segwrap updated to be compatible with cellpose 0.6.5 (#31)

Author: muellerflorian

docs/analysis-overview.md

@@ -1,11 +1,11 @@
 ## ImJoy plugins
 Most of the workflows are implemented as **ImJoy plugins**, with a simple interface to
-specify the different workflow parameters. We describe in a dedicated [sections] how to [**use**](imjoy-overview.md) and  [**install**](imjoy-installation.md) the **Plugin engine** to run these plugins. 
+specify the different workflow parameters. We describe in a dedicated [sections] how to [**use**](imjoy-overview.md) and  [**install**](imjoy-installation.md) the **Plugin engine** to run these plugins.
 
-Once a plugin is installed, it will be available for later use in the ImJoy **workspace `fq-segmentation`** as explained [here](imjoy-overview.md#opening-a-workspace). 
+Once a plugin is installed, it will be available for later use in the ImJoy **workspace `fq-segmentation`** as explained [here](imjoy-overview.md#opening-a-workspace).
 
 !!! abstract "Quick summary for how to connect ImJoy to Jupyter engine"
-    1. Open **anaconda terminal**. 
+    1. Open **anaconda terminal**.
     2.  **Activate environment**: `conda activate fq-segmentation`
     3. **Start Jupyter engine**: `imjoy --jupyter`
     4. **Connect** ImJoy to Jupyter Engine with 🚀 button.
@@ -15,7 +15,7 @@ We also provide **Jupyter notebooks** for these workflows, which we recommend on
 ## Typical workflows
 For most purposes, the segmentation will consist of two steps. Each of these steps is described in a 
 dedicated section. In this section you can also find a recommended workflow, which allows you to also
-process the test data. 
+process the test data.
 
 1. **Pre-processing** of the data: segmentation is performed on 2D images. If your images are 3D, you have to either split them, or project them into 2D images. This can be done with the [pre-processing workflow](analysis-preprocessing.md).
 2. **Actual segmentation** of your images. We provide different plugins to [**segment**](analysis-segmentation.md). one type of structure, e.g. nuclei or cells, or two, e.g. cells and nuclei.

docs/analysis-preprocessing.md

@@ -5,7 +5,7 @@ Segmentation is performed on **2D images**. This plugin allows to obtain 2D imag
 a projection or by saving each z-plane separately. These images are typically stored in a separate folder. 
 2D images by applying a projection, and these images are typically stored in a separate folder.
 
-Install ImJoy plugin `PreProcess` <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/segmentation:PreProcess@stable&upgrade=1" target="_blank">** from here.**</a>
+Install ImJoy plugin `PreProcess` <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/fq-segmentation:PreProcess@stable&upgrade=1" target="_blank">** from here.**</a>
 
 ## Recommended workflow
 

docs/analysis-segmentation.md

@@ -2,8 +2,8 @@
 
 We provide different plugins for different segmentation tasks:
 
-* Install plugin `SegmentObjects` to segment cells OR nuclei <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/segmentation:SegmentObjects@stable&upgrade=1" target="_blank">**from here.**</a>
-* Install plugin  `SegmentCellsNuclei` to segment cells AND nuclei <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/segmentation:SegmentCellsNuclei@stable&upgrade=1" target="_blank">**from here.**</a>
+* Install plugin `SegmentObjects` to segment cells OR nuclei <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/fq-segmentation:SegmentObjects@stable&upgrade=1" target="_blank">**from here.**</a>
+* Install plugin  `SegmentCellsNuclei` to segment cells AND nuclei <a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/fq-segmentation:SegmentCellsNuclei@stable&upgrade=1" target="_blank">**from here.**</a>
 
 ## General behavior
 
@@ -21,32 +21,18 @@ By defining the `Input subfolder` to be `segmentation-input`, the analysis will
 
 ### Results
 
-Results will be saved in the specified folder. For each image the following files, results files with different suffices are created:
+Results will be saved in the specified folder. For each image the following files, results files with different prefixes are created:
 
-* `..._flow`: these are the predicted distance maps of CellPose. They are an intermediate result, and
+* `flow_ ...`: these are the predicted distance maps of CellPose. They are an intermediate result, and
      not needed for most end-users.
-* `..._mask`: these contain the actual segmentation results. Each segmented object is a filled 
+* `mask_ ...`: these contain the actual segmentation results. Each segmented object is a filled 
       object with a constant pixel value. If the images were resized during segmentation, the mask is scaled
       back up to the original image size. The actually obtained (smaller) mask is saved under the name `mask__rescale_...`.  
-* `..._segmentation`: summary plot showing the input image, the predicted distance map, and the segmented
+* `seg_...`: summary plot showing the input image, the predicted distance map, and the segmented
      objects. This plot is also shown in the interface.
 
