Port to ARM64 and add GPU acceleration
This commit is contained in:
Paul DeCarlo
Родитель
6d4eef8942
Коммит
01753791c7
10
.env
10
.env
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@ -1,5 +1,5 @@
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CONTAINER_REGISTRY_URL=<ACR Login Server>
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CONTAINER_REGISTRY_USERNAME=<ACR User Name>
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CONTAINER_REGISTRY_PASSWORD=<ACR Password>
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CONTAINER_MODULE_VERSION=step7-8
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CONTAINER_VIDEO_SOURCE=<URL to Video>
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CONTAINER_REGISTRY_URL=toolboc
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CONTAINER_REGISTRY_USERNAME=
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CONTAINER_REGISTRY_PASSWORD=
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CONTAINER_MODULE_VERSION=latest
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CONTAINER_VIDEO_SOURCE=https://www.youtube.com/watch?v=YZkp0qBBmpw
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@ -0,0 +1,6 @@
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{
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"azure-iot-edge.defaultPlatform": {
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"platform": "arm32v7",
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"alias": null
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}
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}
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@ -0,0 +1,71 @@
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{
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"modulesContent": {
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"$edgeAgent": {
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||||
"properties.desired": {
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||||
"schemaVersion": "1.0",
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"runtime": {
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"type": "docker",
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"settings": {
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"minDockerVersion": "v1.25",
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"loggingOptions": "",
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"registryCredentials": {
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"bootcampfy19acr": {
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"username": "$CONTAINER_REGISTRY_USERNAME",
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"password": "$CONTAINER_REGISTRY_PASSWORD",
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"address": "toolboc"
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}
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}
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}
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},
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"systemModules": {
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"edgeAgent": {
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"type": "docker",
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"settings": {
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"image": "mcr.microsoft.com/azureiotedge-agent:1.0.8-rc1",
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"createOptions": "{}"
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}
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},
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"edgeHub": {
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"type": "docker",
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"status": "running",
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"restartPolicy": "always",
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"settings": {
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"image": "mcr.microsoft.com/azureiotedge-hub:1.0.8-rc1",
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"createOptions": "{\"HostConfig\":{\"PortBindings\":{\"5671/tcp\":[{\"HostPort\":\"5671\"}],\"8883/tcp\":[{\"HostPort\":\"8883\"}],\"443/tcp\":[{\"HostPort\":\"443\"}]}}}"
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}
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}
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},
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"modules": {
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"YoloModule": {
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"version": "1.0",
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"type": "docker",
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"status": "running",
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"restartPolicy": "always",
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"settings": {
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"image": "toolboc/yolomodule:latest-arm32v7",
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"createOptions": "{\"Env\":[\"VIDEO_PATH=https://www.youtube.com/watch?v=YZkp0qBBmpw\",\"VIDEO_WIDTH=0\",\"VIDEO_HEIGHT=0\",\"FONT_SCALE=0.8\"],\"HostConfig\":{\"Devices\":[{\"PathOnHost\":\"/dev/nvhost-ctrl\",\"PathInContainer\":\"/dev/nvhost-ctrl\",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/nvhost-ctrl-gpu\",\"PathInContainer\":\"dev/nvhost-ctrl-gpu\",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/nvhost-prof-gpu\",\"PathInContainer\":\"dev/nvhost-prof-gpu \",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/nvmap\",\"PathInContainer\":\"/dev/nvmap\",\"Cgroup",
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"createOptions01": "Permissions\":\"rwm\"},{\"PathOnHost\":\"dev/nvhost-gpu\",\"PathInContainer\":\"dev/nvhost-gpu\",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/nvhost-as-gpu\",\"PathInContainer\":\"/dev/nvhost-as-gpu\",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/nvhost-vic\",\"PathInContainer\":\"/dev/nvhost-vic\",\"CgroupPermissions\":\"rwm\"},{\"PathOnHost\":\"/dev/tegra_dc_ctrl\",\"PathInContainer\":\"/dev/tegra_dc_ctrl\",\"CgroupPermissions\":\"rwm\"}],\"PortBindings\":{\"80/tcp\":[{\"HostPort\":\"80\"}]}}}"
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}
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}
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}
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}
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},
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"$edgeHub": {
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"properties.desired": {
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"schemaVersion": "1.0",
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"routes": {},
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"storeAndForwardConfiguration": {
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"timeToLiveSecs": 7200
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}
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}
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},
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"YoloModule": {
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"properties.desired": {
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"ConfidenceLevel": "0.3",
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"VerboseMode": 0,
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"Inference": 1,
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"VideoSource": ""
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}
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}
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}
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}
