Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

Related tags

Deep Learning python machine-learning computer-vision forest cv keras ml dataset neural-networks object-detection hacktoberfest ods pascal-voc retina-net hacktoberfest2020 lacmus liza-alert
Overview

lacmus

logo

The program for searching through photos from the air of lost people in the forest using Retina Net neural nwtwork.

The project is being developed by a non-profit organization Liza Alert.

Demonstration

01

Picture 1

02

Picture 2

video

Video 1

See more examples.

Training data

You can download Lacmus Drone Dataset (LaDD) from mail.ru cloud

You also can download Lacmus version of Stenford Drone Dataset (SDD) from mail.ru cloud

Usage

Read more about training steps and atraining data at train documentation to learn how to train the model.

Pretrained models

The models are avalable here.

Partners

ODS DTL JB GitBook Liza alert Novaya Gazeta Teplica

Comments
  • Пользовательская документация - работа с данными

    Пользовательская документация - работа с данными

    • Добавить в вики инструкцию по тому как добавлять данные в проект и как отправлять их на сервер.

    • Добавить инструкцию с тем как снимть данные для операторов БПЛА с перечнем поз.

    enhancement documentation 
    opened by gosha20777 5
  • Dataset format + cropping

    Dataset format + cropping

    1. Класcы для работы с датасетом LADD ("подмножество" формата Pascal VOC).
    • чтение датасета (список изображений, чтение аннотаций к изображению)
    • формирование датасета (добавление изображений, формирование файла аннотаций, формирование файлов ImageSets)
    1. Скрипт для формирования нового датасета путем нарезки изображений из имеющегося датасета. Новый датасет сохраняется в формате Pascal VOC и готов для обучения. Изображение нарезается прямоугольниками "по сетке".
    • настраиваются размеры итоговых изображений, размеры "нахлеста" изображений друг на друга
    • в датасет добавляется равное количество изображений с людьми и без людей (сбалансированный датасет)
    • выполняется параллельная обработка изображений для ускорения работы
    opened by nvsit 4
  • docker image(GPU) failed to built

    docker image(GPU) failed to built

    Hi! i've tried to build GPU-version of docker image on my ubuntu 16.04(nvidia 418.67, Cuda 10.1 and got this error in the end.

    sudo docker build --file Dockerfile.gpu -t rescuer_la . Sending build context to Docker daemon 14.56MB Step 1/24 : FROM tensorflow/tensorflow:1.12.0-gpu-py3 ---> 413b9533f92a Step 2/24 : ENV DEBIAN_FRONTEND noninteractive ---> Using cache ---> 8a52f51116f2 Step 3/24 : RUN apt-get update -qq && apt-get install --no-install-recommends -y build-essential g++ git wget apt-transport-https curl cython libopenblas-base python3-numpy python3-scipy python3-h5py python3-yaml python3-pydot && apt-get clean && rm -rf /var/lib/apt/lists/* ---> Using cache ---> a545cb38439e Step 4/24 : RUN pip3 --no-cache-dir install -U numpy==1.13.3 ---> Using cache ---> 98d345ea0a28 Step 5/24 : ARG KERAS_VERSION=2.2.4 ---> Using cache ---> 7b09457df232 Step 6/24 : ENV KERAS_BACKEND=tensorflow ---> Using cache ---> 85a448c8d80b Step 7/24 : RUN pip3 --no-cache-dir install --no-dependencies git+https://github.com/fchollet/keras.git@${KERAS_VERSION} ---> Using cache ---> 1c91dbe3620b Step 8/24 : RUN python3 -c "import tensorflow; print(tensorflow.version)" && dpkg-query -l > /dpkg-query-l.txt && pip3 freeze > /pip3-freeze.txt ---> Running in 08e7f4469a36 Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in from tensorflow.python.pywrap_tensorflow_internal import * File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in _pywrap_tensorflow_internal = swig_import_helper() File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "/usr/lib/python3.5/imp.py", line 242, in load_module return load_dynamic(name, filename, file) File "/usr/lib/python3.5/imp.py", line 342, in load_dynamic return _load(spec) ImportError: libcuda.so.1: cannot open shared object file: No such file or directory

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "", line 1, in File "/usr/local/lib/python3.5/dist-packages/tensorflow/init.py", line 24, in from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/init.py", line 49, in from tensorflow.python import pywrap_tensorflow File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 74, in raise ImportError(msg) ImportError: Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in from tensorflow.python.pywrap_tensorflow_internal import * File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in _pywrap_tensorflow_internal = swig_import_helper() File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "/usr/lib/python3.5/imp.py", line 242, in load_module return load_dynamic(name, filename, file) File "/usr/lib/python3.5/imp.py", line 342, in load_dynamic return _load(spec) ImportError: libcuda.so.1: cannot open shared object file: No such file or directory

    Failed to load the native TensorFlow runtime.

