Codestyle and Good Practices

.gitignore

@@ -0,0 +1,145 @@
+
+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# End of https://www.toptal.com/developers/gitignore/api/python

cityscapes_gt_converter/cityscapes_panoptic_converter.py

@@ -3,8 +3,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
-import json
+import os
 import glob
 import numpy as np
 import PIL.Image as Image
@@ -14,16 +13,18 @@
 try:
     # set up path for cityscapes scripts
     # sys.path.append('./cityscapesScripts/')
-    from cityscapesscripts.helpers.labels import labels, id2label
+    from cityscapesscripts.helpers.labels import labels
 except Exception:
-    raise Exception("Please load Cityscapes scripts from https://github.com/mcordts/cityscapesScripts")
+    raise Exception(
+        "Please load Cityscapes scripts from https://github.com/mcordts/cityscapesScripts")
 
 original_format_folder = './gtFine/val/'
 # folder to store panoptic PNGs
 out_folder = './cityscapes_data/cityscapes_panoptic_val/'
 # json with segmentations information
 out_file = './cityscapes_data/cityscapes_panoptic_val.json'
 
+
 def panoptic_converter(original_format_folder, out_folder, out_file):
 
     if not os.path.isdir(out_folder):
@@ -42,7 +43,8 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
 
     categories_dict = {cat['id']: cat for cat in categories}
 
-    file_list = sorted(glob.glob(os.path.join(original_format_folder, '*/*_gtFine_instanceIds.png')))
+    file_list = sorted(glob.glob(os.path.join(
+        original_format_folder, '*/*_gtFine_instanceIds.png')))
 
     images = []
     annotations = []
@@ -54,17 +56,17 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
 
         file_name = f.split('/')[-1]
         image_id = file_name.rsplit('_', 2)[0]
-        image_filename= '{}_leftImg8bit.png'.format(image_id)
+        image_filename = '{}_leftImg8bit.png'.format(image_id)
         # image entry, id for image is its filename without extension
         images.append({"id": image_id,
                        "width": original_format.shape[1],
                        "height": original_format.shape[0],
                        "file_name": image_filename})
 
-        pan_format = np.zeros((original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
+        pan_format = np.zeros(
+            (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
         id_generator = IdGenerator(categories_dict)
 
-        idx = 0
         l = np.unique(original_format)
         segm_info = []
         for el in l:
@@ -82,7 +84,7 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
             segment_id, color = id_generator.get_id_and_color(semantic_id)
             pan_format[mask] = color
 
-            area = np.sum(mask) # segment area computation
+            area = np.sum(mask)  # segment area computation
 
             # bbox computation for a segment
             hor = np.sum(mask, axis=0)
@@ -110,9 +112,10 @@ def panoptic_converter(original_format_folder, out_folder, out_file):
     d = {'images': images,
          'annotations': annotations,
          'categories': categories,
-        }
+         }
 
     save_json(d, out_file)
 
+
 if __name__ == "__main__":
     panoptic_converter(original_format_folder, out_folder, out_file)

converters/2channels2panoptic_coco_format.py

@@ -20,35 +20,39 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
 import time
 import multiprocessing
-import itertools
 
 import PIL.Image as Image
 
 from panopticapi.utils import get_traceback, IdGenerator, save_json
 
 OFFSET = 1000
 
+
 @get_traceback
 def convert_single_core(proc_id, image_set, categories, source_folder, segmentations_folder, VOID=0):
     annotations = []
     for working_idx, image_info in enumerate(image_set):
         if working_idx % 100 == 0:
-            print('Core: {}, {} from {} images converted'.format(proc_id, working_idx, len(image_set)))
+            print('Core: {}, {} from {} images converted'.format(
+                proc_id, working_idx, len(image_set)))
 
         file_name = '{}.png'.format(image_info['file_name'].rsplit('.')[0])
         try:
-            original_format = np.array(Image.open(os.path.join(source_folder, file_name)), dtype=np.uint32)
+            original_format = np.array(Image.open(
+                os.path.join(source_folder, file_name)), dtype=np.uint32)
         except IOError:
-            raise KeyError('no prediction png file for id: {}'.format(image_info['id']))
+            raise KeyError(
+                'no prediction png file for id: {}'.format(image_info['id']))
 
         pan = OFFSET * original_format[:, :, 0] + original_format[:, :, 1]
-        pan_format = np.zeros((original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
+        pan_format = np.zeros(
+            (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8)
 
         id_generator = IdGenerator(categories)
 