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@ -22,7 +22,7 @@
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"edgeAgent": {
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"type": "docker",
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"settings": {
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"image": "mcr.microsoft.com/azureiotedge-agent:1.0",
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"image": "mcr.microsoft.com/azureiotedge-agent:1.0.8-rc1",
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"createOptions": {}
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}
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},
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@ -31,7 +31,7 @@
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"status": "running",
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"restartPolicy": "always",
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"settings": {
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"image": "mcr.microsoft.com/azureiotedge-hub:1.0",
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"image": "mcr.microsoft.com/azureiotedge-hub:1.0.8-rc1",
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"createOptions": {
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"HostConfig": {
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"PortBindings": {
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@ -64,7 +64,65 @@
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"restartPolicy": "always",
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"settings": {
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"image": "${MODULES.YoloModule}",
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"createOptions": "{\"Env\":[\"VIDEO_PATH=$CONTAINER_VIDEO_SOURCE\", \"VIDEO_WIDTH=0\", \"VIDEO_HEIGHT=0\", \"FONT_SCALE=0.8\"], \"HostConfig\":{\"PortBindings\":{\"80/tcp\":[{\"HostPort\":\"80\"}]}}}"
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"createOptions": {
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"Env": [
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"VIDEO_PATH=$CONTAINER_VIDEO_SOURCE",
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"VIDEO_WIDTH=0",
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"VIDEO_HEIGHT=0",
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"FONT_SCALE=0.8"
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],
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"HostConfig": {
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"Devices": [
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{
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"PathOnHost": "/dev/nvhost-ctrl",
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"PathInContainer":"/dev/nvhost-ctrl",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/nvhost-ctrl-gpu",
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"PathInContainer":"dev/nvhost-ctrl-gpu",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/nvhost-prof-gpu",
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"PathInContainer":"dev/nvhost-prof-gpu ",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/nvmap",
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"PathInContainer":"/dev/nvmap",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "dev/nvhost-gpu",
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"PathInContainer":"dev/nvhost-gpu",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/nvhost-as-gpu",
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"PathInContainer":"/dev/nvhost-as-gpu",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/nvhost-vic",
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"PathInContainer":"/dev/nvhost-vic",
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"CgroupPermissions":"rwm"
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},
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{
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"PathOnHost": "/dev/tegra_dc_ctrl",
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"PathInContainer":"/dev/tegra_dc_ctrl",
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"CgroupPermissions":"rwm"
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}
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],
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"PortBindings": {
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"80/tcp": [
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{
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"HostPort": "80"
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}
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]
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}
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}
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}
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}
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}
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}
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@ -0,0 +1,21 @@
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FROM toolboc/jetson-nano-l4t-cuda-cudnn-opencv
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RUN apt update && apt install -y libcanberra-gtk-module && \
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rm -rf /var/lib/apt/lists/*
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#GET Darknet sources
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WORKDIR /usr/local/src
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RUN git clone https://github.com/AlexeyAB/darknet.git && \
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cd darknet && \
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sed -i 's/GPU=0/GPU=1/g' Makefile && \
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sed -i 's/CUDNN=0/CUDNN=1/g' Makefile && \
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sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/g' Makefile && \
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sed -i 's/OPENCV=0/OPENCV=1/g' Makefile && \
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sed -i 's/LIBSO=0/LIBSO=1/g' Makefile && \
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make
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RUN cd darknet && LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH
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RUN cd darknet && \
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curl https://pjreddie.com/media/files/yolov3-tiny.weights -o yolov3-tiny.weights
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@ -0,0 +1,31 @@
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FROM toolboc/jetson-nano-l4t-cuda-cudnn
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#Required for libjasper-dev
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RUN echo "deb http://ports.ubuntu.com/ubuntu-ports/ xenial-security main restricted" | sudo tee -a /etc/apt/sources.list
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#INSTALL OPENCV dependencies
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RUN apt update && apt purge *libopencv* && apt install -y build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev \
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libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev \
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python2.7-dev python3.6-dev python-dev python-numpy python3-numpy \
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libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev \
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libv4l-dev v4l-utils qv4l2 v4l2ucp \
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curl unzip && \
