Added Docker environment for easy setup of OTCD

Dockerfile

@@ -0,0 +1,46 @@
+FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04
+
+RUN apt-get update && \
+  apt-get install -y software-properties-common
+
+RUN add-apt-repository ppa:deadsnakes/ppa && \
+  apt-get update && apt-get install -y \
+    pkg-config \
+    python3.6-dev \
+    python3-pip && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1
+
+RUN ln -s $(which python3.6) /usr/local/bin/python
+
+RUN pip3 install numpy pkgconfig
+
+
+WORKDIR /workspace
+RUN chmod -R a+w /workspace
+
+# Install coviar
+WORKDIR /workspace/pytorch-coviar
+COPY pytorch-coviar .
+WORKDIR /workspace
+COPY install.sh .
+RUN chmod +x install.sh && \
+  ./install.sh
+
+# Set environment variables
+ENV PKG_CONFIG_PATH="$PKG_CONFIG_PATH:/ffmpeg_build/lib/pkgconfig"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH":/ffmpeg_build/lib
+
+WORKDIR /workspace/pytorch-coviar/data_loader
+RUN chmod +x install.sh && \
+  ./install.sh
+
+WORKDIR /workspace
+
+# Install Python packages
+COPY requirements.txt /
+RUN pip3 install --upgrade pip
+RUN pip3 install -r /requirements.txt
+
+CMD ["sh", "-c", "tail -f /dev/null"]

README.md

@@ -17,26 +17,42 @@ There maybe a slight gap between the performance obtained by this script and the
 ```
 
 ## 3. Requirements
-```
-PyTorch = 0.3
-python >= 3.5
-```
 
-## 4. Usage
-1) before running `OTCD`, [`CoViAR`](https://github.com/chaoyuaw/pytorch-coviar) needs to be installed. Please click the URL for the usage of `CoViAR`.
+- [Docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
+- [Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0))
+- [Docker-Compose](https://pypi.org/project/docker-compose/)
 
-1) down load this repo.
+## 4. Quickstart
+
+1) Download this repo
 ```
 git clone https://github.com/liuqk3/OTCD.git
 cd OTCD
 ```
-2) download the pretrained model from [BaiduYunPan](https://pan.baidu.com/s/1-Enzek8SQpnr1BY1uzde4w). Then put all models to ```./save```. If you have any problems with the download process, please email me.
 
-3) run the tracker
+2) Build docker image
+```
+sudo docker-compose build
+```
+
+3) Download the pretrained model from [BaiduYunPan](https://pan.baidu.com/s/1-Enzek8SQpnr1BY1uzde4w). Then put all models to ```./save```. If you have any problems with the download process, please email me.
+
+4) Download [MOT Challenge dataset](https://motchallenge.net/) and place into a sub directory of OTCD. If placed outside of the OTCD directory, edit the volume mapping in `docker-compose.yml`.
+
+5) When finished start docker container
+```
+sudo docker-compose up -d
+```
+
+6) Enter the container
+```
+sudo docker exec -it otcd bash
+```
 
+7) Start tracker
 ```
 python tracking_on_mot.py --mot_dir path/to/MOT-dataset
 ```
 
 ## 5. Code for training
-The training scripts are also published in ```useful_scripts```. You can train all the models by the given scripts.
+The training scripts are also published in ```useful_scripts```. You can train all the models by the given scripts.

compute_metrics.py

@@ -0,0 +1,106 @@
+"""py-motmetrics - metrics for multiple object tracker (MOT) benchmarking.
+
+Christoph Heindl, 2017
+https://github.com/cheind/py-motmetrics
+"""
+
+import argparse
+import glob
+import os
+import logging
+import pickle
+import motmetrics as mm
+import pandas as pd
+from collections import OrderedDict
+from pathlib import Path
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="""
+Compute metrics for trackers using MOTChallenge ground-truth data.
+
+Files
+-----
+All file content, ground truth and test files, have to comply with the
+format described in
+
+Milan, Anton, et al.
+"Mot16: A benchmark for multi-object tracking."
+arXiv preprint arXiv:1603.00831 (2016).
