using io.imsave(png_fname, img_as_ubyte(some_binary_mask)) to save compressed mask

histoqc/AnnotationModule.py

@@ -1,7 +1,7 @@
 import logging
 from typing import List, Tuple
 from histoqc.BaseImage import printMaskHelper
-from skimage import io, img_as_ubyte
+from histoqc.SaveModule import saveCompressedMask
 import os
 from pathlib import PurePosixPath, Path
 from shapely.geometry import Polygon
@@ -108,8 +108,9 @@ def saveAnnotationMask(s, params):
     height, width = s["img_mask_use"].shape
     annotationMask = annotation_to_mask(width, height, annot_collection, (off_x, off_y), resize_factor) > 0
 
-    mask_file_name = f"{s['outdir']}{os.sep}{s['filename']}_annot_{ann_format.lower()}.png"
-    io.imsave(mask_file_name, img_as_ubyte(annotationMask))
+    # saving compressed mask
+    saveCompressedMask(f"{s['outdir']}{os.sep}{s['filename']}_annot_{ann_format.lower()}.png", annotationMask)
+
 
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = prev_mask & annotationMask

histoqc/BasicModule.py

@@ -1,10 +1,8 @@
 import logging
 import os
 from histoqc.BaseImage import printMaskHelper
+from histoqc.SaveModule import saveCompressedMask
 from skimage.morphology import remove_small_objects, binary_opening, disk
-from skimage import io, color, img_as_ubyte
-
-import matplotlib.pyplot as plt
 
 
 def getBasicStats(s, params):
@@ -33,7 +31,8 @@ def finalProcessingSpur(s, params):
     mask_opened = binary_opening(mask, selem)
     mask_spur = ~mask_opened & mask
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_spur.png", img_as_ubyte(mask_spur))
+   # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_spur.png", mask_spur)
 
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = mask_opened
@@ -56,7 +55,8 @@ def finalProcessingArea(s, params):
     mask_opened = remove_small_objects(mask, min_size=area_thresh)
     mask_removed_area = ~mask_opened & mask
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_areathresh.png", img_as_ubyte(mask_removed_area))
+     # saving compressed
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_areathresh.png", mask_removed_area)
 
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = mask_opened > 0

histoqc/BlurDetectionModule.py

@@ -3,13 +3,12 @@
 
 import skimage
 from histoqc.BaseImage import printMaskHelper
-from skimage import io, img_as_ubyte, morphology, measure
+from histoqc.SaveModule import saveCompressedMask
+from skimage import morphology, measure
 from skimage.color import rgb2gray
 from skimage.filters import rank
 import numpy as np
 
-import matplotlib.pyplot as plt
-
 
 # Analysis of focus measure operators for shape-from-focus
 # Said Pertuza,, Domenec Puiga, Miguel Angel Garciab, 2012
@@ -31,7 +30,8 @@ def identifyBlurryRegions(s, params):
            1]  # for some reason resize takes a grayscale and produces a 3chan
     mask = s["img_mask_use"] & (mask > 0)
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_blurry.png", img_as_ubyte(mask))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_blurry.png", mask)
     s["img_mask_blurry"] = (mask * 255) > 0
 
     prev_mask = s["img_mask_use"]

histoqc/BubbleRegionByRegion.py

@@ -1,34 +1,17 @@
 import logging
 import os
-import sys
-
-from ast import literal_eval as make_tuple
-
-from distutils.util import strtobool
 from histoqc.BaseImage import printMaskHelper
+from histoqc.SaveModule import saveCompressedMask
 
 import scipy.signal
-
-from skimage import io, img_as_ubyte
 from skimage.filters import gabor_kernel, frangi, gaussian, median, laplace
 from skimage.color import rgb2gray
-from skimage.morphology import remove_small_objects, disk, binary_opening
-from skimage.feature import local_binary_pattern
-
-from skimage.transform import rescale, resize, downscale_local_mean
-
-from math import ceil
-
-from sklearn.naive_bayes import GaussianNB
-from sklearn.ensemble import RandomForestClassifier
-
-from skimage import io, color
+from skimage.morphology import remove_small_objects
 
+from skimage import color
 
 import numpy as np
 
-import matplotlib.pyplot as plt
-
 global_holder = {}
 
 #WARNING: Not as robust as other modules
@@ -85,7 +68,8 @@ def roiWise(s, params):
     #s.addToPrintList(name,
     #                 printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_BubbleBounds.png", img_as_ubyte(mask)) #.astype(np.uint8) * 255)
+    # saving compressd mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_BubbleBounds.png", mask) #.astype(np.uint8) * 255)
 
     return
 
@@ -108,8 +92,8 @@ def detectSmoothness(s, params):
 