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rm -rf /var/lib/apt/lists/*
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|
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#GET OPENCV sources
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WORKDIR /usr/local/src
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RUN curl -L https://github.com/opencv/opencv/archive/4.1.0.zip -o opencv-4.1.0.zip && \
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curl -L https://github.com/opencv/opencv_contrib/archive/4.1.0.zip -o opencv_contrib-4.1.0.zip && \
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unzip opencv-4.1.0.zip && \
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unzip opencv_contrib-4.1.0.zip && \
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rm -rf opencv*.zip
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#INSTALL OPENCV
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RUN cd opencv-4.1.0/ && mkdir release && cd release/ && \
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cmake -D OPENCV_GENERATE_PKGCONFIG=ON -D OPENCV_PC_FILE_NAME=opencv.pc -D WITH_CUDA=ON -D CUDA_ARCH_BIN="5.3" -D CUDA_ARCH_PTX="" -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.1.0/modules -D WITH_GSTREAMER=ON -D WITH_LIBV4L=ON -D BUILD_opencv_python2=ON -D BUILD_opencv_python3=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. && \
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make -j3 && \
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make install && \
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cp unix-install/opencv.pc /usr/local/lib/pkgconfig && \
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rm -rf /usr/local/src/opencv-4.1.0
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RUN ldconfig
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@ -0,0 +1,28 @@
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FROM toolboc/jetson-nano-l4t-cuda
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# NVIDIA CUDA Deep Neural Network library (cuDNN)
|
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ENV CUDNN_VERSION 7.3.1.28
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ENV CUDNN_PKG_VERSION=${CUDA_VERSION}-1
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LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"
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ARG libcudnn7_URL=https://onedrive.live.com/download?cid=54AD8562A32D8752&resid=54AD8562A32D8752%21376196&authkey=ADTDdL0bhMWq4vM
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||||
ARG libcudnn7_dev_URL=https://onedrive.live.com/download?cid=54AD8562A32D8752&resid=54AD8562A32D8752%21376197&authkey=APizXm-di7JPR0Y
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ARG libcudnn7_doc_URL=https://onedrive.live.com/download?cid=54AD8562A32D8752&resid=54AD8562A32D8752%21376195&authkey=ADqH53K9oRnkO-8
|
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|
||||
RUN curl -sL $libcudnn7_URL -o libcudnn7_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
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echo "92867c0a495f84ec11d108f84b776620 libcudnn7_$CUDNN_VERSION-1+cuda10.0_arm64.deb" | md5sum -c - && \
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dpkg -i libcudnn7_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
|
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rm libcudnn7_$CUDNN_VERSION-1+cuda10.0_arm64.deb
|
||||
|
||||
RUN curl -sL $libcudnn7_dev_URL -o libcudnn7-dev_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
|
||||
echo "dd0fbfa225b2374b946febc98e2cdec4 libcudnn7-dev_$CUDNN_VERSION-1+cuda10.0_arm64.deb" | md5sum -c - && \
|
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dpkg -i libcudnn7-dev_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
|
||||
rm libcudnn7-dev_$CUDNN_VERSION-1+cuda10.0_arm64.deb
|
||||
|
||||
RUN curl -sL $libcudnn7_doc_URL -o libcudnn7-doc_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
|
||||
echo "9478c16ceeaaca937d4d26b982e48bd1 libcudnn7-doc_$CUDNN_VERSION-1+cuda10.0_arm64.deb" | md5sum -c - && \
|
||||
dpkg -i libcudnn7-doc_$CUDNN_VERSION-1+cuda10.0_arm64.deb && \
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rm libcudnn7-doc_$CUDNN_VERSION-1+cuda10.0_arm64.deb
|
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|
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@ -0,0 +1,22 @@
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FROM toolboc/jetson-nano-l4t
|
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|
||||
#INSTALL CUDA Toolkit for L4T
|
||||
ARG URL=https://onedrive.live.com/download?cid=54AD8562A32D8752&resid=54AD8562A32D8752%21376191&authkey=APwtvHgdqlgnJzo
|
||||
ARG CUDA_TOOLKIT_PKG="cuda-repo-l4t-10-0-local-10.0.166_1.0-1_arm64.deb"
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --no-install-recommends curl && \
|
||||
curl -sL ${URL} -o ${CUDA_TOOLKIT_PKG} && \
|
||||
echo "5e3eedc3707305f9022d41754d6becde ${CUDA_TOOLKIT_PKG}" | md5sum -c - && \
|
||||
dpkg --force-all -i ${CUDA_TOOLKIT_PKG} && \
|
||||
rm ${CUDA_TOOLKIT_PKG} && \
|
||||
apt-key add var/cuda-repo-*-local*/*.pub && \
|
||||
apt-get update && \
|
||||
apt-get install -y --allow-downgrades cuda-toolkit-10-0 libgomp1 libfreeimage-dev libopenmpi-dev openmpi-bin && \
|
||||
dpkg --purge cuda-repo-l4t-10-0-local-10.0.166 && \
|
||||
apt-get clean && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
ENV CUDA_HOME=/usr/local/cuda
|
||||
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
|
||||
ENV PATH=$PATH:$CUDA_HOME/bin
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
FROM balenalib/jetson-tx2-ubuntu:bionic
|
||||
|
||||
ARG URL=https://onedrive.live.com/download?cid=54AD8562A32D8752&resid=54AD8562A32D8752%21376194&authkey=ADUfVNPnEHviFoU
|
||||
ARG DRIVER_PACK=Jetson-210_Linux_R32.1.0_aarch64.tbz2
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
bzip2 \
|
||||
ca-certificates \
|
||||
curl \
|
||||
lbzip2 \
|
||||
sudo \
|
||||
&& \
|
||||
curl -sSL $URL -o ${DRIVER_PACK} && \
|
||||
echo "9138c7dd844eb290a20b31446b757e1781080f63 *./${DRIVER_PACK}" | sha1sum -c --strict - && \
|
||||
tar -xpj --overwrite -f ./${DRIVER_PACK} && \
|
||||
sed -i '/.*tar -I lbzip2 -xpmf ${LDK_NV_TEGRA_DIR}\/config\.tbz2.*/c\tar -I lbzip2 -xpm --overwrite -f ${LDK_NV_TEGRA_DIR}\/config.tbz2' ./Linux_for_Tegra/apply_binaries.sh && \
|
||||
./Linux_for_Tegra/apply_binaries.sh -r / && \
|
||||
rm -rf ./Linux_for_Tegra && \
|
||||
rm ./${DRIVER_PACK} \
|
||||
&& \
|
||||
apt-get purge --autoremove -y bzip2 curl lbzip2 && \
|
||||
apt-get clean && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
ENV LD_LIBRARY_PATH=/usr/lib/aarch64-linux-gnu/tegra:/usr/lib/aarch64-linux-gnu/tegra-egl:${LD_LIBRARY_PATH}
|
||||
|
||||
RUN ln -s /usr/lib/aarch64-linux-gnu/tegra/libnvidia-ptxjitcompiler.so.32.1.0 /usr/lib/aarch64-linux-gnu/tegra/libnvidia-ptxjitcompiler.so && \
|
||||
ln -s /usr/lib/aarch64-linux-gnu/tegra/libnvidia-ptxjitcompiler.so.32.1.0 /usr/lib/aarch64-linux-gnu/tegra/libnvidia-ptxjitcompiler.so.1 && \
|
||||
ln -sf /usr/lib/aarch64-linux-gnu/tegra/libGL.so /usr/lib/aarch64-linux-gnu/libGL.so && \
|
||||
ln -s /usr/lib/aarch64-linux-gnu/libcuda.so /usr/lib/aarch64-linux-gnu/libcuda.so.1 && \
|
||||
ln -sf /usr/lib/aarch64-linux-gnu/tegra-egl/libEGL.so /usr/lib/aarch64-linux-gnu/libEGL.so
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
FROM ubuntu:xenial
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --no-install-recommends libcurl4-openssl-dev python-pip libboost-python-dev libgtk2.0-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
COPY /build/requirements.txt ./
|
||||
RUN pip install --upgrade pip
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
RUN pip install tornado==4.5.3 trollius && \
|
||||
pip install -U youtube-dl
|
||||
|
||||
ADD /app/ .
