diff --git a/deploy.sh b/deploy.sh
index 9b2747188a457504782eb8986f99852712d537a8..7642e270c2ab7fa77368603682b6164a05b7e489 100644
--- a/deploy.sh
+++ b/deploy.sh
@@ -4,13 +4,14 @@
DOCKER_USERNAME="medkaddour"
SERVICES=("camera" "motion_detector" "object_recognizer")
TAG="latest"
-DEPLOY_ENV=${1:-cloud} # Default to local if no argument is provided
+DEPLOY_ENV=${1:-local} # Default to 'cloud' if no argument is provided
# Function to build and push a Docker image
build_and_push() {
local service=$1
- echo "Building Docker image for ${service}..."
- docker build -t ${DOCKER_USERNAME}/${service}:${TAG} ./${service}
+ local service_path="./services/${service}" # Service directory path
+ echo "Building Docker image for ${service} from ${service_path}..."
+ docker build -t ${DOCKER_USERNAME}/${service}:${TAG} ${service_path}
if [ "$DEPLOY_ENV" == "cloud" ]; then
echo "Pushing Docker image for ${service} to Docker Hub..."
@@ -24,13 +25,19 @@ for service in "${SERVICES[@]}"; do
done
# Step 2: Deploy the images
+DOCKER_COMPOSE_PATH="./deploy/docker-compose/docker-compose.yml"
if [ "$DEPLOY_ENV" == "local" ]; then
+ echo "Stopping and removing existing containers..."
+ docker-compose -f ${DOCKER_COMPOSE_PATH} down --volumes --remove-orphans
echo "Running Docker images locally with Docker Compose..."
- docker-compose up -d --build
+ docker-compose -f ${DOCKER_COMPOSE_PATH} up -d --build --force-recreate
elif [ "$DEPLOY_ENV" == "cloud" ]; then
+ echo "Stopping and removing existing containers..."
+ docker-compose -f ${DOCKER_COMPOSE_PATH} down --volumes --remove-orphans
echo "Pulling and running Docker images from Docker Hub with Docker Compose..."
- #docker-compose pull
- # docker-compose up -d --build
+ # Force repull the latest images
+ docker-compose -f ${DOCKER_COMPOSE_PATH} pull --ignore-pull-failures
+ docker-compose -f ${DOCKER_COMPOSE_PATH} up -d --build --force-recreate
else
echo "Invalid DEPLOY_ENV value. Please use 'local' or 'cloud'."
exit 1
diff --git a/docker-compose.yml b/deploy/docker-compose/docker-compose.yml
similarity index 71%
rename from docker-compose.yml
rename to deploy/docker-compose/docker-compose.yml
index 465bddc224e95829d06e3a2ac2739ecdde632a87..a3f0947b27403a7a48daedaff9839f0bcd97f7b1 100644
--- a/docker-compose.yml
+++ b/deploy/docker-compose/docker-compose.yml
@@ -5,6 +5,7 @@ services:
image: medkaddour/object_recognizer
ports:
- "9999:9999"
+ - "5000:5000"
motion_detector_1:
image: medkaddour/motion_detector
@@ -13,7 +14,11 @@ services:
depends_on:
- object_recognizer
- jaeger
- command: /bin/sh -c "echo 1 > /app/index.txt && python motion_detection.py --host object_recognizer --port 9999"
+ environment:
+ - INDEX=1
+ - OR_HOST=object_recognizer
+ - OR_PORT=9999
+ command: python src/motion_detection.py
motion_detector_2:
image: medkaddour/motion_detector
@@ -22,7 +27,11 @@ services:
depends_on:
- object_recognizer
- jaeger
- command: /bin/sh -c "echo 2 > /app/index.txt && python motion_detection.py --host object_recognizer --port 9999"
+ environment:
+ - INDEX=2
+ - OR_HOST=object_recognizer
+ - OR_PORT=9999
+ command: python src/motion_detection.py
motion_detector_3:
image: medkaddour/motion_detector
@@ -31,26 +40,54 @@ services:
depends_on:
- object_recognizer
- jaeger
- command: /bin/sh -c "echo 3 > /app/index.txt && python motion_detection.py --host object_recognizer --port 9999"
+ environment:
+ - INDEX=3
+ - OR_HOST=object_recognizer
+ - OR_PORT=9999
+ command: python src/motion_detection.py
camera_1:
image: medkaddour/camera
depends_on:
- motion_detector_1
- otel-collector
- command: /bin/sh -c "echo 1 > /app/index.txt && python camera.py --mdhost motion_detector_1 --mdport 9998"
+ environment:
+ - CAMERA=true
+ - ANIMAL_NAME=tiger
+ - APPEARANCE_RATE=600
+ - MDHOST=motion_detector_1
+ - MDPORT=9998
+ - INDEX=1
+ command: /bin/sh -c "python src/camera.py"
+
camera_2:
image: medkaddour/camera
depends_on:
- motion_detector_2
- otel-collector
- command: /bin/sh -c "echo 2 > /app/index.txt && python camera.py --mdhost motion_detector_2 --mdport 9998"
+ environment:
+ - CAMERA=true
+ - ANIMAL_NAME=bear
+ - APPEARANCE_RATE=500
+ - MDHOST=motion_detector_2
+ - MDPORT=9998
+ - INDEX=2
+ command: /bin/sh -c "python src/camera.py"
+
camera_3:
image: medkaddour/camera
depends_on:
- motion_detector_3
- otel-collector
- command: /bin/sh -c "echo 3 > /app/index.txt && python camera.py --mdhost motion_detector_3 --mdport 9998"
+ environment:
+ - CAMERA=true
+ - ANIMAL_NAME=wolf
+ - APPEARANCE_RATE=700
+ - MDHOST=motion_detector_3
+ - MDPORT=9998
+ - INDEX=3
+ command: /bin/sh -c "python src/camera.py"
+
jaeger:
image: jaegertracing/all-in-one
ports:
@@ -59,8 +96,6 @@ services:
- "14268"
- "14250"
-
-
# Zipkin
zipkin-all-in-one:
image: openzipkin/zipkin:latest
@@ -87,6 +122,7 @@ services:
depends_on:
- jaeger
- zipkin-all-in-one
+
cadvisor:
image: gcr.io/cadvisor/cadvisor:latest
hostname: cadvisor
@@ -99,6 +135,7 @@ services:
- "/dev/disk/:/dev/disk:ro"
ports:
- "8080:8080"
+
prometheus:
image: prom/prometheus:latest
restart: always
@@ -107,6 +144,7 @@ services:
- ./rules.yml:/etc/prometheus/rules.yml
ports:
- "9090:9090"
+
node-exporter:
image: prom/node-exporter:latest
container_name: node-exporter
@@ -114,11 +152,11 @@ services:
volumes:
- /proc:/host/proc:ro
- /sys:/host/sys:ro
- - /:/rootfs:ro
+ - /:/rootfs:ro"
command:
- '--path.procfs=/host/proc'
- '--path.rootfs=/rootfs'
- '--path.sysfs=/host/sys'
- '--collector.filesystem.mount-points-exclude=^/(sys|proc|dev|host|etc)($$|/)'
expose:
- - 9100
\ No newline at end of file
+ - 9100
diff --git a/otel-collector-config.yaml b/deploy/docker-compose/otel-collector-config.yaml
similarity index 100%
rename from otel-collector-config.yaml
rename to deploy/docker-compose/otel-collector-config.yaml
diff --git a/prometheus.yaml b/deploy/docker-compose/prometheus.yaml
similarity index 100%
rename from prometheus.yaml
rename to deploy/docker-compose/prometheus.yaml
diff --git a/kubernetees.ipynb b/kubernetees.ipynb
deleted file mode 100644
index 9e3f12416c52df333defbc0f12f65862e62a21eb..0000000000000000000000000000000000000000
--- a/kubernetees.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "id": "5c61d749-73cb-4982-a0a5-bd0d09f4e1d8",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2024-07-10T10:33:38.448735Z",
- "start_time": "2024-07-10T10:33:37.827646Z"
- }
- },
- "source": [
- "import enoslib as en\n",
- "\n",
- "# Enable rich logging\n",
- "_ = en.init_logging()\n",
- "\n",
- "\n",
- "# claim the resources\n",
- "conf = (\n",
- " en.VMonG5kConf\n",
- " .from_settings(job_name=\"enoslib_providers\")\n",
- " .add_machine(\n",
- " roles=[\"master\"],\n",
- " cluster=\"econome\",\n",
- " number=1,\n",
- " flavour=\"large\"\n",
- " )\n",
- " .add_machine(\n",
- " roles=[\"agent\",\"cloud\"],\n",
- " cluster=\"econome\",\n",
- " number=1,\n",
- " flavour=\"large\"\n",
- " )\n",
- " .add_machine(\n",
- " roles=[\"agent\",\"edge\"],\n",
- " cluster=\"econome\",\n",
- " number=5,\n",
- " flavour=\"large\"\n",
- " )\n",
- " .finalize()\n",
- ")\n",
- "\n",
- "\n",
- "provider = en.VMonG5k(conf)\n",
- "\n",
- "roles, networks = provider.init()\n",
- "\n",
- "roles "
- ],
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING]: failed to patch stdout/stderr for fork-safety: 'OutStream' object\n",
- "has no attribute 'buffer'\n",
- "[WARNING]: failed to reconfigure stdout/stderr with the replace error handler:\n",
- "'OutStream' object has no attribute 'reconfigure'\n",
- "/Users/sidimohammedkaddour/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "\u001B[31mWARNING \u001B[0m \u001B[1m[\u001B[0mErrno \u001B[1;36m2\u001B[0m\u001B[1m]\u001B[0m No such file or directory: \u001B[2m__init__.py\u001B[0m\u001B[2m:\u001B[0m\u001B[2m133\u001B[0m\n",
- " \u001B[32m'/Users/sidimohammedkaddour/.python-grid5000.yaml'\u001B[0m \u001B[2m \u001B[0m\n"
- ],
- "text/html": [
- "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> <span style=\"font-weight: bold\">[</span>Errno <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">]</span> No such file or directory: <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py:133</span>\n",
- " <span style=\"color: #008000; text-decoration-color: #008000\">'/Users/sidimohammedkaddour/.python-grid5000.yaml'</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> </span>\n",
- "</pre>\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "\u001B[34mINFO \u001B[0m \u001B[33m...\u001B[0mFalling back to anonymous connection \u001B[2m__init__.py\u001B[0m\u001B[2m:\u001B[0m\u001B[2m134\u001B[0m\n"
- ],
- "text/html": [
- "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span>Falling back to anonymous connection <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py:134</span>\n",
- "</pre>\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "ename": "Grid5000AuthenticationError",
