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LightDarkModule.py
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LightDarkModule.py
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import logging
import os
import numpy as np
from BaseImage import printMaskHelper
from skimage import io, color, img_as_ubyte
from distutils.util import strtobool
from skimage.filters import threshold_otsu, rank
from skimage.morphology import disk
import matplotlib.pyplot as plt
def getIntensityThresholdOtsu(s, params):
logging.info(f"{s['filename']} - \tLightDarkModule.getIntensityThresholdOtsu")
name = "otsu"
local = strtobool(params.get("local", "False"))
radius = float(params.get("radius", 15))
selem = disk(radius)
img = s.getImgThumb(s["image_work_size"])
img = color.rgb2gray(img)
if local:
thresh = rank.otsu(img, selem)
name += "local"
else:
thresh = threshold_otsu(img)
map = img < thresh
s["img_mask_" + name] = map > 0
if strtobool(params.get("invert", "False")):
s["img_mask_" + name] = ~s["img_mask_" + name]
io.imsave(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", img_as_ubyte(s["img_mask_" + name]))
prev_mask = s["img_mask_use"]
s["img_mask_use"] = s["img_mask_use"] & s["img_mask_" + name]
s.addToPrintList(name,
printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
if len(s["img_mask_use"].nonzero()[0]) == 0: # add warning in case the final tissue is empty
logging.warning(f"{s['filename']} - After LightDarkModule.getIntensityThresholdOtsu:{name} NO tissue remains "
f"detectable! Downstream modules likely to be incorrect/fail")
s["warnings"].append(f"After LightDarkModule.getIntensityThresholdOtsu:{name} NO tissue remains detectable! "
f"Downstream modules likely to be incorrect/fail")
return
def getIntensityThresholdPercent(s, params):
name = params.get("name", "classTask")
logging.info(f"{s['filename']} - \tLightDarkModule.getIntensityThresholdPercent:\t {name}")
lower_thresh = float(params.get("lower_threshold", -float("inf")))
upper_thresh = float(params.get("upper_threshold", float("inf")))
lower_var = float(params.get("lower_variance", -float("inf")))
upper_var = float(params.get("upper_variance", float("inf")))
img = s.getImgThumb(s["image_work_size"])
img_var = img.std(axis=2)
map_var = np.bitwise_and(img_var > lower_var, img_var < upper_var)
img = color.rgb2gray(img)
map = np.bitwise_and(img > lower_thresh, img < upper_thresh)
map = np.bitwise_and(map, map_var)
s["img_mask_" + name] = map > 0
if strtobool(params.get("invert", "False")):
s["img_mask_" + name] = ~s["img_mask_" + name]
prev_mask = s["img_mask_use"]
s["img_mask_use"] = s["img_mask_use"] & s["img_mask_" + name]
# io.imsave(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", img_as_ubyte(prev_mask & ~s["img_mask_" + name]))
s.addToPrintList(name,
printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
if len(s["img_mask_use"].nonzero()[0]) == 0: # add warning in case the final tissue is empty
logging.warning(f"{s['filename']} - After LightDarkModule.getIntensityThresholdPercent:{name} NO tissue "
f"remains detectable! Downstream modules likely to be incorrect/fail")
s["warnings"].append(f"After LightDarkModule.getIntensityThresholdPercent:{name} NO tissue remains "
f"detectable! Downstream modules likely to be incorrect/fail")
return