|
||||
|
||||
# Expose the port
|
||||
EXPOSE 80
|
||||
|
||||
ENTRYPOINT [ "python", "-u", "./main.py" ]
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
FROM balenalib/jetson-tx2-ubuntu:bionic as iot-sdk-python-builder
|
||||
|
||||
# Update image
|
||||
SHELL ["/bin/bash", "-c"]
|
||||
|
||||
RUN apt-get update && apt-get install -y cmake build-essential curl libcurl4-openssl-dev \
|
||||
libssl-dev uuid-dev apt-utils python python-pip python-virtualenv python3 python3-pip python3-virtualenv \
|
||||
libboost-python-dev pkg-config valgrind sudo git software-properties-common && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /usr/sdk
|
||||
|
||||
RUN python -m virtualenv --python=python3 env3
|
||||
RUN source env3/bin/activate && pip install --upgrade pip && pip install -U setuptools wheel
|
||||
|
||||
RUN git clone --recursive --depth=1 https://github.com/Azure/azure-iot-sdk-python.git src
|
||||
|
||||
# Build for Python 3
|
||||
RUN add-apt-repository ppa:deadsnakes/ppa
|
||||
RUN source env3/bin/activate && ./src/build_all/linux/setup.sh --python-version 3.6
|
||||
RUN source env3/bin/activate && ./src/build_all/linux/release.sh --build-python 3.6
|
||||
|
||||
# Build for Python 2
|
||||
#RUN pip install --upgrade pip==10.0.1 && python -m pip install -U setuptools wheel
|
||||
#RUN ./src/build_all/linux/setup.sh
|
||||
#RUN ./src/build_all/linux/release.sh
|
||||
|
||||
FROM toolboc/jetson-nano-l4t-cuda-cudnn-opencv-darknet
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
RUN apt-get update && \
|
||||
DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends libcurl4-openssl-dev python3-pip libboost-python-dev libgtk2.0-dev python3-setuptools python3-numpy python3-opencv python-opencv && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
COPY --from=iot-sdk-python-builder /usr/sdk/src/device/doc/package-readme.md /src/device/doc/package-readme.md
|
||||
COPY --from=iot-sdk-python-builder /usr/sdk/src/build_all/linux/release_device_client /src/build_all/linux/release_device_client
|
||||
RUN cd /src/build_all/linux/release_device_client && python3 setup.py install
|
||||
COPY --from=iot-sdk-python-builder /usr/sdk/src/device/samples/iothub_client.so /app/iothub_client.so
|
||||
|
||||
RUN cp /usr/local/src/darknet/libdarknet.so /app/libdarknet.so
|
||||
|
||||
COPY /build/requirements.txt ./
|
||||
RUN pip3 install --upgrade pip
|
||||
RUN pip3 install --no-cache-dir -r requirements.txt
|
||||
RUN pip3 install tornado==4.5.3 trollius && \
|
||||
pip3 install -U youtube-dl
|
||||
|
||||
ADD /app/ .
|
||||
|
||||
# Expose the port
|
||||
EXPOSE 80
|
||||
|
||||
ENTRYPOINT [ "python3", "-u", "./main.py" ]
|
||||
|
|
@ -13,14 +13,9 @@ import ImageServer
|
|||
from ImageServer import ImageServer
|
||||
import VideoStream
|
||||
from VideoStream import VideoStream
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment Start
|
||||
***********************************************************'''
|
||||
# import YoloInference
|
||||
# from YoloInference import YoloInference
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment End
|
||||
***********************************************************'''
|
||||
|
||||
import YoloInference
|
||||
from YoloInference import YoloInference
|
||||
|
||||
class VideoCapture(object):
|
||||
|
||||
|
|
@ -63,13 +58,8 @@ class VideoCapture(object):
|
|||
|
||||
self.imageServer = ImageServer(80, self)
|
||||
self.imageServer.start()
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment Start
|
||||
***********************************************************'''
|
||||
# self.yoloInference = YoloInference(self.fontScale)
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment End
|
||||
***********************************************************'''
|
||||
|
||||
self.yoloInference = YoloInference(self.fontScale)
|
||||
|
||||
def __IsCaptureDev(self, videoPath):
|
||||
try:
|
||||
|
|
@ -287,14 +277,8 @@ class VideoCapture(object):
|
|||
frame = cv2.resize(frame, (self.videoW, self.videoH))
|
||||
|
||||
# Run Object Detection
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment Start
|
||||
***********************************************************'''
|
||||
# if self.inference:
|
||||
# self.yoloInference.runInference(frame, frameW, frameH, self.confidenceLevel)
|
||||
'''***********************************************************
|
||||
Step-10 : Uncomment End
|
||||
***********************************************************'''
|
||||
if self.inference:
|
||||
self.yoloInference.runInference(frame, frameW, frameH, self.confidenceLevel)
|
||||
|
||||
# Calculate FPS
|
||||
timeElapsedInMs = (time.time() - tFrameStart) * 1000
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ if sys.version_info[0] < 3:#e.g python version <3
|
|||
import cv2
|
||||
else:
|
||||
import cv2
|
||||
from cv2 import cv2
|
||||
# from cv2 import cv2
|
||||
# pylint: disable=E1101
|
||||
# pylint: disable=E0401
|
||||
