{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"opencv","slug":"opencv","name":"OpenCV","type":"framework","url":"https://opencv.org","page_url":"https://unfragile.ai/opencv","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"opencv__cap_0","uri":"capability://image.visual.multi.format.image.i.o.with.codec.abstraction","name":"multi-format image i/o with codec abstraction","description":"Reads and writes images across 10+ formats (JPEG, PNG, TIFF, BMP, WebP, etc.) through a unified cv::Mat interface that abstracts underlying codec implementations. Handles color space conversions (RGB, BGR, HSV, Grayscale) automatically during load/save operations, with configurable compression parameters per format. Supports both file-based and in-memory buffer I/O patterns.","intents":["load images from disk in any standard format and convert to a unified processing representation","save processed images back to disk with format-specific compression tuning","convert between color spaces without manual channel manipulation","work with image data in memory buffers instead of files for streaming pipelines"],"best_for":["computer vision engineers building image processing pipelines","robotics developers handling heterogeneous sensor inputs","embedded systems developers with limited codec libraries"],"limitations":["No built-in support for animated formats (GIF, APNG) — requires frame-by-frame extraction","Color space conversion is lossy for certain transformations (e.g., RGB→HSV→RGB may not be bit-identical)","Codec support depends on build configuration (OpenCV must be compiled with codec libraries like libjpeg, libpng)"],"requires":["C++ 11+ or Python 3.6+","OpenCV compiled with codec support (libjpeg, libpng, libtiff, libwebp)","Sufficient disk I/O bandwidth for large image files"],"input_types":["image files (JPEG, PNG, TIFF, BMP, WebP, PBM, PGM, PPM, SR, RAS)","in-memory byte buffers","file paths (string)"],"output_types":["cv::Mat objects (C++) or numpy arrays (Python)","image files in any supported format","in-memory byte buffers"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_1","uri":"capability://image.visual.real.time.video.frame.streaming.and.codec.handling","name":"real-time video frame streaming and codec handling","description":"Captures video from files, camera devices, or network streams using VideoCapture API with frame-by-frame sequential processing. Abstracts codec decoding (H.264, MJPEG, etc.) and frame synchronization, supporting both blocking (frame-at-a-time) and non-blocking (buffer-based) retrieval patterns. Handles variable frame rates and resolution changes mid-stream with automatic resampling.","intents":["read video files frame-by-frame for processing without loading entire file into memory","capture live camera feeds and process frames in real-time loops","handle multi-camera inputs with synchronized frame extraction","process network video streams (RTSP, HTTP) with automatic buffering"],"best_for":["robotics engineers building real-time perception pipelines","surveillance system developers processing multiple video feeds","embedded vision applications on edge devices with limited memory"],"limitations":["No built-in frame buffering — dropped frames if processing slower than capture rate; requires manual queue management for async pipelines","Codec support limited to what's available on the system (platform-dependent; Windows may lack certain codecs without additional libraries)","No native support for variable frame rate (VFR) video — assumes constant frame rate; VFR content may have timing artifacts","Network stream support (RTSP, HTTP) is experimental and may have latency/reliability issues compared to file-based input"],"requires":["C++ 11+ or Python 3.6+","Camera device drivers (for live capture) or video codec libraries (for file/network streams)","OpenCV compiled with ffmpeg or GStreamer backend for advanced codec support","Sufficient CPU for real-time decoding (H.264 decoding ~5-15% CPU per 1080p@30fps stream)"],"input_types":["video files (AVI, MP4, MOV, MKV, FLV, WMV)","camera device indices (0, 1, 2, etc.)","network URLs (RTSP, HTTP)","image sequences (numbered files)"],"output_types":["cv::Mat frames (C++) or numpy arrays (Python)","frame metadata (width, height, FPS, codec, frame count)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_10","uri":"capability://image.visual.camera.calibration.and.distortion.correction","name":"camera calibration and distortion correction","description":"Calibrates camera intrinsics (focal length, principal point, skew) and distortion coefficients (radial, tangential) from checkerboard patterns or other calibration targets. Computes camera matrix and distortion model that can be applied to undistort images or compute 3D-to-2D projections. Supports multi-camera calibration for stereo or multi-view systems with automatic pose estimation between cameras.","intents":["calibrate camera intrinsics for accurate 3D reconstruction and depth estimation","correct