Opencv

OpenCV

Use this skill when the user wants local computer-vision work with OpenCV, especially:

• image filtering, thresholding, morphology, contours, or edge detection
• resize, crop, rotate, perspective transform, or format conversion
• camera or video frame read/write with cv2.VideoCapture or cv2.VideoWriter
• classical vision pipelines that should run locally in Python

Prefer this skill for deterministic image processing. If the task is mainly semantic understanding, captioning, or open-ended visual reasoning, consider an image-understanding expert instead of forcing OpenCV.

Workflow

1. Confirm the real task type first: image enhancement, segmentation, contour extraction, geometry transform, or video I/O.
2. Start with the smallest reproducible pipeline and save intermediate outputs when debugging.
3. Use Python and import OpenCV as cv2 as cv unless the surrounding code clearly uses a different style.
4. For image-processing tasks, prefer a simple pipeline such as read -> colorspace convert -> threshold or filter -> morphology or contours -> save result.
5. For video tasks, check cap.isOpened(), check ret on every frame, and always release capture and writer objects.
6. If parameter choices are unclear, expose them as function arguments instead of hard-coding many magic numbers.
7. When the exact API behavior matters, read the relevant skill reference file before coding.

Reference Map

Read these skill-local references on demand instead of expanding the skill with long examples:

• Installation and package choice: references/install.md
• Image-processing basics: references/image-basics.md
• Thresholding and contours: references/threshold-and-contours.md
• Video capture and writing: references/video-io.md

If a request goes beyond these references, keep the implementation conservative and prefer simple, well-known OpenCV APIs over speculative or heavyweight patterns.

Guardrails

• OpenCV images are usually BGR, not RGB. Convert explicitly when mixing with PIL or matplotlib.
• cv.threshold() and many related operations assume grayscale input; do not skip the colorspace conversion step.
• Contour detection works best on binary images, and the foreground should usually be white on black.
• For server or CI environments, prefer opencv-python-headless or opencv-contrib-python-headless.
• Install exactly one OpenCV PyPI variant per environment unless there is a very specific reason not to.
• Do not drop in heavyweight DNN examples unless the user actually needs them and the model files are available locally.
• When debugging a broken pipeline, save intermediate files rather than guessing which stage failed.

Minimal Patterns

Read and threshold an image

import cv2 as cv

image = cv.imread(input_path, cv.IMREAD_COLOR)
if image is None:
    raise FileNotFoundError(input_path)

gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
_, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
cv.imwrite(output_path, binary)

Find outer contours

import cv2 as cv

image = cv.imread(input_path, cv.IMREAD_GRAYSCALE)
if image is None:
    raise FileNotFoundError(input_path)

_, binary = cv.threshold(image, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
contours, _ = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

Safe video loop

import cv2 as cv

cap = cv.VideoCapture(input_path)
if not cap.isOpened():
    raise RuntimeError(f"Cannot open video: {input_path}")

while True:
    ret, frame = cap.read()
    if not ret:
        break
    # process frame here

cap.release()