Working With Videos
OpenCV library can be used to perform multiple operations on videos. We can perform operations like creating a video using multiple images, extracting images, and frames, drawing shapes, putting text, etc on a video. Let us see a few of these operations.
cv2.VideoCapture(“file_name.mp4”) |
To using OpenCV, we need to create a VideoCapture object. VideoCapture has the device index or the name of a video file. The device index is just the number to specify which camera. If we pass 0 then it is for the first camera, 1 for the second camera so on. |
cv2.VideoCapture(File_path) cv2.read() cv2.imwrite(filename, img[, params]) |
Using OpenCV, techniques such as image scanning, and face recognition can be accomplished quite easily. We can extract images from videos as well using the OpenCV module along with the os module. |
Playing a Video
To capture a video using OpenCV, we need to create a VideoCapture object. VideoCapture has the device index or the name of a video file. The device index is just the number to specify which camera. If we pass 0 then it is for the first camera, 1 for the second camera so on. We capture the video frame by frame.
cv2.VideoCapture(“file_name.mp4”)
Create a Video from Multiple Images
We can create videos from multiple images using OpenCV. It also requires the PIL library which is used to open and resize images to their mean_height and mean_width because the video which will be created using the cv2 library required the input images of the same height and width.
PIL.Image.open(filename, mode)
Extracting Images from Video
Using OpenCV, techniques such as image scanning, and face recognition can be accomplished quite easily. We can extract images from videos as well using the OpenCV module along with the os module.
cv2.VideoCapture(File_path)
cv2.read()
cv2.imwrite(filename, img[, params])
Python OpenCV Cheat Sheet
The Python OpenCV Cheat Sheet is your complete guide to mastering computer vision and image processing using Python. It’s designed to be your trusty companion, helping you quickly understand the important ideas, functions, and techniques in the OpenCV library. Whether you’re an experienced developer needing a quick reminder or a newcomer excited to start, this cheat sheet has got you covered.
In this article, we’ve gathered all the vital OpenCV concepts and explained them in simple terms. We’ve also provided practical examples to make things even clearer. You’ll learn everything from how to handle images to using advanced filters, spotting objects, and even exploring facial recognition. It’s all here to help you on your journey of discovering the amazing world of computer vision.
Table of Content
- Python OpenCV Cheat Sheet 2023
- Core Operations
- Drawing Shapes and Text on Images
- Arithmetic Operations on Images
- Morphological Operations on Images
- Geometric Transformations on Image
- Image Thresholding
- Edge/Line Detection (Features)
- Image Pyramids
- Changing the Colorspace of Images
- Smoothing Images
- Working With Videos
- Camera Calibration and 3D Reconstruction