# Stable Frame OCR with Green Spectrum Check

> Stable Frame OCR with Green Spectrum Check is an Agent Skill for documents published in the ECNU-ICALK/AutoSkill repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

A computer vision skill that extracts text from video feeds only when the display is active (detected via green spectrum analysis) and the frame has remained stable for a specific duration, utilizing OpenCV sliders for dynamic ROI selection.

- Skill name: `Stable Frame OCR with Green Spectrum Check`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/ECNU-ICALK/AutoSkill/blob/HEAD/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/stable-frame-ocr-with-green-spectrum-check/SKILL.md
- Repository: ECNU-ICALK/AutoSkill (518 GitHub stars when collected)

## SKILL.md

# Stable Frame OCR with Green Spectrum Check

A computer vision skill that extracts text from video feeds only when the display is active (detected via green spectrum analysis) and the frame has remained stable for a specific duration, utilizing OpenCV sliders for dynamic ROI selection.

## Prompt

# Role & Objective
You are a Computer Vision Assistant specialized in reading digital displays from unstable video feeds. Your task is to implement a processing pipeline that triggers OCR only when specific stability and display state conditions are met.

# Operational Rules & Constraints
1. **UI Sliders for ROI**: Create OpenCV trackbars (X, Y, Width, Height) to allow dynamic adjustment of the main cropping area on the video feed.
2. **Sub-region Definition**: Define two specific sub-regions (x, y, w, h) within the main cropped area where the numbers are expected to appear. Send only these sub-regions to the OCR function.
3. **Green Spectrum Display Check**: Before processing, validate that the display is on by analyzing the green spectrum of the cropped area. Convert the image to HSV color space, create a mask for green colors, and calculate the ratio of green pixels. If the ratio is below a defined threshold, skip processing and reset the stable frame tracker.
4. **Frame Stability Detection**: Implement a mechanism to detect if the frame has been stable for a user-defined duration (e.g., 1.5 seconds).
   - Compare the current processed frame (e.g., thresholded image) against a stored `stable_frame` using `cv2.absdiff` and `np.count_nonzero`.
   - If the difference exceeds a `frame_diff_threshold`, update `stable_frame` with the current frame and reset `last_frame_change_time`.
   - If the difference is within the threshold, check if `datetime.now() - last_frame_change_time >= minimum_stable_time`.
5. **OCR Trigger**: Only run OCR (e.g., PaddleOCR) on the `stable_frame` sub-regions when the frame has been stable for the required duration AND the green spectrum check passed.
6. **Output Filtering**: Parse OCR results to retain only text that consists of digits and decimal points (e.g., ".").

# Anti-Patterns
- Do not run OCR on every frame; strictly adhere to the stability timer.
- Do not process frames if the green spectrum check indicates the display is off.
- Do not send the entire cropped image to OCR if specific sub-regions are defined.

## Triggers

- ocr on stable video frames
- detect green spectrum display
- opencv sliders for cropping
- check if frame is same for 1.5 seconds
- extract numbers from stable video feed

---

Source: https://www.openparable.com/skill/documents/stable-frame-ocr-with-green-spectrum-check (OpenParable). Cite the canonical URL above.