lens distortion in images for geometric accuracy","estimate relative poses between multiple cameras in a stereo or multi-camera system","compute 3D-to-2D projections for augmented reality or object localization"],"best_for":["robotics engineers setting up stereo vision systems","3D reconstruction and photogrammetry projects","augmented reality applications requiring accurate camera models"],"limitations":["Calibration requires 10-20 images of calibration pattern at different poses; poor coverage leads to inaccurate calibration","Checkerboard detection fails if pattern is partially visible, blurry, or at extreme angles; requires high-quality images","Distortion model assumes standard radial+tangential distortion; fisheye or other exotic distortions require custom models","Calibration is sensitive to initial guess; poor initialization can cause convergence to local minima","No automatic calibration target detection; requires manual specification of pattern type and size","Calibration accuracy degrades for wide-angle lenses (>90° FOV); requires more calibration images"],"requires":["C++ 11+ or Python 3.6+","Calibration images (10-20 images of checkerboard or other pattern)","Known calibration pattern (checkerboard size, square size in mm)","Camera with stable intrinsics (no zoom or focus changes during calibration)"],"input_types":["calibration images (cv::Mat, color or grayscale)","calibration pattern type (checkerboard, circles, asymmetric circles)","pattern dimensions (number of corners, square size in mm)"],"output_types":["camera matrix (3x3 intrinsics matrix)","distortion coefficients (4-8 values for radial and tangential distortion)","reprojection error (RMS error in pixels, indicating calibration quality)","camera poses (rotation, translation for each calibration image)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_11","uri":"capability://image.visual.image.stitching.and.panorama.creation","name":"image stitching and panorama creation","description":"Stitches multiple overlapping images into a seamless panorama using feature matching, homography estimation, and blending. Automatically detects overlaps between image pairs, computes transformation matrices, and blends seams using multi-band blending or Poisson blending. Supports both horizontal and vertical panoramas with automatic exposure compensation and color correction.","intents":["create wide-angle panoramic images from multiple overlapping photos","stitch images from panoramic camera modes or manual panning","automatically align and blend images for document scanning or aerial photography","create high-resolution mosaics from multiple camera views"],"best_for":["photography and image processing applications","document scanning and digitization systems","aerial and satellite image mosaicking"],"limitations":["Stitching fails if images have insufficient overlap (<20%) or poor feature matches","Exposure compensation is global; local exposure differences (shadows, highlights) may remain visible","Blending seams can be visible if images have significant lighting differences or moving objects","No automatic image ordering; images must be provided in correct sequence or manually ordered","Panorama distortion increases with number of images; long panoramas may have noticeable curvature","Real-time stitching is slow (~1-5 seconds per image pair); not suitable for live video"],"requires":["C++ 11+ or Python 3.6+","Multiple overlapping images (minimum 2, typically 3-10 for good panoramas)","Images with 20-50% overlap between adjacent pairs","Sufficient memory for intermediate panorama buffers (typically 2-3x total image size)"],"input_types":["vector of cv::Mat images (color or grayscale)","image order (sequence of image indices)","optional: pre-computed feature matches or homographies"],"output_types":["panoramic image (cv::Mat, typically 2-4x wider than individual images)","blending masks (for debugging or custom blending)","transformation matrices (homographies for each image)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_12","uri":"capability://image.visual.text.detection.and.ocr.integration","name":"text detection and ocr integration","description":"Detects text regions in images using EAST (Efficient and Accurate Scene Text) detector or SSD-based models, outputting bounding boxes around text. Integrates with external OCR engines (Tesseract) for character recognition. Supports text orientation detection and perspective correction for skewed text. No built-in OCR; requires external library or API.","intents":["detect text regions in images for document analysis or scene understanding","extract text from images using external OCR after detection","correct perspective distortion in text for improved OCR accuracy","locate text for redaction, translation, or augmentation"],"best_for":["document digitization and archival systems","scene text understanding for autonomous vehicles or robotics","image-based search and indexing applications"],"limitations":["Text