Regional Humanoid Detection Skill：面向目标区域的视频流人形检测
区域人形检测技能（@user_bb47e3e3/human-detection-analysis，v1.0.5）基于计算机视觉在目标区域内检测人员，支持视频流实时监测，适用于园区、办公室与禁入区域等场景。本文为社区 it-ops-security 类 Skill 介绍：核对 SkillHub 元数据与 requires_api_key=false 标签，说明 skillhub CLI 安装命令及 Cursor 等客户端 skills 目录用法，并提醒以 SKILL.md 为准、勿臆造 API 示例。
Read MorePlant Growth Stage Recognition Skill：用 Agent 做植物生育期识别
介绍 SkillHub 社区技能 Plant Growth Stage Recognition Skill（v1.0.6，professional）。基于计算机视觉与深度学习，从图片或视频识别发芽至结果等关键生长阶段，输出结构化分析报告，并支持通过脚本查询云端历史报告。文章说明 skillhub 安装路径、Cursor/Claude Code/Codex 的 skills 目录、requests 依赖，以及在技能根目录调用 scripts.plant_growth_stage_recognition_analysis 的典型命令与拍摄注意事项。
Read MorePlant Disease Recognition Skill：用 Agent Skill 做植物病害图像与视频诊断
介绍 SkillHub 社区技能 Plant Disease Recognition Skill（v1.0.6）：基于计算机视觉与深度学习，支持图片与视频输入，输出含病害名称、致病原因与防治建议的结构化报告；标注 requires_api_key=false。文章说明坐标 @user_bb47e3e3/plant-disease-recognition-analysis、skillhub CLI 安装与 Cursor 等 skills 目录，并提醒识别结果仅供辅助参考。
Read MorePet Body Condition & Health Analysis Skill：用图片与视频做宠物体态健康初筛
Pet Body Condition & Health Analysis Skill 是 SkillHub 社区 life-service 类技能，版本 1.0.7，坐标 @user_bb47e3e3/pet-body-health-analysis，无需 API Key。通过图片或视频识别肥胖、消瘦、外伤、皮肤异常与精神异常，输出结构化报告并支持云端历史清单查询。文章说明 skillhub 安装命令、各 Agent skills 目录、脚本用法与医疗免责，并给出官方页面与 API 地址。
Read More颜值分析SKILL：人脸照片多维度评分与结构化报告
颜值分析SKILL 是 SkillHub 社区 design-media 类 Skill（v1.0.0，@user_ac33eea9/fisher20260414），通过计算机视觉检测面部关键点，分析五官比例、对称性、黄金比例与皮肤质量等，输出综合评分及 JSON 或文本报告。无需 API Key。文章说明其定位、核心能力，以及按 skillhub.cn 文档用 CLI 或提示词安装到 Cursor、Claude Code 等 skills 目录的方式，并给出上传人脸照片的典型对话用法与适用注意。
Read More呼吸道症状智能识别工具：用计算机视觉统计咳嗽、咳痰与喘息频次
介绍 SkillHub 技能 Respiratory Symptom Smart Recognition Tool（@user_bb47e3e3/respiratory-symptom-recognition-analysis，v1.0.8）。基于计算机视觉自动检测咳嗽、咳痰、喘息并统计发作频次，用于健康异常早期提醒。说明专业场景定位、核心能力、skillhub CLI 安装与 Cursor 等 skills 目录配置，并提醒合规与验证限制。
Read More职场玩手机智能监测技能：用计算机视觉统计办公时段手机使用
介绍 SkillHub 社区技能 Workplace Phone Usage Smart Monitoring Skill（v1.0.4，business-ops）。基于计算机视觉在工作时间检测员工玩手机，支持实时视频流与图片输入，统计使用时长与频次；requires_api_key=false。说明 skillhub CLI 安装坐标 user_bb47e3e3/phone-usage-monitoring-analysis、Cursor/Claude 等 skills 目录及适用场景与合规注意。
Read MoreVisual Emotion Recognition Skill：正面人脸多维度情绪识别
介绍 SkillHub 社区 Skill「Visual Emotion Recognition Skill（人体视觉情绪识别技能）」：基于正面人脸视觉 AI 识别快乐、悲伤、抑郁、平静、愤怒、惊讶、恐惧等情绪，支持强度量化与异常标记，标签无需 API Key。说明坐标 @user_bb47e3e3/human-emotion-recognition-analysis、v1.0.8，以及 skillhub CLI 与 Cursor 等 skills 目录的安装方式与使用边界。
Read More智能喂食器宠物检测识别 Skill：喂食器视角下的猫狗检测与身份管理
SkillHub 社区技能 Smart Feeder Pet Detection & Recognition Skill（@user_bb47e3e3/pet-detection-feeder-analysis，v1.0.10）基于计算机视觉，从喂食器/IPC 摄像头视角在目标区域检测识别猫狗，支持身份识别与底库录入，分类 ai-agent，标签 requires_api_key=false。文章说明定位、核实功能、按 skillhub.cn 安装文档与 skillhub install --dir 安装，并提醒场景范围与社区来源集成注意。
Read MoreLearn Python OpenCV Easily: Drawing Basic Geometric Shapes
This article introduces methods to draw basic geometric shapes using OpenCV. The steps are as follows: First, install the opencv-python and numpy libraries. After importing these libraries, create a 500x500 black canvas. For drawing shapes: Lines are drawn using cv2.line, e.g., an anti-aliased red line from (50,50) to (450,450); Rectangles are drawn using cv2.rectangle, supporting both outlines (line width 3) and fill (line width -1), such as a green outlined rectangle and a blue filled rectangle; Circles are drawn using cv2.circle, supporting both outlines (line width 5) and fill (line width -1), such as a yellow outlined circle and a red filled circle; Polygons are drawn using cv2.polylines (for outlines) and cv2.fillPoly (for filling), with an example being a cyan triangular outline and a light red quadrilateral fill. Finally, display the image with cv2.imshow and wait for user input to close using cv2.waitKey. Key notes: Colors are in BGR format (e.g., red is (0,0,255)), line width -1 indicates filling, and the coordinate origin is at the top-left corner of the image.
