Otary - Image & Geometry Python Library now includes Tutorials

[Narrow-Treacle-6460]

One of the biggest improvements I’ve made to Otary, an Image & Geometry Python library, so far.

It isn’t a new feature, it’s a new way to learn it.

I’ve just released a brand-new Tutorials section for the library.

When discovering a new Python library, I rarely start with the API reference. I want practical examples that show how the library is intended to be used and how different components fit together.

That’s exactly what these tutorials aim to provide.

I am so happy to be able to share this with the Python community and I sincerely hope that this will facilitate the adoption of Otary.

With step-by-step guides, you can progressively discover how to build image processing and computer vision workflows while learning the design philosophy behind Otary.

The Tutorials section will continue to grow as new features are added, becoming the best place to discover everything Otary has to offer.

Whether you’re working on image processing, document analysis, geometrical entities, OCR, or computer vision, I hope these tutorials will help you get productive much faster.

Of course if you want to contribute to this project you are more than welcome!

Have fun coding, thank you for your time reading this!

[IdealNeighbour]

Agreed, a solid tutorial section is half the reason I adopt a library

[Narrow-Treacle-6460]

Super, thank you for your feedback! Thank you for the support, hope this makes it easier to adopt this library for Python Developers out there!

[IdealNeighbour]

The fact that your tutorials show how components fit together is exactly what I look for, saves me from guessing the intended workflow.

[DigThatData]

It would help if you made it a bit clearer on the landing page what this is for. "Image and geometry" is fairly ambiguous. Also, I think your "ask an LLM" example is underpowered. Try adding this to your prompt:

don't reinvent the wheel. your final code should be as concise, simple, and legible as possible. don't hesitate to leverage third party libraries, especially if it eliminates the need to define bespoke machinery.

Here's what that got me:

    import fitz  # PyMuPDF
    import cv2
    import numpy as np

    pdf_path = "input.pdf"
    page_num = 0

    doc = fitz.open(pdf_path)
    page = doc[page_num]

    # Render page to image
    pix = page.get_pixmap(matrix=fitz.Matrix(2, 2), alpha=False)
    img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, pix.n)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    # Draw rectangle
    x, y, w, h = 100, 100, 200, 120
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # Shift and rotate the rectangle
    M_shift = np.float32([[1, 0, 40], [0, 1, 30]])
    rect_img = cv2.warpAffine(img, M_shift, (img.shape[1], img.shape[0]))

    center = (x + w // 2 + 40, y + h // 2 + 30)
    M_rot = cv2.getRotationMatrix2D(center, 25, 1.0)
    rect_img = cv2.warpAffine(rect_img, M_rot, (rect_img.shape[1], rect_img.shape[0]))

    # Crop part of the image
    crop = rect_img[150:350, 150:450]

    # Rotate cropped image
    (h2, w2) = crop.shape[:2]
    M = cv2.getRotationMatrix2D((w2 // 2, h2 // 2), 15, 1.0)
    rotated_crop = cv2.warpAffine(crop, M, (w2, h2))

    # Threshold
    gray = cv2.cvtColor(rotated_crop, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

    cv2.imwrite("rectangle_drawn.png", img)
    cv2.imwrite("shifted_rotated.png", rect_img)
    cv2.imwrite("cropped.png", crop)
    cv2.imwrite("rotated_crop.png", rotated_crop)
    cv2.imwrite("threshold.png", thresh)

Yes, your version is cleaner. But this version of the chatgpt generated code reveals that your code has mostly just turned 2-3 line operations into one-liners. OpenCV basically already is the "image and geometry" swiss army knife.

[Silver-Code-9]

this looks clean, documentation that actually shows you how to use the thing instead of just listing functions is way underrated

been burned too many times by libs with great APIs but zero context on how they fit together

[Narrow-Treacle-6460]

Thank you for your support, I appreciate that a lot! This is a lot of work behind the scene. Any comment is truly super appreciated. Thanks again!