Understanding the process of positive sample selection is crucial for YOLOv5. This guide demonstrates how to visualize these selected samples across different scales to gain deeper insight into the underlying mechanism. Visual Outputs When visualized, the positive sample selection process reveals se...
Preparing the MNIST Dataset The MNIST dataset consists of 28×28 grayscale images of handwritten digits, split into 60,000 training samples and 10,000 test samples. We use torchvision to download and transform the data. import torch from torch.utils.data import DataLoader from torchvision import data...
Developing a real-time fatigue detection system is a critical safety application in computer vision. This implementation utilizes facial landmark detection to monitor physical indicators of exhaustion, specifically focusing on eye closure patterns (EAR), yawning frequency (MAR), and head pose stabil...
1. Classification Basic Data Types Helper Objects Large Array Objects: Mat STL Data Structures: vector, pair 2. Basic Data Structures: Point, Scalar, Size, cv::Rect, RotatedRect, Matx 3. Point 3.1 Point Construction cv::Point2i p; // 2D integer point, e.g., (x, y) cv::Point3f p; // 3D float point, e...
Video content can be sourced from two primary places: pre-recorded local files, or real-time streams from capture devices like computer webcams or phone cameras. OpenCV-Python uses the same core API to read both types of video, with only the input parameter changing to switch sources. Reading Video...
U-Net, formulated by Ronneberger et al. in 2015, adopts an encoder-decoder framework with skip connections to fuse high-resolution feature maps from the contraction path with upsampled outputs in the expansion path. The architecture excels in tasks requiring precise localization, such as biomedical...
Negative Log-Likelihood Loss Mechanics The nn.NLLLoss criterion evaluates classification performance by measuring the negative log-probability assigned to correct classes. Given unnormalized model outputs $z$, the loss first applies the log-softmax operatino: $$\text{log-softmax}(z_i) = z_i - \log\s...
Camera calibration is a foundational process in computer vision, robotics, and 3D reconstruction. It determines the camera's intrinsic properties—including focal length, principal point, and lens distortion parameters—enabling precise correction of image geometry and conversion between pixel cooordi...
Image recognition inovlves identifying objects, scenes, or patterns within digital images through computational analysis. The typical pipeline includes preprocessing, feature extraction, model training, and classification. Preprocessing prepares raw images for analysis—common steps include resizing,...
This project demonstrates a pet cat detection system built around the Seeed Studio XIAO ESP32 S3 Sense board. The solution leverages computer vision techniques and embedded systems to monitor and analyze feline behavior in real time. The hardware setup includes the XIAO ESP32 S3 Sense development bo...