juliussin

from operator import itemgetter
x = [[1, 4], [3, 2]]
max(x, key=itemgetter(0))
# >> [3, 2]
max(x, key=itemgetter(1))
# >> [1, 4]

juliussin

from typing import List
import pandas as pd

def one_hot_to_categorical(df: pd.DataFrame, one_hot_features: List[str], new_feature: str, rm_ori: bool = False) -> pd.DataFrame:
    """
    One Hot Encoding to Categorical Feature
    """
    return_df = df.copy()
    one_hot_df = return_df.loc[:, one_hot_features].astype(bool)
    for row in range(len(one_hot_df)):

juliussin

#!/usr/bin/env python
from WebcamVideoStream import WebcamVideoStream
import cv2
import time

from flask import Flask, render_template, Response

app = Flask(__name__)
first_flag = True
vs = None

juliussin

import cv2
import numpy as np
import argparse


def rescale_frame(frame, scale=2):
    width = int(frame.shape[1] * scale)
    height = int(frame.shape[0] * scale)
    dim = (width, height)
    return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)

juliussin

import cv2
import numpy as np

def xyxy_to_xywh(xyxy):
    """
    Convert XYXY format (x,y top left and x,y bottom right) to XYWH format (x,y center point and width, height).
    :param xyxy: [X1, Y1, X2, Y2]
    :return: [X, Y, W, H]
    """
    if np.array(xyxy).ndim > 1 or len(xyxy) > 4:

juliussin

import cv2
import numpy as np


def point_perspective_transform(point, matrix):
    """
    Perspective transform a point given the transformation matrix
    :param point: [X, Y] point to be transformed
    :param matrix: 3x3 transformation matrix
    :return: [X, Y] transformed point

juliussin

import cv2
import numpy as np

def rescale_frame(frame, percent=75):
    width = int(frame.shape[1] * percent / 100)
    height = int(frame.shape[0] * percent / 100)
    dim = (width, height)
    return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)

juliussin

import shutil
import os
import random


def move_ext(src, dst, ext):
    """
    Move file based on its extension
    :param src: file source path
    :param dst: file destination path

juliussin

import torch

print('Torch CUDA Current Device: {}'.format(torch.cuda.current_device()))
print('Torch CUDA Device: {}'.format(torch.cuda.device(torch.cuda.current_device())))
print('Torch CUDA Device Count: {}'.format(torch.cuda.device_count()))
print('Torch CUDA Device Name: {}'.format(torch.cuda.get_device_name(torch.cuda.current_device())))
print('Torch CUDA Availability: {}'.format(torch.cuda.is_available()))