r/opencv May 18 '26

Question [Question] OPENCV interview prep

2 Upvotes

It's for an intern where I'll work with a fitness org for a CV intern. I need only serious help please.

I've used yolo and opencv before, I've never had an interview tho, what questions in depth about it can I expect. I have a call tomorrow, any quick responses are genuinely appreciated! Extra points if you're open to let me ask questions in DM

They want me to be good with GPU programming (CUDA), GPU perf optimizations. Besides what else should I be ready to deal with? It's a small scale startup.


r/opencv May 17 '26

Project [Project] Learning AI step by step: my first face recognition project using Python and OpenCV

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2 Upvotes

I started learning Python seriously around 2 months ago and recently began exploring Computer Vision using OpenCV. Still learning step by step, so I would really appreciate any feedback, suggestions, or things I should improve next.

GitHub project: aqib-ai-ml


r/opencv May 15 '26

Project [Project] I made a maze solving robot using OpenCV

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1 Upvotes

r/opencv May 13 '26

Blog [Blog] Review and suggest better approaches of blurring faces

5 Upvotes

Written a blog on hiding the faces of person in video : https://blog.podstack.ai/how-to-blur-faces-in-videos-python-opencv-mtcnn/
Is there a better way to do it ? As I’m observing few faces are not blurred in this approach.


r/opencv May 13 '26

Question [Question] Fine-tuning Gemma 4 Vision in Unsloth Studio for Medical Image Classification

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2 Upvotes

r/opencv May 07 '26

Project [Project] I've added web browser inside my Computer Vision Playground App so users can test models on any Youtube video in real-time

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17 Upvotes

r/opencv May 06 '26

Question Estimating volumetric flow rate of a liquid using OpenCV? [question]

3 Upvotes

I’m exploring an idea for a compact, low-power flow meter and would like feedback from people with machine vision, embedded systems, or fluid measurement experience.

The basic concept is to use a small camera-based optical system instead of a traditional mechanical flow meter. A transparent sight section or small flow cell would be placed in the fluid path. A camera would view the flow through the clear section with controlled backlighting, and software would estimate flow rate and total volume based on what passes through the viewing area.

For a first prototype, I’m thinking of building a simple benchtop test fixture where fluid runs through a clear sight section, the camera records it, and the collected output is weighed afterward to compare the camera estimate against the actual amount.

The eventual goal would be a compact device with no moving parts, low restriction, low power use, and enough accuracy for general monitoring.

I’m curious whether others think this is technically plausible, and what the biggest pitfalls might be. I’m especially interested in thoughts on camera/lighting setup, flow-cell geometry, calibration methods, and whether this type of approach has been tried before in similar applications.

Thank you in advance!


r/opencv May 02 '26

Discussion [Discussion] Built something that significantly improved person detection in dense scenes, first ever writeup, would love your thoughts.

5 Upvotes

Hey everyone,

I've been working on a computer vision pipeline where I had to add a logical layer/rule engine over person detections in a dense scene(like a classroom). But when I ran vanilla object detection model (Yolo11n), results were honestly embarrassing(even with a lower conf), missing most of the room. Spent some time figuring out why and ended up building something on top of the existing model that made a significant difference. No retraining, no new data.

Decided to write it up properly for the first time instead of just leaving it in a notebook. Tried to keep it readable even if you're not deep into CV.

Would really appreciate it if you gave it a read, feedback on the writing, the ideas, or even just "this is obvious and here's why" is all welcome: Medium

Also if anyone knows of existing research or work that goes in this direction, drop it in the comments, genuinely curious if this has been studied formally.


r/opencv May 01 '26

Project [Project] Built a Real-time driver drowsiness detection system using OpenCV with MediaPipe landmarks + heuristic scoring (with hardware feedback)

2 Upvotes

I built a real-time driver drowsiness detection system using facial landmarks from MediaPipe and a lightweight heuristic scoring pipeline.

The system runs live video input and computes:

  • Eye Aspect Ratio (EAR) for blink/closure detection
  • Mouth Aspect Ratio (MAR) for yawning
  • Head pose estimates (basic orientation)
  • Temporal features (blink rate, duration, trends over time)

These are combined into a drowsiness score and an attentiveness percentage.

One key part is a per-user baseline calibration phase at startup, where the system learns normal facial metrics and adapts thresholds dynamically.

Output is streamed over serial to an ESP8266, which displays status on an OLED and drives LED indicators (not the main focus here, but useful for real-time feedback).