# Disabling linting that is not supported by Pylint for C extensions such as OpenCV. See issue https://github.com/PyCQA/pylint/issues/1955
|
||||
|
|
|
|||
|
|
@ -2,6 +2,8 @@
|
|||
from __future__ import division
|
||||
from __future__ import absolute_import
|
||||
|
||||
from darknet import darknet
|
||||
|
||||
import cv2
|
||||
#import cv2.cv as cv
|
||||
import numpy as np
|
||||
|
|
@ -11,6 +13,10 @@ import os
|
|||
yolocfg = r'yolo/yolov3-tiny.cfg'
|
||||
yoloweight = r'yolo/yolov3-tiny.weights'
|
||||
classesFile = r'yolo/coco.names'
|
||||
dataFile = r'yolo/coco.data'
|
||||
|
||||
encoding = 'utf-8'
|
||||
|
||||
|
||||
class YoloInference(object):
|
||||
|
||||
|
|
@ -43,7 +49,7 @@ class YoloInference(object):
|
|||
|
||||
# Read pre-trained model and config file
|
||||
print(" - Loading Model and Config")
|
||||
self.net = cv2.dnn.readNetFromDarknet( yolocfg, yoloweight )
|
||||
darknet.performDetect( configPath = yolocfg, weightPath = yoloweight, metaPath= dataFile, initOnly= True )
|
||||
|
||||
def __get_output_layers(self, net):
|
||||
layerNames = net.getLayerNames()
|
||||
|
|
@ -59,8 +65,8 @@ class YoloInference(object):
|
|||
print("draw_rect h :" + str(h))
|
||||
|
||||
label = '%.2f' % confidence
|
||||
label = '%s:%s' % (self.classLabels[class_id], label)
|
||||
color = self.colors[class_id]
|
||||
label = '%s:%s' % (class_id, label)
|
||||
color = self.colors[self.classLabels.index(class_id)]
|
||||
|
||||
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.fontScale, self.fontThickness)
|
||||
|
||||
|
|
@ -71,65 +77,30 @@ class YoloInference(object):
|
|||
|
||||
def runInference(self, frame, frameW, frameH, confidenceLevel):
|
||||
try:
|
||||
# Create input blob
|
||||
blob = cv2.dnn.blobFromImage(frame, 1.0/255.0, (416, 416), (0,0,0), True, crop=False)
|
||||
|
||||
# Set input blob for the network
|
||||
self.net.setInput(blob)
|
||||
detections = darknet.detect(darknet.netMain, darknet.metaMain, frame, confidenceLevel)
|
||||
|
||||
# Run inference
|
||||
outputs = self.net.forward(self.__get_output_layers(self.net))
|
||||
for detection in detections:
|
||||
|
||||
classLabel = detection[0]
|
||||
classID = str(detection[0], encoding)
|
||||
confidence = detection[1]
|
||||
|
||||
# Initialize arrays
|
||||
boxes = []
|
||||
confidences = []
|
||||
classIDs = []
|
||||
if confidence > confidenceLevel:
|
||||
|
||||
for output in outputs:
|
||||
for detection in output:
|
||||
scores = detection[5:]
|
||||
classID = np.argmax(scores)
|
||||
confidence = scores[classID]
|
||||
classLabel = self.classLabels[classID]
|
||||
if self.verbose:
|
||||
print( "Class Label : %s Confidence %f" % (classLabel, confidence))
|
||||
|
||||
if confidence > confidenceLevel:
|
||||
bounds = detection[2]
|
||||
|
||||
xEntent = int(bounds[2])
|
||||
yExtent = int(bounds[3])
|
||||
# Coordinates are around the center
|
||||
xCoord = int(bounds[0] - bounds[2]/2)
|
||||
yCoord = int(bounds[1] - bounds[3]/2)
|
||||
|
||||
if self.verbose:
|
||||
print( "Class Label : %s Confidence %f" % (classLabel, confidence))
|
||||
self.__draw_rect(frame, classID, confidence, xCoord, yCoord, xCoord + xEntent, yCoord + yExtent)
|
||||
|
||||
centerX = int(detection[0] * frameW)
|
||||
centerY = int(detection[1] * frameH)
|
||||
rectWidth = int(detection[2] * frameW)
|
||||
rectHeight = int(detection[3] * frameH)
|
||||
|
||||
left = int(max((centerX - (rectWidth / 2)), 5))
|
||||
top = int(max((centerY - (rectHeight / 2)), 5))
|
||||
|
||||
if rectHeight + top > frameH:
|
||||
rectHeight = frameH - top - 5
|
||||
|
||||
if rectWidth + left > frameW:
|
||||
rectWidth = frameW - left - 5
|
||||
|
||||
boxes.append([left, top, rectWidth, rectHeight])
|
||||
confidences.append(float(confidence))
|
||||
classIDs.append(classID)
|
||||
|
||||
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidenceLevel, self.nmsThreshold)
|
||||
|
||||
for i in idxs:
|
||||
i = i[0]
|
||||
box = boxes[i]
|
||||
# Get the bounding box coordinates
|
||||
x = box[0]
|
||||
y = box[1]
|
||||
w = box[2]
|
||||
h = box[3]
|
||||
|
||||
# draw a bounding box rectangle and label on the image
|
||||
self.__draw_rect(frame, classIDs[i], confidences[i], x, y, x + w, y + h)
|
||||
|
||||
except:
|
||||
except Exception as e:
|
||||
print("Exception during AI Inference")
|
||||
|
||||
|
||||
print(e)
|
||||
|
|
@ -0,0 +1,466 @@
|
|||
#!python3
|
||||
"""
|
||||
Python 3 wrapper for identifying objects in images
|
||||
|
||||
Requires DLL compilation
|
||||
|
||||
Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll".
|
||||
|
||||
On a GPU system, you can force CPU evaluation by any of:
|
||||
|
||||
- Set global variable DARKNET_FORCE_CPU to True
|
||||
- Set environment variable CUDA_VISIBLE_DEVICES to -1
|
||||
- Set environment variable "FORCE_CPU" to "true"
|
||||
|
||||
|
||||
To use, either run performDetect() after import, or modify the end of this file.
|
||||
|
||||
See the docstring of performDetect() for parameters.
