qy-upup/ai-kissing

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.

dev-main 2026-01-07 08:24 UTC

Requires

• php: >=7.4

MIT

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:

1. Kiss Detection: Accurately identifies and locates kissing events within a frame or video sequence.
2. Facial Feature Analysis: Extracts key facial landmarks to analyze the dynamics of the kissing action.
3. Kiss Intensity Measurement: Quantifies the intensity of the kiss based on facial movements and proximity.
4. Multi-Person Kissing Support: Detects and analyzes kissing involving multiple individuals.
5. Kiss Duration Tracking: Measures the duration of each kissing event with millisecond precision.
6. Real-time Processing: Designed for real-time video analysis, enabling interactive applications.
7. 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