detection is slow (~500ms-2s per image for EAST); not suitable for real-time video","Detection accuracy degrades for small text (<20 pixels), curved text, or text at extreme angles","No built-in OCR; requires integration with Tesseract, EasyOCR, or cloud APIs","Perspective correction assumes planar text; 3D text or heavily curved surfaces fail","No language detection; OCR must be configured for specific languages","No confidence scores for detected text regions; difficult to filter low-confidence detections"],"requires":["C++ 11+ or Python 3.6+","Text detection model (EAST or custom DNN model)","External OCR engine (Tesseract, EasyOCR, or cloud API) for character recognition","OpenCV compiled with DNN module"],"input_types":["cv::Mat images (color or grayscale)","text detection model file (EAST or custom)","optional: language configuration for OCR"],"output_types":["bounding boxes (vector of cv::RotatedRect for rotated text)","confidence scores (if using DNN-based detector)","recognized text (from external OCR engine)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_13","uri":"capability://image.visual.contour.detection.and.shape.analysis","name":"contour detection and shape analysis","description":"Detects contours (object boundaries) in binary images using chain approximation algorithms, then analyzes shape properties (area, perimeter, centroid, moments, convex hull, fit ellipse). Supports contour approximation with Douglas-Peucker algorithm to simplify shapes. Computes shape descriptors (Hu moments, contour matching) for shape-based object recognition.","intents":["extract object boundaries from binary images for shape analysis","compute geometric properties (area, perimeter, centroid) for object measurement","match shapes across images using contour similarity metrics","simplify complex contours for visualization or downstream processing"],"best_for":["quality control systems measuring object dimensions","shape-based object recognition and classification","document analysis and form processing"],"limitations":["Contour detection requires binary input; color images must be converted to binary first (threshold, edge detection)","Contour detection is sensitive to noise; binary images must be cleaned (morphological operations) first","Douglas-Peucker approximation is lossy; extreme simplification loses shape detail","Hu moments are rotation-invariant but sensitive to scale; requires normalization for scale-invariant matching","Contour matching is O(n²) for brute-force; requires spatial indexing for large contour sets","No built-in contour tracking across frames; requires manual association for video analysis"],"requires":["C++ 11+ or Python 3.6+","Binary image (cv::Mat with values 0 or 255)","Optional: contour approximation parameters (epsilon for Douglas-Peucker)"],"input_types":["cv::Mat binary images","contour approximation method (CHAIN_APPROX_NONE, CHAIN_APPROX_SIMPLE)","contour retrieval mode (RETR_EXTERNAL, RETR_TREE, etc.)"],"output_types":["vector of contours (vector<vector<cv::Point>>)","shape properties (area, perimeter, centroid, moments, convex hull, fit ellipse)","Hu moments (7-dimensional shape descriptor)","contour matching scores (distance between shapes)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_14","uri":"capability://image.visual.histogram.computation.and.matching.for.color.intensity.analysis","name":"histogram computation and matching for color/intensity analysis","description":"Computes histograms of image intensity or color channels with configurable bin sizes and ranges. Supports multi-dimensional histograms (e.g., 2D histograms of H and S channels in HSV). Compares histograms using multiple distance metrics (Bhattacharyya, Chi-Square, Intersection, Hellinger). Enables color-based object tracking and image retrieval by histogram similarity.","intents":["analyze color distribution in images for quality assessment or color correction","find similar images in a database using histogram matching","track objects by color across video frames using histogram backprojection","detect lighting changes or exposure issues using histogram statistics"],"best_for":["image retrieval and content-based search systems","color-based object tracking in video","image quality assessment and color correction pipelines"],"limitations":["Histograms lose spatial information; two images with same color distribution but different layouts are considered identical","Histogram matching is sensitive to lighting changes; requires normalization or histogram equalization","Multi-dimensional histograms (>2D) become sparse and unreliable; typically limited to 1D or 2D","Histogram backprojection can produce noisy results in cluttered scenes; requires post-processing (morphological operations)","No built-in histogram matching across multiple images; requires manual comparison loops","Bin size selection is manual; poor bin selection reduces discriminative power"],"requires":["C++ 11+ or Python 3.6+","cv::Mat