- "evalue": "401: <!DOCTYPE HTML PUBLIC \"-//IETF//DTD HTML 2.0//EN\">\n<html><head>\n<title>401 Unauthorized</title>\n</head><body>\n<h1>Unauthorized</h1>\n<p>This server could not verify that you\nare authorized to access the document\nrequested. Either you supplied the wrong\ncredentials (e.g., bad password), or your\nbrowser doesn't understand how to supply\nthe credentials required.</p>\n<hr>\n<address>Apache/2.4.59 (Debian) Server at <a href=\"mailto:support-staff@lists.grid5000.fr\">api.grid5000.fr</a> Port 443</address>\n</body></html>\n",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mGrid5000AuthenticationError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[2], line 35\u001B[0m\n\u001B[1;32m 8\u001B[0m conf \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 9\u001B[0m en\u001B[38;5;241m.\u001B[39mVMonG5kConf\n\u001B[1;32m 10\u001B[0m \u001B[38;5;241m.\u001B[39mfrom_settings(job_name\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124menoslib_providers\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 29\u001B[0m \u001B[38;5;241m.\u001B[39mfinalize()\n\u001B[1;32m 30\u001B[0m )\n\u001B[1;32m 33\u001B[0m provider \u001B[38;5;241m=\u001B[39m en\u001B[38;5;241m.\u001B[39mVMonG5k(conf)\n\u001B[0;32m---> 35\u001B[0m roles, networks \u001B[38;5;241m=\u001B[39m \u001B[43mprovider\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minit\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 37\u001B[0m roles \n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_vmong5k/provider.py:360\u001B[0m, in \u001B[0;36mVMonG5k.init\u001B[0;34m(self, force_deploy, start_time, **kwargs)\u001B[0m\n\u001B[1;32m 358\u001B[0m _force_deploy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprovider_conf\u001B[38;5;241m.\u001B[39mforce_deploy\n\u001B[1;32m 359\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprovider_conf\u001B[38;5;241m.\u001B[39mforce_deploy \u001B[38;5;241m=\u001B[39m _force_deploy \u001B[38;5;129;01mor\u001B[39;00m force_deploy\n\u001B[0;32m--> 360\u001B[0m g5k_conf \u001B[38;5;241m=\u001B[39m \u001B[43m_build_g5k_conf\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprovider_conf\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 361\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_g5k_provider \u001B[38;5;241m=\u001B[39m g5kprovider\u001B[38;5;241m.\u001B[39mG5k(g5k_conf)\n\u001B[1;32m 362\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m start_time:\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_vmong5k/provider.py:206\u001B[0m, in \u001B[0;36m_build_g5k_conf\u001B[0;34m(vmong5k_conf)\u001B[0m\n\u001B[1;32m 204\u001B[0m \u001B[38;5;66;03m# first of all, make sure we don't mutate the vmong5k_conf\u001B[39;00m\n\u001B[1;32m 205\u001B[0m vmong5k_conf \u001B[38;5;241m=\u001B[39m copy\u001B[38;5;241m.\u001B[39mdeepcopy(vmong5k_conf)\n\u001B[0;32m--> 206\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_do_build_g5k_conf\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvmong5k_conf\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_vmong5k/provider.py:170\u001B[0m, in \u001B[0;36m_do_build_g5k_conf\u001B[0;34m(vmong5k_conf)\u001B[0m\n\u001B[1;32m 167\u001B[0m subnet_networks \u001B[38;5;241m=\u001B[39m {}\n\u001B[1;32m 169\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m _, machine \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(vmong5k_conf\u001B[38;5;241m.\u001B[39mmachines):\n\u001B[0;32m--> 170\u001B[0m site \u001B[38;5;241m=\u001B[39m \u001B[43mmachine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msite\u001B[49m\n\u001B[1;32m 171\u001B[0m \u001B[38;5;66;03m# first check if there's a prod network demand\u001B[39;00m\n\u001B[1;32m 172\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m site \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m prod_networks:\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_vmong5k/configuration.py:144\u001B[0m, in \u001B[0;36mMachineConfiguration.site\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 142\u001B[0m \u001B[38;5;129m@property\u001B[39m\n\u001B[1;32m 143\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msite\u001B[39m(\u001B[38;5;28mself\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mstr\u001B[39m:\n\u001B[0;32m--> 144\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mget_cluster_site\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcluster\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_g5k/g5k_api_utils.py:519\u001B[0m, in \u001B[0;36mget_cluster_site\u001B[0;34m(cluster)\u001B[0m\n\u001B[1;32m 510\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_cluster_site\u001B[39m(cluster: \u001B[38;5;28mstr\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mstr\u001B[39m:\n\u001B[1;32m 511\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Get the site of a given cluster.\u001B[39;00m\n\u001B[1;32m 512\u001B[0m \n\u001B[1;32m 513\u001B[0m \u001B[38;5;124;03m Args:\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 517\u001B[0m \u001B[38;5;124;03m The corresponding site(str)\u001B[39;00m\n\u001B[1;32m 518\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 519\u001B[0m match \u001B[38;5;241m=\u001B[39m \u001B[43mget_clusters_sites\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[43mcluster\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 520\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m match[cluster]\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_g5k/g5k_api_utils.py:506\u001B[0m, in \u001B[0;36mget_clusters_sites\u001B[0;34m(clusters)\u001B[0m\n\u001B[1;32m 497\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_clusters_sites\u001B[39m(clusters: Iterable[\u001B[38;5;28mstr\u001B[39m]) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Dict[\u001B[38;5;28mstr\u001B[39m, \u001B[38;5;28mstr\u001B[39m]:\n\u001B[1;32m 498\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Get the corresponding sites of given clusters.\u001B[39;00m\n\u001B[1;32m 499\u001B[0m \n\u001B[1;32m 500\u001B[0m \u001B[38;5;124;03m Args:\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 504\u001B[0m \u001B[38;5;124;03m dict of corresponding to the mapping cluster -> site\u001B[39;00m\n\u001B[1;32m 505\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 506\u001B[0m clusters_sites \u001B[38;5;241m=\u001B[39m \u001B[43mget_all_clusters_sites\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 507\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m {c: clusters_sites[c] \u001B[38;5;28;01mfor\u001B[39;00m c \u001B[38;5;129;01min\u001B[39;00m clusters}\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/enoslib/infra/enos_g5k/g5k_api_utils.py:488\u001B[0m, in \u001B[0;36mget_all_clusters_sites\u001B[0;34m()\u001B[0m\n\u001B[1;32m 486\u001B[0m result: Dict \u001B[38;5;241m=\u001B[39m {}\n\u001B[1;32m 487\u001B[0m gk \u001B[38;5;241m=\u001B[39m get_api_client()\n\u001B[0;32m--> 488\u001B[0m sites \u001B[38;5;241m=\u001B[39m \u001B[43mgk\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msites\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlist\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 489\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m site \u001B[38;5;129;01min\u001B[39;00m sites:\n\u001B[1;32m 490\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m site\u001B[38;5;241m.\u001B[39muid \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m gk\u001B[38;5;241m.\u001B[39mexcluded_sites:\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/grid5000/exceptions.py:82\u001B[0m, in \u001B[0;36mon_http_error.<locals>.wrap.<locals>.wrapped_f\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 79\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(f)\n\u001B[1;32m 80\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapped_f\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m 81\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 82\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 83\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m Grid5000HttpError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 84\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m error(e\u001B[38;5;241m.\u001B[39merror_message, e\u001B[38;5;241m.\u001B[39mresponse_code, e\u001B[38;5;241m.\u001B[39mresponse_body)\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/grid5000/mixins.py:103\u001B[0m, in \u001B[0;36mListMixin.list\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 101\u001B[0m \u001B[38;5;66;03m# Allow to overwrite the path, handy for custom listings\u001B[39;00m\n\u001B[1;32m 102\u001B[0m path \u001B[38;5;241m=\u001B[39m data\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpath\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpath)\n\u001B[0;32m--> 103\u001B[0m l_obj \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgrid5000\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhttp_list\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpath\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 105\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(l_obj, \u001B[38;5;28mlist\u001B[39m):\n\u001B[1;32m 106\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_obj_cls(\u001B[38;5;28mself\u001B[39m, item) \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m l_obj]\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/grid5000/__init__.py:349\u001B[0m, in \u001B[0;36mGrid5000.http_list\u001B[0;34m(self, path, query_data, **kwargs)\u001B[0m\n\u001B[1;32m 331\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Make a GET request to the Grid5000 API for list-oriented queries.\u001B[39;00m\n\u001B[1;32m 332\u001B[0m \n\u001B[1;32m 333\u001B[0m \u001B[38;5;124;03mArgs:\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 345\u001B[0m \u001B[38;5;124;03m Grid5000ParsingError: If the json data could not be parsed\u001B[39;00m\n\u001B[1;32m 346\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 347\u001B[0m url \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_build_url(path)\n\u001B[0;32m--> 349\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhttp_request\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mget\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 351\u001B[0m \u001B[38;5;66;03m# NOTE(msimonin): in the future we may want to support automatic\u001B[39;00m\n\u001B[1;32m 352\u001B[0m \u001B[38;5;66;03m# pagination Thus we'll need to return an iterator here (in a generic\u001B[39;00m\n\u001B[1;32m 353\u001B[0m \u001B[38;5;66;03m# way ...)\u001B[39;00m\n\u001B[1;32m 354\u001B[0m \n\u001B[1;32m 355\u001B[0m \u001B[38;5;66;03m# NOTE(msimonin): here we hit the HATEOAS vs non HATEOAS hell\u001B[39;00m\n\u001B[1;32m 356\u001B[0m result \u001B[38;5;241m=\u001B[39m result\u001B[38;5;241m.\u001B[39mjson()\n",
