rename package (#4)

 * rename package
 * update README
 * update setup
 * update asserts
 * fix dividing warning
 * update CI

.shippable.yml

@@ -45,8 +45,8 @@ script:
   - mkdir data && mkdir output && mkdir results
   - python setup.py build_ext --inplace
 
-  - nosetests -v --with-xunit --xunit-file=$CI_REPORTS/nosetests.xml
-  - nosetests segmentation -v --exe --with-doctest --with-xunit --with-coverage --cover-package segmentation
+  # - nosetests -v --with-xunit --xunit-file=$CI_REPORTS/nosetests.xml
+  - nosetests -v --exe --with-doctest --with-xunit --with-coverage --cover-package imsegm --xunit-file=$CI_REPORTS/nosetests.xml
 
   # ANNOTATION section
   - python handling_annotations/run_image_color_quantization.py -imgs "images/drosophila_ovary_slice/segm_rgb/*.png"
@@ -86,6 +86,8 @@ script:
   - python experiments_ovary_detect/run_egg_swap_orientation.py
 
 after_success:
+  - python setup.py install
+
   - coverage xml -o $COVERAGE_REPORTS/coverage.xml
   - codecov -t 80efed4e-ac2b-4fea-a642-0a8b1c82e1c8
   - coverage report

.travis.yml

@@ -42,8 +42,9 @@ before_script:
   - python setup.py build_ext --inplace
 
 script:
-  # - pytest segmentation -v --doctest-modules
-  - nosetests segmentation -v --exe --with-doctest --with-xunit --with-coverage --cover-package=segmentation
+  # - pytest imsegm -v --doctest-modules
+  - nosetests imsegm -v --exe --with-doctest --with-xunit --with-coverage --cover-package=imsegm
+  - python setup.py install
 
 after_success:
   # - codecov  # public repository on Travis CI

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2015-2017, Jiri Borovec
+Copyright (c) 2015-2018, Jiri Borovec
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification,

README.md

@@ -65,34 +65,43 @@ Borovec J., Kybic J., Sugimoto, A. (2017). **Region growing using superpixels wi
 **Configure local environment**
 
 Create your own local environment, for more see the [User Guide](https://pip.pypa.io/en/latest/user_guide.html), and install dependencies requirements.txt contains list of packages and can be installed as
-```
+```bash
 @duda:~$ cd pyImSegm  
 @duda:~/pyImSegm$ virtualenv env
 @duda:~/pyImSegm$ source env/bin/activate  
 (env)@duda:~/pyImSegm$ pip install -r requirements.txt  
 (env)@duda:~/pyImSegm$ python ...
 ```
 and in the end terminating...
-```
+```bash
 (env)@duda:~/pyImSegm$ deactivate
 ```
 
+<!--
 Moreover, we are using python [GraphCut wrapper](https://github.com/Borda/pyGCO) which require to be installed separately (not yet integrated in PIP)
-```
+```bash
 (env)@duda:~/pyImSegm$ mkdir libs && cd libs
 (env)@duda:~/pyImSegm$ git clone https://github.com/Borda/pyGCO.git
 (env)@duda:~/pyImSegm$ pip install -r requirements.txt
 (env)@duda:~/pyImSegm$ python setup.py install
 ```
+-->
 
 **Compilation**
 
 We have implemented cython version of some functions, especially computing descriptors, which require to compile them before using them
-```
+```bash
 python setup.py build_ext --inplace
 ```
 If loading of compiled descriptors in cython fails, it is automatically swapped to numpy which gives the same results, but it is significantly slower.
 
+**Installation**
+
+The package can be installed via pip from the folder
+```bash
+python setup.py install
+```
+
 ---
 
 
@@ -110,32 +119,32 @@ Short description of our three sets of experiments that together compose single
 We introduce some useful tools for work with image annotation and segmentation.
 
 * In case you have some smooth color labeling in your images you can remove them with following quantization script.
-    ```
+    ```bash
     python handling_annotations/run_image_color_quantization.py \
         -imgs "images/drosophila_ovary_slice/segm_rgb/*.png" \
         -m position -thr 0.01 --nb_jobs 2
     ```
 * Concerting image labels into colour space and other way around.
-    ```
+    ```bash
     python handling_annotations/run_image_convert_label_color.py \
         -imgs "images/drosophila_ovary_slice/segm/*.png" \
         -out images/drosophila_ovary_slice/segm_rgb
     ```
 * Having input image and its segmentation we can use simple visualisation which overlap the segmentation over input image. 
-    ```
+    ```bash
     python handling_annotations/run_overlap_images_segms.py \
         -imgs "images/drosophila_ovary_slice/image/*.jpg" \
         -segs images/drosophila_ovary_slice/segm \
         -out results/overlap_ovary_segment
     ```
 * Inpainting selected labels in segmentation.
-    ```
+    ```bash
     python handling_annotations/run_segm_annot_inpaint.py \
         -imgs "images/drosophila_ovary_slice/segm/*.png" \
         --label 4
     ```
 * Change labels in input segmentation into another set of lables in 1:1 schema.
-    ```
+    ```bash
     python handling_annotations/run_segm_annot_relabel.py \
         -imgs "images/drosophila_ovary_slice/center_levels/*.png" \
         -out results/relabel_center_levels \
@@ -148,29 +157,29 @@ We introduce some useful tools for work with image annotation and segmentation.
 We utilize (un)supervised segmentation according to given training examples or some expectations.
 
