide
patch = image.read_region((x, y), level=level, size=(patch_size, patch_size))
# Check if the patch contains enough tissue
res = hsv(patch)
if res is not None:
# If the patch is valid, write its info to the file
output_line = f"{path} {x} {y} {level}\n"
f.write(output_line)
break # Move to the next level/image
if tries >= max_tries:
# If 1000 random patches at this level are invalid, move on
break
image.close()
# Shuffle the collected entries once generation finishes
with open(output_filename, 'r') as f:
lines = f.readlines()
random.shuffle(lines)
with open(output_filename, 'w') as f:
f.writelines(lines)
print("Done")
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