image (single or multi-channel)","Histogram parameters (number of bins, range, channels)"],"input_types":["cv::Mat images (single or multi-channel)","histogram parameters (bins, ranges, channels)","histogram comparison method (BHATTACHARYYA, CHISQR, INTERSECT, HELLINGER)"],"output_types":["cv::MatND histogram (N-dimensional histogram matrix)","histogram statistics (mean, std, min, max)","histogram comparison scores (distance between histograms)","backprojection image (probability map for histogram-based tracking)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_2","uri":"capability://image.visual.spatial.filtering.and.kernel.based.image.convolution","name":"spatial filtering and kernel-based image convolution","description":"Applies 2D convolution operations using custom or predefined kernels (Sobel, Laplacian, Gaussian, etc.) for edge detection, smoothing, and feature enhancement. Implements efficient separable convolution for large kernels, with border handling strategies (replicate, reflect, wrap) and optional GPU acceleration via CUDA. Supports both floating-point and integer kernels with automatic scaling.","intents":["detect edges in images using Sobel or Canny operators for object boundary extraction","smooth/denoise images with Gaussian or bilateral filters for preprocessing","apply custom kernels for domain-specific filtering (e.g., sharpening, embossing)","accelerate large-kernel convolutions on GPU for real-time processing"],"best_for":["image processing engineers building preprocessing pipelines","computer vision researchers prototyping custom filters","embedded vision systems requiring efficient edge detection"],"limitations":["Separable convolution optimization only works for kernels that decompose into rank-1 factors; arbitrary 2D kernels fall back to slower full convolution","Border handling adds latency at image edges; output image is typically 1-2 pixels smaller than input if borders are cropped","GPU acceleration (CUDA) requires NVIDIA GPU and CUDA toolkit; fallback to CPU is automatic but significantly slower","Integer kernel arithmetic can overflow for large kernel sums; requires manual normalization or float conversion"],"requires":["C++ 11+ or Python 3.6+","OpenCV compiled with CUDA support (optional, for GPU acceleration)","NVIDIA CUDA toolkit 10.0+ (if using GPU acceleration)","Sufficient memory for intermediate convolution buffers (typically 2-3x input image size)"],"input_types":["cv::Mat images (single or multi-channel)","custom kernel matrices (float or integer)","predefined kernel types (Sobel, Laplacian, Gaussian, etc.)"],"output_types":["cv::Mat filtered images (same dimensions as input or smaller if borders cropped)","floating-point or integer output depending on kernel type"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_3","uri":"capability://image.visual.morphological.operations.with.structuring.element.composition","name":"morphological operations with structuring element composition","description":"Performs erosion, dilation, opening, closing, and gradient operations using custom or predefined structuring elements (rectangular, elliptical, cross-shaped). Implements efficient multi-pass algorithms for large structuring elements and supports both binary and grayscale morphology. Structuring elements can be composed (e.g., dilate then erode for closing) for complex shape transformations.","intents":["remove noise from binary images using opening (erode then dilate) operations","fill holes in objects using closing (dilate then erode) operations","extract object boundaries using morphological gradient (dilation - erosion)","thin or thicken objects for skeleton extraction or size adjustment"],"best_for":["document image processing engineers cleaning scanned text","medical image analysis researchers segmenting anatomical structures","quality control systems detecting defects in manufactured parts"],"limitations":["Large structuring elements (>50x50) become slow even with multi-pass optimization; requires manual decomposition into smaller elements","Morphological operations are sensitive to structuring element shape; no automatic shape selection — requires domain knowledge","Grayscale morphology is slower than binary morphology (2-3x); binary images should be explicitly used when possible","No built-in morphological reconstruction (conditional dilation) — requires manual iteration"],"requires":["C++ 11+ or Python 3.6+","Binary or grayscale input images (color images must be converted first)","Structuring element definition (predefined or custom matrix)"],"input_types":["cv::Mat binary or grayscale images","structuring element matrices (custom or predefined: MORPH_RECT, MORPH_ELLIPSE, MORPH_CROSS)"],"output_types":["cv::Mat binary or grayscale images (same dimensions as input)","intermediate results from multi-step operations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_4","uri":"capability://image.visual.feature.detection.and.descriptor.extraction.sift.surf.orb.akaze","name":"feature