 ![segmentation__nuclei](img/segmentation__nuclei.png)
 
-### Resizing to speed up prediction
-
-Segmentation speed depends on the image size. In our experience, resizing the images
-can lead to a substantial speed-up. In case you resize the images, we implemented a post-processing
-routine that will resize the predicted masks back to the original image size.
-
-Resizing can be specified in two ways
-
-1. **Scaling factor** recommended]: simply add an integer value. The actual image size will then be divided by this factor
-   to obtain the new size, i.e. a value of 2 will resize an image 512x512 to 256x256.
-2. **New size**: you can directly define the new size of the image, e.g. 256x256. Please note that this size
-   will be applied to all images, independly of their size. This option is hence not suitable if your data-sets 
-   contain differently sized images.
-
 ## Recommended workflow
 
 The default settings of the plugins allow to quickly perform the recommended workflow. You only have 
@@ -70,12 +56,13 @@ to paste your data folder.
     `Path DATA`    | str  |  | Full path to folder containing data to be segmented.
     `Input subfolder`    | str  |  | Name of the subfolder containing the images that should be segmented.
     `Path SAVE` | str  |  | Several options exist. See dedicated section [below](analysis-general-behavior.md#specify-folder-to-save-your-data) for more details.
-    `Object name`    | str  |  `nuclei` | How the object is called.
     `String channel`    | str  |  `dapi` | Unique identifier to identify channel.
-    `String img ext`     | str  | `.png` | File extension of images that should be segmented.
-    `Size object`     | int  | 50 | Typical size of a cell (in resized image).
+    `Object name`    | str  |  `nuclei` | How the object is called.
     `Cellpose model`    | str  |  `nuclei` | Cellpose model for segmentation: `cyto` or `nuclei`. Note that for dense nuclei, the cytoplasmic model might work better. 
-    `New size`     | str  | 512, 512 | String to specify new size of image. No resizing if empty.
+    `Object diameter`     | int  | 50 | Typical diameter of the object. Better to be set a bit to small.
+    `Net Average`     | Bool  | False | Can improve segmentation accuracy, but is slower (Runs the 4 built-in networks and averages them).
+    `Resample`     | Bool  | False | Gives more accurate boundaries, but can be very slow (Runs dynamics at original image size).
+    `String img ext`     | str  | `.png` | File extension of images that should be segmented.
 
 2. Pressing on the plugin name `SegmentObjects` will start the segmentation.
     When using CellPose for the first time, the models for nuclear and cytoplasmic segmentations are downloaded. 
@@ -106,7 +93,9 @@ to paste your data folder.
     `String img ext`     | str  | `.png` | File extension of images that should be segmented.
     `Size CELLS`     | int  | 100 | Typical size of a cell (in resized image).
     `Size NUCLEI`     | int  | 50 | Typical size of a nucleus (in resized image).
-    `New size`     | str  | 512, 512 | String to specify new size of image. No resizing if empty.
+    `Net Average`     | Bool  | False | Can improve segmentation accuracy, but is slower (Runs the 4 built-in networks and averages them).
+    `Resample`     | Bool  | False | Gives more accurate boundaries, but can be very slow (Runs dynamics at original image size).
+    `String img ext`     | str  | `.png` | File extension of images that should be segmented.
 
