Add documentation for heatmap class

docs/reference/engine-target.md

@@ -277,3 +277,17 @@ The [SpeechCommand](#class_engine.target.SpeechCommand) class has the following
 #### SpeechCommand(prediction, confidence=None)
 Construct a new [SpeechCommand](#class_engine.target.SpeechCommand) object based from *prediction*.
 *prediction* is expected to be an integer designating the class and optional *confidence* a float between 0 and 1.
+
+### class engine.target.Heatmap
+Bases: *ToDo: Inherit from engine.target.Target (issue #90)*
+
+This target is used for multi-class segmentation problems or multi-class problems that require heatmap annotations/outputs.
+
+The [Heatmap](#class_engine.target.Heatmap) class has the following public methods:
+#### Heatmap(data, description=None, class_names=None)
+  Construct a new [Heatmap](#class_engine.target.Heatmap) object based from *data*.
+  - *data* is expected to be a numpy array
+  - *description* is expected to be a string [optional]
+  - *class_names* is expected to be a dictionary and can be used for mapping class IDs (integer) to names (string) [optional]
+#### shape()
+  Returns the shape of the underlying *data* object.

src/opendr/engine/target.py

@@ -149,15 +149,14 @@ class BoundingBox(Target):
     This target is used for 2D Object Detection.
     A bounding box is described by the left-top corner and its width and height.
     """
-
     def __init__(
-            self,
-            name,
-            left,
-            top,
-            width,
-            height,
-            score=0,
+        self,
+        name,
+        left,
+        top,
+        width,
+        height,
+        score=0,
     ):
         super().__init__()
         self.name = name
@@ -203,14 +202,15 @@ class BoundingBoxList(Target):
     """
 
     def __init__(
-            self,
-            boxes,
+        self,
+        boxes,
     ):
         super().__init__()
         self.data = boxes
         self.confidence = np.mean([box.confidence for box in self.data])
 
     def mot(self, with_confidence=True):
+
         result = np.array([
             box.mot(with_confidence) for box in self.data
         ])
@@ -239,17 +239,16 @@ class TrackingAnnotation(Target):
     This target is used for 2D Object Tracking.
     A tracking bounding box is described by id, the left-top corner and its width and height.
     """
-
     def __init__(
-            self,
-            name,
-            left,
-            top,
-            width,
-            height,
-            id,
-            score=0,
-            frame=-1,
+        self,
+        name,
+        left,
+        top,
+        width,
+        height,
+        id,
+        score=0,
+        frame=-1,
     ):
         super().__init__()
         self.name = name
@@ -312,10 +311,9 @@ class TrackingAnnotationList(Target):
     This target is used for 2D Object Tracking.
     A bounding box is described by the left and top corners and its width and height.
     """
-
     def __init__(
-            self,
-            boxes,
+        self,
+        boxes,
     ):
         super().__init__()
         self.data = boxes
@@ -330,6 +328,7 @@ def from_mot(data):
         return TrackingAnnotationList(boxes)
 
     def mot(self, with_confidence=True):
+
         result = np.array([
             box.mot(with_confidence) for box in self.data
         ])
@@ -554,20 +553,19 @@ class TrackingAnnotation3D(BoundingBox3D):
     truncation (truncated) and occlusion (occluded) levels, the name of an object (name) and
     observation angle of an object (alpha).
     """
-
     def __init__(
-            self,
-            name,
-            truncated,
-            occluded,
-            alpha,
-            bbox2d,
-            dimensions,
-            location,
-            rotation_y,
-            id,
-            score=0,
-            frame=-1,
+        self,
+        name,
+        truncated,
+        occluded,
+        alpha,
+        bbox2d,
+        dimensions,
+        location,
+        rotation_y,
+        id,
+        score=0,
+        frame=-1,
     ):
         self.data = {
             "name": name,
@@ -584,6 +582,7 @@ def __init__(
         self.confidence = score
 
     def kitti(self, with_tracking_info=True):
+
         result = {}
 
         result["name"] = np.array([self.data["name"]])
@@ -633,10 +632,9 @@ class TrackingAnnotation3DList(Target):
     truncation (truncated) and occlusion (occluded) levels, the name of an object (name) and
     observation angle of an object (alpha).
     """
-
     def __init__(
-            self,
-            tracking_bounding_boxes_3d
+        self,
+        tracking_bounding_boxes_3d
     ):
         super().__init__()
         self.data = tracking_bounding_boxes_3d
@@ -764,7 +762,8 @@ def __str__(self):
             return f"Class {self.data} speech command"
 
 
-#ToDo: Inherit from Target class
+#ToDo: Inherit from Target class:
+# The problem is that the variables in Target are not masked private (leading underscore) preventing proper getter/setters
 class Heatmap():
     """
     This target is used for multi-class segmentation problems or multi-class problems that require heatmap annotations.

src/opendr/perception/panoptic_segmentation/efficient_ps/algorithm/mmdetection/setup.py

@@ -146,7 +146,6 @@ def gen_packages_items():
         name='mmdet',
         version=get_version(),
         description='Open MMLab Detection Toolbox and Benchmark',
-        # long_description=readme(),
         author='OpenMMLab',
         author_email='chenkaidev@gmail.com',
         keywords='computer vision, object detection',
@@ -166,12 +165,6 @@ def gen_packages_items():
         setup_requires=parse_requirements('requirements/build.txt'),
         tests_require=parse_requirements('requirements/tests.txt'),
         install_requires=parse_requirements('requirements/runtime.txt'),
-        #        extras_require={
-        #            'all': parse_requirements('requirements.txt'),
-        #            'tests': parse_requirements('requirements/tests.txt'),
-        #            'build': parse_requirements('requirements/build.txt'),
-        #            'optional': parse_requirements('requirements/optional.txt'),
-        #        },
         ext_modules=[
             make_cuda_ext(name='compiling_info', module='mmdet.ops.utils', sources=['src/compiling_info.cpp']),
             make_cuda_ext(name='nms_cpu', module='mmdet.ops.nms', sources=['src/nms_cpu.cpp']),

src/opendr/perception/panoptic_segmentation/efficient_ps/example_usage.py

@@ -1,3 +1,16 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from pathlib import Path
 from typing import List, Tuple
 
@@ -35,7 +48,7 @@ def train():
         config_file=str(Path(__file__).parent / 'configs' / 'singlegpu_sample.py')
     )
     learner.fit(train_dataset, val_dataset=val_dataset, logging_path=str(Path(__file__).parent / 'work_dir'))
-    learner.save(path=f'{DATA_ROOT}/checkpoints/efficientPS/sample/model.path')
+    learner.save(path=f'{DATA_ROOT}/checkpoints/efficientPS/sample/model.pth')
 
 
 def evaluate():

src/opendr/perception/panoptic_segmentation/efficient_ps/requirements.txt

@@ -9,5 +9,5 @@ cityscapesscripts==2.2.0
 git+git://github.com/waspinator/pycococreator.git@0.2.0
 git+https://github.com/mapillary/inplace_abn.git
 
-./algorithm/efficientNet
-./algorithm/mmdetection
+-e ./algorithm/efficientNet
+-e ./algorithm/mmdetection