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Data Labeling in Machine Learning with Python

You're reading from  Data Labeling in Machine Learning with Python

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781804610541
Pages 398 pages
Edition 1st Edition
Languages
Author (1):
Vijaya Kumar Suda Vijaya Kumar Suda
Profile icon Vijaya Kumar Suda

Table of Contents (18) Chapters

Preface Part 1: Labeling Tabular Data
Chapter 1: Exploring Data for Machine Learning Chapter 2: Labeling Data for Classification Chapter 3: Labeling Data for Regression Part 2: Labeling Image Data
Chapter 4: Exploring Image Data Chapter 5: Labeling Image Data Using Rules Chapter 6: Labeling Image Data Using Data Augmentation Part 3: Labeling Text, Audio, and Video Data
Chapter 7: Labeling Text Data Chapter 8: Exploring Video Data Chapter 9: Labeling Video Data Chapter 10: Exploring Audio Data Chapter 11: Labeling Audio Data Chapter 12: Hands-On Exploring Data Labeling Tools Index Other Books You May Enjoy

Extracting frames from video data for analysis

Once we have loaded the video data, we can start exploring it. One common technique for the EDA of video data is to visualize some frames of the video. This can help us identify patterns and anomalies in the data. Here is example code that displays the first 10 frames of the video:

import cv2
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)
for i in range(10):
    ret, frame = cap.read()
    if not ret:
        break
    cv2.imshow("Frame", frame)
    cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()

This code reads the first 10 frames of the video from the given path and displays them using the cv2.imshow function. The cv2.waitKey(0) function waits for a key press before displaying the next frame. This allows us to inspect each frame before moving on to the next one.

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