@@ -70,7 +74,8 @@ def convert_single_core(proc_id, image_set, categories, source_folder, segmentat
                             'file_name': file_name,
                             "segments_info": segm_info})
 
-        Image.fromarray(pan_format).save(os.path.join(segmentations_folder, file_name))
+        Image.fromarray(pan_format).save(
+            os.path.join(segmentations_folder, file_name))
     print('Core: {}, all {} images processed'.format(proc_id, len(image_set)))
     return annotations
 
@@ -93,7 +98,8 @@ def converter(source_folder, images_json_file, categories_json_file,
     if segmentations_folder is None:
         segmentations_folder = predictions_json_file.rsplit('.', 1)[0]
     if not os.path.isdir(segmentations_folder):
-        print("Creating folder {} for panoptic segmentation PNGs".format(segmentations_folder))
+        print("Creating folder {} for panoptic segmentation PNGs".format(
+            segmentations_folder))
         os.mkdir(segmentations_folder)
 
     print("CONVERTING...")
@@ -106,7 +112,8 @@ def converter(source_folder, images_json_file, categories_json_file,
     print('\n')
     cpu_num = multiprocessing.cpu_count()
     images_split = np.array_split(images, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(images_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(images_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, image_set in enumerate(images_split):

converters/detection2panoptic_coco_format.py

@@ -8,7 +8,7 @@
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-import os, sys
+import os
 import argparse
 import numpy as np
 import json
@@ -22,10 +22,11 @@
 try:
     # set up path for pycocotools
     # sys.path.append('./cocoapi-master/PythonAPI/')
-    from pycocotools import mask as COCOmask
     from pycocotools.coco import COCO as COCO
 except Exception:
-    raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+    raise Exception(
+        "Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+
 
 @get_traceback
 def convert_detection_to_panoptic_coco_format_single_core(
@@ -55,8 +56,9 @@ def convert_detection_to_panoptic_coco_format_single_core(
             if ann['category_id'] not in categories:
                 raise Exception('Panoptic coco categories file does not contain \
                     category with id: {}'.format(ann['category_id'])
-                )
-            segment_id, color = id_generator.get_id_and_color(ann['category_id'])
+                                )
+            segment_id, color = id_generator.get_id_and_color(
+                ann['category_id'])
             mask = coco_detection.annToMask(ann)
             overlaps_map += mask
             pan_format[mask == 1] = color
@@ -66,11 +68,13 @@ def convert_detection_to_panoptic_coco_format_single_core(
             segments_info.append(ann)
 
         if np.sum(overlaps_map > 1) != 0:
-            raise Exception("Segments for image {} overlap each other.".format(img_id))
+            raise Exception(
+                "Segments for image {} overlap each other.".format(img_id))
         panoptic_record['segments_info'] = segments_info
         annotations_panoptic.append(panoptic_record)
 
-        Image.fromarray(pan_format).save(os.path.join(segmentations_folder, file_name))
+        Image.fromarray(pan_format).save(
+            os.path.join(segmentations_folder, file_name))
 
     print('Core: {}, all {} images processed'.format(proc_id, len(img_ids)))
     return annotations_panoptic
@@ -85,7 +89,8 @@ def convert_detection_to_panoptic_coco_format(input_json_file,
     if segmentations_folder is None:
         segmentations_folder = output_json_file.rsplit('.', 1)[0]
     if not os.path.isdir(segmentations_folder):
-        print("Creating folder {} for panoptic segmentation PNGs".format(segmentations_folder))
+        print("Creating folder {} for panoptic segmentation PNGs".format(
+            segmentations_folder))
         os.mkdir(segmentations_folder)
 
     print("CONVERTING...")
@@ -106,7 +111,8 @@ def convert_detection_to_panoptic_coco_format(input_json_file,
 
     cpu_num = multiprocessing.cpu_count()
     img_ids_split = np.array_split(img_ids, cpu_num)
-    print("Number of cores: {}, images per core: {}".format(cpu_num, len(img_ids_split[0])))
+    print("Number of cores: {}, images per core: {}".format(
+        cpu_num, len(img_ids_split[0])))
     workers = multiprocessing.Pool(processes=cpu_num)
     processes = []
     for proc_id, img_ids in enumerate(img_ids_split):