|
||||
|
||||
Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`)
|
||||
|
||||
|
||||
Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py
|
||||
Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py
|
||||
|
||||
@author: Philip Kahn
|
||||
@date: 20180503
|
||||
"""
|
||||
#pylint: disable=R, W0401, W0614, W0703
|
||||
from ctypes import *
|
||||
import math
|
||||
import random
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
def sample(probs):
|
||||
s = sum(probs)
|
||||
probs = [a/s for a in probs]
|
||||
r = random.uniform(0, 1)
|
||||
for i in range(len(probs)):
|
||||
r = r - probs[i]
|
||||
if r <= 0:
|
||||
return i
|
||||
return len(probs)-1
|
||||
|
||||
def c_array(ctype, values):
|
||||
arr = (ctype*len(values))()
|
||||
arr[:] = values
|
||||
return arr
|
||||
|
||||
class BOX(Structure):
|
||||
_fields_ = [("x", c_float),
|
||||
("y", c_float),
|
||||
("w", c_float),
|
||||
("h", c_float)]
|
||||
|
||||
class DETECTION(Structure):
|
||||
_fields_ = [("bbox", BOX),
|
||||
("classes", c_int),
|
||||
("prob", POINTER(c_float)),
|
||||
("mask", POINTER(c_float)),
|
||||
("objectness", c_float),
|
||||
("sort_class", c_int)]
|
||||
|
||||
|
||||
class IMAGE(Structure):
|
||||
_fields_ = [("w", c_int),
|
||||
("h", c_int),
|
||||
("c", c_int),
|
||||
("data", POINTER(c_float))]
|
||||
|
||||
class METADATA(Structure):
|
||||
_fields_ = [("classes", c_int),
|
||||
("names", POINTER(c_char_p))]
|
||||
|
||||
|
||||
|
||||
#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
|
||||
#lib = CDLL("libdarknet.so", RTLD_GLOBAL)
|
||||
hasGPU = True
|
||||
if os.name == "nt":
|
||||
cwd = os.path.dirname(__file__)
|
||||
os.environ['PATH'] = cwd + ';' + os.environ['PATH']
|
||||
winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll")
|
||||
winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll")
|
||||
envKeys = list()
|
||||
for k, v in os.environ.items():
|
||||
envKeys.append(k)
|
||||
try:
|
||||
try:
|
||||
tmp = os.environ["FORCE_CPU"].lower()
|
||||
if tmp in ["1", "true", "yes", "on"]:
|
||||
raise ValueError("ForceCPU")
|
||||
else:
|
||||
print("Flag value '"+tmp+"' not forcing CPU mode")
|
||||
except KeyError:
|
||||
# We never set the flag
|
||||
if 'CUDA_VISIBLE_DEVICES' in envKeys:
|
||||
if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0:
|
||||
raise ValueError("ForceCPU")
|
||||
try:
|
||||
global DARKNET_FORCE_CPU
|
||||
if DARKNET_FORCE_CPU:
|
||||
raise ValueError("ForceCPU")
|
||||
except NameError:
|
||||
pass
|
||||
# print(os.environ.keys())
|
||||
# print("FORCE_CPU flag undefined, proceeding with GPU")
|
||||
if not os.path.exists(winGPUdll):
|
||||
raise ValueError("NoDLL")
|
||||
lib = CDLL(winGPUdll, RTLD_GLOBAL)
|
||||
except (KeyError, ValueError):
|
||||
hasGPU = False
|
||||
if os.path.exists(winNoGPUdll):
|
||||
lib = CDLL(winNoGPUdll, RTLD_GLOBAL)
|
||||
print("Notice: CPU-only mode")
|
||||
else:
|
||||
# Try the other way, in case no_gpu was
|
||||
# compile but not renamed
|
||||
lib = CDLL(winGPUdll, RTLD_GLOBAL)
|
||||
print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.")
|
||||
else:
|
||||
lib = CDLL("./libdarknet.so", RTLD_GLOBAL)
|
||||
lib.network_width.argtypes = [c_void_p]
|
||||
lib.network_width.restype = c_int
|
||||
lib.network_height.argtypes = [c_void_p]
|
||||
lib.network_height.restype = c_int
|
||||
|
||||
copy_image_from_bytes = lib.copy_image_from_bytes
|
||||
copy_image_from_bytes.argtypes = [IMAGE,c_char_p]
|
||||
|
||||
def network_width(net):
|
||||
return lib.network_width(net)
|
||||
|
||||
def network_height(net):
|
||||
return lib.network_height(net)
|
||||
|
||||
predict = lib.network_predict_ptr
|
||||
predict.argtypes = [c_void_p, POINTER(c_float)]
|
||||
predict.restype = POINTER(c_float)
|
||||
|
||||
if hasGPU:
|
||||
set_gpu = lib.cuda_set_device
|
||||
set_gpu.argtypes = [c_int]
|
||||
|
||||
make_image = lib.make_image
|
||||
make_image.argtypes = [c_int, c_int, c_int]
|
||||
make_image.restype = IMAGE
|
||||
|
||||
get_network_boxes = lib.get_network_boxes
|
||||
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int), c_int]
|
||||
get_network_boxes.restype = POINTER(DETECTION)
|
||||
|
||||
make_network_boxes = lib.make_network_boxes
|
||||
make_network_boxes.argtypes = [c_void_p]
|
||||
make_network_boxes.restype = POINTER(DETECTION)
|
||||
|
||||
free_detections = lib.free_detections
|
||||
free_detections.argtypes = [POINTER(DETECTION), c_int]
|
||||
|
||||
free_ptrs = lib.free_ptrs
|
||||
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
|
||||
|
||||
network_predict = lib.network_predict_ptr
|
||||
network_predict.argtypes = [c_void_p, POINTER(c_float)]
|
||||
|
||||
reset_rnn = lib.reset_rnn
|
||||
reset_rnn.argtypes = [c_void_p]
|
||||
|
||||
load_net = lib.load_network
|
||||
load_net.argtypes = [c_char_p, c_char_p, c_int]
|
||||
load_net.restype = c_void_p
|
||||
|
||||
load_net_custom = lib.load_network_custom
|
||||
load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
|
||||
load_net_custom.restype = c_void_p
|
||||
|
||||
do_nms_obj = lib.do_nms_obj
|
||||
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
|
||||
|
||||
do_nms_sort = lib.do_nms_sort
|
||||
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
|
||||
|
||||
free_image = lib.free_image
|
||||
free_image.argtypes = [IMAGE]
|
||||
|
||||
letterbox_image = lib.letterbox_image
|
||||
letterbox_image.argtypes = [IMAGE, c_int, c_int]
|
||||
