Merge pull request #12 from rcland12/jetson_develop

Jetson develop

.gitignore

@@ -5,6 +5,8 @@
 *.m3u8
 stream-*.ts
 tritonserver/
+data/
+triton2/
 
 ### Linux ###
 *~

README.md

@@ -8,8 +8,65 @@ sudo bash -c "echo '/var/swapfile swap swap defaults 0 0'  >> /etc/fstab"
 ```
 
 # Holly Stream
-This application will ingest your computers webcam feed (using ffmpeg), apply an object detection task on the feed with bounding boxes, and send that feed via RTMP to an address of your choice. You have the following options for recording and applying a custom object detection model:
+This application will ingest your Jetson's camera feed, apply an object detection task with bounding boxes, and send that feed via RTMP to an address of your choice. You have the option to send the video stream to another local machine, or an external web server.
 
+# Prerequisites
+On your Jetson Nano, there are a couple of recommended and required steps before you can harness the GPU.
+
+## Allocate more memory (recommended)
+Allocating more swap memory will use storage if your device needs more RAM. You can do so with the following commands:
+```bash
+sudo fallocate -l 4G /var/swapfile 
+sudo chmod 600 /var/swapfile
+sudo mkswap /var/swapfile
+sudo swapon /var/swapfile
+sudo bash -c "echo '/var/swapfile swap swap defaults 0 0'  >> /etc/fstab"
+```
+
+## Allow Docker GPU access (required)
+In order to allow Docker access to your Jetson Nano GPU, you will have to add the following lines to the file `/etc/docker/daemon.json`.
+```bash
+{
+    "runtimes": {
+        "nvidia": {
+            "path": "nvidia-container-runtime",
+            "runtimeArgs": []
+        }
+    },
+    "default-runtime": "nvidia"
+}
+```
+Once you add the line, restart the docker daemon with `sudo systemctl restart docker`.
+
+## Install Docker Compose (required)
+Install this on the Ubuntu system Python environment (not inside a Conda or Virtualenv evironment):
+```bash
+pip3 install --upgrade pip
+pip3 install docker-compose==1.27.4
+```
+
+Check if it was installed correctly:
+```bash
+docker-compose version
+```
+
+# Installation
+You can use Docker or the native operating system:
+
+## Docker Installation
+Pull the Jetson Image:
+```bash
+docker pull rcland12/detection-stream:jetson-latest
+```
+
+## Linux Installation
+This install script will install all dependencies needed for this application, including OpenCV, PyTorch, Torchvision, and the Triton Inference Client. You will be prompted to type your password in multiple times, and this script should take roughly two or three hours to complete.
+```bash
+./install.sh
+```
+
+# Deployment
+# 
 1. [Record a webcam feed from a Jetson Nano architecture.](#deploying-on-jetson-nano)
 2. [Record a webcam feed from a Linux machine.](#deploying-on-a-linux-machine)
 

Dockerfile.jetson → app/Dockerfile

@@ -1,13 +1,7 @@
-FROM nvcr.io/nvidia/l4t-base:r32.7.1
+FROM nvcr.io/nvidia/l4t-ml:r32.7.1-py3
 ARG APPDIR="/root/app"
 ARG BUILDDIR="/root/build"
 
-# OpenCV (4.5.1) installation from source
-# The default OpenCV version on the Jetson Nano (Jetpack 4.6.1) is 4.1.1
-# The default version 4.1.1 causes dependency issues
-WORKDIR ${BUILDDIR}
-ARG OPENCV_VERSION="4.5.1"
-
 RUN sh -c "echo '/usr/local/cuda/lib64' >> /etc/ld.so.conf.d/nvidia-tegra.conf" && \
     ldconfig && \
     apt-get update && \
@@ -30,36 +24,6 @@ RUN sh -c "echo '/usr/local/cuda/lib64' >> /etc/ld.so.conf.d/nvidia-tegra.conf"
     libopenmpi-dev libomp-dev ffmpeg \
     libjpeg-dev zlib1g-dev libpython3-dev libfreetype6-dev
 
-RUN wget -O opencv.zip https://github.com/opencv/opencv/archive/${OPENCV_VERSION}.zip && \
-    wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/${OPENCV_VERSION}.zip && \
-    unzip opencv.zip && unzip opencv_contrib.zip && \
-    mv opencv-${OPENCV_VERSION} opencv && mv opencv_contrib-${OPENCV_VERSION} opencv_contrib && \