         prev_mask = s["img_mask_use"]
         s["img_mask_flat"] = mask_flat
-
-        io.imsave(s["outdir"] + os.sep + s["filename"] + "_flat.png", img_as_ubyte(mask_flat & prev_mask))
+        # saving compressed mask
+        saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_flat.png", mask_flat & prev_mask)
 
         s["img_mask_use"] = s["img_mask_use"] & ~s["img_mask_flat"]
 

histoqc/ClassificationModule.py

@@ -8,7 +8,8 @@
 from distutils.util import strtobool
 
 from histoqc.BaseImage import printMaskHelper
-from skimage import io, img_as_ubyte, img_as_bool
+from histoqc.SaveModule import saveCompressedMask
+from skimage import io, img_as_bool
 from skimage.filters import gabor_kernel, frangi, gaussian, median, laplace
 from skimage.color import rgb2gray
 from skimage.morphology import remove_small_objects, disk, dilation
@@ -21,8 +22,6 @@
 
 import numpy as np
 
-import matplotlib.pyplot as plt
-
 
 def pixelWise(s, params):
     name = params.get("name", "classTask")
@@ -50,7 +49,8 @@ def pixelWise(s, params):
 
     s.addToPrintList(name, str(mask.mean()))
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", img_as_ubyte(mask))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", mask)
     s["img_mask_" + name] = (mask * 255) > 0
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = s["img_mask_use"] & ~s["img_mask_" + name]
@@ -227,7 +227,8 @@ def byExampleWithFeatures(s, params):
 
     mask = s["img_mask_use"] & (mask > 0)
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", img_as_ubyte(mask))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", mask)
     s["img_mask_" + name] = (mask * 255) > 0
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = s["img_mask_use"] & ~s["img_mask_" + name]

histoqc/DeconvolutionModule.py

@@ -9,6 +9,7 @@
 from skimage.color import gdx_from_rgb, hax_from_rgb, bro_from_rgb, bpx_from_rgb, ahx_from_rgb, \
     hpx_from_rgb  # need to load all of these in case the user selects them
 from distutils.util import strtobool
+from histoqc.SaveModule import saveCompressedMask
 
 import matplotlib.pyplot as plt
 
@@ -36,7 +37,8 @@ def separateStains(s, params):
         for c in range(3):
             s.addToPrintList(f"deconv_c{c}_std", str(-100))
             s.addToPrintList(f"deconv_c{c}_mean", str(-100))
-            io.imsave(s["outdir"] + os.sep + s["filename"] + f"_deconv_c{c}.png", img_as_ubyte(np.zeros(mask.shape)))
+            # saving compressed mask
+            saveCompressedMask(s["outdir"] + os.sep + s["filename"] + f"_deconv_c{c}.png", np.zeros(mask.shape))
 
         logging.warning(f"{s['filename']} - DeconvolutionModule.separateStains: NO tissue "
                              f"remains detectable! Saving Black images")
@@ -71,6 +73,8 @@ def separateStains(s, params):
             s.addToPrintList(f"deconv_c{c}_std", str(dc.std()))
 
         dc = (dc - dc_min) / float(dc_max - dc_min) * mask
-        io.imsave(s["outdir"] + os.sep + s["filename"] + f"_deconv_c{c}.png", img_as_ubyte(dc))
+
+        # saving compressed mask
+        saveCompressedMask(s["outdir"] + os.sep + s["filename"] + f"_deconv_c{c}.png", dc)
 
     return

histoqc/LightDarkModule.py

@@ -2,7 +2,8 @@
 import os
 import numpy as np
 from histoqc.BaseImage import printMaskHelper
-from skimage import io, color, img_as_ubyte
+from histoqc.SaveModule import saveCompressedMask
+from skimage import color
 from distutils.util import strtobool
 from skimage.filters import threshold_otsu, rank
 from skimage.morphology import disk
@@ -34,7 +35,8 @@ def getIntensityThresholdOtsu(s, params):
     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]))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", s["img_mask_" + name])
 
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = s["img_mask_use"] & s["img_mask_" + name]
@@ -84,7 +86,8 @@ def getIntensityThresholdPercent(s, params):
     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]))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_" + name + ".png", prev_mask & ~s["img_mask_" + name])
 
     s.addToPrintList(name,
                      printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
@@ -128,7 +131,8 @@ def removeBrightestPixels(s, params):
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = s["img_mask_use"] & s["img_mask_bright"]
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_bright.png", img_as_ubyte(prev_mask & ~s["img_mask_bright"]))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_bright.png", prev_mask & ~s["img_mask_bright"])
 
     s.addToPrintList("brightestPixels",
                      printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
@@ -163,7 +167,8 @@ def minimumPixelIntensityNeighborhoodFiltering(s,params):
     prev_mask = s["img_mask_use"]
     s["img_mask_use"] = s["img_mask_use"] & s["img_mask_bright"]
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_bright.png", img_as_ubyte(prev_mask & ~s["img_mask_bright"]))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_bright.png", prev_mask & ~s["img_mask_bright"])
 