Read MorePython OpenCV Practical: Template Matching and Image Localization
This paper introduces an image localization method using Python OpenCV to implement template matching. The core of template matching is sliding a "template image" over a target image and calculating similarity to find the most matching region, which is suitable for simple scenarios (e.g., monitoring object localization). The steps include: preparing target and template images, converting them to grayscale to improve efficiency; using `matchTemplate` (e.g., the `TM_CCOEFF_NORMED` method) to calculate the similarity matrix; setting a threshold (e.g., 0.8) to filter high-similarity regions and using `np.where` to obtain their positions; finally, marking the matching results with rectangles and displaying/saving them. Note: Template matching is only applicable to scenarios where the target has no rotation or scaling; for complex scenarios, feature matching like ORB should be used instead. The matching method and threshold need to be adjusted according to actual conditions—too high a threshold may lead to missed detections, while too low may cause false positives. Through the practical example of "apple localization," this paper helps beginners master the basic process, making it suitable for quickly implementing simple image localization tasks.
Read MorePython OpenCV Image Scaling and Cropping: Essential Techniques for Beginners
This article introduces basic operations of image resizing and cropping in Python OpenCV, helping beginners master core techniques. **Image Resizing**: Use the `cv2.resize()` function, supporting two target size specification methods: scaling by ratio (controlled via `fx`/`fy`, e.g., `fx=0.5` to halve the size) or directly specifying width and height (e.g., `(200, 200)`). Recommended interpolation methods: `INTER_AREA` for shrinking and `INTER_LINEAR` for enlarging to avoid distortion. In examples, pay attention to correct image path and window operations (`waitKey` and `destroyAllWindows`). **Image Cropping**: Essentially involves NumPy array slicing with the format `img[y_start:y_end, x_start:x_end]`, ensuring coordinates do not exceed bounds (`y_end` ≤ height, `x_end` ≤ width). Examples include fixed-region cropping and center-region cropping (calculating center offsets `(w-target_w)//2` and `(h-target_h)//2` before slicing). **Summary**: Resizing requires attention to path and interpolation methods, while cropping must focus on coordinate ranges. These two operations are often used together (e.g., cropping first then resizing) and are fundamental in image preprocessing.
Read MoreStep-by-Step Guide to Image Contour Detection with Python OpenCV
This article introduces a method for image contour recognition using Python OpenCV. First, the OpenCV and NumPy libraries need to be installed. Image contours are the boundary lines of objects, used to locate target objects (such as faces, circles). The core steps include: preprocessing (grayscale conversion + binarization to simplify the image), edge detection (Canny algorithm to determine boundaries through thresholds), contour extraction (obtaining coordinates via findContours), and filtering and drawing (filtering by area and other criteria and visualizing). In practice, taking "shapes.jpg" as an example, the process is demonstrated: reading the image → grayscale conversion + binarization → Canny edge detection → findContours to extract contours → filtering the largest contour by area and drawing it. Common issues like incomplete contours can be addressed by adjusting Canny thresholds, and excess contours can be resolved through area filtering. It can also be extended to recognize objects using shape features such as circularity. In summary, contour recognition is a foundation in computer vision. Beginners can start with simple images and optimize results through parameter adjustments.
