Con-Mi

#!/bin/bash

TEXT_RESET='\e[0m'
TEXT_YELLOW='\e[1;33m'

wget https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64
echo -e $TEXT_YELLOW
echo 'WEBGET finished..'
echo -e $TEXT_RESET

pknowledge

import cv2
import numpy as np

cap = cv2.VideoCapture('vtest.avi')
frame_width = int( cap.get(cv2.CAP_PROP_FRAME_WIDTH))

frame_height =int( cap.get( cv2.CAP_PROP_FRAME_HEIGHT))

fourcc = cv2.VideoWriter_fourcc('X','V','I','D')

shubham0204

import numpy as np
import pickle
import os

# You need to download the GloVe embeddings ( in txt files ) from here -> http://nlp.stanford.edu/data/glove.6B.zip

# The vocabulary size. It is the value of `len( tokenizer.word_index )+1`
vocab_size = int(input('Enter Vocabulary Size : '))

# The file to the GloVe file. For instance, "glove.6B/glove.6B.50d" where "50" represents the output dimension of the embedding.

Pawandeep-prog

import cv2
import numpy as np 

cap = cv2.VideoCapture(0)

_, prev = cap.read()
prev = cv2.flip(prev, 1)
_, new = cap.read()
new = cv2.flip(new, 1)

Pawandeep-prog

import cv2

cap = cv2.VideoCapture(0)

_, first = cap.read()

while True:
    _, second = cap.read()

    orig = second.copy()

theachoem

// Copyright 2021, Thea Choem, All rights reserved.

import 'package:badges/badges.dart';
import 'package:flutter/material.dart';

class CustomTabBarItem {
  final String label;
  final String? value;

  CustomTabBarItem({

Pawandeep-prog

import cv2 
import mediapipe as mp
import pyautogui
import time

def count_fingers(lst):
    cnt = 0

    thresh = (lst.landmark[0].y*100 - lst.landmark[9].y*100)/2

Pawandeep-prog

person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light