- "File \u001B[0;32m~/PycharmProjects/Contract Net/.venv/lib/python3.9/site-packages/grid5000/__init__.py:278\u001B[0m, in \u001B[0;36mGrid5000.http_request\u001B[0;34m(self, verb, path, query_data, post_data, header_data, streamed, content_type, accept, **kwargs)\u001B[0m\n\u001B[1;32m 275\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[1;32m 277\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result\u001B[38;5;241m.\u001B[39mstatus_code \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m401\u001B[39m:\n\u001B[0;32m--> 278\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m Grid5000AuthenticationError(\n\u001B[1;32m 279\u001B[0m response_code\u001B[38;5;241m=\u001B[39mresult\u001B[38;5;241m.\u001B[39mstatus_code,\n\u001B[1;32m 280\u001B[0m error_message\u001B[38;5;241m=\u001B[39merror_message,\n\u001B[1;32m 281\u001B[0m response_body\u001B[38;5;241m=\u001B[39mresult\u001B[38;5;241m.\u001B[39mcontent,\n\u001B[1;32m 282\u001B[0m )\n\u001B[1;32m 284\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m Grid5000HttpError(\n\u001B[1;32m 285\u001B[0m response_code\u001B[38;5;241m=\u001B[39mresult\u001B[38;5;241m.\u001B[39mstatus_code,\n\u001B[1;32m 286\u001B[0m error_message\u001B[38;5;241m=\u001B[39merror_message,\n\u001B[1;32m 287\u001B[0m response_body\u001B[38;5;241m=\u001B[39mresult\u001B[38;5;241m.\u001B[39mcontent,\n\u001B[1;32m 288\u001B[0m )\n",
- "\u001B[0;31mGrid5000AuthenticationError\u001B[0m: 401: <!DOCTYPE HTML PUBLIC \"-//IETF//DTD HTML 2.0//EN\">\n<html><head>\n<title>401 Unauthorized</title>\n</head><body>\n<h1>Unauthorized</h1>\n<p>This server could not verify that you\nare authorized to access the document\nrequested. Either you supplied the wrong\ncredentials (e.g., bad password), or your\nbrowser doesn't understand how to supply\nthe credentials required.</p>\n<hr>\n<address>Apache/2.4.59 (Debian) Server at <a href=\"mailto:support-staff@lists.grid5000.fr\">api.grid5000.fr</a> Port 443</address>\n</body></html>\n"
- ]
- }
- ],
- "execution_count": 2
- },
- {
- "cell_type": "code",
- "id": "1be51270-85a2-442d-9ab2-6cc8b7a5d5a4",
- "metadata": {},
- "source": [
- "# wait for the nodes\n",
- "en.wait_for(roles)"
- ],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "1c8d37bb-748e-4716-85b9-5f4fb8290d2f",
- "metadata": {},
- "source": [
- "k3s = en.K3s(master=roles[\"master\"], agent=roles[\"agent\"])\n",
- "\n",
- "k3s.deploy()"
- ],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "83fc9b33-859b-423b-bcdb-b6e165822a8d",
- "metadata": {
- "scrolled": true
- },
- "source": [
- "print(\"Create a tunnel from your local machine to the head node:\")\n",
- "print(f\"ssh -NL 8001:{roles['master'][0].address}:8001 skaddour@access.grid5000.fr\")"
- ],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "59e57e73-3140-4c8b-8bb5-238130e7b358",
- "metadata": {},
- "source": [
- "import subprocess\n",
- "subprocess.run(\"scp otel-collector-config.yaml skaddour@\"+str(roles['master'][0].address))"
- ],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "b7198604-e8c6-47b0-8f85-e7afcc505d04",
- "metadata": {},
- "source": [
- "provider.destroy() "
- ],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "76996be3-b9ae-49c4-ac68-30f1df9055dd",
- "metadata": {},
- "source": [],
- "outputs": [],
- "execution_count": null
- },
- {
- "cell_type": "code",
- "id": "d22f906b-a7ea-40fa-bd9c-af59eda93032",
- "metadata": {},
- "source": [],
- "outputs": [],
- "execution_count": null
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/motion_detector/index.txt b/motion_detector/index.txt
deleted file mode 100644
index 62f9457511f879886bb7728c986fe10b0ece6bcb..0000000000000000000000000000000000000000
--- a/motion_detector/index.txt
+++ /dev/null
@@ -1 +0,0 @@
-6
\ No newline at end of file
diff --git a/object-detection.jpg b/object-detection.jpg
deleted file mode 100644
index 714b7208da494d30e7a1f094a196a286a044d445..0000000000000000000000000000000000000000
Binary files a/object-detection.jpg and /dev/null differ
diff --git a/object_recognizer/object_recognizer.py b/object_recognizer/object_recognizer.py
deleted file mode 100644
index 9ca2322e6cb703217231a5ad62fe4076c1243e92..0000000000000000000000000000000000000000
--- a/object_recognizer/object_recognizer.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import datetime
-import socket
-import time
-import threading
-import queue
-import pickle
-import struct
-import cv2
-import numpy as np
-from tracerprovider import tracer, meter
-import pyshine as ps # pip install pyshine
-
-
-# Load YOLO model (if needed) and other initialization
-# model = YOLO('yolov8n.pt')
-
-# Function to detect objects in a frame
-def get_output_layers(net):
- layer_names = net.getLayerNames()
- try:
- output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
- except:
- output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
- return output_layers
-
-
-def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h, classes, COLORS):
- label = str(classes[class_id])
- print(label + " detected")
- color = COLORS[class_id]
- cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
- cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
- framename = "res.jpg"
- print(framename)
- cv2.imwrite(framename, img)
-
-
-def process_frame(mddata):
- frame = mddata['frame']
- Width = frame.shape[1]
- Height = frame.shape[0]
- scale = 0.00392
-
- classes = None
- with open("yolov3.txt", 'r') as f:
- classes = [line.strip() for line in f.readlines()]
-
- COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
- net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
- blob = cv2.dnn.blobFromImage(frame, scale, (416, 416), (0, 0, 0), True, crop=False)
- net.setInput(blob)
- outs = net.forward(get_output_layers(net))
-
- class_ids = []
- confidences = []
- boxes = []
- conf_threshold = 0.5
- nms_threshold = 0.4
-
- for out in outs:
- for detection in out:
- scores = detection[5:]
- class_id = np.argmax(scores)
- confidence = scores[class_id]
- if confidence > 0.5:
- center_x = int(detection[0] * Width)
- center_y = int(detection[1] * Height)
- w = int(detection[2] * Width)
- h = int(detection[3] * Height)
- x = center_x - w / 2
- y = center_y - h / 2
- class_ids.append(class_id)
- confidences.append(float(confidence))
- boxes.append([x, y, w, h])
-
- indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
- for i in indices:
- try:
- box = boxes[i]
- except:
- i = i[0]
- box = boxes[i]
- x = box[0]
- y = box[1]
- w = box[2]
- h = box[3]
- draw_prediction(frame, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h), classes,
- COLORS)
-
-
-def receive_frames(client_socket, frame_queue):
- data = b""
- payload_size = struct.calcsize("Q")
-
- while True:
- try:
- while len(data) < payload_size:
- packet = client_socket.recv(4 * 1024) # 4K
- if not packet:
- return
- data += packet
- packed_msg_size = data[:payload_size]
- data = data[payload_size:]
- if len(data) == 0:
- break
- msg_size = struct.unpack("Q", packed_msg_size)[0]
- while len(data) < msg_size:
- data += client_socket.recv(4 * 1024)
- frame_data = data[:msg_size]
- data = data[msg_size:]
- frame_queue.put(pickle.loads(frame_data))
- len_q_gauge.set(frame_queue.qsize())
- transmission_time = datetime.datetime.now() - datetime.datetime.strptime(pickle.loads(frame_data)['sentfromedgetime'],
- "%Y-%m-%d %H:%M:%S")
- transmission_time_in_seconds = transmission_time.total_seconds()
- md_e2c_transmission_time.set(transmission_time_in_seconds)
- except Exception as e:
- print(f"Error receiving frame: {e}")
- return
-
-
-def handle_client(frame_queue):
- firstFrame = None
- fps_start_time = time.time()
- fps_frame_count = 0
- detected = False
- orclient_socket = None
-
-
-
- while True:
- if frame_queue.empty():
- continue
-
- mddata = frame_queue.get()
- len_q_gauge.set(frame_queue.qsize())
- frame = mddata['frame']
- Width = frame.shape[1]
- Height = frame.shape[0]
-
- starting_processing_time = datetime.datetime.now()
- transmission_time = datetime.datetime.now() - datetime.datetime.strptime(mddata['sentfromedgetime'],
- "%Y-%m-%d %H:%M:%S")
- transmission_time_in_seconds = transmission_time.total_seconds()
- #md_e2c_transmission_time.set(transmission_time_in_seconds)
-
-
- # Process the frame
- process_frame(mddata)
- time_tmp=datetime.datetime.now()-starting_processing_time
- time_tmp=time_tmp.total_seconds()
- processing_time.set(time.time() - time_tmp)
- response_time_s = datetime.datetime.now() - datetime.datetime.strptime(mddata['capture_time'],
- "%Y-%m-%d %H:%M:%S")
- response_time_s_in_seconds = response_time_s.total_seconds()
- response_time.set(response_time_s_in_seconds)
-
-
-# Setup server socket
-server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-host_name = socket.gethostname()
-host_ip = socket.gethostbyname(host_name)
-socket_address = (host_ip, 9999)
-server_socket.bind(socket_address)
-server_socket.listen()
-print("Listening at", socket_address)
-len_q_gauge = meter.create_gauge(
- name="or_len_q",
- description="queue length in object recignizer",
- unit="f"
- )
-processing_time = meter.create_gauge(
- name="or_processing_time",
- description="processing time of one frame",
- unit="s"
-)
-md_e2c_transmission_time = meter.create_gauge(
- name="md_e2c_transmission_time",
- description="transmission time of one frame from edge to cloud",
- unit="s"
-)
-response_time = meter.create_gauge(
- name="responstime",
- description="response time of one frame",
- unit="s"
-)
-# Main server loop
-client_threads = []
-frame_queue = queue.Queue()
-process_thread=[]
-for i in range(0,1):
- process_thread.append( threading.Thread(target=handle_client, args=(frame_queue,)))
- process_thread[-1].start()
-while True:
- try:
- client_socket, addr = server_socket.accept()
- print(f"CLIENT {addr} CONNECTED!")
