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IoT device to detect any early signs of GI issues by analyzing images of individual's stool using computer vision and machine learning

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ToI

Team

Bertha Hu

Sung Woo Park

Baekchun Kim

User Interface

Login Page

Landing Page

Menu

Graph

Milestones

Week 1 - 2 (Due Feb. 20)

  1. Use python3, OpenCV and numpy to read stool image and color balance the image, emulating Photoshop's "auto levels" command

  2. Use python3, OpenCV and numpy to produce a new stool image to mark the red pixels and mask the remaining non-red pixels

  3. Compare the original image with the masked image

Week 3 - 4 (Due Mar. 6)

  1. Calendar implemented

  2. Drawer menu implemented

  3. Signup, login, logout working

  4. Implemented k-means clustering to find the most dominant colors in the image

Week 5 - 6 (Due Mar. 27)

Week 7 - 8 (Due Apr. 10)

Week 9 - 10 (Due Apr. 24)

BPC

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IoT device to detect any early signs of GI issues by analyzing images of individual's stool using computer vision and machine learning

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