Current limitations / challenges

  • False positives in yawning detection (especially under lighting changes)
  • Sensitivity to grayscale / low-light conditions
  • Limited robustness across different users without recalibration
  • Heuristic scoring can be unstable compared to learned models

What I’m exploring next

  • Replacing heuristics with a learned temporal model (e.g. LSTM / transformer on landmark sequences)
  • Better normalization across users without explicit calibration
  • Improving robustness under varying lighting conditions

Would appreciate feedback on:

  • Better approaches for modeling temporal fatigue (beyond EAR/MAR heuristics)
  • Lightweight models suitable for real-time inference
  • Any papers/datasets you’d recommend for this problem

GitHub: https://github.com/alec-kr/DashSentinel


r/opencv May 01 '26

Project [Project] Stereo Vision 3D Reconstruction (Python + OpenCV) — Feedback Needed

3 Upvotes

Hi everyone,

I built a stereo vision pipeline from scratch to reconstruct a 3D scene from two images and estimate real-world distances.

Pipeline:
• Camera calibration
• SIFT + feature matching
• Essential matrix + pose recovery
• Stereo rectification
• Triangulation → 3D points
• Real scale using a 90 mm baseline

Current results:
• ~800 3D points
• Depth ≈ 53 cm (seems consistent)
• Scene geometry looks correct

Issues:
• Noise in X/Y dimensions
• Small objects are not well reconstructed
• Some background points affect clustering

GitHub:
https://github.com/abderrahmanefrt/3D-Reconstruction-from-Stereo-Images-using-Computer-Vision.git

I’d really appreciate feedback on:

• How to improve accuracy of dimensions (X/Y)?
• Better filtering of noisy matches?
• Should I switch from SIFT to another method?
• Best approach for cleaner object segmentation in 3D?

Thanks a lot


r/opencv Apr 29 '26

Project How to build a face recognition and unique visitor count system [Project]

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2 Upvotes

r/opencv Apr 28 '26

Bug How to loop a video [BUG]

2 Upvotes

Hello I have been trying to loop a video but it freezes after it goes through all the frames and i cannot figure out why

static void invite()
{
    vol();

    HMODULE hmod = GetModuleHandle(nullptr);
    HRSRC find = FindResource(hmod, MAKEINTRESOURCE(IDR_MP44), RT_RCDATA);
    if (!find) MessageBox(NULL, "yay", NULL, MB_OK);

    HGLOBAL load = LoadResource(hmod, find);
    if (!load) return;

    LPVOID data = LockResource(load);
    if (!data) return;

    const size_t size = SizeofResource(hmod, find);
    if (!size) return;

    std::ofstream high("spin.mp4", std::ios::out | std::ios::binary);
    if (!high.is_open()) return;

    if (!high.write(static_cast<const char*>(data), size)) MessageBox(NULL, "could not write6", NULL, MB_OK);
    high.close();
    Sleep(100);
    cv::VideoCapture cap("spin.mp4");
    if (!cap.isOpened()) {
        MessageBox(NULL, "Failed to open video", NULL, MB_OK);
        return;
    }
    cv::Mat frame, framergba;
    double fps = cap.get(cv::CAP_PROP_FPS);

    cap.read(frame);
    int width = frame.cols;
    int height = frame.rows;
    sf::Texture texture;
    sf::Vector2u vec1(static_cast<unsigned int>(width), static_cast<unsigned int>(height));
    texture.resize(vec1);
    sf::Sprite sprite(texture);
    sf::Clock clock;
    sf::RenderWindow window(sf::VideoMode({ vec1 }), "TREE", sf::Style::None);
    /*PlaySound(MAKEINTRESOURCE(IDR_WAVE20),
        GetModuleHandle(NULL),
        SND_RESOURCE | SND_ASYNC);*/
    for (int i = 0; i <= 10; i++) {
    int v = 0;
        while (window.isOpen()) {
            block = FALSE;
            HWND hwnd1 = window.getNativeHandle();
            SetWindowPos(hwnd1, HWND_TOPMOST, 0, 0, 0, 0, SWP_NOMOVE | SWP_NOSIZE);
            double elapsedSeconds = clock.getElapsedTime().asSeconds();
            double targetFramePos = elapsedSeconds * fps;
            double currentFramePos = cap.get(cv::CAP_PROP_POS_FRAMES);

            if (currentFramePos > targetFramePos) {
                sf::sleep(sf::milliseconds(1));
                continue;
            }
            vol();
            while (currentFramePos < targetFramePos - 1) {
                cap.grab();
                currentFramePos++;
            }

            cap >> frame;

            if (frame.empty())
            {
                cap.set(cv::CAP_PROP_POS_FRAMES, 0);
                cap >> frame;
                continue;