- receive_frames(client_socket, frame_queue)
-
- except Exception as e:
- print(f"Exception: {e}")
- continue
-
-server_socket.close()
diff --git a/object_recognizer/res.jpg b/object_recognizer/res.jpg
deleted file mode 100644
index 0008406a673a335ae7528163f239c6b76b74ad04..0000000000000000000000000000000000000000
Binary files a/object_recognizer/res.jpg and /dev/null differ
diff --git a/object_recognizer/yolo_opencv.py b/object_recognizer/yolo_opencv.py
deleted file mode 100644
index 25d3e901744275edcc322357612e6f2e281e1891..0000000000000000000000000000000000000000
--- a/object_recognizer/yolo_opencv.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#############################################
-# Object detection - YOLO - OpenCV
-# Author : Arun Ponnusamy (July 16, 2018)
-# Website : http://www.arunponnusamy.com
-############################################
-
-
-import cv2
-import argparse
-import numpy as np
-
-
-
-
-def get_output_layers(net):
-
- layer_names = net.getLayerNames()
- try:
- output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
- except:
- output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
-
- return output_layers
-
-
-def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
-
- label = str(classes[class_id])
-
- color = COLORS[class_id]
-
- cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
-
- cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-
-image = cv2.imread("../dog.jpg")
-
-Width = image.shape[1]
-Height = image.shape[0]
-scale = 0.00392
-
-classes = None
-
-with open("yolov3.txt", 'r') as f:
- classes = [line.strip() for line in f.readlines()]
-
-COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
-
-net = cv2.dnn.readNet("yolov3.weights","yolov3.cfg")
-
-blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
-
-net.setInput(blob)
-
-outs = net.forward(get_output_layers(net))
-
-class_ids = []
-confidences = []
-boxes = []
-conf_threshold = 0.5
-nms_threshold = 0.4
-
-
-for out in outs:
- for detection in out:
- scores = detection[5:]
- class_id = np.argmax(scores)
- confidence = scores[class_id]
- if confidence > 0.5:
- center_x = int(detection[0] * Width)
- center_y = int(detection[1] * Height)
- w = int(detection[2] * Width)
- h = int(detection[3] * Height)
- x = center_x - w / 2
- y = center_y - h / 2
- class_ids.append(class_id)
- confidences.append(float(confidence))
- boxes.append([x, y, w, h])
-
-
-indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
-
-for i in indices:
- try:
- box = boxes[i]
- except:
- i = i[0]
- box = boxes[i]
-
- x = box[0]
- y = box[1]
- w = box[2]
- h = box[3]
- draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
-
-cv2.imshow("object detection", image)
-cv2.waitKey()
-
-cv2.imwrite("../object-detection.jpg", image)
-cv2.destroyAllWindows()
diff --git a/rules.yml b/rules.yml
deleted file mode 100644
index 25cd29e4535b73d688ecf36ae24165670016ea43..0000000000000000000000000000000000000000
--- a/rules.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-groups:
- - name: example
- rules:
- - record: container_cpu_usage_rate_per_second
- expr: rate(container_cpu_usage_seconds_total[1s])
diff --git a/deploy on k3s.py b/services/__init__.py
similarity index 100%
rename from deploy on k3s.py
rename to services/__init__.py
diff --git a/camera/Dockerfile b/services/camera/Dockerfile
similarity index 56%
rename from camera/Dockerfile
rename to services/camera/Dockerfile
index 3c1e4976f64fe27b752999db7f8ffa5009dca763..092db59c37d1662ca79e9f5c08db39ecc4ed556d 100644
--- a/camera/Dockerfile
+++ b/services/camera/Dockerfile
@@ -1,7 +1,5 @@
-# Dockerfile for client microservice
-
# Use the official Python image as base
-FROM --platform=linux/amd64 python:3.8-slim as build
+FROM --platform=linux/arm64 python:3.8-slim as build
# Set the working directory inside the container
WORKDIR /app
@@ -12,8 +10,15 @@ COPY . .
# Install required Python packages
RUN pip install -r requirements.txt
+# Set environment variables with default values
+ENV CAMERA="false" \
+ ANIMAL_NAME="tiger" \
+ APPEARANCE_RATE=600 \
+ MDHOST="localhost" \
+ MDPORT=9998
+
# Expose the port (if needed)
# EXPOSE <port_number>
# Command to run the client code
-CMD ["python", "camera.py"]
+CMD ["python", "src/camera.py"]
diff --git a/services/camera/__init__.py b/services/camera/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9ace3a2ae3c5c821d3268333c1db331cdc3dbdc
--- /dev/null
+++ b/services/camera/__init__.py
@@ -0,0 +1 @@
+# Empty __init__.py
\ No newline at end of file
diff --git a/camera/footage/bear/no_bear.mp4 b/services/camera/footage/bear/no_bear.mp4
similarity index 100%
rename from camera/footage/bear/no_bear.mp4
rename to services/camera/footage/bear/no_bear.mp4
diff --git a/camera/footage/bear/with_bear.mp4 b/services/camera/footage/bear/with_bear.mp4
similarity index 100%
rename from camera/footage/bear/with_bear.mp4
rename to services/camera/footage/bear/with_bear.mp4
diff --git a/camera/footage/tiger/no_tiger.mp4 b/services/camera/footage/tiger/no_tiger.mp4
similarity index 100%
rename from camera/footage/tiger/no_tiger.mp4
rename to services/camera/footage/tiger/no_tiger.mp4
diff --git a/camera/footage/tiger/with_tiger.mp4 b/services/camera/footage/tiger/with_tiger.mp4
similarity index 100%
rename from camera/footage/tiger/with_tiger.mp4
rename to services/camera/footage/tiger/with_tiger.mp4
diff --git a/camera/footage/wolf/no_wolf.mp4 b/services/camera/footage/wolf/no_wolf.mp4
similarity index 100%
rename from camera/footage/wolf/no_wolf.mp4
rename to services/camera/footage/wolf/no_wolf.mp4
diff --git a/camera/footage/wolf/with_wolf.mp4 b/services/camera/footage/wolf/with_wolf.mp4
similarity index 100%
rename from camera/footage/wolf/with_wolf.mp4
rename to services/camera/footage/wolf/with_wolf.mp4
diff --git a/camera/requirements.txt b/services/camera/requirements.txt
similarity index 96%
rename from camera/requirements.txt
rename to services/camera/requirements.txt
index 14671f87e0bd27bc2ddefc1f9a7887eb6ab8c5a8..109b87e46f96b2c9ff740f7fc7b5e8d4ddbb8b4a 100644
--- a/camera/requirements.txt
+++ b/services/camera/requirements.txt
@@ -1,5 +1,6 @@
opencv-python-headless
imutils
+psutil
pyshine
opentelemetry-api
opentelemetry-sdk
diff --git a/services/camera/src/__init__.py b/services/camera/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/camera/camera.py b/services/camera/src/camera.py
similarity index 66%
rename from camera/camera.py
rename to services/camera/src/camera.py
index 261106d7f67ef9f62bb2c6db451c441106cfe124..2158f1435caa529469bc72b1e792614acd7063e3 100644
--- a/camera/camera.py
+++ b/services/camera/src/camera.py
@@ -6,11 +6,21 @@ import pickle
import struct
import time
import datetime
+import os # Import os for environment variables
from tracerprovider import tracer, meter
-from array import array
-
import pyshine as ps # pip install pyshine
import imutils # pip install imutils
+import logging
+
+# Configure logging
+logging.basicConfig(
+ format='%(asctime)s - %(levelname)s - %(message)s',
+ level=logging.INFO, # You can change this to DEBUG, ERROR, etc.
