MarcoForte

""" This module shows how to retrieve the duration of an audio or video file on Windows by getting the file details
with Powershell, tested on Powershell 4. """

import subprocess
myFileLocation = r"'C:\Users\Marco\Untitled.wma'" # or myFileLocation = 'C:\\Users\\yourname\\test.mp4'

def getFileDuration(fileLocation):
    # Calls the Powershell script that gets the file duration
    return subprocess.check_output([r'C:\WINDOWS\system32\WindowsPowerShell\v1.0\powershell.exe',
    '(((New-Object -COMObject Shell.Application).Namespace((Split-Path {0})))).GetDetailsOf((((New-Object -COMObject Shell.Application).Namespace((Split-Path {0}))).ParseName((Split-Path {0} -Leaf))), 183);'.format(myFileLocation)]).decode("utf-8").splitlines()[0]

MarcoForte

#Newbie programmer
def factorial(x):
    if x == 0:
        return 1
    else:
        return x * factorial(x - 1)
print factorial(6)


#First year programmer, studied Pascal

MarcoForte

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primarily about computing factorials"
   ]
  },
  {

MarcoForte

name: "RED-Net"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 256
input_dim: 256

# conv1
layer {
  name: "conv1"

MarcoForte

name: "color"
input: "data"
input_dim: 1
input_dim: 4
input_dim: 256
input_dim: 256
layer {
  name: "data_l_ab_mask"
  type: "Input"
  top: "data_l_ab_mask"

MarcoForte

def group_weight(module, lr_encoder, lr_decoder, WD):
    group_decay = { 'encoder': [], 'decoder':[]}
    group_bias = { 'encoder': [], 'decoder':[]}
    group_GN = { 'encoder': [], 'decoder':[]}


    for name, m in module.named_modules():
        # if hasattr(m, 'requires_grad'):
        #     if m.requires_grad:
        #         continue

MarcoForte

########################

# https://gist.github.com/alper111/b9c6d80e2dba1ee0bfac15eb7dad09c8


def gauss_kernel(size=5, device=torch.device('cpu'), channels=3):
    kernel = torch.tensor([[1., 4., 6., 4., 1],
                           [4., 16., 24., 16., 4.],
                           [6., 24., 36., 24., 6.],
                           [4., 16., 24., 16., 4.],

MarcoForte

def add_zoom_crop(img, crop_xs, crop_ys, r, inset_coord):
    x1,x2 = crop_xs
    y1,y2 = crop_ys
    img = (img*255).astype(np.uint8)
    inset_coord_end = ( int(inset_coord[0]+(x2-x1)*r), int(inset_coord[1] +(y2-y1)*r))  
    patch = img[y1:y2,x1:x2]

    patch_bigger = cv2.resize(patch, ( int(patch.shape[1]*r), int(patch.shape[0]*r)))
    img[inset_coord[1]:inset_coord[1]+patch_bigger.shape[0], inset_coord[0]:inset_coord[0]+patch_bigger.shape[1]] = np.atleast_3d(patch_bigger)