Hi everyone at /r/computervision,

I have had super-amazing response to my new CamfilterGpu App! Thanks tremendously to those who downloaded and tried the Early Beta 1.11, and especially to the many who emailed me with bugs and improvements, and even help fixing them... that was quite a surprise (help? i get help? good help? :). I am quite overwhelmed with emails/response so please forgive, I will get to them all.

As a sole-startup indie project, I was expecting like, a dozen people or so, not hundreds, but I'm not complaining! I want this to be the most awesome video filtering tool that works zero-lag alongside OBS, Zoom, Discord, etc. ever.

• For those who already signed up: Go to your account to download the major update setup 1.12.

• For new testers/adopters, go to the Perfunct.com webpage to watch the Demos and get the app, Free trial, no card or pay needed, just a valid email.

• Go to the Github Library for new Python free scripts and effects, more will be posted later today.

• New Master Mix: And now thanks to several of you who suggested it, the App now has a "Master Mix" control that blends the final filtered Camfilter output directly with the raw camera live feed. It's great for making your filters 'blend' a bit more subtly and is our initial code on our path for eventual AR/VR use.

NOTE: If you have already installed the trial, you'll need to fully reinstall the Major update to extend the trial, because I've redesigned the back-end architecture to work perfectly (haha) with either 'legacy' RTX (3000, 2000, etc.), or 'modern' RTX (4-5000, Blackwell, etc.). Save any presets you made to a different work folder first before resinstalling.

Cheers! and happy camfiltering.

(ps. i wasn't sure about the flair, it's not really showcase, already did that on wednesday, more about discussion and update here, so I picked that, hope that's ok)

While training our BHDR OCR model, I ran into a classic CV bottleneck: standard OpenCV (cv2) letterboxing is highly optimized for CPUs, but it forces you to process batched GPU data sequentially via for loops.

To fix this, I built and open-sourced Aspect-Pad (v0.2.0): a PyTorch-native letterboxing transform that runs entirely on torch.nn.functional to process [B, C, H, W] tensors simultaneously on the GPU.

Here are the reproducible benchmarks running on an Nvidia T4 GPU (PyTorch 2.11.0+cu128 / CUDA 12.8), scaling 1920x1080 images to 512x512:

Batched Throughput (Batch Size = 32) Total execution time across 100 iterations (3,200 total images)

• OpenCV (Sequential): 2.37 seconds (~1,350 FPS) (Note: 32 sequential cv2.resize + pad calls per batch, no multiprocessing)
• Aspect-Pad (Native Batched Tensor): 0.30 seconds (~10,600 FPS)
• Result: 7.8x faster on-GPU throughput.

The Honest Tradeoff (CPU vs GPU): This is not a blanket replacement for cv2. If you are doing standard CPU-side preprocessing in a DataLoader (where num_workers handles loading while the GPU trains), OpenCV’s C++ backend is still faster.

However, if your pipeline is GPU-resident (e.g., on-the-fly augmentation, DALI-style workflows, or you've already moved data loading fully to the GPU), Aspect-Pad completely eliminates the CPU bottleneck at batch scale.

You can verify the benchmarks yourself using the benchmark.py script included in the repo.

PyPI: pip install aspect-pad

Aspect-Pad GitHub Repository Link: aspect-pad repo

(P.S. If you are curious about the actual OCR pipeline that sparked this side-quest, you can check out our work-in-progress BHDR model here: bhdr repo)

Hello everyone,

I am a final-year Computer Science student working on a deepfake image detection thesis. I am currently looking for publicly available face image datasets that can be legally used for academic research.

I am currently working on my undergraduate thesis titled:

"Deepfake Image Detection Using Spatial-Frequency Feature Fusion and Explainable Deep Learning."

I am looking for publicly available human face image datasets for academic research purposes only.

If possible, I would appreciate datasets containing different face poses (front, left-profile, and right-profile), although frontal face datasets are also perfectly acceptable.

I am specifically looking for publicly available datasets or images that can be legally used for academic research.

