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Kevin Baensch 2021-03-10 09:47:54 +01:00
commit 4f9af8446c
Signed by: derped
GPG key ID: C0F1D326C7626543
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.gitignore vendored Normal file
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in/
out/
cache/
imageConfig.json
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/

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Copyright 2021 Kevin Baensch
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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from typing import List, Generator, Optional, Tuple
from fileutils import FILE, dslice, search, DIRIN, DIROUT, DIRCACHE
import window
from math import pi
import cv2
import pandas as pd
import numpy as np
from scipy import ndimage as ndi
from skimage.filters import threshold_otsu, threshold_li
from skimage import feature, measure, restoration, segmentation
from pythreshold.global_th.entropy.kapur import kapur_threshold
PIXELS_TO_UM = 3.2 # 1 pixel equals 3.2 um
def rball(img, *argv, **args):
return img - restoration.rolling_ball(img, *argv, **args)
def watershed_new(img, blur: int, min_thresh: int, dist: int, min_size: int, min_roundness: float, min_mean_brightness: int, ignore: List[Tuple[int, int]]):
MAX_VAL = 255 if img.dtype == "uint8" else 65535
BRIGHT_MUL = 65535/255 if img.dtype == "uint8" else 1
c_img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
if blur > 0:
b_img = cv2.GaussianBlur(img, (blur, blur), 0)
thresh_val = kapur_threshold(b_img)
thresh_val = thresh_val if thresh_val >= min_thresh else min_thresh
thresh = cv2.threshold(cv2.bitwise_not(b_img), MAX_VAL-thresh_val, MAX_VAL, cv2.THRESH_BINARY_INV)[1]
else:
thresh_val = kapur_threshold(img)
thresh_val = thresh_val if thresh_val >= min_thresh else min_thresh
thresh = cv2.threshold(cv2.bitwise_not(img), MAX_VAL-thresh_val, MAX_VAL, cv2.THRESH_BINARY_INV)[1]
thresh = segmentation.clear_border(thresh)
thresh = ndi.binary_fill_holes(thresh)
distance = ndi.distance_transform_edt(thresh)
if dist < 1:
localMax = distance > distance.min()
else:
localMax = np.zeros_like(img, dtype=np.bool)
localMax[tuple(feature.peak_local_max(distance, exclude_border=False, min_distance=dist, labels=thresh).T)] = True
markers = ndi.label(localMax, structure=np.ones((3, 3)))[0]
labels = segmentation.watershed(-img, markers, mask=thresh)
grain_num = 1
meta = {
"outlines": np.full_like(img, MAX_VAL, dtype=img.dtype),
"data": [[ "Grain","Area","Mean","Min","Max","Circ", "Eccentricity", "IntDen","RawIntDen","AR","Round","Solidity" ]]
}
for prop, label in list(zip(measure.regionprops(labels, intensity_image=img), np.unique(labels)[1:]))[:50]:
bbox = prop["bbox"]
if prop["area"]*PIXELS_TO_UM**2 < min_size \
or 1-prop["eccentricity"] < min_roundness \
or prop["mean_intensity"]*BRIGHT_MUL < min_mean_brightness*257 \
or grain_num > 50 \
or any([bbox[1] <= x <= bbox[3] and bbox[0] <= y <= bbox[2]
