Prerelease bugfixes (#191)

* Fix LD_LIBRARY_PATH

* Typo fix

* Install usb cam deps

* Update README.MD

* Update demo setup

Co-authored-by: Illia Oleksiienko <io@ece.au.dk>

bin/activate.sh

@@ -2,7 +2,7 @@
 export OPENDR_HOME=$PWD
 export PYTHONPATH=$OPENDR_HOME/src:$PYTHONPATH
 export PYTHON=python3
-export LD_LIBRARY_PATH=$OPENDR_HOME/src:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$OPENDR_HOME/lib:$LD_LIBRARY_PATH
 
 if [[ -z "${OPENDR_DEVICE}" ]]; then
   echo "[INFO] Set available device to CPU. You can manually change this by running 'export OPENDR_DEVICE=gpu'."

projects/opendr_ws/README.md

@@ -15,12 +15,13 @@ For running a minimal working example you can follow the instructions below:
    ```source /opt/ros/noetic/setup.bash```
 
 1. Make sure you are inside opendr_ws
-2. If you are planning to use a usb camera for the demos, install the corresponding package:
+2. If you are planning to use a usb camera for the demos, install the corresponding package and its dependencies:
 
 ```shell
 cd src
 git clone https://github.com/ros-drivers/usb_cam
 cd ..
+rosdep install --from-paths src/ --ignore-src
 ```
 3. Install the following dependencies, required in order to use the OpenDR ROS tools:
 ```shell

projects/perception/heart_anomaly_detection/README.MD

@@ -10,9 +10,12 @@ python3 demo.py -model [anbof|gru]
 By setting ` --input_data /path/to/data.npy` inference can be performed from a single ECG sequence. 
 
 Additional parameters can be specified to control the model and data parameters:
-` --attention_type` sets the attention type for ANBOF model
-` --checkpoint` defines the path to a pretrained model (AF-pretrained temporal ANBOF or GRU will be downloaded by default, otherwise a path to existing checkpoint should be specified)
-` --channels` defines the number of channels in input sequence
-` --series_length` defines the length of input sequence
-` --n_class` defines the number of classes
+
+- ` --attention_type` sets the attention type for ANBOF model
+- ` --checkpoint` defines the path to a pretrained model (AF-pretrained temporal ANBOF or GRU will be downloaded by default, otherwise a path to existing checkpoint should be specified)
+- ` --channels` defines the number of channels in input sequence
+- ` --series_length` defines the length of input sequence
+- ` --n_class` defines the number of classes
+
+If you switch between the two available models (ANBOF and GRU) without specifying the checkpoint folder, please make sure that you delete the previous model files (e.g., `rf -rf checkpoint`).
 

projects/perception/object_detection_3d/demos/voxel_object_detection_3d/setup.py

@@ -0,0 +1,53 @@
+# Copyright 2020-2021 OpenDR European Project
+#
+# 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 setuptools import find_packages, setup
+
+
+def from_file(file_name: str = "requirements.txt", comment_char: str = "#"):
+    """Load requirements from a file"""
+    with open(file_name, "r") as file:
+        lines = [ln.strip() for ln in file.readlines()]
+    reqs = []
+    for ln in lines:
+        # filer all comments
+        if comment_char in ln:
+            ln = ln[: ln.index(comment_char)].strip()
+        # skip directly installed dependencies
+        if ln.startswith("http"):
+            continue
+        if ln:  # if requirement is not empty
+            reqs.append(ln)
+    return reqs
+
+
+def long_description():
+    text = open("README.md", encoding="utf-8").read()
+    # SVG images are not readable on PyPI, so replace them  with PNG
+    text = text.replace(".svg", ".png")
+    return text
+
+
+setup(
+    name="object_detection_3d_demo",
+    version="0.1.0",
+    description="Example of in-browser video streaming and processing using the OpenDR toolkit.",
+    long_description=long_description(),
+    long_description_content_type="text/markdown",
+    author="Illia Oleksiienko, Lukas Hedegaard",
+    author_email="{io, lhm}@ece.au.dk",
+    install_requires=from_file("requirements.txt"),
+    packages=find_packages(exclude=["test"]),
+    keywords=["deep learning", "pytorch", "AI", "OpenDR", "lidar"],
+)

projects/perception/object_tracking_2d/demos/fair_mot_deep_sort/requirements.txt

@@ -1,7 +1,4 @@
 flask
-# black
-# isort>=5.7
-# flake8-black
 numpy
 opencv-contrib-python
 imutils

projects/perception/object_tracking_2d/demos/fair_mot_deep_sort/setup.py

@@ -40,13 +40,13 @@ def long_description():
 
 
 setup(
-    name="online_activity_recognition",
+    name="object_tracking_2d_demo",
     version="0.1.0",
     description="Example of in-browser video streaming and processing using the OpenDR toolkit.",
     long_description=long_description(),
     long_description_content_type="text/markdown",
-    author="Lukas Hedegaard",
-    author_email="lhm@ece.au.dk",
+    author="Illia Oleksiienko, Lukas Hedegaard",
+    author_email="{io, lhm}@ece.au.dk",
     install_requires=from_file("requirements.txt"),
     packages=find_packages(exclude=["test"]),
     keywords=["deep learning", "pytorch", "AI", "OpenDR", "video", "webcam"],

src/c_api/README.md

@@ -12,7 +12,7 @@ In order to build the library you should:
 1. Build the OpenDR C API library
 
 ```sh
-make liboperdr
+make libopendr
 ```
 
 After building the API you will find the library (`libopendr.so`) under the `lib` folder.