adding more documentation in gui

README.md

@@ -25,9 +25,12 @@ If you want to improve Cellpose for yourself and for everyone else, please consi
 
 Cellpose 2.0 now allows human-in-the-loop training of models! Check out the twitter [thread](twitter) and [preprint](biorxiv) for details.
 
-Read how to use it yourself in the [docs](docs), and check out the full human-in-the-loop 
+Read how to use it yourself in the [docs](https://cellpose.readthedocs.io/en/latest/gui.html#training-your-own-cellpose-model), and check out the full human-in-the-loop video:
+
 [![video](https://img.youtube.com/vi/3Y1VKcxjNy4/default.jpg)](https://youtu.be/3Y1VKcxjNy4)
 
+![gui screenshot](https://www.cellpose.org/static/images/cellpose_gui.png)
+
 ### UPDATE v1.0 (Jan 2022)
 
 Cellpose has been relatively stable for a while now. Small bugs will continue to be fixed, but we are now releasing a reference 1.0 version. Larger updates to Cellpose will go towards a new 2.0 candidate version to be released soon. 

cellpose/gui/gui.py

@@ -10,7 +10,6 @@
 
 import numpy as np
 import cv2
-from scipy.ndimage import gaussian_filter
 
 from . import guiparts, menus, io
 from .. import models, core, dynamics
@@ -132,7 +131,7 @@ def run():
  print('downloading logo')
  download_url_to_file('https://www.cellpose.org/static/images/cellpose_transparent.png', icon_path, progress=True)
  if not guip_path.is_file():
- download_url_to_file('https://github.com/MouseLand/cellpose/raw/master/docs/_static/cellpose_gui.png', guip_path, progress=True)
+ download_url_to_file('https://www.cellpose.org/static/images/cellpose_gui.png', guip_path, progress=True)
  icon_path = str(icon_path.resolve())
  app_icon = QtGui.QIcon()
  app_icon.addFile(icon_path, QtCore.QSize(16, 16))
@@ -255,6 +254,10 @@ def help_window(self):
  HW = guiparts.HelpWindow(self)
  HW.show()
 
+ def train_help_window(self):
+ THW = guiparts.TrainHelpWindow(self)
+ THW.show()
+
  def gui_window(self):
  EG = guiparts.ExampleGUI(self)
  EG.show()
@@ -303,8 +306,14 @@ def make_buttons(self):
  self.RGBDropDown.currentIndexChanged.connect(self.color_choose)
  #self.RGBDropDown.setFixedWidth(60)
  self.RGBDropDown.setStyleSheet(self.dropdowns)
+
 
  self.l0.addWidget(self.RGBDropDown, b,0,1,4)
+ label = QLabel('[press R / G / B to \n toggle RGB and color ]')
+ label.setStyleSheet(label_style)
+ label.setFont(self.smallfont)
+ self.l0.addWidget(label, b+1,0,1,4)
+
  b+=3
 
  self.resize = -1
@@ -451,7 +460,7 @@ def make_buttons(self):
  # post-hoc paramater tuning
 
  b+=1
- label = QLabel('flow threshold:')
+ label = QLabel('flow_threshold:')
  label.setToolTip('threshold on flow error to accept a mask (set higher to get more cells, e.g. in range from (0.1, 3.0), OR set to 0.0 to turn off so no cells discarded);\n press enter to recompute if model already run')
  label.setStyleSheet(label_style)
  label.setFont(self.medfont)
@@ -463,7 +472,7 @@ def make_buttons(self):
  self.l0.addWidget(self.flow_threshold, b,5,1,4)
 
  b+=1
- label = QLabel('cellprob threshold:')
+ label = QLabel('cellprob_threshold:')
  label.setToolTip('threshold on cellprob output to seed cell masks (set lower to include more pixels or higher to include fewer, e.g. in range from (-6, 6)); \n press enter to recompute if model already run')
  label.setStyleSheet(label_style)
  label.setFont(self.medfont)
@@ -782,7 +791,7 @@ def model_choose(self, index):
 
  def calibrate_size(self):
  self.initialize_model(model_name='cyto')
- diams, _ = self.model.sz.eval(self.stack[self.currentZ].copy(), invert=self.invert.isChecked(),
+ diams, _ = self.model.sz.eval(self.stack[self.currentZ].copy(),
  channels=self.get_channels(), progress=self.progress)
  diams = np.maximum(5.0, diams)
  logger.info('estimated diameter of cells using %s model = %0.1f pixels'%
@@ -1624,9 +1633,6 @@ def train_model(self):
  self.diameter = diam_labels
  self.Diameter.setText('%0.2f'%self.diameter) 
  logger.info(f'>>>> diameter set to diam_labels ( = {diam_labels: 0.3f} )')
- if self.train_files[0] == self.filename:
- print(f'GUI_INFO: trained on all images + masks in folder --> auto-end training')
- return 
  self.compute_model()
  logger.info(f'!!! computed masks for {os.path.split(self.filename)[1]} from new model !!!')
 