detection and descriptor extraction (sift, surf, orb, akaze)","description":"Detects keypoints (corners, blobs, edges) in images using scale-invariant algorithms (SIFT, SURF, ORB, AKAZE) and computes local descriptors for each keypoint. Implements multi-scale pyramid processing to detect features at different image resolutions, with configurable sensitivity and non-maximum suppression. Descriptors are binary (ORB, AKAZE) or floating-point (SIFT, SURF) for downstream matching.","intents":["find distinctive points in images for image matching and alignment tasks","extract local feature descriptors for object recognition and retrieval","detect corners and edges for camera calibration and structure-from-motion","match features across multiple images for panorama stitching or 3D reconstruction"],"best_for":["computer vision researchers building feature-based matching systems","robotics engineers implementing visual SLAM and localization","image stitching and panorama creation applications"],"limitations":["SIFT and SURF are patented algorithms; OpenCV includes them but commercial use may require licensing","ORB and AKAZE are free alternatives but less robust to extreme scale/rotation changes than SIFT","Descriptor matching is O(n²) for brute-force; requires KD-tree or LSH indexing for large feature sets (>10k features)","Feature detection is sensitive to image quality; low-contrast or blurry images produce few/unreliable features","Multi-scale pyramid processing adds 30-50% latency compared to single-scale detection"],"requires":["C++ 11+ or Python 3.6+","OpenCV compiled with non-free modules (for SIFT/SURF; ORB/AKAZE are in main library)","Sufficient memory for multi-scale pyramid (typically 3-4x input image size)"],"input_types":["cv::Mat grayscale or color images","detector type (SIFT, SURF, ORB, AKAZE, BRISK, etc.)","configuration parameters (number of features, scale levels, threshold)"],"output_types":["vector of cv::KeyPoint objects (location, scale, orientation, response)","cv::Mat descriptor matrix (rows=keypoints, cols=descriptor dimension)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_5","uri":"capability://image.visual.feature.matching.and.geometric.verification.with.outlier.rejection","name":"feature matching and geometric verification with outlier rejection","description":"Matches keypoint descriptors across images using brute-force or FLANN (Fast Library for Approximate Nearest Neighbors) indexing, then filters matches using geometric constraints (RANSAC, homography, fundamental matrix). Automatically rejects outliers and computes transformation matrices (rotation, translation, perspective) between matched image pairs. Supports both binary (Hamming distance) and floating-point (L2 distance) descriptor matching.","intents":["find corresponding points between two images for image alignment and registration","compute homography or fundamental matrix for perspective correction or epipolar geometry","filter false matches using RANSAC to improve robustness in cluttered scenes","estimate camera motion and 3D structure from matched features across multiple views"],"best_for":["image stitching and panorama creation systems","structure-from-motion and 3D reconstruction pipelines","visual odometry and SLAM systems for robotics"],"limitations":["FLANN indexing requires tuning (number of trees, branching factor) for optimal performance; poor tuning can make matching slower than brute-force","RANSAC is probabilistic; requires multiple iterations for high confidence, adding 50-200ms latency per image pair","Homography estimation assumes planar scenes; fails for non-planar objects or significant depth variation","Fundamental matrix estimation requires 8+ good matches; sparse feature sets may fail to converge","No built-in multi-view geometry (bundle adjustment); requires external libraries for large-scale reconstruction"],"requires":["C++ 11+ or Python 3.6+","Two or more images with extracted keypoints and descriptors","Minimum 4 matches for homography, 8 for fundamental matrix"],"input_types":["vector of cv::KeyPoint objects from both images","descriptor matrices (cv::Mat) from both images","matcher type (BFMatcher for brute-force, FlannBasedMatcher for FLANN)","geometric verification method (RANSAC, LMedS, RHO)"],"output_types":["vector of cv::DMatch objects (matched keypoint pairs with distances)","homography matrix (3x3) or fundamental matrix (3x3)","inlier mask (boolean array indicating which matches are geometrically valid)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_6","uri":"capability://image.visual.object.detection.with.pre.trained.cascade.classifiers.and.dnn.inference","name":"object detection with pre-trained cascade classifiers and dnn inference","description":"Detects objects (faces, eyes, pedestrians, etc.) using Haar cascade classifiers (fast, lightweight) or deep neural networks (more accurate, slower). Cascade classifiers use boosted weak learners with integral image acceleration