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person | |
bicycle | |
car | |
motorcycle | |
airplane | |
bus | |
train | |
truck | |
boat | |
traffic light |
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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 |
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// 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({ |
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import cv2 | |
cap = cv2.VideoCapture(0) | |
_, first = cap.read() | |
while True: | |
_, second = cap.read() | |
orig = second.copy() |
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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) |
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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. |
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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') |
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#!/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 |