I primarily divided the whole task into two parts which are as follows:
Part 1:
- Developed a GStreamer pipeline using the
gst-launchcommand-line tool to read an MP4 video file. - Scaled down the input video to a 640x640 resolution.
- Output JPEG-encoded frames from the scaled-down video.
Part 2:
- Extended Part 1 by writing a C++ application to process the JPEG frames sequentially.
- Utilized OpenCV’s
haarcascade_frontalface_alt.xmlclassifier to detect and crop faces from each frame. - Organized the cropped faces into a frame-wise folder structure.
- GStreamer: For creating the multimedia pipeline and processing the video file.
- OpenCV: For image processing, including face detection and cropping using the
haarcascade_frontalface_alt.xmlclassifier. - C++: For implementing the application to read, process, and organize JPEG frames.
- Linux Ubuntu: Development environment.
- Makefile: For compiling the C++ application.
-
GStreamer Pipeline Creation:
- Constructed a pipeline to read the MP4 video, scaled it to 640x640 resolution, and encoded the frames as JPEG images.
- Executed the pipeline using the
gst-launchcommand line tool.
- Frame Processing Application:
- Developed a C++ application to read the JPEG frames sequentially.
- Utilized OpenCV’s
haarcascade_frontalface_alt.xmlclassifier to detect faces in each frame. - Cropped detected faces and saved them in a structured folder hierarchy based on the frame number.
- Created a Makefile to streamline the compilation and build process of the C++ application.
- Successfully created a GStreamer pipeline to process and encode video frames.
- Developed a robust C++ application that effectively reads JPEG frames from an MP4 video, detects and crops faces using OpenCV, and organizes them into a clear and manageable directory structure.
- Ensured a seamless development process using Linux Ubuntu and a well-defined Makefile for efficient compilation.