Merge pull request #129 from MouseLand/move_resize

Move resize

.travis.yml

@@ -0,0 +1,73 @@
+language: python
+jobs:
+  include:
+  - name: Python 3.7.1 on Linux
+    dist: xenial
+    services:
+    - xvfb
+    python: 3.7
+    before_install:
+    - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O
+      miniconda.sh
+    - bash ./miniconda.sh -b
+    - export PATH=~/miniconda3/bin:$PATH
+    - conda update --yes conda
+    - export MPLBACKEND=Agg
+  - name: Python 3.7.1 on Linux Bionic
+    dist: bionic
+    services:
+    - xvfb
+    python: 3.7
+    before_install:
+    - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O
+      miniconda.sh
+    - bash ./miniconda.sh -b
+    - export PATH=~/miniconda3/bin:$PATH
+    - conda update --yes conda
+    - export MPLBACKEND=Agg
+  - name: Python 3.7.7 on macOS
+    os: osx
+    osx_image: xcode12
+    language: shell
+    before_install:
+    - wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+      -O miniconda.sh
+    - bash ./miniconda.sh -b
+    - export PATH=~/miniconda3/bin:$PATH
+    - conda update --yes conda
+    - export MPLBACKEND=Agg
+  - name: Python 3.7.7 on Windows
+    os: windows
+    language: shell
+    before_install:
+    - choco install python --version 3.7.7
+    - export MINICONDA=/c/miniconda
+    - MINICONDA_WIN=$(cygpath --windows $MINICONDA)
+    - choco install openssl.light
+    - choco install miniconda3 --params="'/AddToPath:0 /D:$MINICONDA_WIN'"
+    - PATH=$(echo "$PATH" | sed -e 's|:/c/ProgramData/chocolatey/bin||')
+    - PATH=$(echo "$PATH" | sed -e 's|:/c/ProgramData/chocolatey/lib/mingw/tools/install/mingw64/bin||')
+    - source $MINICONDA/Scripts/activate
+    - source $MINICONDA/etc/profile.d/conda.sh
+  allow_failures:
+  - os: osx
+  - os: windows
+install:
+- conda env create -f environment.yml
+- source activate cellpose
+- pip install .
+- pip install matplotlib
+- pip install coveralls
+script:
+- coverage run --source=cellpose --omit=cellpose/gui.py,cellpose/guiparts.py,cellpose/menus.py,cellpose/__main__.py
+  setup.py test
+after_success: coveralls
+deploy:
+  skip_cleanup: true
+  skip_existing: true
+  provider: pypi
+  user: __token__
+  password:
+    secure: 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
+  on:
+    tags: true

