{"id":147,"date":"2021-03-13T23:56:37","date_gmt":"2021-03-14T04:56:37","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamc0\/?p=147"},"modified":"2021-03-13T23:59:34","modified_gmt":"2021-03-14T04:59:34","slug":"joons-status-report-for-3-13","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamc0\/2021\/03\/13\/joons-status-report-for-3-13\/","title":{"rendered":"Joon\u2019s Status Report for 3\/13"},"content":{"rendered":"

This week, I worked with my teammates for the Design Report. Aside from the regular lab meetings where we spent all of the time listening to the Section\u2019s Design report presentations, we had our regular meetings outside of the regular lab sessions to discuss our current progress of the project and design report. From the feedback received from the faculty, TAs, and peers in Section C, we look forward to improving our project.<\/span><\/p>\n

For the item recognition part, I collected the data (a collection of images) for the items that students carry in their backpack. From 21 items, which includes textbooks, notebooks, pencil case, etc, I was able to collect 250 images per item. I was initially planning to write a Python scraper to scrape images from Google. In order to scrape from Google Images, I had to download ChromeDriver, a web driver for Google Chrome. But, I wasn\u2019t able to download it. However, I was able to find a library called IDT, a Image Dataset Tool, which enabled me to scrape 250 images for each student item. I decided to download and use the IDT library for a few main reasons. (For more information about the library, refer to <\/span>https:\/\/pypi.org\/project\/idt\/<\/span><\/a>, <\/span>https:\/\/github.com\/deliton\/idt<\/span><\/a> )<\/span><\/p>\n