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Gen2 Class Saver (JPEG)

This example demonstrates how to run MobilenetSSD and collect images of detected objects, grouped by detection label. After running this script, DepthAI will start MobilenetSSD, and whenever it detects objects, it will add a dataset entry

Dataset is stored under data directory, and a main dataset file is located under data.dataset.csv. For each detected object, a new dataset entry is created, with each entry having files with a precise purpose:

• timestamp uses int(time.time() * 10000) to store a timestamp of the capture. Please note that this value can be duplicated, if multiple objects are detected on a single image

• label is a human-readable label of the detected object

• left, top, right, top are object bounding box coordinates

• raw_frame represents a path to raw RGB frame captured on DepthAI when detection occured

  

• overlay_frame represents a path to RGB frame with detection overlays (bounding box and label)

  

• cropped_frame represents a path to cropped RGB frame containing only ROI of the detected object

  

An example entries in dataset.csv are shown below

timestamp,label,left,top,right,bottom,raw_frame,overlay_frame,cropped_frame
16125187249289,bottle,0,126,79,300,data/raw/16125187249289.jpg,data/bottle/16125187249289_overlay.jpg,data/bottle/16125187249289_cropped.jpg
16125187249289,person,71,37,300,297,data/raw/16125187249289.jpg,data/person/16125187249289_overlay.jpg,data/person/16125187249289_cropped.jpg
16125187249653,bottle,0,126,79,300,data/raw/16125187249653.jpg,data/bottle/16125187249653_overlay.jpg,data/bottle/16125187249653_cropped.jpg
16125187249653,person,71,36,300,297,data/raw/16125187249653.jpg,data/person/16125187249653_overlay.jpg,data/person/16125187249653_cropped.jpg
16125187249992,bottle,0,126,80,300,data/raw/16125187249992.jpg,data/bottle/16125187249992_overlay.jpg,data/bottle/16125187249992_cropped.jpg
16125187249992,person,71,37,300,297,data/raw/16125187249992.jpg,data/person/16125187249992_overlay.jpg,data/person/16125187249992_cropped.jpg
16125187250374,person,37,38,300,299,data/raw/16125187250374.jpg,data/person/16125187250374_overlay.jpg,data/person/16125187250374_cropped.jpg
16125187250769,bottle,0,126,79,300,data/raw/16125187250769.jpg,data/bottle/16125187250769_overlay.jpg,data/bottle/16125187250769_cropped.jpg
16125187250769,person,71,36,299,297,data/raw/16125187250769.jpg,data/person/16125187250769_overlay.jpg,data/person/16125187250769_cropped.jpg
16125187251120,bottle,0,126,80,300,data/raw/16125187251120.jpg,data/bottle/16125187251120_overlay.jpg,data/bottle/16125187251120_cropped.jpg
16125187251120,person,77,37,300,298,data/raw/16125187251120.jpg,data/person/16125187251120_overlay.jpg,data/person/16125187251120_cropped.jpg
16125187251492,bottle,0,126,79,300,data/raw/16125187251492.jpg,data/bottle/16125187251492_overlay.jpg,data/bottle/16125187251492_cropped.jpg
16125187251492,person,74,38,300,297,data/raw/16125187251492.jpg,data/person/16125187251492_overlay.jpg,data/person/16125187251492_cropped.jpg

Demo

Pre-requisites

1. Purchase a DepthAI model (see shop.luxonis.com)
2. Install requirements

   python3 -m pip install -r requirements.txt
   

Usage

python3 main.py

The dataset will be stored inside data directory