 * Evaluate superpixels (with given SLIC parameters) quality against given segmentation. It helps find out best SLIC configuration.
-    ```
+    ```bash
     python experiments_segmentation/run_eval_superpixels.py \
         -imgs "images/drosophila_ovary_slice/image/*.jpg" \
         -segm "images/drosophila_ovary_slice/annot_eggs/*.png" \
         --img_type 2d_gray \
         --slic_size 20 --slic_regul 0.25 --slico 0
     ```
 * Perform **Unsupervised** segmentation.
-    ```
+    ```bash
     python experiments_segmentation/run_segm_slic_model_graphcut.py \
        -list images/langerhans_islets/list_lang-isl_imgs-annot.csv \
        -imgs "images/langerhans_islets/image/*.jpg" \
        -out results -n langIsl --nb_classes 3 --visual --nb_jobs 2
     ```
 * Perform **Supervised** segmentation with afterwards evaluation.
-    ```
+    ```bash
     python experiments_segmentation/run_segm_slic_classif_graphcut.py \
         -list images/drosophila_ovary_slice/list_imgs-annot-struct.csv \
         -imgs "images/drosophila_ovary_slice/image/*.jpg" \
         -out results -n Ovary --img_type 2d_gray --visual --nb_jobs 2
     ```
 * For both experiment you can evaluate segmentation results.
-    ```
+    ```bash
     python experiments_segmentation/run_compute-stat_annot-segm.py \
         -annot "images/drosophila_ovary_slice/annot_struct/*.png" \
         -segm "results/experiment_segm-supervise_ovary/*.png" \
@@ -188,49 +197,49 @@ In general, the input is a formatted list (CSV file) of input images and annotat
 **Experiment sequence is following:**
 
 1. We can create the annotation completely manually or use following script which uses annotation of individual objects and create the zones automatically.
-    ```
+    ```bash
     python experiments_ovary_centres/run_create_annotation.py
     ```
 1. With zone annotation, we train a classifier for center candidate prediction. The annotation can be a CSV file with annotated centers as points, and the zone of positive examples is set uniformly as the circular neighborhood around these points. Another way (preferable) is to use annotated image with marked zones for positive, negative and neutral examples.
-    ```
+    ```bash
     python experiments_ovary_centres/run_center_candidate_training.py -list none \
         -segs "images/drosophila_ovary_slice/segm/*.png" \
         -imgs "images/drosophila_ovary_slice/image/*.jpg" \
         -centers "images/drosophila_ovary_slice/center_levels/*.png" \
         -out results -n ovary
     ```
 1. Having trained classifier we perfom center prediction composed from two steps: i. center candidate clustering and candidate clustering.
-    ```
+    ```bash
     python experiments_ovary_centres/run_center_prediction.py -list none \
         -segs "images/drosophila_ovary_slice/segm/*.png" \
         -imgs "images/drosophila_ovary_slice/image/*.jpg" \
         -centers results/detect-centers-train_ovary/classifier_RandForest.pkl \
         -out results -n ovary
     ```
 1. Assuming you have an expert annotation you can compute static such as missed eggs.
-    ```
+    ```bash
     python experiments_ovary_centres/run_center_evaluation.py
     ```
 1. This is just cut out clustering in case you want to use different parameters.
-    ```
+    ```bash
     python experiments_ovary_centres/run_center_clustering.py
     ```
 1. Matching the ellipses to the user annotation.
-    ```
+    ```bash
     python experiments_ovary_detect/run_ellipse_annot_match.py \
         -info "~/Medical-drosophila/all_ovary_image_info_for_prague.txt" \
         -ells "~/Medical-drosophila/RESULTS/3_ellipse_ransac_crit_params/*.csv" \
         -out ~/Medical-drosophila/RESULTS
     ```
 1. Cut eggs by stages and norm to mean size.
-    ```
+    ```bash
     python experiments_ovary_detect/run_ellipse_cut_scale.py \
         -info ~/Medical-drosophila/RESULTS/info_ovary_images_ellipses.csv \
         -imgs "~/Medical-drosophila/RESULTS/0_input_images_png/*.png" \
         -out ~/Medical-drosophila/RESULTS/images_cut_ellipse_stages
     ```
 1. Rotate (swap) extrated eggs according the larger mount of mass.
-    ```
+    ```bash
     python experiments_ovary_detect/run_egg_swap_orientation.py
     ```
 
@@ -241,7 +250,7 @@ In general, the input is a formatted list (CSV file) of input images and annotat
 In case you do not have estimated object centers, you can use [plugins](ij_macros) for landmarks import/export for [Fiji](http://fiji.sc/).
 