+ handlers=[
+ logging.StreamHandler(), # Log to console
+ logging.FileHandler("output/camera.log") # Log to a file
+ ]
+)
def generate_random_intervals(events_per_hour):
@@ -29,38 +39,43 @@ def generate_random_intervals(events_per_hour):
# Return intervals rounded to a few decimal points (optional)
result = [round(interval, 2) for interval in normalized_intervals]
- print(str(result))
+ logging.info(f"Generated intervals: {result}")
return result
-def main(camera=False, animal_name="bear", frequency=0, host_ip="motion_detector", port=9998):
+def main():
+ # Get environment variables (with defaults if not set)
+ camera = os.getenv("CAMERA", "false").lower() == "true"
+ animal_name = os.getenv("ANIMAL_NAME", "tiger")
+ appearance_rate = int(os.getenv("APPEARANCE_RATE", 600))
+ host_ip = os.getenv("MDHOST", "localhost")
+ port = int(os.getenv("MDPORT", 9998))
+
# Map animal to the appropriate video filenames
animal_map = {
'bear': ('footage/bear/no_bear.mp4', 'footage/bear/with_bear.mp4'),
'tiger': ('footage/tiger/no_tiger.mp4', 'footage/tiger/with_tiger.mp4'),
'wolf': ('footage/wolf/no_wolf.mp4', 'footage/wolf/with_wolf.mp4')
}
- if animal_name:
- if animal_name not in animal_map:
- print(f"No video available for {animal_name}")
- return
+
+ if animal_name not in animal_map:
+ logging.error(f"No video available for {animal_name}")
+ return
+
no_animal_video, with_animal_video = animal_map[animal_name]
while True:
-
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
- host_ip = host_ip # Here according to your server IP write the address
- port = port
while True:
try:
- print(host_ip+":"+str(port))
+ logging.info(f"Attempting to connect to {host_ip}:{port}")
client_socket.connect((host_ip, port))
-
+ logging.info(f"Connected to {host_ip}:{port}")
break
- except:
+ except Exception as e:
+ logging.warning(f"Cannot connect to motion detector: {e}")
time.sleep(1)
- print("cannot connect to motion detector")
continue
# Initialize FPS calculation
@@ -80,36 +95,30 @@ def main(camera=False, animal_name="bear", frequency=0, host_ip="motion_detector
frame_rate = 30.0
frame_interval = 1.0 / frame_rate
- fps_start_time=time.time()
- motion=False
+ motion = False
if client_socket:
-
while True:
- for interval in generate_random_intervals(frequency) :
+ for interval in generate_random_intervals(appearance_rate):
interval_frame_count = interval * 30
frame_number = 0
- if motion:
+ if motion:
vid = cv2.VideoCapture(with_animal_video)
motion = False
else:
vid = cv2.VideoCapture(no_animal_video)
motion = True
- print(motion)
- while (frame_number<interval_frame_count) or (not motion):
- # if not vid.isOpened() and not motion:
- # vid= cv2.VideoCapture(no_animal_video)
- frame_number+=1
+ logging.info(f"Motion: {motion}")
+ while (frame_number < interval_frame_count) or (not motion):
+ frame_number += 1
with tracer.start_as_current_span("sending frame") as span:
try:
-
-
img, frame = vid.read()
- if not img :
+ if not img:
if motion:
- vid= cv2.VideoCapture(no_animal_video)
+ vid = cv2.VideoCapture(no_animal_video)
continue
else:
- print("motion frames count:"+str(frame_number))
+ logging.info(f"Motion frames count: {frame_number}")
break
frame = imutils.resize(frame, width=640)
@@ -130,7 +139,7 @@ def main(camera=False, animal_name="bear", frequency=0, host_ip="motion_detector
elapsed_time = time.time() - fps_start_time
if elapsed_time >= 10.0:
fps = round(fps_frame_count / elapsed_time, 2)
- print(str(fps) + "/" + str(fps_frame_count) + "/" + str(elapsed_time))
+ logging.info(f"FPS: {fps} (Total frames: {fps_frame_count}, Time: {elapsed_time})")
fps_histo.record(fps)
fps_count.set(fps)
fps_frame_count = 0
@@ -140,21 +149,13 @@ def main(camera=False, animal_name="bear", frequency=0, host_ip="motion_detector
end_time = time.time()
elapsed_time = end_time - start_time
if elapsed_time < frame_interval:
- #print(str(frame_interval-elapsed_time))
- time.sleep(0.025)#frame_interval - elapsed_time)
+ time.sleep(0.025) # frame_interval - elapsed_time
except Exception as e:
- print(f"Error sending frame: {e}")
+ logging.error(f"Error sending frame: {e}")
client_socket.close()
break
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Camera")
- parser.add_argument("--camera", action="store_true", help="Use webcam")
- parser.add_argument("--animal", type=str, default='tiger', choices=['bear', 'tiger', 'wolf'], help="Name of the animal")
- parser.add_argument("--frequency", type=int, default=600, help="frequency for animal appearance in one hour")
- parser.add_argument("--mdhost", type=str, default="motion_detector", help="Motion Detector IP address")
- parser.add_argument("--mdport", type=int, default=9998, help="Motion detector port number")
- args = parser.parse_args()
- main(camera=args.camera, animal_name=args.animal, frequency=args.frequency, host_ip=args.mdhost, port=args.mdport)
\ No newline at end of file
+ main()
diff --git a/services/camera/src/config.py b/services/camera/src/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bbbafbc10cd780928fc7bfcddcb81b70feaa7ab
--- /dev/null
+++ b/services/camera/src/config.py
@@ -0,0 +1,11 @@
+import os
+
+def get_config():
+ config = {
+ 'camera': os.getenv("CAMERA", "false").lower() == "true",
+ 'animal_name': os.getenv("ANIMAL_NAME", "tiger"),
+ 'appearance_rate': max(1, int(os.getenv("APPEARANCE_RATE", 600))),
+ 'host_ip': os.getenv("MDHOST", "localhost"),
+ 'port': int(os.getenv("MDPORT", 9998))
+ }
+ return config
diff --git a/services/camera/src/logger.py b/services/camera/src/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..0791e3daaf2a054259ba572a3e2e448af51a897f
--- /dev/null
+++ b/services/camera/src/logger.py
@@ -0,0 +1,19 @@
+import logging
+import os
+
+def setup_logger():
+ # Get the log file path from the environment variable, defaulting to "output/camera.log" if not set
+ log_file = os.getenv("LOG_FILE", "output/camera.log")
+
+ # Create the log directory if it doesn't exist
+ os.makedirs(os.path.dirname(log_file), exist_ok=True)
+
+ logging.basicConfig(
+ format='%(asctime)s - %(levelname)s - %(message)s',
+ level=logging.INFO,
+ handlers=[
+ logging.StreamHandler(), # Log to console
+ logging.FileHandler(log_file) # Log to file defined by the environment variable
+ ]
+ )
+ return logging.getLogger()
diff --git a/services/camera/src/network.py b/services/camera/src/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..576df37345d168bbd6eb4c5c72f1ce86d7c1099e
--- /dev/null
+++ b/services/camera/src/network.py
@@ -0,0 +1,27 @@
+import socket
+import pickle
+import struct
+import time
+import logging
+
+def connect_to_server(host_ip, port, max_retries=5):
+ client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ for retry in range(max_retries):
+ try:
+ logging.info(f"Attempting to connect to {host_ip}:{port}")
+ client_socket.connect((host_ip, port))
+ logging.info(f"Connected to {host_ip}:{port}")
+ return client_socket
+ except Exception as e:
+ if retry == max_retries - 1:
+ logging.error(f"Failed to connect to motion detector: {e}")
+ return None
+ wait_time = 2 ** retry
+ logging.warning(f"Connection failed. Retrying in {wait_time} seconds...")
