Shamim-38

from google.colab import drive
drive.mount('/content/drive', force_remount=True)


# Importing all necessary libraries 
import cv2 
import os 
  
# Read the video from specified path 
cam = cv2.VideoCapture("Video/Path/h.mp4") #Video path

Shamim-38

from sklearn.model_selection import train_test_split
import numpy as np 


a = ["1", "3", "4", "1", "3", "4"]
b = [[2, 4, 3],[0, 4, 5], [8, 9, 5], [5, 4, 5],[8, 4, 5], [2, 9, 5]]
y_train = np.array(a)
x_train = np.array(b)

Shamim-38

Pretty good solution. One can resize a video as:
ffmpeg -i AigCblw--hk_000067_000077.mp4 -vf "scale='if(gt(ih,iw),256,trunc(oh*a/2)*2):if(gt(ih,iw),trunc(ow/a/2)*2,256)'" output.mp4

For multiple videos, one can perform (please mkdir a videos_resized folder in prepath ../):
for f in *.mp4 ; do ffmpeg -i "$f" -vf "scale='if(gt(ih,iw),256,trunc(oh*a/2)*2):if(gt(ih,iw),trunc(ow/a/2)*2,256)'" "../videos_resized/$f" ; done

Shamim-38

Try to find max in list:

l = [1,2,3,4,4]
num = max(l, key=lambda x:l.count(x))  # num will be 4

the you can get the count of num.

l.count(num)  # this wil returns 2

in the other hand as @buddemat said(Here), it is better to:

Shamim-38

To reduce the size of your mp4 you have two good choices with FFmpeg:

    Experiment with Constant Rate Factor (CRF) settings to find a match between video output size and video quality
    Experiment with the set bitrate of your choice and use a 2 pass encode.

Details of both below:

1. Resize using FFmpeg's Constant Rate Factor (CRF) option:

In FFmpeg the CRF settings vary from 0-51 with 0 being lossless and 51 being shockingly poor quality. The default is 23 and you need to find the 'sweet spot' for your particular video of file size and video quality with some experimentation.

Shamim-38

Shamim-38

import numpy as np
import pandas as pd
eps = np.finfo(float).eps
from numpy import log2 as log

dataset = {'Taste':['Salty','Spicy','Spicy','Spicy','Spicy','Sweet','Salty','Sweet','Spicy','Salty'],
       'Temperature':['Hot','Hot','Hot','Cold','Hot','Cold','Cold','Hot','Cold','Hot'],
       'Texture':['Soft','Soft','Hard','Hard','Hard','Soft','Soft','Soft','Soft','Hard'],
       'Eat':['No','No','Yes','No','Yes','Yes','No','Yes','Yes','Yes']}

Shamim-38

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
from tensorflow.keras.datasets import fashion_mnist

#(x_train, _), (x_test, _) = fashion_mnist.load_data()

import os

Shamim-38

#Writer: Md Shamimul Islam

import cv2
import numpy as np
import os
from matplotlib import pyplot as plt
import time
import mediapipe as mp

import glob

Shamim-38

Create a new repository on GitHub. You can also add a gitignore file, a readme and a licence if you want
 Open Git Bash
Change the current working directory to your local project.
Initialize the local directory as a Git repository.
git init
Add the files in your new local repository. This stages them for the first commit.
git add .
 Commit the files that you’ve staged in your local repository.
git commit -m "initial commit"
 Copy the https url of your newly created repo