cellpose/gui/guiparts.py

@@ -256,7 +256,7 @@ def __init__(self, parent=None):
 class HelpWindow(QDialog):
  def __init__(self, parent=None):
  super(HelpWindow, self).__init__(parent)
- self.setGeometry(100,100,700,800)
+ self.setGeometry(100,50,700,850)
  self.setWindowTitle('cellpose help')
  self.win = QWidget(self)
  layout = QGridLayout()
@@ -278,6 +278,8 @@ def __init__(self, parent=None):
  <p class="has-line-data" data-line-start="17" data-line-end="18">!NOTE!: The GUI automatically saves after you draw a mask in 2D but NOT after 3D mask drawing and NOT after segmentation. Save in the file menu or with Ctrl+S. The output file is in the same folder as the loaded image with <code>_seg.npy</code> appended.</p>
  <table class="table table-striped table-bordered">
  <br><br>
+ FYI there are tooltips throughout the GUI (hover over text to see)
+ <br>
  <thead>
  <tr>
  <th>Keyboard shortcuts</th>
@@ -310,6 +312,10 @@ def __init__(self, parent=None):
  <td>SAVE MASKS IN IMAGE to <code>_seg.npy</code> file</td>
  </tr>
  <tr>
+ <td>CTRL+T</td>
+ <td>train model using _seg.npy files in folder
+ </tr>
+ <tr>
  <td>CTRL+P</td>
  <td>load <code>_seg.npy</code> file (note: it will load automatically with image if it exists)</td>
  </tr>
@@ -318,10 +324,6 @@ def __init__(self, parent=None):
  <td>load masks file (must be same size as image with 0 for NO mask, and 1,2,3… for masks)</td>
  </tr>
  <tr>
- <td>CTRL+N</td>
- <td>load numpy stack (NOT WORKING ATM)</td>
- </tr>
- <tr>
  <td>A/D or LEFT/RIGHT</td>
  <td>cycle through images in current directory</td>
  </tr>
@@ -330,12 +332,12 @@ def __init__(self, parent=None):
  <td>change color (RGB/gray/red/green/blue)</td>
  </tr>
  <tr>
- <td>PAGE-UP / PAGE-DOWN</td>
- <td>change to flows and cell prob views (if segmentation computed)</td>
+ <td>R / G / B </td>
+ <td>toggle between RGB and Red or Green or Blue</td>
  </tr>
  <tr>
- <td>, / .</td>
- <td>increase / decrease brush size for drawing masks</td>
+ <td>PAGE-UP / PAGE-DOWN</td>
+ <td>change to flows and cell prob views (if segmentation computed)</td>
  </tr>
  <tr>
  <td>X</td>
@@ -346,8 +348,8 @@ def __init__(self, parent=None):
  <td>toggle outlines ON or OFF</td>
  </tr>
  <tr>
- <td>C</td>
- <td>cycle through labels for image type (saved to <code>_seg.npy</code>)</td>
+ <td>, / .</td>
+ <td>increase / decrease brush size for drawing masks</td>
  </tr>
  </tbody>
  </table>
@@ -364,6 +366,35 @@ def __init__(self, parent=None):
  layout.addWidget(label, 0, 0, 1, 1)
  self.show()
 
+
+class TrainHelpWindow(QDialog):
+ def __init__(self, parent=None):
+ super(TrainHelpWindow, self).__init__(parent)
+ self.setGeometry(100,50,700,300)
+ self.setWindowTitle('training instructions')
+ self.win = QWidget(self)
+ layout = QGridLayout()
+ self.win.setLayout(layout)
+
+ text = ('''
+ Check out this <a href="https://youtu.be/3Y1VKcxjNy4">video</a> to learn the process.
+ <ol>
+ <li>Drag and drop an image from a folder of images with a similar style (like similar cell types).</li>
+ <li>Run the built-in models on one of the images using the "model zoo" and find the one that works best for your data. Make sure that if you have a nuclear channel you have selected it for CHAN2.</li>
+ <li>Fix the labelling by drawing new ROIs (right-click) and deleting incorrect ones (CTRL+click). The GUI autosaves any manual changes (but does not autosave after running the model, for that click CTRL+S). The segmentation is saved in a "_seg.npy" file.</li>
+ <li> Go to the "Models" menu in the File bar at the top and click "Train new model..." or use shortcut CTRL+T. </li>
+ <li> Choose the pretrained model to start the training from (the model you used in #2), and type in the model name that you want to use. The other parameters should work well in general for most data types. Then click OK. </li>
+ <li> The model will train (much faster if you have a GPU) and then auto-run on the next image in the folder. Next you can repeat #3-#5 as many times as is necessary. </li>
+ <li> The trained model is available to use in the future in the GUI in the "custom model" section and is saved in your image folder. </li>
+ </ol>
+ ''')
+ label = QLabel(text)
+ label.setFont(QtGui.QFont("Arial", 8))
+ label.setWordWrap(True)
+ layout.addWidget(label, 0, 0, 1, 1)
+ self.show()
+
+
 class TypeRadioButtons(QButtonGroup):
  def __init__(self, parent=None, row=0, col=0):
  super(TypeRadioButtons, self).__init__()