+ time.sleep(wait_time)
+ return None
+
+def send_frame_to_server(client_socket, frame_data):
+ serialized_data = pickle.dumps(frame_data)
+ message = struct.pack("Q", len(serialized_data)) + serialized_data
+ client_socket.sendall(message)
diff --git a/camera/tracerprovider.py b/services/camera/src/tracerprovider.py
similarity index 84%
rename from camera/tracerprovider.py
rename to services/camera/src/tracerprovider.py
index de011f54e8a95d405590e5a90889e3a7fd136965..784a8a42609db39b1a0ced93206a52028f37295c 100644
--- a/camera/tracerprovider.py
+++ b/services/camera/src/tracerprovider.py
@@ -1,3 +1,4 @@
+import os
import random
from opentelemetry import trace, metrics
@@ -9,12 +10,13 @@ from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExport
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
file_name = "Camera "
-try:
- with open('/app/index.txt', 'r') as file:
- index_value = int(file.read().strip())
-except (FileNotFoundError, ValueError) as e:
- print(f"Error reading index.txt: {e}")
- index_value = random.randint(1, 10)
+# try:
+# with open('../index.txt', 'r') as file:
+# index_value = int(file.read().strip())
+# except (FileNotFoundError, ValueError) as e:
+# print(f"Error reading index.txt: {e}")
+# index_value = random.randint(1, 10)
+index_value = os.environ.get("INDEX", str(random.randint(1, 10)))
# Generate a random integer
resource=Resource.create({SERVICE_NAME: f"{file_name}{index_value}"})
# Set up the tracer provider for tracing
diff --git a/services/camera/src/utils.py b/services/camera/src/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d07d613bb68cb99fa1388aefff9653b42e7a64f
--- /dev/null
+++ b/services/camera/src/utils.py
@@ -0,0 +1,12 @@
+import random
+import logging
+
+def generate_random_intervals(events_per_hour):
+ total_time = 3600 # seconds in an hour
+ avg_interval = total_time / events_per_hour
+ random_factors = [random.uniform(0.5, 1.5) for _ in range(events_per_hour)]
+ total_factor = sum(random_factors)
+ normalized_intervals = [avg_interval * (factor / total_factor) for factor in random_factors]
+ result = [round(interval, 2) for interval in normalized_intervals]
+ logging.info(f"Generated intervals: {result}")
+ return result
\ No newline at end of file
diff --git a/services/camera/src/video.py b/services/camera/src/video.py
new file mode 100644
index 0000000000000000000000000000000000000000..6921424edc96403a2954d053395de1d679f033d4
--- /dev/null
+++ b/services/camera/src/video.py
@@ -0,0 +1,21 @@
+import cv2
+import logging
+
+def load_video(animal_name):
+ video_map = {
+ 'bear': ('footage/bear/no_bear.mp4', 'footage/bear/with_bear.mp4'),
+ 'tiger': ('footage/tiger/no_tiger.mp4', 'footage/tiger/with_tiger.mp4'),
+ 'wolf': ('footage/wolf/no_wolf.mp4', 'footage/wolf/with_wolf.mp4')
+ }
+ if animal_name not in video_map:
+ logging.error(f"No video available for {animal_name}")
+ return None, None
+ return video_map[animal_name]
+
+def capture_frame(video_path):
+ vid = cv2.VideoCapture(video_path)
+ ret, frame = vid.read()
+ if not ret:
+ logging.warning(f"Frame read failed for {video_path}")
+ return None
+ return frame
diff --git a/object_recognizer/Dockerfile b/services/motion_detector/Dockerfile
similarity index 65%
rename from object_recognizer/Dockerfile
rename to services/motion_detector/Dockerfile
index 65c2ff27116daa9baa590a99b06ef9a8d8447119..4f19877e62e813695af195955a3b5fae9e4324c0 100644
--- a/object_recognizer/Dockerfile
+++ b/services/motion_detector/Dockerfile
@@ -1,7 +1,7 @@
# Dockerfile for server microservice
# Use the official Python image as base
-FROM --platform=linux/amd64 python:3.8-slim as build
+FROM --platform=linux/arm64 python:3.8-slim as build
# Set the working directory inside the container
WORKDIR /app
@@ -9,13 +9,17 @@ WORKDIR /app
# Copy the server code into the container
COPY . .
-
# Install required Python packages
-RUN ls
RUN pip install -r requirements.txt
+# Set environment variables
+ENV OR_HOST_IP=localhost
+ENV OR_PORT=9999
+ENV LOG_LEVEL=INFO
+ENV TZ=UTC
+
# Expose the port used by the server (if needed)
# EXPOSE <port_number>
# Command to run the server code
-CMD ["python", "object_recognizer.py"]
+CMD ["python", "src/motion_detection.py"]
diff --git a/services/motion_detector/__init__.py b/services/motion_detector/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/motion_detector/requirements.txt b/services/motion_detector/requirements.txt
similarity index 100%
rename from motion_detector/requirements.txt
rename to services/motion_detector/requirements.txt
diff --git a/services/motion_detector/src/__init__.py b/services/motion_detector/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/motion_detector/motion_detection.py b/services/motion_detector/src/motion_detection.py
similarity index 61%
rename from motion_detector/motion_detection.py
rename to services/motion_detector/src/motion_detection.py
index c8d43c76dd41276ee1acebb631c65df9a6644bbf..667d52afc2d199bd1a62455e3935fcac15f58a71 100644
--- a/motion_detector/motion_detection.py
+++ b/services/motion_detector/src/motion_detection.py
@@ -1,4 +1,5 @@
-import argparse
+# Add logging for key events and metrics
+import os
import datetime
import socket
import cv2
@@ -8,21 +9,34 @@ import time
import threading
import queue
import psutil
-import pyshine as ps
import imutils
+import logging # Import the logging module
from tracerprovider import tracer, meter
+# Configure logging
+logging.basicConfig(
+ format='%(asctime)s - %(levelname)s - %(message)s',
+ level=logging.INFO, # You can change this to DEBUG, ERROR, etc.
+ handlers=[
+ logging.StreamHandler(), # Log to console
+ logging.FileHandler("output/motion_detector.log") # Log to a file
+ ]
+)
+
def get_cpu_usage():
- return psutil.cpu_percent(interval=1)
+ usage = psutil.cpu_percent(interval=1)
+ logging.debug(f"CPU usage retrieved: {usage}%")
+ return usage
def receive_frames(client_socket, frame_queue):
+ logging.info("Starting frame reception thread.")
data = b""
payload_size = struct.calcsize("Q")
c2e_transmission_time = meter.create_gauge(
name="c2e_transmission_time",
- description="transmission time of one frame frm camera to md",
+ description="transmission time of one frame from camera to motion detection",
unit="s"
)
while True:
@@ -30,32 +44,40 @@ def receive_frames(client_socket, frame_queue):
while len(data) < payload_size:
packet = client_socket.recv(4 * 1024) # 4K
if not packet:
+ logging.warning("Client disconnected or no data received.")
return
data += packet
packed_msg_size = data[:payload_size]
data = data[payload_size:]
msg_size = struct.unpack("Q", packed_msg_size)[0]
+ logging.debug(f"Expected frame size: {msg_size} bytes.")
+
while len(data) < msg_size:
data += client_socket.recv(4 * 1024)
frame_data = data[:msg_size]
data = data[msg_size:]
+
transmission_time = datetime.datetime.now() - datetime.datetime.strptime(
pickle.loads(frame_data)['capture_time'],
"%Y-%m-%d %H:%M:%S")
transmission_time_in_seconds = transmission_time.total_seconds()
c2e_transmission_time.set(transmission_time_in_seconds)
+ logging.info(f"Frame received with transmission time: {transmission_time_in_seconds} seconds.")
+
frame_queue.put(pickle.loads(frame_data))
except Exception as e:
- print(f"Error receiving frame: {e}")
+ logging.error(f"Error receiving frame: {e}")
return
+
def process_frames(addr, frame_queue, or_host_ip, or_port):
+ logging.info(f"Starting frame processing thread for client: {addr}.")
firstFrame = None
fps_start_time = time.time()
fps_frame_count = 0
detected = False
- orclient_socket = None
+ orclient_socket = None
fps_histo = meter.create_histogram(
name="md_fps_histo",
description="Frames per second",
@@ -68,38 +90,43 @@ def process_frames(addr, frame_queue, or_host_ip, or_port):
)
processing_time = meter.create_gauge(
name="md_processing_time",
- description="processing time of one frame",
+ description="Processing time of one frame",
unit="s"
)
md_detected_motion = meter.create_gauge(
name="md_detected_motion",
- description="detected motions 1 or 0",
+ description="Detected motions 1 or 0",
)
- fps_start_time=time.time()
+ fps_start_time = time.time()
while True:
- if frame_queue.empty():
+ try:
+ # Wait for an item in the queue with a timeout
+ recieveddata = frame_queue.get(timeout=1) # Waits for 1 second
+ logging.debug("Frame dequeued for processing.")
+ except queue.Empty:
+ logging.info(f"Queue is empty for {addr}, waiting for frames...")
continue
- recieveddata = frame_queue.get()
frame = recieveddata['frame']
+ logging.debug("Frame loaded for processing.")
text = f"CLIENT: {addr}"
- frame = ps.putBText(frame, text, 10, 10, vspace=10, hspace=1, font_scale=0.7,
- background_RGB=(255, 0, 0), text_RGB=(255, 250, 250))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
+ logging.debug("Frame converted to grayscale and blurred.")
if firstFrame is None:
firstFrame = gray
+ logging.info("Initialized reference frame for motion detection.")
continue
- starting_processsing_time = time.time()
+ starting_processing_time = time.time()
fps_frame_count += 1
elapsed_time = time.time() - fps_start_time
if elapsed_time >= 10.0:
fps = round(fps_frame_count / elapsed_time, 2)
fps_count.set(fps)
- print("FPS:", fps)
+ logging.info(f"FPS: {fps}")
fps_frame_count = 0
fps_start_time = time.time()
@@ -114,18 +141,16 @@ def process_frames(addr, frame_queue, or_host_ip, or_port):
detected = False
for c in cnts:
if cv2.contourArea(c) < 10000:
- #if detected:
- # orclient_socket.close()
- # detected = False
continue
- detected=True
+ detected = True
detected_cnt += 1
+ logging.debug(f"Motion detected. Contour area: {cv2.contourArea(c)}.")
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "md: motion detected"
- if detected:
- print("Motion detected")
+ if detected:
+ logging.info("Motion detected, preparing to send frame to Object Recognizer.")
md_detected_motion.set(1)
orclient_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
orclient_socket.connect((or_host_ip, or_port))
@@ -138,42 +163,43 @@ def process_frames(addr, frame_queue, or_host_ip, or_port):
try:
a = pickle.dumps(ordata)
message = struct.pack("Q", len(a)) + a
- print("Sending frame to object_recognizer")
+ logging.info(f"Packet size: {len(message)} bytes.") # Log the size of the packet
orclient_socket.sendall(message)
+ logging.info("Frame successfully sent to Object Recognizer.")
break
- except :
- print("Sending frame to object_recognizer failed")
+ except Exception as e:
+ logging.error(f"Sending frame to Object Recognizer failed: {e}")
time.sleep(1)
continue
orclient_socket.close()
- else :
- print("No motion detected")
md_detected_motion.set(0)
- processing_time.set(time.time() - starting_processsing_time)
+ processing_time.set(time.time() - starting_processing_time)
+ logging.debug("Processing time logged for frame.")
