example source code of python for converting numpy ndarray to pandas dataframe

 refer to code:

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import numpy as np

import pandas as pd

# Create a numpy ndarray

array = np.random.rand(5, 3)

print('array: \n', array)

# Convert the numpy ndarray to a pandas DataFrame

df = pd.DataFrame(array, columns=['Column1', 'Column2', 'Column3'])

# Print the DataFrame

print('df: \n',df)

..

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Get YouTube url list from YouTube playlist url.

refer to code: 

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from pytube import Playlist

# Replace with your playlist URL

playlist_url = 'https://www.youtube.com/playlist?list=yourlist'

playlist = Playlist(playlist_url)

# Fetch video URLs

video_urls = playlist.video_urls

# Print video URLs

for url in video_urls:

print(url)

..

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DownLoad Youtube video - I fall in love too easily

Download app

🗓️ Version 2.0 - 2023-04-29

🙅🏽 Don't worry there is no virus!! It's very clean code.

📦 Download link -> By me a coffee : https://www.buymeacoffee.com/trurg28/e/131820

refer to code 

.

#pip install pytube

from pytube import YouTube

# Replace the URL below with the URL of the video you want to download

video_url = 'https://www.youtube.com/watch?v=YOUR_VIDEO_ID'

# Creating a YouTube object

yt = YouTube(video_url)

# Getting the highest resolution video stream

video = yt.streams.get_highest_resolution()

# Downloading the video

video.download()

print("Video downloaded successfully.")

..

you can fine source code here:

https://study.marearts.com/2023/09/download-youtube-video-url-to-local.html

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Create the output folder if it doesn't exist, python example

 refer to code:

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# Create the output folder if it doesn't exist

output_folder = os.path.dirname(ioutput_file)

os.makedirs(output_folder, exist_ok=True)

..

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Count object(file) amount in certain s3 folder. python and cli example

 refer to code.

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import boto3

def count_files_in_bucket(bucket_name, prefix=''):

s3 = boto3.client('s3')

paginator = s3.get_paginator('list_objects_v2')

file_count = 0

for page in paginator.paginate(Bucket=bucket_name, Prefix=prefix):

for obj in page['Contents']:

if not obj['Key'].endswith('/'):

file_count += 1

return file_count

bucket_name = 'your_bucket_name'

prefix = 'sub_folder_name'

file_count = count_files_in_bucket(bucket_name, prefix)

print(f'There are {file_count} files in the "{prefix}" folder of the "{bucket_name}" bucket.')

..

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The long-tail problem in an unbalanced dataset

The long-tail problem in an unbalanced dataset is a situation where a few classes have a large number of samples, while a majority of classes have few samples. This can lead to biased models that perform poorly on underrepresented classes. To address this issue, you can use various techniques, including:

1. Resampling methods:
   a. Oversampling: Increase the number of instances in the underrepresented classes by creating copies or generating synthetic samples.
• Random oversampling: Duplicate random instances from the minority classes.
• Synthetic Minority Over-sampling Technique (SMOTE): Generate synthetic samples by interpolating between instances in the minority class.
• Adaptive Synthetic (ADASYN): Similar to SMOTE, but with a focus on generating samples for difficult-to-classify instances.
• b. Undersampling: Reduce the number of instances in the overrepresented classes.
• Random undersampling: Randomly remove instances from the majority class.
• Tomek links: Identify and remove majority class instances that are close to minority class instances.
• Neighborhood Cleaning Rule (NCR): Remove majority class instances that are misclassified by their nearest neighbors.
  
2. Cost-sensitive learning: Assign higher misclassification costs to underrepresented classes during the training process, encouraging the model to be more sensitive to these classes.
   
3. Ensemble methods: Combine multiple models to improve classification performance.
   a. Balanced Random Forest: A variation of the Random Forest algorithm that balances the class distribution by either undersampling the majority class or oversampling the minority class in each tree.
   b. EasyEnsemble: Train an ensemble of classifiers, each using a random under-sampling of the majority class.
   c. RUSBoost: An adaptation of the boosting algorithm that incorporates random under-sampling of the majority class during the training process.
   
4. Transfer learning: Pre-train a model on a balanced dataset or a dataset from a related domain, then fine-tune it on the imbalanced dataset.
   
5. Evaluation metrics: Use appropriate evaluation metrics such as precision, recall, F1-score, or the area under the precision-recall curve (AUPRC) to measure the model's performance on the minority class. This helps ensure that the model's performance is not skewed by the imbalanced class distribution.

Remember to experiment with different techniques to find the best approach for your specific dataset and problem.

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Combine Two Videos Side by Side with OpenCV python

 refer to code:

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import cv2

# Open the two video files

video1 = cv2.VideoCapture('video1.mp4')

video2 = cv2.VideoCapture('video2.mp4')

# Get video properties

width1 = int(video1.get(cv2.CAP_PROP_FRAME_WIDTH))

height1 = int(video1.get(cv2.CAP_PROP_FRAME_HEIGHT))

fps1 = video1.get(cv2.CAP_PROP_FPS)

width2 = int(video2.get(cv2.CAP_PROP_FRAME_WIDTH))

height2 = int(video2.get(cv2.CAP_PROP_FRAME_HEIGHT))

fps2 = video2.get(cv2.CAP_PROP_FPS)

# Check if videos have the same FPS and height

assert fps1 == fps2, "Videos should have the same FPS"

assert height1 == height2, "Videos should have the same height"

# Create a VideoWriter object to save the combined video

fourcc = cv2.VideoWriter_fourcc(*'mp4v')

out = cv2.VideoWriter('output.mp4', fourcc, fps1, (width1 + width2, height1))

while video1.isOpened() and video2.isOpened():

ret1, frame1 = video1.read()

ret2, frame2 = video2.read()

if not ret1 or not ret2:

break

# Concatenate the frames side by side

combined_frame = cv2.hconcat([frame1, frame2])

# Write the combined frame to the output video

out.write(combined_frame)

# Display the combined frame

cv2.imshow('Combined Video', combined_frame)

# Press 'q' to stop the process and close the window

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# Release the video files and the output video

video1.release()

video2.release()

out.release()

cv2.destroyAllWindows()

..

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