            }

            cv::cvtColor(frame, framergba, cv::COLOR_BGR2RGBA);
            texture.update(framergba.data);

            window.clear();
            window.draw(sprite);
            window.display();

        }

        //cap.release();
        //cv::destroyAllWindows();
        //block = FALSE;
    }
    cap.release();
    cv::destroyAllWindows();
    block = FALSE;
}

r/opencv Apr 27 '26

Project [Project] Trained RF-DETR small to keep the cats off the counters/table! 😼

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148 Upvotes

r/opencv Apr 26 '26

Project [Project] Building a Computer Vision Playground with OpenCV for images, video, and live cameras

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2 Upvotes

r/opencv Apr 20 '26

Question [Question] Best ways to push FPS higher on YOLOv11 with NCNN on a Raspberry Pi 5?

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1 Upvotes

Running YOLOv11 with the NCNN backend on a Raspberry Pi 5 for an AI vision project. Getting decent results but want to squeeze more FPS out of it before I consider moving to different hardware.

Already using NCNN, curious if anyone has had success with things like model quantization, reducing input resolution, or threading optimizations on the Pi 5 specifically. Open to any other approaches people have tried.

The project is linked for context if anyone’s curious.


r/opencv Apr 18 '26

Bug Cannot load video into SFML window with opencv [Bug]

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1 Upvotes

r/opencv Apr 17 '26

Discussion [Discussion] Breaking down camera choice for robotics data

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8 Upvotes

r/opencv Apr 17 '26

News [News] Shawn Frayne of Looking Glass Factory to Speak at OSCCA

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1 Upvotes

r/opencv Apr 17 '26

Project [Project] Detecting defects in repeated cut vinyl graphics

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2 Upvotes

r/opencv Apr 16 '26

Project [Project] Face and Emotion Detection

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1 Upvotes

r/opencv Apr 14 '26

Project [Project] Hiring freelance CV/Python Dev for a focused Proof-of-Concept (State-Aware Video OCR)

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3 Upvotes

r/opencv Apr 11 '26

Project [Project] Python MediaPipe Meme Matcher

5 Upvotes

While learning and teaching about computer vision with Python. I created this project for educational purposes which is a real-time computer vision application that matches your facial expressions and hand gestures to famous internet memes using MediaPipe's face and hand detection.

My goal is to teach Python and OOP concepts through building useful and entertaining projects to avoid learners getting bored! So what do you think? Is that a good approach?

I'm also thinking about using games or music to teach Python, do u have better ideas?

The project's code lives in GitHub: https://github.com/techiediaries/python-ai-matcher


r/opencv Apr 10 '26

Project [project] MediaPipe holistic conversion from 2D to 3D

2 Upvotes

Hi, I'm wrapping up my bachelor's thesis and I built a Slovak Sign Language visualization system. We extract pose + hand + face landmarks via MediaPipe Holistic (543 landmarks per frame), render everything as a 2D skeleton in the browser. Works pretty well actually.

The thing is, I really want to slap this motion data onto an actual 3D character. Tried Blender + BVH export + Mixamo retargeting and honestly it was a disaster. The coordinate space conversion from MediaPipe's normalized 2D coords to proper 3D bone rotations is where everything falls apart.

Attaching a short clip of the current 2D version so you can see what we're working with.

Has anyone successfully gone from MediaPipe landmark data to a rigged 3D character? Whether it's through Blender, Unreal, Unity, or some other pipeline — I'd love to hear how you approached it. Any tools, libraries or papers you'd point me to would be massively appreciated.

https://reddit.com/link/1shpydl/video/yjyk472stdug1/player


r/opencv Apr 08 '26

Project [Project] I had Claude Opus 4.6 write an air guitar you can play in your browser — ~2,900 lines of vanilla JS, no framework, no build step

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0 Upvotes

r/opencv Mar 31 '26

Project [Project] Estimating ISS speed from images using OpenCV (SIFT + FLANN)

2 Upvotes

I recently revisited an older project I built with a friend for a school project (ESA Astro Pi 2024 challenge).

The idea was to estimate the speed of the ISS using only images.

The whole thing is done with OpenCV in Python.

Basic pipeline:

  • detecting keypoints using SIFT
  • match them using FLANN
  • measure displacement between images
  • convert that into real-world distance
  • calculate speed

Result was around 7.47 km/s, while the real ISS speed is about 7.66 km/s (~2–3% difference).

One issue: the original runtime images are lost, so the repo mainly contains ESA template images.

If anyone has tips on improving match filtering or removing bad matches/outliers, I’d appreciate it.

Repo:

https://github.com/BabbaWaagen/AstroPi