for (x, y) in ignore]):
continue
# Create mask of image size and mark labeled area
mask = np.zeros(img.shape, dtype=img.dtype)
mask[labels == label] = MAX_VAL
# cnts = measure.find_contours(mask.copy())
# print(cnts)
# c_img[cnts] = (65535,65535)
# measure.drawContours()
# c_img = segmentation.mark_boundaries(c_img, mask, (0,255,255))
# Create and draw contour around labeled area
if cv2.__version__.startswith("3."):
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[1]
else:
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[0]
c_img = cv2.drawContours(c_img, cnts, -1, (0,MAX_VAL,MAX_VAL), 1)
meta["outlines"] = cv2.drawContours(meta["outlines"], cnts, -1, (0,0), 1)
c = max(cnts, key=cv2.contourArea)
(x,y), r = cv2.minEnclosingCircle(c)
x = int(float(x) + float(r)/2)
y = int(float(y) + float(r)/2)
cv2.putText(c_img, str(grain_num), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
cv2.putText(meta["outlines"], str(grain_num), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0), 1)
meta["data"].append([
grain_num,
round(prop["area"]*PIXELS_TO_UM**2, 4),
round(prop["mean_intensity"]*BRIGHT_MUL, 4),
round(prop["min_intensity"]*BRIGHT_MUL, 4),
round(prop["max_intensity"]*BRIGHT_MUL, 4),
(1 if (4 * pi * prop.area) / (prop.perimeter_crofton * prop.perimeter_crofton) >=1 else round((4 * pi * prop.area) / (prop.perimeter_crofton * prop.perimeter_crofton), 4)),
round(1-prop["eccentricity"], 4), # 4*pi*(prop["area"])/prop["perimeter"]**2,
round((prop["area"]*PIXELS_TO_UM**2)*prop["mean_intensity"]*BRIGHT_MUL, 4),
round(prop["intensity_image"].sum()*BRIGHT_MUL, 4),
(None if prop["minor_axis_length"] == 0 else round(prop["major_axis_length"]/prop["minor_axis_length"], 4)),
(None if prop["major_axis_length"] == 0 else round(4*prop["area"]*PIXELS_TO_UM**2/(pi*prop["major_axis_length"]**2)/10, 4)),
round(prop["solidity"], 4)
])
grain_num += 1
return c_img, meta
def main():
"""
tif metadata documentation:
threshold-low -> lower bound bit range (0)
threshold-high -> upper bound bit range (65535)
https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.normalize
"""
files = [FILE(p) for p in search([DIRIN]) if p.endswith(".tif")]
if files != []:
files.sort()
normalize = ["normalized", (cv2.normalize, [], {"dst": None, "alpha": 0, "beta": 65535, "norm_type": cv2.NORM_MINMAX}, True)]
to8bit = ["8bit", (cv2.convertScaleAbs, [] , {"alpha": 255.0/65535.0}, True)]
rollingball = ["rball", (rball, [], {"radius": 50.0}, True)]
wat = ["wshed", (watershed_new, [], {"blur": 0, "dist": 0, "min_thresh": 20, "min_size": 250, "min_roundness": 0.2, "min_mean_brightness": 50, "ignore": []}, False)]
[f.opqueue.__init__([normalize, to8bit, rollingball, wat]) for f in files]
[f.apply(dslice(f.opqueue,None,-1)) for f in files]
# [(f.apply(f.opqueue), f.save(True, f.opqueue)) for f in files]
window.init(files)
window.reset()
return True
print(f"ERR: No files in {DIRIN}, please add some.")