    See https://www.tensorflow.org/install/errors

    for some common reasons and solutions. Include the entire stack trace above this error message when asking for help. The command '/bin/sh -c python3 -c "import tensorflow; print(tensorflow.version)" && dpkg-query -l > /dpkg-query-l.txt && pip3 freeze > /pip3-freeze.txt' returned a non-zero code: 1

    opened by aprentis 3
  • CUDNN_STATUS_INTERNAL_ERROR

    CUDNN_STATUS_INTERNAL_ERROR

    CUDNN_STATUS_INTERNAL_ERROR while loading the model

    2021-04-05 20:44:46.086918: E tensorflow/stream_executor/cuda/cuda_dnn.cc:328] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2021-04-05 20:44:46.087682: E tensorflow/stream_executor/cuda/cuda_dnn.cc:328] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
[2021-04-05 20:44:46,090] ERROR in app: Exception on /image [POST]
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/flask/app.py", line 2447, in wsgi_app
    response = self.full_dispatch_request()
  File "/usr/local/lib/python3.6/dist-packages/flask/app.py", line 1952, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/usr/local/lib/python3.6/dist-packages/flask/app.py", line 1821, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "/usr/local/lib/python3.6/dist-packages/flask/_compat.py", line 39, in reraise
    raise value
  File "/usr/local/lib/python3.6/dist-packages/flask/app.py", line 1950, in full_dispatch_request
    rv = self.dispatch_request()
  File "/usr/local/lib/python3.6/dist-packages/flask/app.py", line 1936, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "inference.py", line 132, in predict_image
    caption = run_detection_image(request.json['data'])
  File "inference.py", line 49, in run_detection_image
    boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1788, in predict_on_batch
    outputs = predict_function(iterator)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 780, in call
    result = self._call(*args, **kwds)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py", line 814, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 2829, in call
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1848, in _filtered_call
    cancellation_manager=cancellation_manager)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1924, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 550, in call
    ctx=ctx)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute
    inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
  (0) Unknown:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[node retinanet-bbox/conv1/Conv2D (defined at inference.py:49) ]]
     [[retinanet-bbox/filtered_detections/map/while/body/_1/retinanet-bbox/filtered_detections/map/while/strided_slice_2/_32]]
  (1) Unknown:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[node retinanet-bbox/conv1/Conv2D (defined at inference.py:49) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_predict_function_7071]

Function call stack:
predict_function -> predict_function

    Mon Apr  5 23:06:13 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.102.04   Driver Version: 450.102.04   CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce MX230       Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   64C    P3    N/A /  N/A |    218MiB /  2002MiB |     22%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A       956      G   /usr/lib/xorg/Xorg                 95MiB |
|    0   N/A  N/A      1301      G   /usr/bin/gnome-shell              121MiB |
+-----------------------------------------------------------------------------+

    docker version: Docker version 19.03.8, build afacb8b7f0

    opened by gosha20777 2
  • Bug: Could not find registration proxy for IID: {ADD8BA80-002B-8F0F-00C04FD062}

    Bug: Could not find registration proxy for IID: {ADD8BA80-002B-8F0F-00C04FD062}

    Describe the bug Version - 0.3.2 os - Win10

    при попытке загрузить директорию с файлами через USB выдает ошибку Сообщение программы:

    Error Интерфейс не зарегистрирован
Не удалось найти регистрацию прокси-сервера для IID: {ADD8BA80-002B-8F0F-00C04FD062}
    bug 
    opened by Denaizer 2
  • Bug: Program crash with exit code 134

    Bug: Program crash with exit code 134

    Describe the bug Program crashes with 134 status code when material.avalonia theme is installed.

