Eduardo Ottone
ee92581211
- Implements YOLOv8-based object detection with batch output - Supports multiple YOLOv8 model sizes (nano to extra-large) - Configurable confidence threshold, IoU threshold, and padding - Outputs separate images for each detected object - Includes installation scripts for Windows, Linux, and Mac - Complete documentation and example workflow - MIT license for open source distribution
59 lines
1.3 KiB
Batchfile
59 lines
1.3 KiB
Batchfile
@echo off
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echo ================================
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echo AI Object Segmentation Suite Setup
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echo ================================
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REM Check Python
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python --version >nul 2>&1
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if %errorlevel% neq 0 (
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echo ERROR: Python not found! Please install Python 3.8+
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pause
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exit /b 1
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)
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echo Found Python
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REM Create virtual environment
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echo.
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echo Creating virtual environment...
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if not exist "venv" (
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python -m venv venv
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echo Virtual environment created
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) else (
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echo Virtual environment already exists
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)
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REM Activate virtual environment
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echo.
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echo Activating virtual environment...
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call venv\Scripts\activate.bat
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REM Upgrade pip
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echo.
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echo Upgrading pip...
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python -m pip install --upgrade pip
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REM Install requirements
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echo.
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echo Installing requirements...
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echo This may take a few minutes...
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pip install -r requirements.txt
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REM Create output directory
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echo.
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echo Creating output directories...
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if not exist "output" mkdir output
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echo.
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echo ================================
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echo Setup Complete!
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echo ================================
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echo.
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echo To get started:
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echo 1. Activate environment: venv\Scripts\activate
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echo 2. Test with an image: python segment_practical.py your_image.jpg
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echo 3. Check results in: practical_output\
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echo.
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echo For more options, see README.md
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pause
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