letterbox_image.restype = IMAGE
|
||||
|
||||
load_meta = lib.get_metadata
|
||||
lib.get_metadata.argtypes = [c_char_p]
|
||||
lib.get_metadata.restype = METADATA
|
||||
|
||||
load_image = lib.load_image_color
|
||||
load_image.argtypes = [c_char_p, c_int, c_int]
|
||||
load_image.restype = IMAGE
|
||||
|
||||
rgbgr_image = lib.rgbgr_image
|
||||
rgbgr_image.argtypes = [IMAGE]
|
||||
|
||||
predict_image = lib.network_predict_image
|
||||
predict_image.argtypes = [c_void_p, IMAGE]
|
||||
predict_image.restype = POINTER(c_float)
|
||||
|
||||
predict_image_letterbox = lib.network_predict_image_letterbox
|
||||
predict_image_letterbox.argtypes = [c_void_p, IMAGE]
|
||||
predict_image_letterbox.restype = POINTER(c_float)
|
||||
|
||||
def array_to_image(arr):
|
||||
# need to return old values to avoid python freeing memory
|
||||
arr = arr.transpose(2,0,1)
|
||||
c = arr.shape[0]
|
||||
h = arr.shape[1]
|
||||
w = arr.shape[2]
|
||||
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
|
||||
data = arr.ctypes.data_as(POINTER(c_float))
|
||||
im = IMAGE(w,h,c,data)
|
||||
return im, arr
|
||||
|
||||
def classify(net, meta, im):
|
||||
out = predict_image(net, im)
|
||||
res = []
|
||||
for i in range(meta.classes):
|
||||
if altNames is None:
|
||||
nameTag = meta.names[i]
|
||||
else:
|
||||
nameTag = altNames[i]
|
||||
res.append((nameTag, out[i]))
|
||||
res = sorted(res, key=lambda x: -x[1])
|
||||
return res
|
||||
|
||||
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
|
||||
if isinstance(image, bytes):
|
||||
# image is a filename
|
||||
# i.e. image = b'/darknet/data/dog.jpg'
|
||||
im = load_image(image, 0, 0)
|
||||
else:
|
||||
# image is an nparray
|
||||
# i.e. image = cv2.imread('/darknet/data/dog.jpg')
|
||||
im, image = array_to_image(image)
|
||||
rgbgr_image(im)
|
||||
num = c_int(0)
|
||||
pnum = pointer(num)
|
||||
letter_box = 0
|
||||
predict_image(net, im)
|
||||
dets = get_network_boxes(net, im.w, im.h, thresh,
|
||||
hier_thresh, None, 0, pnum, letter_box)
|
||||
num = pnum[0]
|
||||
if nms: do_nms_obj(dets, num, meta.classes, nms)
|
||||
|
||||
res = []
|
||||
for j in range(num):
|
||||
a = dets[j].prob[0:meta.classes]
|
||||
if any(a):
|
||||
ai = np.array(a).nonzero()[0]
|
||||
for i in ai:
|
||||
b = dets[j].bbox
|
||||
res.append((meta.names[i], dets[j].prob[i],
|
||||
(b.x, b.y, b.w, b.h)))
|
||||
|
||||
res = sorted(res, key=lambda x: -x[1])
|
||||
if isinstance(image, bytes): free_image(im)
|
||||
free_detections(dets, num)
|
||||
return res
|
||||
|
||||
def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
|
||||
#import cv2
|
||||
#custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,)
|
||||
#custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
|
||||
#custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR)
|
||||
#import scipy.misc
|
||||
#custom_image = scipy.misc.imread(image)
|
||||
#im, arr = array_to_image(custom_image) # you should comment line below: free_image(im)
|
||||
num = c_int(0)
|
||||
if debug: print("Assigned num")
|
||||
pnum = pointer(num)
|
||||
if debug: print("Assigned pnum")
|
||||
predict_image(net, im)
|
||||
letter_box = 0
|
||||
#predict_image_letterbox(net, im)
|
||||
#letter_box = 1
|
||||
if debug: print("did prediction")
|
||||
#dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV
|
||||
dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box)
|
||||
if debug: print("Got dets")
|
||||
num = pnum[0]
|
||||
if debug: print("got zeroth index of pnum")
|
||||
if nms:
|
||||
do_nms_sort(dets, num, meta.classes, nms)
|
||||
if debug: print("did sort")
|
||||
res = []
|
||||
if debug: print("about to range")
|
||||
for j in range(num):
|
||||
if debug: print("Ranging on "+str(j)+" of "+str(num))
|
||||
if debug: print("Classes: "+str(meta), meta.classes, meta.names)
|
||||
for i in range(meta.classes):
|
||||
if debug: print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i]))
|
||||
if dets[j].prob[i] > 0:
|
||||
b = dets[j].bbox
|
||||
if altNames is None:
|
||||
nameTag = meta.names[i]
|
||||
else:
|
||||
nameTag = altNames[i]
|
||||
if debug:
|
||||
print("Got bbox", b)
|
||||
print(nameTag)
|
||||
print(dets[j].prob[i])
|
||||
print((b.x, b.y, b.w, b.h))
|
||||
res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h)))
|
||||
if debug: print("did range")
|
||||
res = sorted(res, key=lambda x: -x[1])
|
||||
if debug: print("did sort")
|
||||
free_detections(dets, num)
|
||||
if debug: print("freed detections")
|
||||
return res
|
||||
|
||||
|
||||
netMain = None
|
||||
metaMain = None
|
||||
altNames = None
|
||||
|
||||
def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False):
|
||||
"""
|
||||
Convenience function to handle the detection and returns of objects.
|
||||
|
||||
Displaying bounding boxes requires libraries scikit-image and numpy
|
||||
|
||||
Parameters
|
||||
----------------
|
||||
imagePath: str
|
||||
Path to the image to evaluate. Raises ValueError if not found
|
||||
|
||||
thresh: float (default= 0.25)
|
||||
The detection threshold
|
||||
|
||||
configPath: str
|
||||
Path to the configuration file. Raises ValueError if not found
|
||||
|
||||
weightPath: str
|
||||
Path to the weights file. Raises ValueError if not found
|
||||
|
||||
metaPath: str
|
||||
Path to the data file. Raises ValueError if not found
|
||||
|
||||
showImage: bool (default= True)
|
||||
Compute (and show) bounding boxes. Changes return.