 **Note:** install multi-snake package which is used in multi-method segmentation experiment.
-```
+```bash
 cd libs 
 git clone https://github.com/Borda/morph-snakes.git
 cd morph-snakes 
@@ -252,13 +261,13 @@ python setup.py install
 **Experiment sequence is following:**
 
 1. Estimating shape model from set training images containing single egg annotation.
-    ```
+    ```bash
     python experiments_ovary_detect/run_RG2Sp_estim_shape-models.py  \
         -annot "~/Medical-drosophila/egg_segmentation/mask_2d_slice_complete_ind_egg/*.png" \
         -out data -nb 15
     ```
 1. Run several segmentation techniques on each image.
-    ```
+    ```bash
     python experiments_ovary_detect/run_ovary_egg-segmentation.py  \
         -list images/drosophila_ovary_slice/list_imgs-segm-center-points.csv \
         -out output -n ovary_image --nb_jobs 1 \
@@ -274,18 +283,18 @@ python setup.py install
            watershed_morph
     ```
 1. Evaluate your segmentation results to expert annotation.
-    ```
+    ```bash
     python experiments_ovary_detect/run_ovary_segm_evaluation.py --visual
     ```
 1. In the end, cut individual segmented objects comes as minimal bounding box.
-    ```
+    ```bash
     python experiments_ovary_detect/run_cut_segmented_objects.py \
         -annot "images/drosophila_ovary_slice/annot_eggs/*.png" \
         -img "images/drosophila_ovary_slice/segm/*.png" \
         -out results/cut_images --padding 50
     ```
 1. Finally, performing visualisation of segmentation results toghter with expert annotation.
-    ```
+    ```bash
     python experiments_ovary_detect/run_export_user-annot-segm.py
     ```
     ![user-annnot](figures/insitu7545_user-annot-segm.jpg)
@@ -296,6 +305,6 @@ python setup.py install
 ## References
 
 For complete references see [BibTex](docs/references.bib).
-1. Borovec J., Svihlik J., Kybic J., Habart D. (2017). **Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.** SPIE Journal of Electronic Imaging 26(6), 061610, http://doi.org/10.1117/1.JEI.26.6.061610
-1. Borovec J., Kybic J., Nava R. (2017) **Detection and Localization of Drosophila Egg Chambers in Microscopy Images.** In: Wang Q., Shi Y., Suk HI., Suzuki K. (eds) Machine Learning in Medical Imaging. MLMI 2017. LNCS, vol 10541. Springer, Cham. http://doi.org/10.1007/978-3-319-67389-9_3
-1. Borovec J., Kybic J., Sugimoto, A. (2017). **Region growing using superpixels with learned shape prior.** SPIE Journal of Electronic Imaging 26(6),  061611, http://doi.org/10.1117/1.JEI.26.6.061611
+1. Borovec J., Svihlik J., Kybic J., Habart D. (2017). **Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.** SPIE Journal of Electronic Imaging 26(6), 061610, [DOI: 10.1117/1.JEI.26.6.061610](http://doi.org/10.1117/1.JEI.26.6.061610).
+1. Borovec J., Kybic J., Nava R. (2017) **Detection and Localization of Drosophila Egg Chambers in Microscopy Images.** In: Wang Q., Shi Y., Suk HI., Suzuki K. (eds) Machine Learning in Medical Imaging. MLMI 2017. LNCS, vol 10541. Springer, Cham. [DOI: 10.1007/978-3-319-67389-9_3](http://doi.org/10.1007/978-3-319-67389-9_3).
+1. Borovec J., Kybic J., Sugimoto, A. (2017). **Region growing using superpixels with learned shape prior.** SPIE Journal of Electronic Imaging 26(6),  061611, [DOI: 10.1117/1.JEI.26.6.061611](http://doi.org/10.1117/1.JEI.26.6.061611).

circle.yml

@@ -29,7 +29,7 @@ test:
     - mkdir -p $CIRCLE_TEST_REPORTS
 
   override:
-    - unset DISPLAY && coverage run --source segmentation -m py.test segmentation -v --doctest-modules --junitxml=$CIRCLE_TEST_REPORTS/pytest_junit.xml
+    - unset DISPLAY && coverage run --source imsegm -m py.test imsegm -v --doctest-modules --junitxml=$CIRCLE_TEST_REPORTS/pytest_junit.xml
 
   post:
     - coverage report && coverage xml -o $CIRCLE_TEST_REPORTS/coverage.xml

experiments_ovary_centres/gui_annot_center_correction.py

@@ -45,8 +45,8 @@
 from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')] # Add path to root
-import segmentation.utils.data_io as tl_io
-import segmentation.utils.drawing as tl_visu
+import imsegm.utils.data_io as tl_io
+import imsegm.utils.drawing as tl_visu
 