    To Reproduce Steps to reproduce the behavior:

    1. Go to 'file - open directory'
    2. Click on 'predict all' button
    3. Open another directory with images (file - open directory)
    4. Exuted with code 134

    Desktop (please complete the following information):

    • OS: Ubuntu 19.04
    • CPU: Intel Core i7-6500U
    • GPU: GeForce GTX 950M

    Additional context It seems to me that this is due to incorrect visualization errors at material.avalonia theme.

    bug 
    opened by gosha20777 2
  • docker image(CPU) failed to start.

    docker image(CPU) failed to start.

    Hi, i`ve successfully built CPU image, but it failed to start with this error.

    sudo docker run --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY --workdir=$(pwd) --volume="/home/$USER:/home/$USER" --volume="/etc/group:/etc/group:ro" --volume="/etc/passwd:/etc/passwd:ro" --volume="/etc/shadow:/etc/shadow:ro" --volume="/etc/sudoers.d:/etc/sudoers.d:ro" rescuer_la No protocol specified No protocol specified

    Unhandled Exception: System.Exception: XOpenDisplay failed at Avalonia.X11.AvaloniaX11Platform.Initialize(X11PlatformOptions options) at Avalonia.Controls.AppBuilderBase1.Setup() at Avalonia.Controls.AppBuilderBase1.Start[TMainWindow](Func`1 dataContextProvider) at RescuerLaApp.Program.Main(String[] args) in /app/install/RescuerLaApp/Program.cs:line 14

    opened by aprentis 2
  • Thread safe issue with visual_effect_generator

    Thread safe issue with visual_effect_generator

    Bug description The visual_effect_generator, as all python generators, is not thread-safe. That can cause an exception when using several worker threads, especially within single process.

    How to reproduce Enter the lacmus directory and run train.py with --workers > 1 but without --multiprocessing:

    keras_retinanet/bin/train.py --backbone mobilenet_v3_small --no-snapshots --batch-size 8 --max-queue-size=10 --workers=8 --epoch 1 --steps 200 pascal ../../../data/ful

    Actual result On one or another training step the process stops with an exception "ValueError: generator already executing"

    Callstack: Traceback (most recent call last): File "keras_retinanet/bin/train.py", line 546, in main() File "keras_retinanet/bin/train.py", line 541, in main initial_epoch=args.initial_epoch File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/engine/training.py", line 1732, in fit_generator initial_epoch=initial_epoch) File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/engine/training_generator.py", line 185, in fit_generator generator_output = next(output_generator) File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/utils/data_utils.py", line 625, in get six.reraise(*sys.exc_info()) File "/home/jupyter-kseniia/.conda/envs/lacmus-k/lib/python3.7/site-packages/six.py", line 703, in reraise raise value File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/utils/data_utils.py", line 610, in get inputs = future.get(timeout=30) File "/home/jupyter-kseniia/.conda/envs/lacmus-k/lib/python3.7/multiprocessing/pool.py", line 657, in get raise self._value File "/home/jupyter-kseniia/.conda/envs/lacmus-k/lib/python3.7/multiprocessing/pool.py", line 121, in worker result = (True, func(*args, **kwds)) File "/home/jupyter-kseniia/.local/lib/python3.7/site-packages/keras/utils/data_utils.py", line 406, in get_index return _SHARED_SEQUENCES[uid][i] File "keras_retinanet/bin/../../keras_retinanet/preprocessing/generator.py", line 375, in getitem inputs, targets = self.compute_input_output(group) File "keras_retinanet/bin/../../keras_retinanet/preprocessing/generator.py", line 347, in compute_input_output image_group, annotations_group = self.random_visual_effect_group(image_group, annotations_group) File "keras_retinanet/bin/../../keras_retinanet/preprocessing/generator.py", line 212, in random_visual_effect_group image_group[index], annotations_group[index] File "keras_retinanet/bin/../../keras_retinanet/preprocessing/generator.py", line 195, in random_visual_effect_group_entry visual_effect = next(self.visual_effect_generator) ValueError: generator already executing terminate called without an active exception terminate called recursively Aborted (core dumped)

    bug 
    opened by prickly-u 1
  • Fix readme logo and organization logo

    Fix readme logo and organization logo

    • Удалить лого Лиза Алерт из ридми.
    • Добавить лого lacmus
    • Выкинуть лого Лиза Алерт из организации lacmus foundation и заменить его на lacmus
    • добавить секцию partners в readme
    • добавить лого DTL, Сбер.Клауд, Liza Alert в партнёры.
    bug documentation 
    opened by gosha20777 1
  • Bug: System.NullReferenceException throws while loading file