+https://motchallenge.net/
+
+Structure
+---------
+
+Layout for ground truth data
+    <GT_ROOT>/<SEQUENCE_1>/gt/gt.txt
+    <GT_ROOT>/<SEQUENCE_2>/gt/gt.txt
+    ...
+
+Layout for test data
+    <TEST_ROOT>/<SEQUENCE_1>.txt
+    <TEST_ROOT>/<SEQUENCE_2>.txt
+    ...
+
+Sequences of ground truth and test will be matched according to the `<SEQUENCE_X>`
+string.""", formatter_class=argparse.RawTextHelpFormatter)
+
+    parser.add_argument('groundtruths', type=str, help='Directory containing ground truth files.')
+    parser.add_argument('tests', type=str, help='Directory containing tracker result files')
+    parser.add_argument('--loglevel', type=str, help='Log level', default='info')
+    parser.add_argument('--fmt', type=str, help='Data format', default='mot15-2D')
+    parser.add_argument('--solver', type=str, help='LAP solver to use')
+    return parser.parse_args()
+
+def compare_dataframes(gts, ts):
+    accs = []
+    names = []
+    for k, tsacc in ts.items():
+        if k in gts:
+            logging.info('Comparing {}...'.format(k))
+            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))
+            names.append(k)
+        else:
+            logging.warning('No ground truth for {}, skipping.'.format(k))
+
+    return accs, names
+
+if __name__ == '__main__':
+
+    args = parse_args()
+
+    loglevel = getattr(logging, args.loglevel.upper(), None)
+    if not isinstance(loglevel, int):
+        raise ValueError('Invalid log level: {} '.format(args.loglevel))
+    logging.basicConfig(level=loglevel, format='%(asctime)s %(levelname)s - %(message)s', datefmt='%I:%M:%S')
+
+    if args.solver:
+        mm.lap.default_solver = args.solver
+
+    gtfiles = glob.glob(os.path.join(args.groundtruths, '*/gt/gt.txt'))
+    tsfiles = [f for f in glob.glob(os.path.join(args.tests, '*.txt')) if not os.path.basename(f).startswith('eval')]
+
+    logging.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))
+    logging.info('Available LAP solvers {}'.format(mm.lap.available_solvers))
+    logging.info('Default LAP solver \'{}\''.format(mm.lap.default_solver))
+    logging.info('Loading files.')
+
+    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt=args.fmt, min_confidence=1)) for f in gtfiles])
+    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt=args.fmt)) for f in tsfiles])
+
+    mh = mm.metrics.create()
+    accs, names = compare_dataframes(gt, ts)
+
+    logging.info('Running metrics')
+
+    summary = mh.compute_many(accs, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)
+    str_summary = mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names)
+    print(str_summary)
+
+    # print to text file
+    with open(os.path.join(args.tests, "mot_metrics.log"), mode="w") as text_file:
+        print(str_summary, file=text_file)
+
+    # save dataframe to pickle file
+    pickle.dump(summary, open(os.path.join(args.tests, "mot_metrics.pkl"), "wb"))
+
+    logging.info('Completed')

docker-compose.yml

@@ -0,0 +1,18 @@
+version: "2.3"
+
+services:
+
+  otcd:
+    container_name: otcd
+    build: .
+    runtime: nvidia
+    environment:
+      - DISPLAY
+    ipc: host # for GUI
+    volumes:
+      - /tmp/.X11-unix:/tmp/.X11-unix:rw  # for GUI access
+      - .:/workspace
+    # example for volume mapping if MOt data is outside the OTCD directory
+      - /home/lukas/mv-tracker/data:/workspace/data
+    entrypoint: ./docker-entrypoint.sh
+    command: tail -f /dev/null

docker-entrypoint.sh

@@ -0,0 +1,7 @@
+#!/bin/sh
+set -e
+
+cd /workspace/lib/model/roi_align && chmod +x install.sh && ./install.sh
+cd /workspace/lib/model/nms && chmod +x make.sh && ./make.sh
+
+exec "$@"

install.sh

@@ -0,0 +1,92 @@
+#!/bin/bash
+
+INSTALL_BASE_DIR="$PWD/.."