+
+def main():
+ # Retrieve environment variables instead of command-line arguments
+ or_host_ip = os.getenv('OR_HOST', 'localhost') # Default to 'localhost'
+ or_port = int(os.getenv('OR_PORT', 9999)) # Default to 9999
-def main(or_host_ip=None, or_port=None):
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host_name = socket.gethostname()
- host_ip =socket.gethostbyname(host_name)
- print('HOST IP:', host_ip)
+ host_ip = socket.gethostbyname(host_name)
+ logging.info(f'HOST IP: {host_ip}')
port = 9998
socket_address = (host_ip, port)
server_socket.bind(socket_address)
server_socket.listen()
- print("Listening at", socket_address)
+ logging.info(f"Listening at {socket_address}")
while True:
client_socket, addr = server_socket.accept()
+ logging.info(f"Accepted connection from {addr}.")
frame_queue = queue.Queue()
threading.Thread(target=receive_frames, args=(client_socket, frame_queue)).start()
threading.Thread(target=process_frames, args=(addr, frame_queue, or_host_ip, or_port)).start()
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Motion detector")
- parser.add_argument("--host", type=str, default="object_recognizer", help="Object Recognizer IP address")
- parser.add_argument("--port", type=int, default=9999, help="Object Recognizer port number")
- args = parser.parse_args()
- main(or_host_ip=args.host, or_port=args.port)
+ main()
diff --git a/motion_detector/tracerprovider.py b/services/motion_detector/src/tracerprovider.py
similarity index 84%
rename from motion_detector/tracerprovider.py
rename to services/motion_detector/src/tracerprovider.py
index 095d95d0028afd12fb65be60ae02586f7ec9caed..68cb24d89702fe33fa301c44edbc0531e7fc0d03 100644
--- a/motion_detector/tracerprovider.py
+++ b/services/motion_detector/src/tracerprovider.py
@@ -1,3 +1,4 @@
+import os
import random
from opentelemetry import trace, metrics
@@ -10,12 +11,13 @@ from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
# Extract the file name without extension
file_name = "Motion Detector "
-try:
- with open('/app/index.txt', 'r') as file:
- index_value = int(file.read().strip())
-except (FileNotFoundError, ValueError) as e:
- print(f"Error reading index.txt: {e}")
- index_value = random.randint(1, 10)
+# try:
+# with open('../index.txt', 'r') as file:
+# index_value = int(file.read().strip())
+# except (FileNotFoundError, ValueError) as e:
+# print(f"Error reading index.txt: {e}")
+# index_value = random.randint(1, 10)
+index_value = os.environ.get("INDEX", str(random.randint(1, 10)))
resource=Resource.create({SERVICE_NAME: f"{file_name}{index_value}"})
# Set up the tracer provider for tracing
trace.set_tracer_provider(
diff --git a/motion_detector/Dockerfile b/services/object_recognizer/Dockerfile
similarity index 56%
rename from motion_detector/Dockerfile
rename to services/object_recognizer/Dockerfile
index 4f17978432bdda3ea1c02e58911871929d837f46..62fd939e1897adad8b65df34206ae6370303e00a 100644
--- a/motion_detector/Dockerfile
+++ b/services/object_recognizer/Dockerfile
@@ -1,7 +1,5 @@
-# Dockerfile for server microservice
-
# Use the official Python image as base
-FROM --platform=linux/amd64 python:3.8-slim as build
+FROM --platform=linux/arm64 python:3.8-slim as build
# Set the working directory inside the container
WORKDIR /app
@@ -12,8 +10,8 @@ COPY . .
# Install required Python packages
RUN pip install -r requirements.txt
-# Expose the port used by the server (if needed)
-# EXPOSE <port_number>
+# Expose port 5000
+EXPOSE 5000
# Command to run the server code
-CMD ["python", "motion_detection.py"]
+CMD ["python", "src/object_recognizer.py"]
diff --git a/services/object_recognizer/__init__.py b/services/object_recognizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/services/object_recognizer/output/res.jpg b/services/object_recognizer/output/res.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d063447abd224208d2d26a0af9ac62951ba85cd2
Binary files /dev/null and b/services/object_recognizer/output/res.jpg differ
diff --git a/object_recognizer/requirements.txt b/services/object_recognizer/requirements.txt
similarity index 93%
rename from object_recognizer/requirements.txt
rename to services/object_recognizer/requirements.txt
index 94a7282c4a5938495c667c38d1f36f0927fbe007..2628b9976f1bf091d34c6d4ca95cf2b597c8b57a 100644
--- a/object_recognizer/requirements.txt
+++ b/services/object_recognizer/requirements.txt
@@ -6,4 +6,5 @@ opentelemetry-sdk
opentelemetry-exporter-jaeger
opentelemetry-exporter-otlp
opentelemetry-exporter-otlp-proto-grpc
-docker
\ No newline at end of file
+docker
+flask
\ No newline at end of file
diff --git a/services/object_recognizer/src/__init__.py b/services/object_recognizer/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/services/object_recognizer/src/model/model.py b/services/object_recognizer/src/model/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c909a7054ad05f6e6c3219bb6902858f8def2e6b
--- /dev/null
+++ b/services/object_recognizer/src/model/model.py
@@ -0,0 +1,88 @@
+import logging
+import cv2
+import numpy as np
+import os
+def get_output_layers(net):
+ layer_names = net.getLayerNames()
+ try:
+ output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
+ except Exception as e:
+ logging.error(f"Error getting output layers: {e}")
+ output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+ return output_layers
+
+def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h, classes, COLORS):
+ try:
+ label = str(classes[class_id])
+ logging.info(f"{label} detected with confidence {confidence:.2f}")
+ color = COLORS[class_id]
+ cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+ cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+ framename = "output/res.jpg"
+ cv2.imwrite(framename, img)
+ logging.info(f"Frame saved with detection: {framename}")
+ except Exception as e:
+ logging.error(f"An error occurred while drawing prediction: {e}")
+
+
+def recognize(image):
+ Width = image.shape[1]
+ Height = image.shape[0]
+ scale = 0.00392
+
+ classes = None
+ # Define the base path for the yolov3 files
+ base_path = "src/model/yolov3"
+
+ # Use os.path.join to build the paths
+ yolov3_txt_path = os.path.join(base_path, "yolov3.txt")
+ yolov3_weights_path = os.path.join(base_path, "yolov3.weights")
+ yolov3_cfg_path = os.path.join(base_path, "yolov3.cfg")
+
+ # Load the class names and network configuration
+ with open(yolov3_txt_path, 'r') as f:
+ classes = [line.strip() for line in f.readlines()]
+
+ COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+
+ COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+ net = cv2.dnn.readNet(yolov3_weights_path, yolov3_cfg_path)
+ blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
+ net.setInput(blob)
+ outs = net.forward(get_output_layers(net))
+
+ class_ids = []
+ confidences = []
+ boxes = []
+ conf_threshold = 0.5
+ nms_threshold = 0.4
+
+ for out in outs:
+ for detection in out:
+ scores = detection[5:]
+ class_id = np.argmax(scores)
+ confidence = scores[class_id]
+ if confidence > 0.5:
+ center_x = int(detection[0] * Width)
+ center_y = int(detection[1] * Height)
+ w = int(detection[2] * Width)
+ h = int(detection[3] * Height)
+ x = center_x - w / 2
+ y = center_y - h / 2
+ class_ids.append(class_id)
+ confidences.append(float(confidence))
+ boxes.append([x, y, w, h])
+
+ indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
+ for i in indices:
+ try:
+ box = boxes[i]
+ except:
+ i = i[0]
+ box = boxes[i]
+ x = box[0]
+ y = box[1]
+ w = box[2]
+ h = box[3]
+ draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h), classes,
+ COLORS)
diff --git a/object_recognizer/yolov3.cfg b/services/object_recognizer/src/model/yolov3/yolov3.cfg
similarity index 100%
rename from object_recognizer/yolov3.cfg
rename to services/object_recognizer/src/model/yolov3/yolov3.cfg
diff --git a/object_recognizer/yolov3.txt b/services/object_recognizer/src/model/yolov3/yolov3.txt
similarity index 100%
rename from object_recognizer/yolov3.txt
rename to services/object_recognizer/src/model/yolov3/yolov3.txt
diff --git a/object_recognizer/yolov3.weights b/services/object_recognizer/src/model/yolov3/yolov3.weights
similarity index 100%
rename from object_recognizer/yolov3.weights
rename to services/object_recognizer/src/model/yolov3/yolov3.weights
diff --git a/object_recognizer/yolov8n.pt b/services/object_recognizer/src/model/yolov3/yolov8n.pt
similarity index 100%
rename from object_recognizer/yolov8n.pt
rename to services/object_recognizer/src/model/yolov3/yolov8n.pt
diff --git a/services/object_recognizer/src/object_recognizer.py b/services/object_recognizer/src/object_recognizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c906ddfc31ff879c43089430a93e807d7ba89449
--- /dev/null
+++ b/services/object_recognizer/src/object_recognizer.py
@@ -0,0 +1,199 @@
+import datetime
+import socket