|
||||
|
||||
makeImageOnly: bool (default= False)
|
||||
If showImage is True, this won't actually *show* the image, but will create the array and return it.
|
||||
|
||||
initOnly: bool (default= False)
|
||||
Only initialize globals. Don't actually run a prediction.
|
||||
|
||||
Returns
|
||||
----------------------
|
||||
|
||||
|
||||
When showImage is False, list of tuples like
|
||||
('obj_label', confidence, (bounding_box_x_px, bounding_box_y_px, bounding_box_width_px, bounding_box_height_px))
|
||||
The X and Y coordinates are from the center of the bounding box. Subtract half the width or height to get the lower corner.
|
||||
|
||||
Otherwise, a dict with
|
||||
{
|
||||
"detections": as above
|
||||
"image": a numpy array representing an image, compatible with scikit-image
|
||||
"caption": an image caption
|
||||
}
|
||||
"""
|
||||
# Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again
|
||||
global metaMain, netMain, altNames #pylint: disable=W0603
|
||||
assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
|
||||
if not os.path.exists(configPath):
|
||||
raise ValueError("Invalid config path `"+os.path.abspath(configPath)+"`")
|
||||
if not os.path.exists(weightPath):
|
||||
raise ValueError("Invalid weight path `"+os.path.abspath(weightPath)+"`")
|
||||
if not os.path.exists(metaPath):
|
||||
raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`")
|
||||
if netMain is None:
|
||||
netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
|
||||
if metaMain is None:
|
||||
metaMain = load_meta(metaPath.encode("ascii"))
|
||||
if altNames is None:
|
||||
# In Python 3, the metafile default access craps out on Windows (but not Linux)
|
||||
# Read the names file and create a list to feed to detect
|
||||
try:
|
||||
with open(metaPath) as metaFH:
|
||||
metaContents = metaFH.read()
|
||||
import re
|
||||
match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE)
|
||||
if match:
|
||||
result = match.group(1)
|
||||
else:
|
||||
result = None
|
||||
try:
|
||||
if os.path.exists(result):
|
||||
with open(result) as namesFH:
|
||||
namesList = namesFH.read().strip().split("\n")
|
||||
altNames = [x.strip() for x in namesList]
|
||||
except TypeError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
if initOnly:
|
||||
print("Initialized detector")
|
||||
return None
|
||||
if not os.path.exists(imagePath):
|
||||
raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`")
|
||||
# Do the detection
|
||||
#detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image)
|
||||
detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh)
|
||||
if showImage:
|
||||
try:
|
||||
from skimage import io, draw
|
||||
image = io.imread(imagePath)
|
||||
print("*** "+str(len(detections))+" Results, color coded by confidence ***")
|
||||
imcaption = []
|
||||
for detection in detections:
|
||||
label = detection[0]
|
||||
confidence = detection[1]
|
||||
pstring = label+": "+str(np.rint(100 * confidence))+"%"
|
||||
imcaption.append(pstring)
|
||||
print(pstring)
|
||||
bounds = detection[2]
|
||||
shape = image.shape
|
||||
# x = shape[1]
|
||||
# xExtent = int(x * bounds[2] / 100)
|
||||
# y = shape[0]
|
||||
# yExtent = int(y * bounds[3] / 100)
|
||||
yExtent = int(bounds[3])
|
||||
xEntent = int(bounds[2])
|
||||
# Coordinates are around the center
|
||||
xCoord = int(bounds[0] - bounds[2]/2)
|
||||
yCoord = int(bounds[1] - bounds[3]/2)
|
||||
boundingBox = [
|
||||
[xCoord, yCoord],
|
||||
[xCoord, yCoord + yExtent],
|
||||
[xCoord + xEntent, yCoord + yExtent],
|
||||
[xCoord + xEntent, yCoord]
|
||||
]
|
||||
# Wiggle it around to make a 3px border
|
||||
rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
|
||||
rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
|
||||
rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
|
||||
rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape= shape)
|
||||
rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape= shape)
|
||||
boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0)
|
||||
draw.set_color(image, (rr, cc), boxColor, alpha= 0.8)
|
||||
draw.set_color(image, (rr2, cc2), boxColor, alpha= 0.8)
|
||||
draw.set_color(image, (rr3, cc3), boxColor, alpha= 0.8)
|
||||
draw.set_color(image, (rr4, cc4), boxColor, alpha= 0.8)
|
||||
draw.set_color(image, (rr5, cc5), boxColor, alpha= 0.8)
|
||||
if not makeImageOnly:
|
||||
io.imshow(image)
|
||||
io.show()
|
||||
detections = {
|
||||
"detections": detections,
|
||||
"image": image,
|
||||
"caption": "\n<br/>".join(imcaption)
|
||||
}
|
||||
except Exception as e:
|
||||
print("Unable to show image: "+str(e))
|
||||
return detections
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(performDetect())
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
classes= 80
|
||||
train = /home/pjreddie/data/coco/trainvalno5k.txt
|
||||
valid = coco_testdev
|
||||
#valid = data/coco_val_5k.list
|
||||
names = /app/yolo/coco.names
|
||||
backup = /home/pjreddie/backup/
|
||||
eval=coco
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
azure-iothub-device-client~=1.4.3
|
||||
setuptools
|
||||
numpy
|
||||
#azure-iothub-device-client~=1.4.3
|
||||
#setuptools
|
||||
#numpy
|
||||
requests
|
||||
opencv-contrib-python
|
||||
#opencv-contrib-python
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
"tag": {
|
||||
"version": "$CONTAINER_MODULE_VERSION",
|
||||
"platforms": {
|
||||
"amd64": "./Dockerfile.amd64"
|
||||
"arm32v7": "./Dockerfile.arm64v8"
|
||||
}
|
||||
},
|
||||
"buildOptions": []
|
||||
|
|
|
|||
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