     s.addToPrintList("brightestPixels",
                      printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))
@@ -183,6 +188,7 @@ def saveEqualisedImage(s,params):
     img = color.rgb2gray(img)
 
     out = exposure.equalize_hist((img*255).astype(np.uint8))
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_equalized_thumb.png", img_as_ubyte(out))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_equalized_thumb.png", out)
 
     return

histoqc/MorphologyModule.py

@@ -2,7 +2,8 @@
 import os
 import numpy as np
 from histoqc.BaseImage import printMaskHelper
-from skimage import io, morphology, img_as_ubyte, measure
+from histoqc.SaveModule import saveCompressedMask
+from skimage import morphology, measure
 
 from scipy import ndimage as ndi
 
@@ -16,7 +17,8 @@ def removeSmallObjects(s, params):
     img_reduced = morphology.remove_small_objects(s["img_mask_use"], min_size=min_size)
     img_small = np.invert(img_reduced) & s["img_mask_use"]
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_small_remove.png", img_as_ubyte(img_small))
+    # saving commpresed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_small_remove.png", img_small)
     s["img_mask_small_filled"] = (img_small * 255) > 0
 
     prev_mask = s["img_mask_use"]
@@ -81,7 +83,8 @@ def removeFatlikeTissue(s, params):
 
     mask_fat = mask_dilate & ~mask_dilate_removed
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_fatlike.png", img_as_ubyte(mask_fat))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_fatlike.png", mask_fat)
     s["img_mask_fatlike"] = (mask_fat * 255) > 0
 
     prev_mask = s["img_mask_use"]
@@ -118,8 +121,8 @@ def fillSmallHoles(s, params):
     img_reduced = morphology.remove_small_holes(s["img_mask_use"], area_threshold=min_size)
     img_small = img_reduced & np.invert(s["img_mask_use"])
 
-
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_small_fill.png", img_as_ubyte(img_small))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_small_fill.png", img_small)
     s["img_mask_small_removed"] = (img_small * 255) > 0
 
     prev_mask = s["img_mask_use"]

histoqc/SaveModule.py

@@ -5,8 +5,6 @@
 from skimage import color
 import numpy as np
 
-import matplotlib.pyplot as plt
-
 
 def blend2Images(img, mask):
     if (img.ndim == 3):
@@ -26,12 +24,13 @@ def saveFinalMask(s, params):
     for mask_force in s["img_mask_force"]:
         mask[s[mask_force]] = 0
 
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_mask_use.png", img_as_ubyte(mask))
+    # saving compressed mask
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_mask_use.png", mask)
 
     if strtobool(params.get("use_mask", "True")):  # should we create and save the fusion mask?
         img = s.getImgThumb(s["image_work_size"])
         out = blend2Images(img, mask)
-        io.imsave(s["outdir"] + os.sep + s["filename"] + "_fuse.png", img_as_ubyte(out))
+        saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_fuse.png" ,out)
 
     return
 
@@ -60,7 +59,7 @@ def saveAssociatedImage(s, key:str, dim:int):
 
     associated_img = associated_img.resize(size)
     associated_img = np.asarray(associated_img)[:, :, 0:3]
-    io.imsave(f"{s['outdir']}{os.sep}{s['filename']}_{key}.png", associated_img)
+    saveCompressedMask(f"{s['outdir']}{os.sep}{s['filename']}_{key}.png" ,associated_img)
 
 def saveMacro(s, params):
     dim = params.get("small_dim", 500)
@@ -78,14 +77,22 @@ def saveMask(s, params):
         return
 
     # save mask
-    io.imsave(f"{s['outdir']}{os.sep}{s['filename']}_{suffix}.png", img_as_ubyte(s["img_mask_use"]))
+    path = f"{s['outdir']}{os.sep}{s['filename']}_{suffix}.png"    
+    # saving mask with RLE
+    saveCompressedMask(path, s["img_mask_use"])
 
 def saveThumbnails(s, params):
     logging.info(f"{s['filename']} - \tsaveThumbnail")
     # we create 2 thumbnails for usage in the front end, one relatively small one, and one larger one
     img = s.getImgThumb(params.get("image_work_size", "1.25x"))
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_thumb.png", img)
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_thumb.png" ,img)
 
     img = s.getImgThumb(params.get("small_dim", 500))
-    io.imsave(s["outdir"] + os.sep + s["filename"] + "_thumb_small.png", img)
+    saveCompressedMask(s["outdir"] + os.sep + s["filename"] + "_thumb.png" ,img)
     return
+
+def saveCompressedMask(f_path, img_mask, optimize: bool=True):
+    # Check if the image is binarized
+    unique_values = np.unique(img_mask)
+    bits = 1 if len(unique_values) == 2 else 8
+    io.imsave(f_path, img_as_ubyte(img_mask),bits=bits, optimize=optimize)