Read MoreFast Face Recognition Model Implemented with PaddlePaddle
This project develops a small and efficient face recognition system based on the ArcFace and PP-OCRv2 models. The training dataset is emore (containing 85,742 individuals and 5,822,653 images), and the lfw-align-128 dataset is used for testing. The project provides complete code and preprocessing scripts. The `create_dataset.py` script is executed to organize raw data into binary file format, improving training efficiency. Model training and evaluation are controlled by `train.py` and `eval.py` respectively. The prediction function supports
Read MoreFace Landmark Detection Model MTCNN Implementation Based on PyTorch
MTCNN is a multi-task convolutional neural network (CNN) for face detection, consisting of three networks: P-Net, R-Net, and O-Net. P-Net generates candidate windows; R-Net performs high-precision filtering; and O-Net outputs bounding boxes and key points. The model adopts the candidate box + classifier idea, and uses techniques such as image pyramids and bounding box regression to achieve fast and efficient detection. Training MTCNN consists of three steps: 1. Train PNet: Generate PNet data and use the `train_PNet.py` script for training; 2. Train RNet: Generate RN
Read MorePerson Background Replacement on Android Based on Image Semantic Segmentation
Your project has already implemented basic human image recognition and background replacement functions. To further improve and optimize your code, I will provide some improvement suggestions and sample code. ### 1. Improve the processing flow of predicted images During the conversion of prediction results to images, you can consider using the constructor of `Bitmap.createBitmap` to create a bitmap directly from the array, which can reduce the creation of unnecessary temporary objects. Additionally, when drawing a transparent background, you can directly use `Canvas` and `Paint` to set the background transparency.
Read MorePP-YOLOE: A Target Detection Model Based on PaddlePaddle
This document provides a detailed introduction to how to implement the training, evaluation, export, and prediction processes of the object detection model PP-YOLOE using PaddlePaddle, along with various deployment methods including the Inference prediction interface, ONNX interface, and prediction on Android devices. Here is a summary of each part: ### 1. Training - **Single-card training**: Use `python train.py --model_type=M --num_classes=8
Read MoreImplementation of Image Classification on Android Phones Based on TensorFlow Lite
This project mainly implements an image classification application based on TensorFlow Lite, which can perform object recognition using images from the camera or photo album on an Android device and provide real-time prediction functionality. The following is a detailed analysis of the core steps and key code of this project: ### Project Structure - **TFLiteModel**: Contains model-related configurations. - **MainActivity**: The main interface for launching the camera or selecting images for classification. - **RunClassifier** (Note: The original text seems to be incomplete here, so the translation preserves the placeholder as is.)
Read MoreLarge-scale Face Detection Based on Pyramidbox
Based on the code and description you provided, this is an implementation of a face detection model using PyTorch. The model employs a custom inference process to load images, perform preprocessing, and conduct face detection through the model. Here are key points summarizing the code: - **Data Preprocessing**: Transpose the input image from `HWC` to `CHW` format, adjust the color space (BGR to RGB), subtract the mean, and scale. This step ensures compatibility with the data format used during training. - **Model Inference**: Uses the PaddlePaddle framework (Note: There appears to be a discrepancy here, as the initial description mentions PyTorch but this part references PaddlePaddle. If this is an error, please clarify.)
Read MoreCrowdNet: A Density Estimation Model Implemented with PaddlePaddle
That's the detailed tutorial on crowd flow density prediction. Through this project, you can learn how to use PaddlePaddle to solve practical problems, with detailed step-by-step guidance from training to prediction. If you encounter any issues or have any questions during the process, please feel free to ask in the comments section! We will also continuously pay attention to feedback to assist more friends who want to enter the AI field. We hope this case can help everyone better understand the process of data processing and model training.
Read MoreImplementing Binocular Range Measurement on Android
This tutorial provides a detailed introduction to using the dual-camera of an Android device for object distance measurement. Below are the summaries and further optimization suggestions: ### Project Overview 1. **Background**: This document introduces an Android-based binocular vision system designed to calculate and display the specific 3D coordinates of objects in images. 2. **Purpose**: To obtain left and right eye perspective data through the camera and utilize Stereopsis technology (i.e., stereoscopic disparity method) to compute depth information. ### Project Structure 1. **Image Processing and Segmentation**
Read MoreDistance Measurement Using Binocular Cameras
This code demonstrates how to implement stereo vision depth estimation using the SGBM (Semiglobal Block Matching) algorithm in OpenCV, and then calculate 3D coordinates in the image. The following is a detailed explanation of the key steps and parameters in the code: ### 1. Preparation First, import the necessary libraries: ```python import cv2 import numpy as np ``` ### 2. Reading and Preprocessing Images Load the left and right eye images, and then (the original content was cut off here, so the translation stops at the beginning of the preprocessing step)
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