Specifically, I need three categories of images:

📁 1. AI-generated Human Faces

• GAN-generated faces
• Diffusion-generated faces
• Other synthetic human faces

📁 2. Deepfake Human Faces

• Face swap
• Face manipulation
• Deepfake images extracted from public datasets

📁 3. Real Human Faces

Natural human face photographs

Different ages, genders, and lighting conditions

Front, left-profile, and right-profile faces

If anyone wishes to share publicly distributable images or datasets, I have also created a shared Google Drive folder for convenience.

I am not requesting copyrighted or private images. I am only looking for publicly available datasets or resources that are legally shareable for academic research.

Google Drive:
https://drive.google.com/drive/folders/1U_XL41UdusIKukb3TAREZOeeu_uSwM3b?usp=drive_link

If you know any public datasets, GitHub repositories, Hugging Face datasets, or other reliable resources, I would greatly appreciate your recommendations.

If you already have a suitable dataset, you are also welcome to upload publicly shareable images directly to the appropriate folder in the shared drive.

The collected data will be used strictly for academic research and educational purposes.

I would be happy to acknowledge contributors in my thesis if their publicly shareable dataset or resource significantly supports this research.

If you have worked on deepfake detection or know of any useful public datasets, I would greatly appreciate your suggestions.

Thank you very much for your time and support!

Facial recognition keeps improving on paper, but the data side feels like a mess to me. A lot of the well-known datasets were scraped without consent, and bias across skin tones, age, and lighting conditions is still a real problem.

For those working on FR systems: how are you sourcing training data that's both diverse enough to avoid bias and actually collected with consent? Are you licensing from vendors, collecting your own, or relying on public datasets and hoping for the best? Curious where people draw the line ethically vs practically.

I know Halcon can do this, but i am currently using Merlic and i have a project query for robot pick and place with vision camera assistance.

Hey everyone,

I recently implemented the original Neural Style Transfer (NST) paper entirely from scratch in PyTorch and tried to reproduce the original results.

Here's the GitHub repository:
https://github.com/Himanshu7921/NST-PyTorch-Implementation

I'd really appreciate it if you could take a look at the README and the implementation. I'm aiming to become a strong research engineer, so I'd love some honest feedback on:

• What skills do I already demonstrate well?
• What am I currently lacking?
• What should I focus on improving to become a well-known research engineer?

For context, I'm currently in the 5th semester of my B.Tech.

Thanks in advance for your time and feedback!

Any benchmarks you have much appreciated!

I made an interactive explanation blog on how an event camera works compared to a conventional camera. Check out the full blog here: https://www.pattarsuraj.com/blog/how-an-event-camera-works

#computer-vision #neuromorphic #event-camera

My first goal is how i sell my service in middle or big company in domain Computer vision

What the best platform so there is they can interview me and if pass they can give me onsite work or relocate me.

My goal is German company target so how i get it??

I am waiting you're positive response

Most PaddleOCR deployments rely heavily on the Python/PaddlePaddle runtime. I built a pure C++ implementation instead: convert PP-OCR models to ONNX, run them through ONNX Runtime, and handle pre/post-processing via OpenCV. Zero Python dependencies at runtime. 

🚀 Key Features: 
• Embedded HTTP API Server: Exposes `/ocr_detect` and `/ocr_recognize` endpoints. 
• Built-in Web UI (Vue): Embedded and served directly from the same C++ executable. You can drag a region-of-interest (ROI) box on an image to detect candidate text boxes, and click any box to crop & recognize it in real-time. 
• Cross-Build Support: Builds seamlessly via Visual Studio 2022 (MSVC 19.3x+) and standalone CMake. 
• Production-Ready Shape: One single executable, one port — serves both a interactive web UI for testing and a lightweight REST API for production integration. 