cellpose/__main__.py

@@ -19,60 +19,6 @@
     GUI_IMPORT = False
     raise
 
-def get_image_files(folder, mask_filter, imf=None):
-    mask_filters = ['_cp_masks', '_cp_output', '_flows', mask_filter]
-    image_names = []
-    if imf is None:
-        imf = ''
-    image_names.extend(glob.glob(folder + '/*%s.png'%imf))
-    image_names.extend(glob.glob(folder + '/*%s.jpg'%imf))
-    image_names.extend(glob.glob(folder + '/*%s.jpeg'%imf))
-    image_names.extend(glob.glob(folder + '/*%s.tif'%imf))
-    image_names.extend(glob.glob(folder + '/*%s.tiff'%imf))
-    image_names = natsorted(image_names)
-    imn = []
-    for im in image_names:
-        imfile = os.path.splitext(im)[0]
-        igood = all([(len(imfile) > len(mask_filter) and imfile[-len(mask_filter):] != mask_filter) or len(imfile) < len(mask_filter) 
-                        for mask_filter in mask_filters])
-        if len(imf)>0:
-            igood &= imfile[-len(imf):]==imf
-        if igood:
-            imn.append(im)
-    image_names = imn
-
-    return image_names
-
-def get_label_files(image_names, mask_filter, imf=None):
-    nimg = len(image_names)
-    label_names0 = [os.path.splitext(image_names[n])[0] for n in range(nimg)]
-
-    if imf is not None and len(imf) > 0:
-        label_names = [label_names0[n][:-len(imf)] for n in range(nimg)]
-    else:
-        label_names = label_names0
-
-    # check for flows
-    if os.path.exists(label_names0[0] + '_flows.tif'):
-        flow_names = [label_names0[n] + '_flows.tif' for n in range(nimg)]
-    else:
-        flow_names = [label_names[n] + '_flows.tif' for n in range(nimg)]
-    if not all([os.path.exists(flow) for flow in flow_names]):
-        flow_names = None
-
-    # check for masks
-    if os.path.exists(label_names[0] + mask_filter + '.tif'):
-        label_names = [label_names[n] + mask_filter + '.tif' for n in range(nimg)]
-    elif os.path.exists(label_names[0] + mask_filter + '.png'):
-        label_names = [label_names[n] + mask_filter + '.png' for n in range(nimg)]
-    else:
-        raise ValueError('labels not provided with correct --mask_filter')
-    if not all([os.path.exists(label) for label in label_names]):
-        raise ValueError('labels not provided for all images in train and/or test set')
-
-    return label_names, flow_names
-
-
 def main():
     parser = argparse.ArgumentParser(description='cellpose parameters')
     parser.add_argument('--check_mkl', action='store_true', help='check if mkl working')
@@ -161,10 +107,7 @@ def main():
         else:
             imf = None
 
-        image_names = get_image_files(args.dir, args.mask_filter, imf=imf)
-        nimg = len(image_names)
-        images = [io.imread(image_names[n]) for n in range(nimg)]
-
+
 
         if args.use_gpu:
             use_gpu = utils.use_gpu()
@@ -183,6 +126,10 @@ def main():
                     print('model path does not exist, using cyto model')
                     args.pretrained_model = 'cyto'
 
+            image_names = io.get_image_files(args.dir, args.mask_filter, imf=imf)
+            nimg = len(image_names)
+            images = [io.imread(image_names[n]) for n in range(nimg)]
+
             if args.pretrained_model=='cyto' or args.pretrained_model=='nuclei':
                 model = models.Cellpose(device=device, model_type=args.pretrained_model)
 
@@ -243,25 +190,9 @@ def main():
             if args.all_channels:
                 channels = None  
 
-            # training data
-            label_names, flow_names = get_label_files(image_names, args.mask_filter, imf=imf)
-            nimg = len(image_names)
-            labels = [io.imread(label_names[n]) for n in range(nimg)]
-            if flow_names is not None and not args.unet:
-                labels = [np.concatenate((labels[n][np.newaxis,:,:], io.imread(flow_names[n])), axis=0) 
-                          for n in range(nimg)]
-
-            # testing data
-            test_images, test_labels, image_names_test = None, None, None
-            if len(args.test_dir) > 0:
-                image_names_test = get_image_files(args.test_dir, args.mask_filter, imf=imf)
-                label_names_test, flow_names_test = get_label_files(image_names_test, args.mask_filter, imf=imf)
-                nimg = len(image_names_test)
-                test_images = [io.imread(image_names_test[n]) for n in range(nimg)]
-                test_labels = [io.imread(label_names_test[n]) for n in range(nimg)]
-                if flow_names_test is not None and not args.unet:
-                    test_labels = [np.concatenate((test_labels[n][np.newaxis,:,:], io.imread(flow_names_test[n])), axis=0) 
-                                   for n in range(nimg)]
+            test_dir = None if len(args.test_dir)==0 else args.test_dir
+            output = io.load_train_test_data(args.dir, test_dir, imf, args.mask_filter, args.unet)
+            images, labels, image_names, test_images, test_labels, image_names_test = output
 
             # model path
             if not os.path.exists(cpmodel_path):