 2. Pressing on the plugin name `SegmentCellsNuclei` will start the segmentation. 
     When using CellPose for the first time, the models for nuclear and cytoplasmic segmentations are downloaded.

docs/contact.md

@@ -1,6 +1,6 @@
 # Reporting bugs
 
-If you encounter a bug, best is to create a [**GitHub issue**](https://github.com/fish-quant/segmentation/issues). This would allow other users to see 
+If you encounter a bug, best is to create a [**GitHub issue**](https://github.com/fish-quant/fq-segmentation/issues). This would allow other users to see 
 reported bugs and proposed solutions. If possible, please provide the following information
 
 1. **How** can the bug be produced?

docs/img/imjoy-segment-cells-nuclei-ui.png

@@ -34,7 +34,7 @@ To **calculate these images** for all masks in a folder, you can use either
 
 * **Jupyter notebook**: notebooks\distance-closest-object.ipynb
 * **ImJoy plugin**:  `ObjectDist`, which you  
-<a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/segmentation:ObjectDist@stable&upgrade=1" target="_blank">**install from here.**</a>
+<a href="https://imjoy.io/#/app?w=fq-segmentation&plugin=fish-quant/fq-segmentation:ObjectDist@stable&upgrade=1" target="_blank">**install from here.**</a>
 
 In either case, the following **parameters** have to be specified
 

docs/img/imjoy-segment-objects-ui.png

@@ -1,8 +1,8 @@
 
 Here we provide a description of less frequently used workflows.  
 
-* [**Split multi-channel images**](workflows-fiji-split-channels.md) to obain single-channel images for analysis. 
+* [**Split multi-channel images**](workflows-fiji-split-channels.md) to obain single-channel images for analysis.
 * [**Calculate distance to objects**](workflows-distance-objects.md) to calculate images summarizing which object is closest to each pixel.
-* [**Convert segmentation results in FQ outlines**](workflows-create-fq-outlines.md) that can be used by the older Matlab version of FISH-quant. 
+* [**Convert segmentation results in FQ outlines**](workflows-create-fq-outlines.md) that can be used by the older Matlab version of FISH-quant.
 
 Each workflow is documented in a dedicated section.

docs/workflows-distance-objects.md

@@ -6,7 +6,7 @@
 {
   "name": "ObjectDist",
   "type": "native-python",
-  "version": "0.1.5",
+  "version": "0.2.0",
   "description": "Pre-process 3D images to obtain 2D images for segmentation.",
   "tags":  ["stable","dev"],
   "ui": [
@@ -28,7 +28,7 @@
   "permissions": [],
   "requirements": {
                     "dev":   [""],
-                    "stable":["pip: -U git+https://github.com/fish-quant/segmentation@master"]
+                    "stable":["pip: -U git+https://github.com/fish-quant/fq-segmentation@master"]
                    },
   "dependencies": []
 }

docs/workflows-overview.md

@@ -7,7 +7,7 @@
 {
   "name": "PreProcess",
   "type": "native-python",
-  "version": "0.1.7",
+  "version": "0.2.0",
   "description": "Pre-process 3D images to obtain 2D images for segmentation.",
   "tags":  ["stable","dev"],
   "ui": [
@@ -29,7 +29,7 @@
   "permissions": [],
   "requirements": {
                     "dev":   [""],
-                    "stable":["pip: -U git+https://github.com/fish-quant/segmentation@master"]
+                    "stable":["pip: -U git+https://github.com/fish-quant/fq-segmentation@master"]
                    },
   "dependencies": []
 }