 PATH_BASE = tl_io.update_path(os.path.join('images', 'drosophila_ovary_slice'))
 PATH_IMAGES = os.path.join(PATH_BASE, 'image', '*.jpg')
@@ -90,7 +90,7 @@ def arg_parse_params():
         if params[k] is None: continue
         params[k] = os.path.abspath(os.path.expanduser(params[k]))
         p = os.path.dirname(params[k]) if '*' in params[k] else params[k]
-        assert os.path.exists(p), '%s' % p
+        assert os.path.exists(p), 'missing: %s' % p
     logging.info('ARG PARAMETERS: \n %s', repr(params))
     return params
 

experiments_ovary_centres/run_center_candidate_training.py

@@ -44,13 +44,13 @@
 import matplotlib.pyplot as plt
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')]  # Add path to root
-import segmentation.utils.data_io as tl_io
-import segmentation.utils.experiments as tl_expt
-import segmentation.utils.drawing as tl_visu
-import segmentation.superpixels as seg_spx
-import segmentation.descriptors as seg_fts
-import segmentation.classification as seg_clf
-import segmentation.labeling as seg_lbs
+import imsegm.utils.data_io as tl_io
+import imsegm.utils.experiments as tl_expt
+import imsegm.utils.drawing as tl_visu
+import imsegm.superpixels as seg_spx
+import imsegm.descriptors as seg_fts
+import imsegm.classification as seg_clf
+import imsegm.labeling as seg_lbs
 
 # whether skip loading triplest CSV from previous run
 FORCE_RELOAD = False
@@ -353,7 +353,7 @@ def estim_points_compute_features(name, img, segm, params):
     """
     # superpixels on image
     assert img.shape[:2] == segm.shape[:2], \
-        'shapes: %s : %s' % (repr(img.shape), repr(segm.shape))
+        'not matching shapes: %s : %s' % (repr(img.shape), repr(segm.shape))
     slic = seg_spx.segment_slic_img2d(img, params['slic_size'],
                                       params['slic_regul'])
     slic_centers = seg_spx.superpixel_centers(slic)
@@ -437,7 +437,7 @@ def label_close_points(centers, points, params):
         labels = [-1] * len(points)
     assert len(points) == len(labels), \
         'not equal lenghts of points (%i) and labels (%i)' \
-        % (len(points),len(labels))
+        % (len(points), len(labels))
     return labels
 
 

experiments_ovary_centres/run_center_clustering.py

@@ -27,9 +27,9 @@
 import matplotlib.pylab as plt
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')]  # Add path to root
-import segmentation.utils.data_io as tl_io
-import segmentation.utils.experiments as tl_expt
-import segmentation.utils.drawing as tl_visu
+import imsegm.utils.data_io as tl_io
+import imsegm.utils.experiments as tl_expt
+import imsegm.utils.drawing as tl_visu
 import run_center_candidate_training as run_train
 # import run_center_prediction as run_pred
 

experiments_ovary_centres/run_center_evaluation.py

@@ -35,9 +35,9 @@
 import matplotlib.pyplot as plt
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')]  # Add path to root
-import segmentation.utils.experiments as tl_expt
-import segmentation.utils.drawing as tl_visu
-import segmentation.annotation as seg_annot
+import imsegm.utils.experiments as tl_expt
+import imsegm.utils.drawing as tl_visu
+import imsegm.annotation as seg_annot
 import run_center_candidate_training as run_train
 import run_center_prediction as run_detect
 import run_center_clustering as run_clust

experiments_ovary_centres/run_center_prediction.py

@@ -24,9 +24,9 @@
 import pandas as pd
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')] # Add path to root
-import segmentation.utils.experiments as tl_expt
-import segmentation.utils.data_io as tl_io
-import segmentation.classification as seg_clf
+import imsegm.utils.experiments as tl_expt
+import imsegm.utils.data_io as tl_io
+import imsegm.classification as seg_clf
 import run_center_candidate_training as run_train
 import run_center_clustering as run_clust
 

experiments_ovary_centres/run_create_annotation.py

@@ -95,7 +95,7 @@ def draw_circle(pos_center, radius, img_shape):
 
 
 def segm_set_center_levels(name, seg_labels, path_out, levels=DISTANCE_LEVELS):
-    """ set segmentation levels according distance inside object segmentation
+    """ set segmentation levels according distance inside object imsegm
 
     :param str name: image name
     :param ndarray seg_labels: