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Author | SHA1 | Date |
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Kevin Baensch | b744cb9e93 | |
Kevin Baensch | ca803a53c9 |
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[MIT License](./LICENSE)
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# Colony Counter
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A simple Utility to count and measure bright spots on dark images 😸
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## Features
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- Image transformation results are cached and incremental.
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- Provides an ugly user interface with intuitive key binds.
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- Batch process/export image analysis results.
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- Adjust colony recognition and filtering through a couple of simple sliders.
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![](./readme_img/Filter_Colony.gif)
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- Remove false positives by clicking on them.
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![](./readme_img/Remove_Colony.gif)
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## Getting Started
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- Install python (>=3.8 recommended... I made sure to explicitly use ordered dictionaries but just in case).
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- Install dependencies in whichever way you prefer.
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- Create the folders "in", "out" and "cache" (or run colonycounter.py).
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- Adjust queue functions in source code to suit your needs (and read through [Caveats](#caveats)).
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- Copy/Move your images into the "in" folder.
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- Start the program by running the colonycounter.py file.
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## Key Binds
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| Key | Function |
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|-----------------|----------------------------------------------------------------------------------------|
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| q, left arrow | load previous image |
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| w, right arrow | load next image |
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| a | apply changes (always do this before switching images, otherwise changes will be lost) |
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| r | reset changes that have not yet been applied |
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| l | load saved settings from disk |
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| s | save currently applied settings to disk |
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| left mouse btn | add colony to list of ignored colonies |
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| right mouse btn | remove colonies in the vicinity from ignore list |
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| c | clear list of ignored colonies |
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| e | batch process and export all images |
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## Caveats
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- The current processing queue expects 16bit gray scale images with the file extension .tif (tagged image file).
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- watershed function does not work on 16 bit images... so there are some hard coded values to bring measurements back to 16bit brightness values
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- this means a loss of resolution
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- To get hour and culture type values the following folder structure is expected:
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- "$DIRIN/OPTIONAL\_FOLDERS/$HOUR/$CULTURETYPE/image.tif"
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- $HOUR = integer followed by the letter h
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- $CULTURETYPE = whatever string identifies your culture type
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- Code documentation is currently lacking
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- Not all types are properly annotated
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@ -122,7 +122,6 @@ def main():
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[f.opqueue.__init__([normalize, to8bit, rollingball, wat]) for f in files]
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[f.apply(dslice(f.opqueue,None,-1)) for f in files]
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# [(f.apply(f.opqueue), f.save(True, f.opqueue)) for f in files]
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window.init(files)
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window.reset()
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return True
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@ -53,7 +53,7 @@ class FILE:
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self.meta = pd.DataFrame(None if self.data[1]["data"][1:] == [] else self.data[1]["data"][1:], columns=self.data[1]["data"][0])
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self.meta.insert(loc=0, column="Label", value="".join([self.fname, self.fext]))
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spath = self.path.split(path.sep)
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if len(spath) > 2 and spath[-1] in ["BFP", "YFP"] and spath[-2][:-1].isdigit():
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if len(spath) > 2 and spath[-2][:-1].isdigit():
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hour, culture = self.path.split(path.sep)[-2:]
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hour = int(hour[:-1])
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else:
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@ -82,11 +82,7 @@ class FILE:
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if self.outlines is not None:
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imwrite(path.join(DIROUT, self.path, f"{self.fname}.outlines.jpg"), self.outlines)
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if self.meta is not None:
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print(path.join(DIROUT, self.path, f"{self.fname}.csv"))
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self.meta.to_csv(path.join(DIROUT, self.path, f"{self.fname}.csv"), index=False)
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# [l.insert(1, self.getName(False, {})) for l in self.meta["data"][1:]]
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# with open(path.join(DIROUT, self.path, f"{self.fname}.csv"), "w") as f:
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# [f.write(",".join([str(e) for e in l]) + "\n") for l in self.meta["data"]]
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else:
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if not path.exists(path.join(DIRCACHE, self.path)):
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makedirs(path.join(DIRCACHE, self.path))
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@ -1,5 +1,5 @@
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numpy
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opencv-python == 4.3.0
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pythreshold
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scikit-image
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scikit-image >= 0.18.1
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scipy
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