+INSTALL_DIR="$PWD"
+
+echo "Installing module into: $INSTALL_DIR"
+
+# Install build tools
+apt-get update && \
+apt-get upgrade -y && \
+apt-get install -y \
+    wget \
+    unzip \
+    build-essential \
+    cmake \
+    git \
+    pkg-config \
+    autoconf \
+    automake \
+    git-core \
+    python3-dev \
+    python3-pip \
+    python3-numpy \
+    python3-pkgconfig && \
+    rm -rf /var/lib/apt/lists/*
+
+
+###############################################################################
+#
+#							FFMPEG
+#
+###############################################################################
+
+# Install FFMPEG dependencies
+apt-get update -qq && \
+apt-get -y install \
+    libass-dev \
+    libfreetype6-dev \
+    libsdl2-dev \
+    libtool \
+    libva-dev \
+    libvdpau-dev \
+    libvorbis-dev \
+    libxcb1-dev \
+    libxcb-shm0-dev \
+    libxcb-xfixes0-dev \
+    texinfo \
+    zlib1g-dev \
+    nasm \
+    yasm \
+    libx264-dev \
+    libx265-dev \
+    libnuma-dev \
+    libvpx-dev \
+    libfdk-aac-dev \
+    libmp3lame-dev \
+    libopus-dev
+
+
+# Download FFMPEG source
+FFMPEG_VERSION="4.1.3"
+mkdir -p "$INSTALL_BASE_DIR"/ffmpeg_sources/ffmpeg "$INSTALL_BASE_DIR"/bin
+cd "$INSTALL_BASE_DIR"/ffmpeg_sources
+wget -O ffmpeg-snapshot.tar.bz2 https://ffmpeg.org/releases/ffmpeg-"$FFMPEG_VERSION".tar.bz2
+tar xjvf ffmpeg-snapshot.tar.bz2 -C "$INSTALL_BASE_DIR"/ffmpeg_sources/ffmpeg --strip-components=1
+rm -rf "$INSTALL_BASE_DIR"/ffmpeg_sources/ffmpeg-snapshot.tar.bz2
+cd "$INSTALL_BASE_DIR"/ffmpeg_sources/ffmpeg
+
+# Compile FFMPEG
+cd "$INSTALL_BASE_DIR"/ffmpeg_sources/ffmpeg && \
+./configure \
+--prefix="$INSTALL_BASE_DIR/ffmpeg_build" \
+--pkg-config-flags="--static" \
+--extra-cflags="-I$INSTALL_BASE_DIR/ffmpeg_build/include" \
+--extra-ldflags="-L$INSTALL_BASE_DIR/ffmpeg_build/lib" \
+--extra-libs="-lpthread -lm" \
+--bindir="$INSTALL_BASE_DIR/bin" \
+--enable-gpl \
+--enable-libass \
+--enable-libfdk-aac \
+--enable-libfreetype \
+--enable-libmp3lame \
+--enable-libopus \
+--enable-libvorbis \
+--enable-libvpx \
+--enable-libx264 \
+--enable-libx265 \
+--enable-nonfree \
+--enable-pic && \
+make -j $(nproc) && \
+make install && \
+hash -r

lib/model/nms/src/nms_cuda.c

@@ -9,10 +9,10 @@ extern THCState *state;
 int nms_cuda(THCudaIntTensor *keep_out, THCudaTensor *boxes_host,
 		     THCudaIntTensor *num_out, float nms_overlap_thresh) {
 
-	nms_cuda_compute(THCudaIntTensor_data(state, keep_out), 
-		         THCudaIntTensor_data(state, num_out), 
-      	                 THCudaTensor_data(state, boxes_host), 
-		         boxes_host->size[0], 
+	nms_cuda_compute(THCudaIntTensor_data(state, keep_out),
+		         THCudaIntTensor_data(state, num_out),
+      	                 THCudaTensor_data(state, boxes_host),
+		         boxes_host->size[0],
 		         boxes_host->size[1],
 		         nms_overlap_thresh);