return False
if __name__ == '__main__':
if main():
window.run()

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import re
from collections import OrderedDict
from os import path, makedirs, listdir
from typing import Callable, Dict, Optional, Tuple, List, Generator, Any, Union
import numpy as np
import pandas as pd
from cv2 import imread, imwrite
DIRIN = path.abspath("in")
DIROUT = path.abspath("out")
DIRCACHE = path.abspath("cache")
[makedirs(d, exist_ok=True) for d in [DIRIN, DIROUT, DIRCACHE]]
def dslice(odict: Dict[Any, Any], start: Optional[int] = None, end: Optional[int] = None) -> Dict[Any, Any]:
return OrderedDict([
(k, odict[k]) for k in list(odict.keys())[start:end]
])
def lval(odict: Dict[Any, Any]) -> Any:
return odict[list(odict.keys())[-1]]
def summarize(dlist):
summary = pd.concat(dlist)
summary.sort_values(["Hour", "Culture", "Label", "Grain"], ignore_index=True, inplace=True)
return summary
class FILE:
def __init__(self, fpath: str, esum: Optional[str] = None):
self.path, self.fname = path.split(path.relpath(fpath, start=DIRIN))
self.fname, self.fext = path.splitext(self.fname)
self.opqueue: Dict[str, Tuple[str, Callable, List[int]]] = OrderedDict()
self.meta: Optional[pd.DataFrame] = None
self.outlines: Optional[np.ndarray] = None
self.load({})
def __repr__(self) -> str:
return path.join(self.path, "".join([self.fname, self.fext]))
def __gt__(self, other) -> bool:
return [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', repr(self))] > \
[int(c) if c.isdigit() else c for c in re.split('([0-9]+)', repr(other))]
def apply(self, opts: Dict[str, Tuple[str, Callable, List[int]]]):
if self.load(opts):
pass
else:
self.apply(dslice(opts, None, -1))
try:
self.data = lval(opts)[0](self.data, *lval(opts)[1], **lval(opts)[2])
if type(self.data) is tuple:
self.meta = pd.DataFrame(None if self.data[1]["data"][1:] == [] else self.data[1]["data"][1:], columns=self.data[1]["data"][0])
self.meta.insert(loc=0, column="Label", value="".join([self.fname, self.fext]))
spath = self.path.split(path.sep)
if len(spath) > 2 and spath[-1] in ["BFP", "YFP"] and spath[-2][:-1].isdigit():
hour, culture = self.path.split(path.sep)[-2:]
hour = int(hour[:-1])
else:
hour, culture = None, None
self.meta.insert(loc=0, column="Hour", value=hour)
self.meta.insert(loc=1, column="Culture", value=culture)
self.outlines = self.data[1]["outlines"]
self.data = self.data[0]
if lval(opts)[3]:
self.save(False, opts)
except SyntaxError as error:
print(f"Error: {error}")
def getOpStr(self, opts: Dict[str, Tuple[str, Callable, List[int]]]) -> str:
return ';'.join([f"{x}_{','.join([str(y) for y in opts[x][1]])}" for x in opts.keys()])
def getName(self, cache: bool, opts: Dict[str, Tuple[str, Callable, List[int]]]) -> str:
return f"{self.fname}{(cache and self.getOpStr(opts) + '.png') or self.fext}"
def save(self, toDirOut: bool, opts: Dict[str, Tuple[str, Callable, List[int]]]):
if toDirOut:
if not path.exists(path.join(DIROUT, self.path)):
makedirs(path.join(DIROUT, self.path))
imwrite(path.join(DIROUT, self.path, f"{self.fname}.jpg"), self.data)
if self.outlines is not None:
imwrite(path.join(DIROUT, self.path, f"{self.fname}.outlines.jpg"), self.outlines)
if self.meta is not None:
print(path.join(DIROUT, self.path, f"{self.fname}.csv"))
self.meta.to_csv(path.join(DIROUT, self.path, f"{self.fname}.csv"), index=False)
# [l.insert(1, self.getName(False, {})) for l in self.meta["data"][1:]]
# with open(path.join(DIROUT, self.path, f"{self.fname}.csv"), "w") as f:
# [f.write(",".join([str(e) for e in l]) + "\n") for l in self.meta["data"]]
else:
if not path.exists(path.join(DIRCACHE, self.path)):
makedirs(path.join(DIRCACHE, self.path))
imwrite(path.join(DIRCACHE, self.path, self.getName(True, opts)), self.data)
def load(self, opts: Dict[str, Tuple[str, Callable, List[int]]]) -> bool:
if opts == {} and path.exists(path.join(DIRIN, self.path, self.getName(False, []))):
self.data = imread(path.join(DIRIN, self.path, self.getName(False, [])), -1)
return True
if path.exists(path.join(DIRCACHE, self.path, self.getName(True, opts))):
self.data = imread(path.join(DIRCACHE, self.path, self.getName(True, opts)), -1)
return True
return False
def search(pathlist: List[str]) -> Generator[str, None, None]:
"""
Generate file objects from given list of Paths.
