lxg2015

import os
os.environ['CUDA_VISIBLE_DEVICES']='7'
import sys
import time
import copy
import torch
import numpy as np
import gradio as gr
from PIL import Image

lxg2015

import sys
import logging

# logging.basicConfig()
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
handler = logging.StreamHandler(sys.stdout)
log.addHandler(handler)
formatter = logging.Formatter(f'%(asctime)s %(levelname)s:%(name)s: %(message)s', datefmt='%m/%d/%Y %H:%M:%S')
handler.setFormatter(formatter)

lxg2015

#include <cuda_runtime_api.h>
#include <cuda.h>
#include <stdio.h>

int main()
{
  cudaDeviceProp prop;
  int gpu_index = 0;
  int i = 0;
  cudaGetDeviceProperties(&prop, gpu_index);

lxg2015

import tensorflow as tf

def map_func_single(example):
  feature_dict = {
    "url": tf.FixedLenFeature(1, tf.string),
    "pic_data": tf.FixedLenFeature(1, tf.string),
    "dt": tf.FixedLenFeature(1, tf.string),
    "pic_size": tf.FixedLenFeature(1, tf.int64),
    "origin_pic_width": tf.FixedLenFeature(1, tf.int64),
    "origin_pic_height": tf.FixedLenFeature(1, tf.int64),

lxg2015

import cv2
import math
import numpy as np


def affine(path, theta, scalex):
    img = cv2.imread(path)
    h, w, c = img.shape
    x0 = w // 2
    y0 = h // 2

lxg2015

def calc_bias(a, b, theta):
    '''
    Args:
        a: (real), long axis in ellipse
        b: (real), short axis in ellipse
        theta: (real), line slope [0, pi), anticlockwise

    Returns: 
        x,y: tangent line point in first quadrant
    '''