for real-time detection. DNN module supports inference from TensorFlow, PyTorch, Caffe, and ONNX models with automatic quantization and GPU acceleration via CUDA/OpenCL.","intents":["detect faces in images or video for face recognition or anonymization pipelines","detect pedestrians or vehicles for surveillance or autonomous driving applications","run custom-trained object detectors (YOLO, SSD, Faster R-CNN) without external inference engines","accelerate inference on embedded devices using quantized models and GPU acceleration"],"best_for":["real-time surveillance and security systems using cascade classifiers","embedded vision applications on Raspberry Pi or Jetson with limited compute","computer vision engineers prototyping detectors before deploying to production inference engines"],"limitations":["Cascade classifiers are outdated; high false positive rates compared to modern deep learning detectors (YOLO, SSD)","Cascade classifiers require manual tuning of detection parameters (scale factor, min neighbors) for each use case","DNN inference is slower than specialized inference engines (TensorRT, ONNX Runtime) because OpenCV prioritizes simplicity over performance","No built-in batching for DNN inference; processing multiple images requires manual loops","GPU acceleration (CUDA) requires NVIDIA GPU; OpenCL support is experimental and slower","Model quantization is automatic but may reduce accuracy by 1-5% depending on model architecture"],"requires":["C++ 11+ or Python 3.6+","Pre-trained cascade classifier XML files (included for face/eye detection) or DNN model files (TensorFlow, PyTorch, Caffe, ONNX)","OpenCV compiled with DNN module (included in standard builds)","CUDA toolkit 10.0+ (optional, for GPU acceleration)","Sufficient memory for model weights (typically 50MB-500MB for modern detectors)"],"input_types":["cv::Mat images (color or grayscale)","cascade classifier XML file paths","DNN model files (.pb, .pth, .caffemodel, .onnx)","detection parameters (scale factor, min neighbors, min/max object size)"],"output_types":["vector of cv::Rect bounding boxes","confidence scores (for DNN-based detectors)","detection metadata (scale, orientation for some classifiers)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_7","uri":"capability://image.visual.face.recognition.and.biometric.analysis","name":"face recognition and biometric analysis","description":"Detects faces and extracts facial landmarks (eyes, nose, mouth, jawline) using pre-trained models, then computes face embeddings for identity matching. Supports multiple recognition backends (LBP histograms, Fisher faces, Eigenfaces, deep learning embeddings). Embeddings can be compared using distance metrics (L2, cosine) for 1:1 verification or 1:N identification. Includes face alignment preprocessing to normalize pose and lighting.","intents":["detect and extract faces from images for identity verification systems","compute face embeddings for matching against a database of known faces","extract facial landmarks for face alignment, expression analysis, or augmentation","build face recognition systems with configurable accuracy/speed trade-offs"],"best_for":["security and access control systems requiring face verification","photo organization and tagging applications","biometric authentication for mobile or desktop applications"],"limitations":["Face detection accuracy degrades significantly for faces <50 pixels, extreme angles (>45°), or heavy occlusion","Landmark detection assumes frontal or near-frontal faces; profile faces have poor landmark accuracy","Embedding-based matching requires threshold tuning for acceptable false positive/negative rates; no automatic threshold selection","No built-in face database or indexing; 1:N matching requires manual nearest-neighbor search (slow for >10k identities)","Deep learning embeddings require external model files; OpenCV doesn't include pre-trained embedding models","Lighting and pose normalization is limited; extreme lighting or pose still causes matching failures"],"requires":["C++ 11+ or Python 3.6+","Face detection model (cascade classifier or DNN model)","Landmark detection model (included in contrib modules or external)","Face embedding model (external; OpenCV doesn't include pre-trained embeddings)","Sufficient memory for embedding vectors (typically 128-512 dimensions per face)"],"input_types":["cv::Mat images containing faces","face detection results (bounding boxes)","landmark detection model files","embedding model files (TensorFlow, PyTorch, etc.)"],"output_types":["facial landmark coordinates (x, y for each landmark)","face embedding vectors (128-512 dimensional float arrays)","distance scores for face matching (L2 or cosine distance)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_8","uri":"capability://image.visual.motion.tracking.and.optical.flow.estimation","name":"motion tracking and optical flow estimation","description":"Tracks objects across video frames using multiple algorithms: dense