+------------+
| Parameters |
+------------+
| pathlist: List[str]
| List of files and directories.
"""
for fpath in pathlist:
if path.isfile(fpath):
yield path.abspath(fpath)
continue
if path.isdir(fpath):
yield from search([path.join(fpath, x) for x in listdir(fpath)])
continue
print(f"[WARN]: No such file or directory: {fpath}")

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requirements.txt Normal file
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numpy
opencv-python == 4.3.0
pythreshold
scikit-image
scipy

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{ pkgs ? import /nixpkgs {} }:
let
pythreshold = pkgs.python3Packages.buildPythonPackage rec {
pname = "pythreshold";
version = "0.3.1";
propagatedBuildInputs = with pkgs.python3Packages; [
numpy
scipy
scikitimage
matplotlib
pkgs.python3Packages.opencv4
];
src = pkgs.python3Packages.fetchPypi {
inherit pname version;
sha256 = "149f4dkx6dm8rlh3disas9xpr13cfglpkrqfx2manaahgwkgpx12";
};
preBuild = ''
substituteInPlace setup.py --replace "'opencv-python'" ""
'';
};
in pkgs.mkShell rec {
name = "ColonyCounter";
version = "0.1";
pyEnv = pkgs.python38.withPackages (ps: with pkgs.python3Packages; [
numpy
pandas
pythreshold
scikitimage
scipy
opencv4
]);
nativeBuildInputs = [ pyEnv ];
}

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from copy import deepcopy
import tkinter as tk
from os.path import join
from PIL import Image, ImageTk
from cv2 import convertScaleAbs, cvtColor, COLOR_BGR2RGB
from json import load, dump
from fileutils import dslice, summarize, DIROUT
IMAGES = []
CURIMINDEX = 0
CURQUEUE = {}
WIDTH = 1028
HEIGHT = 1080
def init(imgList):
global CURQUEUE
IMAGES.extend(imgList)
CURQUEUE = deepcopy(IMAGES[CURIMINDEX].opqueue)
loadCMPImage()
loadImage()
def convertImage(img):
# Currently only expect grayscale and RGB images
if img.dtype == "uint16":
img = convertScaleAbs(img, alpha=255.0/65535.0)
if len(img.shape) > 2:
img = cvtColor(img, COLOR_BGR2RGB)
img = Image.fromarray(img)
return ImageTk.PhotoImage(img)
def loadImage(event=None):
IMAGES[CURIMINDEX].apply(CURQUEUE)
label = IMAGES[CURIMINDEX].__repr__()
img = convertImage(IMAGES[CURIMINDEX].data)
IMLABEL.configure(text=label)
IMLABEL.text = label
CURIMAGE.configure(image=img)
CURIMAGE.image = img
def loadCMPImage(event=None):
IMAGES[CURIMINDEX].apply(dslice(CURQUEUE, 0, int(S_CMPIMAGE.get())))
img = convertImage(IMAGES[CURIMINDEX].data)
CMPIMAGE.configure(image=img)
CMPIMAGE.image = img
def loadImageNext(event=None):
global CURIMINDEX
CURIMINDEX = (CURIMINDEX+1) % (len(IMAGES))
reset()
loadCMPImage()
def loadImagePrevious(event=None):
global CURIMINDEX
CURIMINDEX = (CURIMINDEX-1) % (len(IMAGES))
reset()
loadCMPImage()
def reset(event=None):
global CURQUEUE
CURQUEUE = deepcopy(IMAGES[CURIMINDEX].opqueue)
if "wshed" in CURQUEUE:
S_BLURSIZE.set(CURQUEUE["wshed"][2]["blur"])
S_THRESH.set(CURQUEUE["wshed"][2]["min_thresh"])
S_SIZE.set(CURQUEUE["wshed"][2]["min_size"])