-    rm opencv.zip && rm opencv_contrib.zip && \
-    mkdir ./opencv/build
-
-WORKDIR ${BUILDDIR}/opencv/build
-
-RUN cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr \
-    -D OPENCV_EXTRA_MODULES_PATH=${BUILDDIR}/opencv_contrib/modules \
-    -D EIGEN_INCLUDE_PATH=/usr/include/eigen3 -D WITH_OPENCL=OFF \
-    -D WITH_CUDA=ON -D CUDA_ARCH_BIN=5.3 -D CUDA_ARCH_PTX="" \
-    -D WITH_CUDNN=ON -D WITH_CUBLAS=ON -D ENABLE_FAST_MATH=ON \
-    -D CUDA_FAST_MATH=ON -D OPENCV_DNN_CUDA=ON -D ENABLE_NEON=ON \
-    -D WITH_QT=OFF -D WITH_OPENMP=ON -D WITH_OPENGL=ON -D BUILD_TIFF=ON \
-    -D WITH_FFMPEG=ON -D WITH_GSTREAMER=ON -D WITH_TBB=ON -D BUILD_TBB=ON \
-    -D BUILD_TESTS=OFF -D WITH_EIGEN=ON -D WITH_V4L=ON -D WITH_LIBV4L=ON \
-    -D OPENCV_ENABLE_NONFREE=ON -D INSTALL_C_EXAMPLES=OFF \
-    -D INSTALL_PYTHON_EXAMPLES=OFF -D BUILD_NEW_PYTHON_SUPPORT=ON \
-    -D BUILD_opencv_python3=TRUE -D OPENCV_GENERATE_PKGCONFIG=ON \
-    -D BUILD_EXAMPLES=OFF .. && \
-    make -j4
-
-RUN make install && \
-    ldconfig && \
-    make clean && \
-    apt-get update && \
-    cd ../.. && rm -rf opencv opencv_contrib
-
 
 # PyTorch (1.8.0) and Torchvision (0.9.0) installation via pip wheel
 WORKDIR ${BUILDDIR}
@@ -82,15 +46,17 @@ RUN git clone --branch ${TORCHVISION_BRANCH} https://github.com/pytorch/vision t
 
 
 # Nvidia Triton Inference Server Client installation
-RUN wget https://github.com/triton-inference-server/server/releases/download/v2.16.0/tritonserver2.16.0-jetpack4.6.tgz && \
+ARG TRITON_VERSION="2.19.0"
+RUN wget https://github.com/triton-inference-server/server/releases/download/v${TRITON_VERSION}/tritonserver${TRITON_VERSION}-jetpack4.6.1.tgz && \
     mkdir tritonserver && \
-    tar -xzf tritonserver2.16.0-jetpack4.6.tgz -C ${BUILDDIR}/tritonserver/
+    tar -xzf tritonserver${TRITON_VERSION}-jetpack4.6.1.tgz -C ${BUILDDIR}/tritonserver/
 
 RUN pip3 install --upgrade pip && \
-    pip3 install --upgrade grpcio-tools numpy==1.19.4 future attrdict nanocamera && \
-    pip3 install --upgrade ${BUILDDIR}/tritonserver/clients/python/tritonclient-2.16.0-py3-none-any.whl[all] && \
-    rm tritonserver2.16.0-jetpack4.6.tgz
+    pip3 install --upgrade grpcio-tools numpy==1.19.4 future attrdict nanocamera imutils python-dotenv && \
+    pip3 install --upgrade ${BUILDDIR}/tritonserver/clients/python/tritonclient-${TRITON_VERSION}-py3-none-any.whl[all] && \
+    rm tritonserver${TRITON_VERSION}-jetpack4.6.1.tgz
 
 WORKDIR ${APPDIR}
-COPY jetson.py utilities.py ./
-CMD ["python3", "jetson.py"]
+COPY main.py utilities.py ./
+COPY images/*.png ./images/
+CMD ["python3", "main.py"]

app/collect_data.py

@@ -0,0 +1,54 @@
+import cv2
+from nanocamera import Camera
+
+
+
+def main(
+    save_path,
+    number_to_save=100,
+    period=5,
+    camera_index=0,
+    camera_width=1280,
+    camera_height=720,
+    camera_fps=30
+):
+    camera = Camera(
+        device_id=camera_index,
+        flip=0,
+        width=camera_width,
+        height=camera_height,
+        fps=camera_fps
+	)
+
+    frame_index = 0
+    picture_index = 0
+    take_picture = camera_fps * period
+
+    while camera.isReady():
+        frame = camera.read()
+
+        if frame_index % take_picture:
+            cv2.imwrite(save_path + f"pic_{picture_index}.png", frame)
+            print(f"Saving image {picture_index}/{number_to_save}")
+            picture_index += 1
+
+            if picture_index == number_to_save:
+                break
+
+        frame_index += 1
+
+    print(f"Saved {number_to_save} picture to `{save_path}` successfully.")
+    return
+
+
+
+if __name__ == "__main__":
+    main(
+        save_path="../data/images/train/",
+        number_to_save=100,
+        period=5,
+        camera_index=0,
+        camera_width=1280,
+        camera_height=720,
+        camera_fps=30
+    )