Added readme.

README.md

@@ -0,0 +1,51 @@
+[MIT License](./LICENSE)
+
+# Colony Counter
+A simple Utility to count and measure bright spots on dark images 😸
+
+## Features
+- Image transformation results are cached and incremental.
+- Provides an ugly user interface with intuitive key binds.
+- Batch process/export image analysis results.
+
+- Adjust colony recognition and filtering through a couple of simple sliders.
+
+![](./readme_img/Filter_Colony.gif)
+
+
+- Remove false positives by clicking on them.
+
+![](./readme_img/Remove_Colony.gif)
+
+## Getting Started
+- Install python (>=3.8 recommended... I made sure to explicitly use ordered dictionaries but just in case).
+- Install dependencies in whichever way you prefer.
+- Create the folders "in", "out" and "cache" (or run colonycounter.py).
+- Adjust queue functions in source code to suit your needs (and read through [Caveats](#caveats)).
+- Copy/Move your images into the "in" folder.
+- Start the program by running the colonycounter.py file.
+
+## Key Binds
+| Key             | Function                                                                               |
+|-----------------|----------------------------------------------------------------------------------------|
+| q, left arrow   | load previous image                                                                    |
+| w, right arrow  | load next image                                                                        |
+| a               | apply changes (always do this before switching images, otherwise changes will be lost) |
+| r               | reset changes that have not yet been applied                                           |
+| l               | load saved settings from disk                                                          |
+| s               | save currently applied settings to disk                                                |
+| left mouse btn  | add colony to list of ignored colonies                                                 |
+| right mouse btn | remove colonies in the vicinity from ignore list                                       |
+| c               | clear list of ignored colonies                                                         |
+| e               | batch process and export all images                                                    |
+
+## Caveats
+- The current processing queue expects 16bit gray scale images with the file extension .tif (tagged image file).
+- watershed function does not work on 16 bit images... so there are some hard coded values to bring measurements back to 16bit brightness values
+  - this means a loss of resolution
+- To get hour and culture type values the following folder structure is expected:
+  - "$DIRIN/OPTIONAL\_FOLDERS/$HOUR/$CULTURETYPE/image.tif"
+  - $HOUR = integer followed by the letter h
+  - $CULTURETYPE = whatever string identifies your culture type
+- Code documentation is currently lacking
+- Not all types are properly annotated

colonycounter.py

@@ -122,7 +122,6 @@ def main():
         [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

fileutils.py

@@ -53,7 +53,7 @@ class FILE:
                     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():
+                    if len(spath) > 2 and spath[-2][:-1].isdigit():
                         hour, culture = self.path.split(path.sep)[-2:]
                         hour = int(hour[:-1])
                     else:
@@ -82,11 +82,7 @@ class FILE:
             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))

requirements.txt

@@ -1,5 +1,5 @@
 numpy
 opencv-python == 4.3.0
 pythreshold
-scikit-image
+scikit-image >= 0.18.1
 scipy