S_DISTANCE.set(CURQUEUE["wshed"][2]["dist"])
S_MBRIGHT.set(CURQUEUE["wshed"][2]["min_mean_brightness"])
S_ROUND.set(CURQUEUE["wshed"][2]["min_roundness"])
loadImage()
def apply(event=None):
IMAGES[CURIMINDEX].opqueue = deepcopy(CURQUEUE)
def set_blursize(ksize):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["blur"] = int(ksize)-1
loadImage()
def set_min_thresh(min_thresh):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["min_thresh"] = int(min_thresh)
loadImage()
def set_min_size(min_size):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["min_size"] = int(min_size)
loadImage()
def set_distance(dist):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["dist"] = int(dist)
loadImage()
def set_min_mean_brightness(min_mean_brightness):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["min_mean_brightness"] = int(min_mean_brightness)
loadImage()
def set_min_roundness(min_roundness):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["min_roundness"] = float(min_roundness)
loadImage()
def set_comp(qslice):
loadCMPImage()
def add_pos(event):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["ignore"].append((event.x, event.y))
loadImage()
def remove_pos(event):
if "wshed" in CURQUEUE:
# print(f"box_x: {event.x-5}, {event.x+5}")
# print(f"box_y: {event.y-5}, {event.y+5}")
CURQUEUE["wshed"][2]["ignore"] = [
pos for pos in
CURQUEUE["wshed"][2]["ignore"]
if (((event.x-15) < pos[0] < (event.x+15)) is False)
and (((event.y-15) < pos[1] < (event.y+15)) is False)
]
loadImage()
def clear_pos(event):
if "wshed" in CURQUEUE:
CURQUEUE["wshed"][2]["ignore"] = []
loadImage()
def export(event=None):
[(f.apply(f.opqueue), f.save(True, f.opqueue)) for f in IMAGES]
summary = summarize([f.meta for f in IMAGES])
summary.to_csv(join(DIROUT, "summary.csv"), index=False)
summary.groupby(["Hour", "Culture"]).mean().round(4).to_csv(join(DIROUT, "summary_mean.csv"))
summary.groupby(["Hour", "Culture"]).std().round(4).to_csv(join(DIROUT, "summary_std.csv"))
def settings_load(event=None):
with open("imageConfig.json", "r") as f:
settings = load(f)
for (n, i) in enumerate(IMAGES):
if repr(i) in settings:
[
IMAGES[n].opqueue.__setitem__(k,
[i.opqueue[k][0]] +
settings[repr(i)][k]
)
for k in i.opqueue
if k in settings[repr(i)]
]
reset()
def settings_save(event=None):
with open("imageConfig.json", "w") as f:
dump(dict([[repr(i), dict([[o, i.opqueue[o][1:]] for o in i.opqueue])] for i in IMAGES]), f)
def run():
ROOT.mainloop()
ROOT = tk.Tk()
ROOT.wm_title("FP Analysis")
ROOT.geometry(f"{WIDTH}x{HEIGHT}")
ROOT.rowconfigure(0, weight=3)
ROOT.rowconfigure(1, weight=10)
ROOT.rowconfigure(2, weight=1)
ROOT.bind("<Left>", loadImagePrevious)
ROOT.bind("<Right>", loadImageNext)
ROOT.bind("q", loadImagePrevious)
ROOT.bind("w", loadImageNext)
ROOT.bind("a", apply)
ROOT.bind("r", reset)
ROOT.bind("s", settings_save)
ROOT.bind("l", settings_load)
ROOT.bind("e", export)
ROOT.bind("c", clear_pos)