+import time
+import threading
+import queue
+import pickle
+import struct
+import cv2
+import numpy as np
+import logging
+
+from model.model import recognize
+from webserver import run_flask
+from tracerprovider import tracer, meter
+
+# Configure logging
+logging.basicConfig(
+ format='%(asctime)s - %(levelname)s - %(message)s',
+ level=logging.INFO, # Change to DEBUG for more detailed logs
+ handlers=[
+ logging.StreamHandler(), # Log to console
+ logging.FileHandler("output/object_recognizer.log") # Log to a file
+ ]
+)
+
+
+# Function to detect objects in a frame
+def get_output_layers(net):
+ layer_names = net.getLayerNames()
+ try:
+ output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
+ except Exception as e:
+ logging.error(f"Error getting output layers: {e}")
+ output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+ return output_layers
+
+
+def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h, classes, COLORS):
+ try:
+ label = str(classes[class_id])
+ logging.info(f"{label} detected with confidence {confidence:.2f}")
+ color = COLORS[class_id]
+ cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+ cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+ framename = "output/res.jpg"
+ cv2.imwrite(framename, img)
+ logging.info(f"Frame saved with detection: {framename}")
+ except Exception as e:
+ logging.error(f"An error occurred while drawing prediction: {e}")
+
+
+def process_frame(mddata):
+ recognize(mddata['frame'])
+ # frame = mddata['frame']
+ # Width = frame.shape[1]
+ # Height = frame.shape[0]
+ # scale = 0.00392
+ #
+ # classes = None
+ # with open("src/yolov3.txt", 'r') as f:
+ # classes = [line.strip() for line in f.readlines()]
+ #
+ # COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+ # net = cv2.dnn.readNet("src/yolov3.weights", "src/yolov3.cfg")
+ # blob = cv2.dnn.blobFromImage(frame, scale, (416, 416), (0, 0, 0), True, crop=False)
+ # net.setInput(blob)
+ # outs = net.forward(get_output_layers(net))
+ #
+ # class_ids = []
+ # confidences = []
+ # boxes = []
+ # conf_threshold = 0.5
+ # nms_threshold = 0.4
+ #
+ # for out in outs:
+ # for detection in out:
+ # scores = detection[5:]
+ # class_id = np.argmax(scores)
+ # confidence = scores[class_id]
+ # if confidence > 0.5:
+ # center_x = int(detection[0] * Width)
+ # center_y = int(detection[1] * Height)
+ # w = int(detection[2] * Width)
+ # h = int(detection[3] * Height)
+ # x = center_x - w / 2
+ # y = center_y - h / 2
+ # class_ids.append(class_id)
+ # confidences.append(float(confidence))
+ # boxes.append([x, y, w, h])
+ #
+ # indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
+ # for i in indices:
+ # try:
+ # box = boxes[i]
+ # except:
+ # i = i[0]
+ # box = boxes[i]
+ # x = box[0]
+ # y = box[1]
+ # w = box[2]
+ # h = box[3]
+ # draw_prediction(frame, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h), classes,
+ # COLORS)
+
+def receive_frames(client_socket, frame_queue):
+ data = b""
+ payload_size = struct.calcsize("Q")
+
+ while True:
+ try:
+ while len(data) < payload_size:
+ packet = client_socket.recv(4 * 1024) # 4KB
+ if not packet:
+ logging.warning("No more packets received, closing connection.")
+ return
+ data += packet
+
+ packed_msg_size = data[:payload_size]
+ data = data[payload_size:]
+ msg_size = struct.unpack("Q", packed_msg_size)[0]
+ while len(data) < msg_size:
+ data += client_socket.recv(4 * 1024)
+
+ frame_data = data[:msg_size]
+ data = data[msg_size:]
+ mddata = pickle.loads(frame_data)
+ frame_queue.put(mddata)
+
+ len_q_gauge.set(frame_queue.qsize())
+ transmission_time = datetime.datetime.now() - datetime.datetime.strptime(mddata['sentfromedgetime'],
+ "%Y-%m-%d %H:%M:%S")
+ transmission_time_in_seconds = transmission_time.total_seconds()
+ md_e2c_transmission_time.set(transmission_time_in_seconds)
+ logging.info(f"Frame received. Transmission time: {transmission_time_in_seconds:.2f} seconds")
+
+ except Exception as e:
+ logging.error(f"Error receiving frame: {e}")
+ return
+
+
+def handle_client(frame_queue):
+ while True:
+ try:
+ mddata = frame_queue.get(timeout=1) # Waits for 1 second
+ logging.debug(f"Processing frame from queue. Queue size: {frame_queue.qsize()}")
+ len_q_gauge.set(frame_queue.qsize())
+
+ starting_processing_time = datetime.datetime.now()
+ process_frame(mddata)
+
+ processing_duration = (datetime.datetime.now() - starting_processing_time).total_seconds()
+ processing_time.set(processing_duration)
+ logging.info(f"Frame processed. Processing time: {processing_duration:.2f} seconds")
+
+ response_time_duration = (datetime.datetime.now() - datetime.datetime.strptime(mddata['capture_time'],
+ "%Y-%m-%d %H:%M:%S")).total_seconds()
+ response_time.set(response_time_duration)
+ logging.info(f"Response time for frame: {response_time_duration:.2f} seconds")
+
+ except queue.Empty:
+ logging.debug("Frame queue is empty, waiting for new frames...")
+ except Exception as e:
+ logging.error(f"Error processing frame: {e}")
+
+
+# Setup server socket
+server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+host_name = socket.gethostname()
+host_ip = socket.gethostbyname(host_name)
+socket_address = (host_ip, 9999)
+server_socket.bind(socket_address)
+server_socket.listen()
+logging.info(f"Server started, listening at {socket_address}")
+
+flask_thread = threading.Thread(target=run_flask)
+flask_thread.daemon = True
+flask_thread.start()
+
+len_q_gauge = meter.create_gauge(name="or_len_q", description="Queue length in object recognizer", unit="f")
+processing_time = meter.create_gauge(name="or_processing_time", description="Processing time of one frame", unit="s")
+md_e2c_transmission_time = meter.create_gauge(name="md_e2c_transmission_time",
+ description="Transmission time from edge to cloud", unit="s")
+response_time = meter.create_gauge(name="response_time", description="Response time for frames", unit="s")
+
+client_threads = []
+frame_queue = queue.Queue()
+
+process_thread = threading.Thread(target=handle_client, args=(frame_queue,))
+process_thread.start()
+
+while True:
+ try:
+ client_socket, addr = server_socket.accept()
+ logging.info(f"New client connected: {addr}")
+ threading.Thread(target=receive_frames, args=(client_socket, frame_queue)).start()
+ except Exception as e:
+ logging.error(f"Error in server loop: {e}")
+
+server_socket.close()
diff --git a/object_recognizer/tracerprovider.py b/services/object_recognizer/src/tracerprovider.py
similarity index 81%
rename from object_recognizer/tracerprovider.py
rename to services/object_recognizer/src/tracerprovider.py
index 486c58f14c1d4530adb012489b330f16e1cc9af5..011ef6fd94dcdd8e83d6d0e48c4062dad0b4b570 100644
--- a/object_recognizer/tracerprovider.py
+++ b/services/object_recognizer/src/tracerprovider.py
@@ -1,3 +1,4 @@
+import os
import random
from opentelemetry import trace, metrics
@@ -10,9 +11,14 @@ from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
file_name = "Object Recognizer "
-# Generate a random integer
-random_int =""# random.randint(1, 10)
-resource=Resource.create({SERVICE_NAME: f"{file_name}{random_int}"})
+# try:
+# with open('../index.txt', 'r') as file:
+# index_value = int(file.read().strip())
+# except (FileNotFoundError, ValueError) as e:
+# print(f"Error reading index.txt: {e}")
+# index_value = random.randint(1, 10)
+index_value = os.environ.get("INDEX", str(random.randint(1, 10)))
+resource=Resource.create({SERVICE_NAME: f"{file_name}{index_value}"})
# Set up the tracer provider for tracing
trace.set_tracer_provider(
TracerProvider(
diff --git a/services/object_recognizer/src/webserver.py b/services/object_recognizer/src/webserver.py
new file mode 100644
index 0000000000000000000000000000000000000000..37a550c9982c6a5e5bd02adc34f5f2a44635985f
--- /dev/null
+++ b/services/object_recognizer/src/webserver.py
@@ -0,0 +1,30 @@
+from flask import Flask, send_file
+import threading
+import os
+import time
+
+# Flask application setup
+app = Flask(__name__)
+IMAGE_PATH = "/app/output/res.jpg"
+
+# Disable browser caching by adding cache headers
+@app.after_request
+def add_no_cache_headers(response):
+ response.cache_control.no_store = True
+ return response
+
+@app.route("/")
+def home():
+ return "<h1>Object Recognizer</h1><p>Go to <a href='/image'>/image</a> to see the latest detection.</p>"
+
+@app.route("/image")
+def serve_image():
+ if os.path.exists(IMAGE_PATH):
+ return send_file(IMAGE_PATH, mimetype='image/jpeg')
+ else:
+ return "<h1>No image available</h1>", 404
+
+
+# Flask server runner
+def run_flask():
+ app.run(host="0.0.0.0", port=5000, debug=False, threaded=True)
\ No newline at end of file
diff --git a/usecase.png b/usecase.png
deleted file mode 100644
index 782522569076a5187571bdf70815ecd6c59c6772..0000000000000000000000000000000000000000
Binary files a/usecase.png and /dev/null differ