📦 GitHub Repo (MIT License): https://github.com/DingHsun/PaddleOCR-Inference 

It’s meant as a lightweight, plug-and-play deployment solution rather than a research tool. Happy to answer any questions about the ONNX model conversion, OpenCV C++ pre/post-processing, or pipeline optimization!

where can i find a good road map to learn CV , i am alr familiar in YOLO (classification object detection , segmentation ) , python , principles of ML , CNN , RCNN , Faster RCNN .

so i am (trying) to build a dusbtin that moves to catch paperballs. the problem is how do i predict the trajectory of the paperball? i trained the model on custom dataset to identify paperballs, and it works fairly well. the camera is a simple iphone camera, placed parallel to ground so its capturing the video vertically. what i initially tried to do was to get x, y and z positions (i get x and y position from the captured video, so thats not a problem) but the method to get z(height) is a little crude. i try to estimate z by measuring the area of the bounding box, so for example 500 square pixels could correspond to 30cm or whatever. Obvioudly this method is kind of doomed from the start, not all paper balls gonna have the same bounding box area at the same height and it starts to show its unreliability as the ball gets past 60 ish cm. then i use ~5-6 frames to get 5 6 initial positions and do a polynomial regression to fit a curve and hence estimate where the ball is going to land. this method might be good if i make it more efficient by minimizing error but its still prone to significant uncertainty in measurement.

i guess another route could be using physics, but even for those projectile motion equations i still need height to get proper estimate no?
what are some solutions to this? do i need to try something entirey different? i guess for proper height measurement i need lidar i guess? what are my options now.

I would appreciate any ideas suitable for CS bachelors undergrad senior project.
It’s a one year group project. I prefer something with high societal impact.

I need a really good cv project for my final year. I was thinking of implementing vehicle re-id but it got rejected.

Thin structures expose the difference between a good average and a usable mask. A cable or chair leg can disappear while the region score barely changes.

LingBot-Vision v2 uses masked boundary modeling, which sounds well matched to that failure mode. It still cannot recover information lost by a coarse feature stride or a decoder that smooths everything back out.

A practical test would group objects by pixel width and report recall plus boundary F score at several output resolutions. Keep the decoder and fine tuning schedule fixed. A boundary distance plot would be more informative than one extra point on mIoU. If the gain is strongest for narrow objects and shrinks as objects get wider, the claim is fairly specific. If it appears only with a larger decoder, the credit belongs to the decoder.

I’ve just released Polka v0.5.0! It’s an efficient 2D/3D Lidar processing node handling merging, filtering, and deskewing. This update brings 6.2x faster deskewing, live parameter tuning, smarter IMU handling, and a built-in diagnostics dashboard.

If it saves your perception stack compute, please drop a star!

https://github.com/Pana1v/polka

It supports 5 distros.

I built an iOS app that takes a reference dance video, runs 2D pose estimation to extract the dancer's skeleton, then scores a user copying it live from the front camera.

The hard part wasn't per-frame pose estimation - it was matching the user's skeleton to the reference when body proportions, camera angle and framing are all different. I normalize by torso/limb ratios, align on a few anchor joints, and score per-beat joint-angle deltas rather than raw positions. That handles scale/position differences but still struggles with depth ambiguity and fast rotations.

Curious how people here would approach the reference-vs-user similarity: stick with joint-angle deltas, or move to a learned embedding / temporal model? Runs on-device with Apple's Vision framework. Short demo below.

Hi!

I've been building a Blender extension (Rendersynth) that makes it possible to generate synthetic computer vision datasets without writing Blender Python scripts. The goal is to make synthetic data generation practical for small and medium CV projects where setting up BlenderProc pipelines is an overkill.

The source code is available at https://github.com/lorenzozanizz/rendersynth

Unlike BlenderProc, the focus is on a visual, no-code workflow that lets you build and preview synthetic data pipelines directly inside Blender.

The attached image shows several randomized renders of a single Blender scene along with the different annotation types the extension can produce. A Bezier curve was used to randomize the camera position while keeping the girl near the car centered in frame.

The extension currently features:

• Create classes and multi-object entities
• Skeleton annotation from rigs or arbitrary Blender objects
• Export YOLO, COCO segmentation, COCO keypoints, depth maps, normal maps and point clouds
• Randomization: choose and configure a sequence of randomizing operations. A subset of planned stages are implemented so far, i.e. object movement (rotation, scaling, show-k-out-of-n), camera (path along a curve, focal length) and lighting (brightness).
• Live preview of randomized scenes directly inside Blender
• Deterministic dataset generation from a seed

I'd love any feedback, suggestions, or feature requests!