F_SLIDERS = tk.Frame(ROOT, width=WIDTH, height=50)
F_SLIDERS.grid(row=0)
F_IMAGE = tk.Canvas(ROOT, width=WIDTH, height=500)
F_IMAGE.grid(row=1)
F_IMAGE.rowconfigure(0, weight=0)
F_IMAGE.rowconfigure(1, weight=10)
F_BUTTONS = tk.Frame(ROOT, width=WIDTH, height=50)
F_BUTTONS.grid(row=2)
S_BLURSIZE = tk.Scale(F_SLIDERS, label='Blur Size', from_=0, to=255, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=25, resolution=2, command=set_blursize)
S_BLURSIZE.grid(row=0, column=0, sticky="N")
S_THRESH = tk.Scale(F_SLIDERS, label='Min Threshold', from_=0, to=255, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=25, resolution=1, command=set_min_thresh)
S_THRESH.grid(row=1, column=0, sticky="N")
S_DISTANCE = tk.Scale(F_SLIDERS, label='Distance Transform', from_=0, to=50, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=10, resolution=1, command=set_distance)
S_DISTANCE.grid(row=2, column=0, sticky="N")
S_SIZE = tk.Scale(F_SLIDERS, label='Filter: Min Size', from_=0, to=500, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=25, resolution=1, command=set_min_size)
S_SIZE.grid(row=3, column=0, sticky="N")
S_MBRIGHT = tk.Scale(F_SLIDERS, label='Filter: Min Mean Brightness', from_=0, to=255, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=25, resolution=1, command=set_min_mean_brightness)
S_MBRIGHT.grid(row=4, column=0, sticky="N")
S_ROUND = tk.Scale(F_SLIDERS, label='Filter: Min Roundness', from_=0, to=1, orient=tk.HORIZONTAL, length=WIDTH-10, showvalue=True, tickinterval=0.1, resolution=0.05, command=set_min_roundness)
S_ROUND.grid(row=5, column=0, sticky="N")
S_CMPIMAGE = tk.Scale(F_IMAGE, label=None, from_=0, to=4, orient=tk.HORIZONTAL, length=WIDTH/2-10, showvalue=False, tickinterval=1, resolution=1, command=set_comp)
S_CMPIMAGE.grid(row=0, column=0, sticky="N")
IMLABEL = tk.Label(F_IMAGE, text="", height=1)
IMLABEL.grid(row=0, column=1)
CURIMAGE = tk.Label(F_IMAGE, image=None)
CURIMAGE.grid(row=1, column=1)
CMPIMAGE = tk.Label(F_IMAGE, image=None)
CMPIMAGE.grid(row=1, column=0)
CURIMAGE.bind("<Button 1>", add_pos)
CURIMAGE.bind("<Button 3>", remove_pos)
CURIMAGE.bind("<Button-1>", add_pos)
CURIMAGE.bind("<Button-3>", remove_pos)
B_RESET = tk.Button(F_BUTTONS, text="Reset", command=reset)
B_RESET.grid(row=0, column=0, sticky="W")
B_APPLY = tk.Button(F_BUTTONS, text="Apply", command=apply)
B_APPLY.grid(row=0, column=1, sticky="E")
B_PREVIOUS = tk.Button(F_BUTTONS, text="Previous", command=loadImagePrevious)
B_PREVIOUS.grid(row=0, column=2, sticky="E")
B_NEXT = tk.Button(F_BUTTONS, text="Next", command=loadImageNext)
B_NEXT.grid(row=0, column=3, sticky="E")
B_SLOAD = tk.Button(F_BUTTONS, text="Load Settings", command=settings_load)
B_SLOAD.grid(row=0, column=4, sticky="E")
B_SSAVE = tk.Button(F_BUTTONS, text="Save Settings", command=settings_save)
B_SSAVE.grid(row=0, column=5, sticky="E")
B_EXPORT = tk.Button(F_BUTTONS, text="Export Images", command=export)
B_EXPORT.grid(row=0, column=6, sticky="E")