    Bug: System.NullReferenceException throws while loading file

    Version - 0.3.2 OS - Linux/Windows

    На последнем и предпоследнем релизе есть плавающий глюк, систематику еще не выявил. При попытке загрузить с жесткого диска файлы программа падает с таким сообщением:

     Unhandled Exception: System.NullReferenceException: Object reference not set to an instance of an object.
   at RescuerLaApp.Models.Frame.<>c__DisplayClass38_0.<Load>b__0(Object o)
   in /home/user/files/projects/lacmus/RescuerLaApp/Models/Frame.cs:line 77
   at System.Threading.ExecutionContext.RunInternal(ExecutionContext executionContext, ContextCallback callback, Object state)
--- End of stack trace from previous location where exception was thrown ---
   at System.Threading.ThreadPoolWorkQueue.Dispatch()

    От количества файлов это не зависит, битых файлов нет.

    bug 
    opened by Denaizer 1
  • Feat: add fucnction to reset image back to 100% size

    Feat: add fucnction to reset image back to 100% size

    Пилоты очень просили доп. опцию - центрирования и возврата обработанной фотографии к 100%, т.к. при работе на трекпаде фото может "улетать" за пределы галактики.

    enhancement 
    opened by Denaizer 1
  • Detections are of shape `(1, 200700, 6)` but decode_openvino_detections uses the wrong number of dims

    Detections are of shape `(1, 200700, 6)` but decode_openvino_detections uses the wrong number of dims

    https://github.com/lacmus-foundation/lacmus/blob/f1dd0da5fb1c04d12ad25e7f67b5dd2a98f595d4/cli_inference_openvino.py#L57

    As seen here this is assuming 4 dims when output has 3.

    opened by suvojit-0x55aa 1
  • Add Cutmix

    Add Cutmix

    Add cutmix generator for beter results. The cutmix generator can be useful for training models and achieving better results. This will help take existing models to a higher level of quality.

    Resources that may be useful:

    • https://arxiv.org/abs/1905.04899 - the original article
    • https://github.com/clovaai/CutMix-PyTorch - a pytorch implementation
    • https://github.com/DevBruce/CutMixImageDataGenerator_For_Keras - keras implenentation (not compartable with retinanet)
    enhancement 
    opened by gosha20777 0
Releases(2.5.0)

  • 2.5.0(Aug 18, 2021)

  • 0.3.2(Nov 13, 2019)

    Change log

    • update to latest avaloniaUI-0.9-preview6
    • fix critical bug with windows #48
    • fix multiple bugs with osx
    • fix multiple bugs Linux
    • better performance
    • windows fully supported
    • osx Catalina fully supported
    • add show and hide bounding box function
    • add favorites images
    • convert GPS tags to correct format (Google, Yandex comparable)
    • add material disign

    Ussage

    system requirements CPU support for Windows/Linux/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    1. Installation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Usage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(35.33 MB)
    linux.zip(35.33 MB)
    osx.zip(33.38 MB)
    ubuntu16-gpu.zip(35.33 MB)
    ubuntu16.zip(35.33 MB)
    ubuntu18-gpu.zip(35.33 MB)
    ubuntu18.zip(35.33 MB)
    win10.zip(37.02 MB)

  • 0.3.2-preview(Nov 8, 2019)

    Change log

    • update to latest avaloniaUI-0.9-preview6
    • fix criticlal bug with windows #48
    • fix multiple bugs with osx
    • fix multiple bugs linux
    • better performance
    • windows fully supported
    • osx catalina fully supported

    Ussage

    system requirements CPU support for Windows/Linux/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    1. Installation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Usage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(35.00 MB)
    linux.zip(35.00 MB)
    osx.zip(33.04 MB)
    ubuntu16-gpu.zip(35.00 MB)
    ubuntu16.zip(35.00 MB)
    ubuntu18-gpu.zip(35.00 MB)
    ubuntu18.zip(35.00 MB)
    win10.zip(36.69 MB)

  • 0.3.1(Oct 26, 2019)

    Change log

    • fix bugs with model updating
    • add auth function and crypt keys

    Ussage

    system requirements CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    Attention: Windows version have a critical bug with CPU cash. It happens cause Windows works with glx incorrect. We are working on ti. See #48 for more details.