optical flow (Farnebäck, TV-L1) computes motion for every pixel, sparse optical flow (Lucas-Kanade) tracks selected features, and template matching tracks rectangular regions. Optical flow outputs 2D motion vectors (u, v) per pixel or feature. Includes background subtraction for foreground/background separation in static camera scenarios.","intents":["estimate pixel-level motion between consecutive frames for video analysis","track sparse features (corners, edges) across video for motion estimation","detect moving objects in surveillance video using background subtraction","compute optical flow for motion-based segmentation or video stabilization"],"best_for":["video surveillance systems detecting moving objects","motion capture and activity recognition applications","video stabilization and frame interpolation pipelines"],"limitations":["Dense optical flow (Farnebäck) is slow (~100-500ms per frame for 1080p); sparse optical flow is faster but less complete","Optical flow fails at occlusions, large displacements (>50 pixels), and textureless regions","Background subtraction assumes static camera; moving camera requires ego-motion compensation","Background subtraction is sensitive to lighting changes and shadows; requires tuning of model parameters","No built-in multi-object tracking; requires manual association of tracks across frames","Optical flow is noisy in low-texture regions; post-processing (median filtering) often required"],"requires":["C++ 11+ or Python 3.6+","Video frames (consecutive frames for optical flow, or video stream for background subtraction)","Optional: feature points for sparse optical flow (from feature detection)"],"input_types":["cv::Mat video frames (grayscale or color)","previous frame for optical flow computation","feature points (cv::KeyPoint) for sparse optical flow","background subtraction algorithm type (MOG2, KNN, etc.)"],"output_types":["optical flow field (cv::Mat with 2 channels: u, v motion vectors)","foreground/background mask (binary cv::Mat)","tracked feature positions (vector of cv::Point2f)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"opencv__cap_9","uri":"capability://image.visual.stereo.vision.and.3d.reconstruction.from.multiple.views","name":"stereo vision and 3d reconstruction from multiple views","description":"Computes depth maps from stereo image pairs using block matching (StereoBM) or semi-global matching (StereoSGBM) algorithms. Requires camera calibration (intrinsics, distortion) and stereo rectification to align image pairs. Outputs disparity maps (inverse depth) that can be converted to 3D point clouds. Supports multi-view stereo for structure-from-motion pipelines with automatic camera pose estimation.","intents":["compute depth maps from stereo camera pairs for 3D scene understanding","generate 3D point clouds from calibrated stereo images for 3D reconstruction","estimate camera poses and 3D structure from multiple uncalibrated views","perform camera calibration and distortion correction for accurate depth estimation"],"best_for":["robotics engineers building 3D perception systems with stereo cameras","3D reconstruction and photogrammetry applications","autonomous vehicle perception pipelines"],"limitations":["Stereo matching fails in textureless regions (white walls, sky); requires texture or active illumination","Disparity estimation is noisy at depth discontinuities (object boundaries); post-processing (median filtering, bilateral filtering) required","Camera calibration requires careful setup with checkerboard patterns; poor calibration causes systematic depth errors (5-10%)","Stereo rectification assumes rigid camera geometry; 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requires manual queue management for async pipelines","Codec support limited to what's available on the system (platform-dependent; Windows may lack certain codecs without additional libraries)","No native support for variable frame rate (VFR) video — assumes constant frame rate; VFR content may have timing artifacts","Network stream support (RTSP, HTTP) is experimental and may have latency/reliability issues compared to file-based input","Calibration requires 10-20 images of calibration pattern at different poses; poor coverage leads to inaccurate calibration","Checkerboard detection fails if pattern is partially visible, blurry, or at extreme angles; requires high-quality images","Distortion model assumes standard radial+tangential distortion; fisheye or other exotic distortions require custom models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=opencv","compare_url":"https://unfragile.ai/compare?artifact=opencv"}},"signature":"PHH0tXEvRntgMVlWeY4odCb9Nr04pdxrvkW4+XjumgwSCPj0+Gr7zzPoA3AtrxwV8wwrV9hYhDsIFRz0MZf+DA==","signedAt":"2026-06-20T22:07:33.381Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/opencv","artifact":"https://unfragile.ai/opencv","verify":"https://unfragile.ai/api/v1/verify?slug=opencv","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}