managed-portal/managed/people_flow_project/docs/plans/2026-04-08-x86-docker-migration.md

x86 Docker Migration Implementation Plan

For Claude: REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.

Goal: Make the RTSP people-flow project directly usable on Ubuntu 24.04 x86_64 with an RTX 3080 by transferring code and weights and building a Docker image on the target host.

Architecture: Use a Docker-based runtime for Python 3.12, GPU-enabled PyTorch, DeepFace, and the existing project CLI. Keep weights in a deterministic project directory and bake them into the image during build so runtime startup does not trigger downloads.

Tech Stack: Docker, NVIDIA Container Runtime, Python 3.12, PyTorch, Ultralytics, DeepFace

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Task 1: Add Docker packaging files

Files:

• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/Dockerfile
• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/docker-compose.yml
• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/.dockerignore
• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/scripts/run_rtsp_docker.sh
• Modify: /Users/zxmacmini1/Documents/人流检测/people_flow_project/README.md

Step 1: Define the image build

• Base the image on Python 3.12.
• Install required OS packages for OpenCV and ffmpeg.
• Install GPU-enabled PyTorch and project dependencies.
• Copy project source and weights into the image.

Step 2: Add a Docker run wrapper

• Provide a shell script that accepts RTSP URL and output directory.
• Use --gpus all.

Step 3: Update the README

• Document the Docker build and run commands.
• Document where weights must live if the host directory is rebuilt.

Task 2: Prepare the project tree for weights

Files:

• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/weights/.gitkeep
• Create: /Users/zxmacmini1/Documents/人流检测/people_flow_project/weights/deepface/.gitkeep

Step 1: Create weight directories

• Reserve stable paths for YOLO and DeepFace weights.

Task 3: Transfer the project and weights to the target host

Files:

• No code changes required

Step 1: Copy the project

• Transfer the project directory to /home/x/people/people_flow_project.

Step 2: Copy YOLO and DeepFace weights

• Place YOLO and DeepFace weights into the target project weights/ tree.

Task 4: Build and validate on the target host

Files:

• No code changes required

Step 1: Build the image

• Run docker build under /home/x/people/people_flow_project.

Step 2: Validate the CLI

• Run the container with python main.py rtsp --help.

Step 3: Provide the final RTSP run command

• Document the exact docker run invocation for the target host.