    1. Installation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Usage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(35.00 MB)
    linux.zip(35.00 MB)
    osx.zip(33.04 MB)
    ubuntu16-gpu.zip(35.00 MB)
    ubuntu16.zip(35.00 MB)
    ubuntu18-gpu.zip(35.00 MB)
    ubuntu18.zip(35.00 MB)
    win10.zip(36.69 MB)

  • 0.3.0(Sep 26, 2019)

    Change log

    • fix bugs with model updating
    • add message boxes
    • add geo-tags support
    • add help and about function
    • add ability to save images with objects in specific folder

    Ussage

    system requirements CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    Attention: Windows version have a critical bug with CPU cash. It happens cause Windows works with glx incorrect. We are working on ti. See #48 for more details.

    1. Installation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Usage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(34.96 MB)
    linux.zip(34.96 MB)
    osx.zip(33.00 MB)
    ubuntu16-gpu.zip(34.96 MB)
    ubuntu16.zip(34.96 MB)
    ubuntu18-gpu.zip(34.96 MB)
    ubuntu18.zip(34.96 MB)
    win10.zip(36.65 MB)

  • 0.2.9(Sep 26, 2019)

    Change log

    • fix bugs with model updating
    • add message boxes
    • add geo-tags support
    • add help and about function
    • add ability to save images with objects in specific folder

    Ussage

    system requirements

    CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    Attention: Windows version have a critical bug with CPU cashe. It heppens couse Windows works with glx incorrect. We are working on ti.

    1. Instalation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Ussage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(34.96 MB)
    linux.zip(34.96 MB)
    osx.zip(33.00 MB)
    ubuntu16-gpu.zip(34.96 MB)
    ubuntu16.zip(34.96 MB)
    ubuntu18-gpu.zip(34.96 MB)
    ubuntu18.zip(34.96 MB)
    win10.zip(36.65 MB)

  • 0.2.8(Sep 5, 2019)

    Change log

    • fix bugs with docker
    • fix render timer bug at avalonia 0.8.x
    • update avalonia ui 0.8.0 => 0.8.2
    • speed up image loading on linux
    • smaller docker images size
    • add gpu support
    • add api versioning support

    Ussage

    system requirements CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    Attention: Windows version have a critical bug with CPU cashe. It heppens couse Windows works with glx incorrect. We are working on ti.

    1. Instalation

    CPU

    • install docker and docker service (and add user to docker group for linux)
    • unzip archive with your runtime

    GPU

    • install docker and docker service (and add user to docker group for linux)
    • install nvidia-docker and run it
    • unzip archive with your runtime use runtime with -gpu suffix
    1. Ussage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux-gpu.zip(34.50 MB)
    linux.zip(34.50 MB)
    osx.zip(32.53 MB)
    ubuntu16-gpu.zip(34.62 MB)
    ubuntu16.zip(34.50 MB)
    ubuntu18-gpu.zip(34.62 MB)
    ubuntu18.zip(34.50 MB)
    win10.zip(36.50 MB)

  • 0.2.7(Aug 23, 2019)

    Change log

    • fix bugs with docker tags
    • smaller archive sizes

    Ussage

    system requirements CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    OS: Windows 10 / Lunux / MacOS X (x64 only!). CPU: 2x+ Core CPU (with AVX, SSE SSE2 SSE3 SSE4 SSE4.1) Intel Core i3/i5/i7/xeon Sandy Bridge / AMD Buldozer and higher (Note: intel celeron does not support!) GPU (Optional): GTX, 4Gb vRAM, CUDA 9.1+ comparable (including CUDA 10 and higher), no CUDA driver required, e.g. GTX 950m and higher RAM: 4096 mb RAM and higher Storage: 5gb free disk space (10gb free disk space for GPU version)

    UPD: Windows version have a critical bug with CPU cashe. It heppens couse Windows works with glx incorrect. We are working on ti.

    1. Instalation

    CPU

    • install docker and docker service
    • unzip archive with your runtime

    GPU (experemental support in this release)

    • install docker and docker service
    • install nvidia-docker and run it
    • unzip archive with your runtime
    1. Ussage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes (x64 only!)

