qy-upup/ai-kissing
Composer 安装命令:
composer require qy-upup/ai-kissing
包简介
A robust and well-structured library providing seamless technical integration for AI-driven kissing detection and analysis. Facilitates the development of applications requiring sophisticated understanding of kissing events in video or image data.
README 文档
README
Enhance your video applications with advanced AI-powered kissing detection and analysis.
Installation
To integrate ai-kissing into your project, use the following command:
bash
pip install ai-kissing
This package requires Python 3.7 or higher and a compatible version of TensorFlow or PyTorch. Please ensure these dependencies are installed before proceeding.
Core API/Feature Overview
The ai-kissing library provides a comprehensive suite of features for analyzing kissing instances within video and image content:
- Kiss Detection: Accurately identifies and locates kissing events within a frame or video sequence.
- Facial Feature Analysis: Extracts key facial landmarks to analyze the dynamics of the kissing action.
- Kiss Intensity Measurement: Quantifies the intensity of the kiss based on facial movements and proximity.
- Multi-Person Kissing Support: Detects and analyzes kissing involving multiple individuals.
- Kiss Duration Tracking: Measures the duration of each kissing event with millisecond precision.
- Real-time Processing: Designed for real-time video analysis, enabling interactive applications.
- Customizable Sensitivity: Allows adjustment of detection sensitivity to cater to different video qualities and scenarios.
Usage Examples
Here are some concise examples demonstrating the core functionality:
Image Analysis: python from ai_kissing import KissAnalyzer import cv2
analyzer = KissAnalyzer() image = cv2.imread("sample_image.jpg") results = analyzer.analyze_image(image)
if results: print("Kiss detected!") for box in results: x, y, w, h = box cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.imwrite("output_image.jpg", image) else: print("No kiss detected.")
Video Analysis: python from ai_kissing import KissAnalyzer import cv2
analyzer = KissAnalyzer() video_path = "sample_video.mp4" cap = cv2.VideoCapture(video_path)
while cap.isOpened(): ret, frame = cap.read() if not ret: break
results = analyzer.analyze_image(frame)
if results:
print("Kiss detected in frame!")
for box in results:
x, y, w, h = box
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Video", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release() cv2.destroyAllWindows()
Enterprise Solutions
For advanced features, including cloud-based processing, customized models, and priority support, explore our enterprise solutions at https://supermaker.ai/video/ai-kissing/. These solutions are designed to meet the demanding requirements of large-scale deployments.
We offer tailored solutions for specific use cases, such as social media monitoring, content moderation, and behavioral analysis. Our team of experts can help you integrate ai-kissing seamlessly into your existing infrastructure.
Looking for more ways to leverage AI in video analysis? Check out https://supermaker.ai/video/ai-kissing/ to discover how we can help you.
To learn more about the underlying technology, visit https://supermaker.ai/video/ai-kissing/.
License
MIT License
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其他信息
- 授权协议: MIT
- 更新时间: 2026-01-07