imjoy-plugins/ObjectDist.imjoy.html

@@ -7,7 +7,7 @@
 {
   "name": "SegmentCellsNuclei",
   "type": "native-python",
-  "version": "0.1.11",
+  "version": "0.2.0",
   "description": "Segment cells AND nuclei.",
   "tags":  ["stable","dev"],
   "ui": [
@@ -16,9 +16,10 @@
         "<span style='font-weight: 550; color:#666362'>Path SAVE</span>: {id: 'path_save', type: 'string', placeholder: 'segmentation-input˃˃segmentation-results'}",
         "<span style='font-weight: 550; color:#666362'>String CELLS</span>: {id: 'str_cyto', type: 'string', placeholder: 'cy3'}",
         "<span style='font-weight: 550; color:#666362'>String NUCLEI</span>: {id: 'str_nuclei', type: 'string', placeholder: 'dapi'}",
-        "<span style='font-weight: 550; color:#666362'>Scaling factor/New size:</span>: {id: 'new_size', type: 'string', placeholder: '2'}",
-        "<span style='font-weight: 550; color:#666362'>Size CELLS</span>: {id: 'size_cells', type: 'number', placeholder: 100}",
-        "<span style='font-weight: 550; color:#666362'>Size NUCLEI</span>: {id: 'size_nuclei', type: 'number', placeholder: 50}",
+        "<span style='font-weight: 550; color:#666362'>Size CELLS</span>: {id: 'size_cells', type: 'number', placeholder: 200}",
+        "<span style='font-weight: 550; color:#666362'>Size NUCLEI</span>: {id: 'size_nuclei', type: 'number', placeholder: 100}",
+        "<span style='font-weight: 550; color:#666362'>Net Average</span>: {id: 'net_avg', type: 'choose', options: ['false', 'true'], placeholder: 'false'}",
+        "<span style='font-weight: 550; color:#666362'>Resample</span>: {id: 'resample', type: 'choose', options: ['false', 'true'], placeholder: 'false'}",
         "<span style='font-weight: 550; color:#666362'>String img ext</span>: {id: 'img_ext', type: 'string', placeholder: '.png'}"
         ],
   "cover": "",
@@ -31,10 +32,10 @@
   "permissions": [],
   "requirements": {
                     "dev":   [""],
-                    "stable":["pip: -U git+https://github.com/fish-quant/segmentation@master"]
+                    "stable":["pip: -U git+https://github.com/fish-quant/fq-segmentation@master"]
                    },
   "dependencies": [],
-  "cover": "https://fish-quant.github.io/segmentation/img/segmentation__cells.png"
+  "cover": "https://fish-quant.github.io/fq-segmentation/img/segmentation__cells.png"
 }
 </config>
 
@@ -66,28 +67,10 @@
         # Get path for debugging
         if api.TAG == 'dev':
             if path_scan == 'paste-path-to-data':
-                path_scan = r'D:\Documents\data-test\fish-quant\fq-imjoy-demo\analysis\segmentation-input'
+                path_scan = r'D:\Work\Documents\data-test\fish-quant\fq-imjoy-demo\analysis\segmentation-input'
 
         # >>> CHECK INPUT PARAMETERS
 
-
-        # >> Check new size
-        new_size_str = ctx.config.new_size
-
-        if len(new_size_str) == 0: 
-            new_size = ()
-
-        else:
-            if new_size_str.count(',') == 0:
-                new_size = (int(new_size_str),)
-
-            elif new_size_str.count(',') == 1:
-                new_size = tuple(int(x) for x in new_size_str.split(','))
-
-            else:
-                api.alert('String to define new size has to contain one or zero \",\" to separate the new dimensions in XY.')
-                return
-
         # >> Path containing the data
         if path_scan == 'paste-path-to-data':
             api.alert('Path containing data to be processed has to be specified.')
@@ -117,16 +100,28 @@
         else:
             path_save = Path(path_save)
 
+        # >> Interpret boolean parameters
+        if ctx.config.resample == 'true':
+            resample = True 
+        else:
+            resample = False
+
+        if ctx.config.net_avg == 'true':
+            net_avg = True 
+        else:
+            net_avg = False
+
 
         # >>>> Call pre-processing function
         api.showStatus('Performing segmentation ... see log for details')
         utils_cellpose.segment_cells_nuclei_indiv(
                                       path_scan=path_scan, 
-                                      strings=(ctx.config.str_cyto, ctx.config.str_nuclei), 
+                                      str_channels=(ctx.config.str_cyto, ctx.config.str_nuclei), 
                                       img_ext=ctx.config.img_ext, 
-                                      new_size=new_size,
-                                      sizes=(ctx.config.size_cells, ctx.config.size_nuclei), 
-                                      models=('cyto','nuclei'),
+                                      model_types=('cyto','nuclei'),
+                                      diameters=(ctx.config.size_cells, ctx.config.size_nuclei), 
+                                      net_avg=net_avg, 
+                                      resample=resample,         
                                       path_save=path_save,
                                       input_subfolder=input_subfolder,
                                       callback_log=api.log,