Real time video stitching with face detection
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real-time video stitching with face detection is a fascinating process that involves combining multiple video streams into a single panoramic view while also detecting and tracking faces within the individual video streams. in this tutorial, we will use python with opencv to achieve this. opencv is a powerful computer vision library that provides various tools and algorithms for image and video processing.
here's a step-by-step guide on how to create a real-time video stitching application with face detection using opencv:
step 1: install the necessary libraries
make sure you have python installed on your system, along with the opencv library. you can install opencv using pip:
step 2: import the required libraries
step 3: initialize the video capture objects
step 4: create the face detection classifier
step 5: main loop for real-time video stitching with face detection
this code snippet captures frames from two cameras, detects faces in each frame, draws rectangles around the detected faces, stitches the frames horizontally, and displays the stitched frame in real-time. press 'q' to exit the application.
feel free to customize and enhance this code further by adding features such as face tracking, image stabilization, or blending techniques for smoother stitching.
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