Pawandeep-prog

package com.programminghut.edge_detector;

import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import androidx.core.content.ContextCompat;

import android.Manifest;
import android.content.pm.PackageManager;
import android.os.Bundle;
import android.util.Log;

Pawandeep-prog

person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light

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

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

df = np.load('archive/olivetti_faces.npy')

df = df.reshape(400, 64*64)

y = np.random.randn(40000).reshape(400,100)

Pawandeep-prog

import cv2

cap = cv2.VideoCapture(0)

_, first = cap.read()

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

    orig = second.copy()

Pawandeep-prog

import numpy as np
import random

X = []
logx=[]
logy=[]

for i in range(10000):
    X.append([random.randint(1,1000), random.randint(1,1000)])
    logx.append([np.log(X[i][0]), np.log(X[i][1])])

Pawandeep-prog

### video tutorial https://youtu.be/yt20FGrZmbM

from PIL import ImageGrab
import cv2
import numpy as np
from pynput.keyboard import Controller

flaga = True
flagb = True
flagc = True

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 random
import numpy as np

X =[]
y =[]
for i in range(1000):
    X.append([random.randint(1, 1000), random.randint(1, 1000)])
    y.append(sum(X[i]))

X = np.array(X)

Pawandeep-prog

import tensorflow as tf
import cv2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model

dataset = tf.keras.datasets.mnist

#### train - test - split ####
(X_train, y_train), (X_test, y_test) = dataset.load_data()