cellpose/gui/menus.py

@@ -103,20 +103,24 @@ def modelmenu(parent):
  parent.newmodel.setEnabled(False)
  model_menu.addAction(parent.newmodel)
 
+ openTrainHelp = QAction("Training instructions", parent)
+ openTrainHelp.triggered.connect(parent.train_help_window)
+ model_menu.addAction(openTrainHelp)
+
 def helpmenu(parent):
  main_menu = parent.menuBar()
  help_menu = main_menu.addMenu("&Help")
-
- checkMKL = QAction("Check CPU MKL -- see terminal", parent)
- checkMKL.triggered.connect(lambda: models.check_mkl(istorch=parent.torch))
- help_menu.addAction(checkMKL)
 
- openHelp = QAction("&Help window", parent)
+ openHelp = QAction("&Help with GUI", parent)
  openHelp.setShortcut("Ctrl+H")
  openHelp.triggered.connect(parent.help_window)
  help_menu.addAction(openHelp)
-
+ 
  openGUI = QAction("&GUI layout", parent)
  openGUI.setShortcut("Ctrl+G")
  openGUI.triggered.connect(parent.gui_window)
- help_menu.addAction(openGUI)
+ help_menu.addAction(openGUI)
+
+ openTrainHelp = QAction("Training instructions", parent)
+ openTrainHelp.triggered.connect(parent.train_help_window)
+ help_menu.addAction(openTrainHelp)

docs/gui.rst

@@ -69,10 +69,10 @@ CHAN2 (OPT): if *cytoplasm* model is chosen, then choose the nuclear channel for
 Training your own cellpose model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Check out this `video <https://youtu.be/3Y1VKcxjNy4>`_ to watch Marius going through the process.
+Check out this `video <https://youtu.be/3Y1VKcxjNy4>`_ to learn the process.
 
 1. Drag and drop an image from a folder of images with a similar style (like similar cell types).
-2. Run the built-in models on the data in the "model zoo" and find the one that works best for your data. Make sure that if you have a nuclear channel you have selected it for CHAN2.
+2. Run the built-in models on one of the images using the "model zoo" and find the one that works best for your data. Make sure that if you have a nuclear channel you have selected it for CHAN2.
 3. Fix the labelling by drawing new ROIs (right-click) and deleting incorrect ones (CTRL+click). The GUI autosaves any manual changes (but does not autosave after running the model, for that click CTRL+S). The segmentation is saved in a ``_seg.npy`` file.
 4. Go to the "Models" menu in the File bar at the top and click "Train new model..." or use shortcut CTRL+T.
 5. Choose the pretrained model to start the training from (the model you used in #2), and type in the model name that you want to use. The other parameters should work well in general for most data types. Then click OK.
@@ -114,6 +114,8 @@ Keyboard shortcuts
 +---------------------+-----------------------------------------------+
 | CTRL+S | SAVE MASKS IN IMAGE to ``_seg.npy`` file |
 +---------------------+-----------------------------------------------+
+| CTRL+T | start model training using ``_seg.npy`` files |
++---------------------+-----------------------------------------------+
 | CTRL+P | load ``_seg.npy`` file (note: it will load |
 | | automatically with image if it exists) |
 +---------------------+-----------------------------------------------+
@@ -124,17 +126,16 @@ Keyboard shortcuts
 +---------------------+-----------------------------------------------+
 | W/S or UP/DOWN | change color (RGB/gray/red/green/blue) |
 +---------------------+-----------------------------------------------+
+| R / G / B | press to toggle RGB and Red or Green or Blue |
++---------------------+-----------------------------------------------+
 | PAGE-UP / PAGE-DOWN | change to flows and cell prob views (if |
 | | segmentation computed) |
 +---------------------+-----------------------------------------------+
-| , / . | increase / decrease brush size for drawing |
-+---------------------+-----------------------------------------------+
 | X | turn masks ON or OFF |
 +---------------------+-----------------------------------------------+
 | Z | toggle outlines ON or OFF |
 +---------------------+-----------------------------------------------+
-| C | cycle through labels for image type (saved to |
-| | ``_seg.npy``) |
+| , / . | increase / decrease brush size for drawing |
 +---------------------+-----------------------------------------------+