> For the complete documentation index, see [llms.txt](https://campus-rover.gitbook.io/lab-notebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://campus-rover.gitbook.io/lab-notebook/fiiva/computer-vision-suggestion.md).

# How to approach computer vision

Using machine learning models for object detection/ classification) might be harder than you expect, from our own groups’ experience, as machine learning models are usually uninterpretable and yield stochastic results. Therefore, if you don’t have a solid understanding of how to build/train a model or if you’re not confident that you know what to do when the model JUST doesn’t work as expected, I recommend you to start from more interpretable and stable computer vision techniques to avoid confusion/frustration, and also try to simplify the object you want to detect/classify – try to make them easily differentiable with other background objects from color and shape. For examples, several handy functions from OpenCV that I found useful and convenient-to-use include color masking (cv2.inRange), contour detection (cv2.findContours), indices extraction (cv2.convexHul) etc.. These functions suffice for easy object detection (like colored circles, colored balls, arrows, cans, etc.); and you can use cv2.imshow to easily see the transformation from each step -- this would help you debug faster and have something functional built first.

For one example (this is what our team used for traffic sign classification): Using this piece of code: ![Screenshot 2023-05-05 at 7 38 05 PM](https://user-images.githubusercontent.com/59838570/236585107-8e69b398-36c1-4977-8116-ad4ff33f6aba.jpg) You can detect the contour of the arrow ![Screenshot 2023-05-05 at 7 38 59 PM](https://user-images.githubusercontent.com/59838570/236585145-c92799a8-d5ae-4da9-9db8-935fe0263750.jpg)

After which you can find the tip of the arrow, and then determine the direction of the arrow ![Screenshot 2023-05-05 at 7 40 25 PM](https://user-images.githubusercontent.com/59838570/236585224-9bb3d2d1-0daf-4bfa-b313-0c4551323ece.jpg)

The algorithm of finding the tip might be a little complicated to understand (it uses the "convexity defects" of the "hull"). But the point I'm making here is that with these few lines of code, you can achieve probably more than you expected. So do start with these "seamingly easy" techniques first before you use something more powerful but confusing.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://campus-rover.gitbook.io/lab-notebook/fiiva/computer-vision-suggestion.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.