    • win10 - windows 10 pro (x64 only!)
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04 (x64 only!)
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra or higher (x64 only!)
    Source code(tar.gz)
    Source code(zip)
    linux.zip(34.49 MB)
    osx.zip(32.53 MB)
    ununtu16.zip(34.50 MB)
    ununtu18.zip(34.50 MB)
    win10.zip(34.59 MB)

  • 0.2.6(Aug 14, 2019)

    Change log

    • client app works without docker
    • add docker manager
    • add neoro-model auto updator
    • better models
    • update dataset

    Ussage

    CPU support for Windows/Linux (recommend)/CentOS/MacOS X GPU support ONLY for linux, only nvidia graphic support

    1. Instalation

    CPU

    • install docker and docker service
    • unzip archive with your runtime

    GPU (experemental support in this release)

    • install docker and docker service
    • install nvidia-docker and run it
    • unzip archive with your runtime
    1. Ussage

    Linux\CentOS\OSX

    cd /directory/with/app/
./ReacuerLaApp

    Windows

    go to directory with app
run ReacuerLaApp.exe

    Supported Runtimes

    • win10 - windows 10 x64 pro
    • ubuntu16; ubuntu18 - linux ubuntu 16.04 and 18.04
    • linux - most computer linux distributions (x64 only!), such as CentOS, Debian, Fedora, Arch, Gentoo and their derivatives
    • osx - macOS 10.12 Sierra x64 or higher
    Source code(tar.gz)
    Source code(zip)
    linux.zip(34.61 MB)
    osx.zip(34.35 MB)
    ununtu16.zip(34.61 MB)
    ununtu18.zip(34.61 MB)
    win10.zip(34.68 MB)

  • 0.2.5(Jun 28, 2019)

    Change log

    • Update zoom feature - add more useful navigation. (press up down left right arrows on keyboard to move image)
    • Fix some critical bugs

    Ussage

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la_gpu .
    1. Ussage

    In some distributive (e.g. Debian / Ubuntu) you should run this command before (see issue #8)

    sudo xhost +

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la_gpu
    Source code(tar.gz)
    Source code(zip)

  • 0.2.4(Jun 27, 2019)

    Change log

    • Add zoom feature
    • Fix bugs
    • Speedup image zooming

    Ussage

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la_gpu .
    1. Ussage

    In some distributive (e.g. Debian / Ubuntu) you should run this command before (see issue #8)

    sudo xhost +

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la_gpu
    Source code(tar.gz)
    Source code(zip)

  • 0.2.3(Jun 26, 2019)

    Change log

    • Add save annotation function
    • Add console applications to work with datasets
    • Update LADD dataset
    • Fix bugs

    Liza Alert Drone Ddataset v2

    You can download Liza Alert Drone Dataset

    Ussage

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la_gpu .
    1. Ussage

    In some distributive (e.g. Debian / Ubuntu) you should run this command before (see issue #8)

    sudo xhost +

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la_gpu
    Source code(tar.gz)
    Source code(zip)

  • 0.2.2(Jun 19, 2019)

    Change log

    • Update model inference
    • Fix bugs
    • Speedup image loading
    • Speedup image proсessing
    • Go to the newest version avalonUI
    • Reduce resource consumption
    • Add automatic build (by @ortho)
    • Code refactoring (by @worldbeater)

    Ussage

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la_gpu .
    1. Ussage

    In some distributive (e.g. Debian / Ubuntu) you should run this command before (see issue #8)

    sudo xhost +

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la_gpu
    Source code(tar.gz)
    Source code(zip)

  • 0.2.1(Jun 3, 2019)

    • Fix the Issue #7

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    Ussage

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la .
    1. Ussage

    In some distributive (e.g. Debian / Ubuntu) you should run this command before (see issue #8)

    sudo xhost +

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la
    Source code(tar.gz)
    Source code(zip)

  • 0.2.0(May 17, 2019)

    Create cross platform gui application

    Use dockerfile to launch it.

    CPU support for Windows/Linux (recommend)/MacOS X GPU support ONLY for linux, only nvidia graphic support

    Ussage

    1. Instalation

    CPU

    docker build -t rescuer_la .

    GPU

    docker build --file Dockerfile.gpu -t rescuer_la .
    1. Ussage

    CPU

    docker run --rm \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la

    GPU

    docker run --rm \
--runtime=nvidia
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
rescuer_la
    Source code(tar.gz)
    Source code(zip)
    screen.png(279.21 KB)

  • 0.1.1(Mar 17, 2019)

  • 0.1.0(Mar 13, 2019)

Owner
Lacmus Foundation
open-source fondation engaged in the search for missing people and developments in the field of computer vision and deep learning
Lacmus Foundation
GitHub https:/lacmus.ml/
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

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