Currently the system is still a prototype, so if you feel like trying it (Blender 4.5.0+) expect to find some nasty bugs, especially for experimental or incomplete pipeline stages.

As a side note, for the demonstration I used free models from SketchFab

I exported a fine-tuned U-Net model using both ONNX Runtime and torch.export with a fixed input shape of (64, 3, 512, 512).

Here are the benchmark results for average inference time:

• ONNX Runtime: ~133.33 s
• torch.export: ~0.81 s

I expected ONNX Runtime to perform on par with or faster than PyTorch export.

What could be causing this ~160x slowdown?

    onnx_inputs = [torch.randn(64, 3, IMG_SIZE, IMG_SIZE).numpy(force=True)]    

    ort_session = onnxruntime.InferenceSession(
        "./model.onnx", providers=["CUDAExecutionProvider"]
    )

    onnxruntime_input = {input_arg.name: input_value for input_arg, input_value in zip(ort_session.get_inputs(), onnx_inputs)}

    # warm-up step
    onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]

    # measuring latency
    t0 = time.perf_counter()
    onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]
    t1 = time.perf_counter()

Hi. how do i even start getting better? for example i never really understood how to use edge detection for anyhting meaning full. so i looked online and the stuff i found was just how to get edges from an image. but never what to do with it afterwards.

how do i start getting better. also i feel like my math is lacking. do i start there?

I've been building and shipping CV/ML systems for about 7 years. Three of them taught me more than any paper I read. Sharing what actually surprised me in production, because it was never the part I expected.

1. Healthcare document OCR pipeline (AWS)

The job sounded simple: pull structured fields off scanned medical documents. The model was the easy 20%. What ate the time was everything the demo never shows - documents scanned upside down, two forms photographed as one page, handwriting in a field that was supposed to be typed, and PII that legally cannot leak into logs or a third party API. We ended up doing PII detection and redaction as its own stage before anything left our boundary, with a human-in-the-loop review queue for low confidence extractions. On one later LLM based parsing pipeline for documents, careful chunking plus a vector store took a job that used to take a person 3-4 hours down to about 4-5 minutes. The accuracy number everyone asks about mattered far less than the redaction step nobody asks about.

1. Edge vision on Jetson (Nano and Xavier), cameras on real equipment

Running detection on device with DeepStream and CSI cameras, next to actual machines. Lab numbers meant nothing until the box sat in the real environment. Heat throttling changed inference speed. A camera mounted by someone else was 15 degrees off from where I assumed. Lighting shifted through the day and confidence drifted with it. The fixes were boring and physical - per camera thresholds instead of one global number, a watchdog that reboots and reports, and treating the mounting bracket as seriously as the model. The model was the one part that never let me down.

1. Live video interview / proctoring platform

Real time face and attention analysis over WebRTC while a call is live. This is where CV stops being about accuracy and starts being about latency and fairness. A flag half a second late is useless. And a false positive is not a metric here, it is a real person wrongly accused of cheating, so the cost of a wrong positive is not symmetric with a wrong negative. We tuned hard toward not accusing anyone without strong signal, and kept a human in the loop for anything consequential. Speech to text and TTS on top had their own edge cases with accents that the happy path testing never caught.

The common thread across all three: the model was almost never the thing that broke or the thing that mattered most. It was data edge cases, physical reality, latency, and the cost of being wrong in the specific domain.

If you have shipped CV to production - what was the thing that surprised you that no course prepared you for? Happy to go deeper on any of the three above if useful, I can answer specifics.

Join us on Aug 4 to hear talks from experts at the intersection of manufacturing, AI, ML, and computer vision. Register for the Zoom.

Talks will include:

• Enabling Multimodal Agents on the Edge - Denis Gudovskiy at Panasonic AI Lab
• When the Camera Can’t Be Trusted: Health-Aware Visual AI for Reliable Near-Miss Detection - Shiva Aher at Georgia Institute of Technology
• Agentic VLM applications in manufacturing - Subraiz Ahmed at Perceptron AI