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def split_to_batches(img, trained_size=512):
    img = skimage.img_as_float32(img)
    direction = 'landscape'
    if img.shape[0] > img.shape[1]: direction = 'portrait'
    
    # resize into 512x[width|height]
    width, height = img.shape[1], img.shape[0]    
    if direction == 'landscape': 
        new_height = trained_size
        new_width = int(new_height * (width / height))  

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# Copy from https://github.com/bbonik/image_enhancement/blob/f0c286a166443202d592b920b63ee6901a0ee727/source/image_enhancement.py#L1388

import numpy as np
from skimage import img_as_float

def change_color_saturation(
        image_color, 
        image_ph_mask=None, 
        sat_degree=1.5,):
    LOCAL_BOOST = 0.2

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//
// Step 3: In the file 'AgoraSdk.cpp'
//
void AgoraSdk::videoFrameReceivedImpl(
    agora::linuxsdk::uid_t uid,
    const agora::linuxsdk::VideoFrame *pframe) const {

  char uidbuf[65];
  snprintf(uidbuf, sizeof(uidbuf), "%u", uid);

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If you want to connect to a local workstation behind a firewall you can do that by bouncing traffic off of an AWS instance.

An example would be accessing a Jupyter Lab from the outside world to a machine with no static IP or behind a firewall.

The steps are:

1. Create a paid AWS Ubuntu instance and save the '.pem' file.
2. Make sure the AWS instance's Security Rules allows traffic in.
3. Test that you can connect to this instance from the outside world (e.g. python3 -m http.server or jupyter lab --ip=*) then visit the instance using its public IP.
4. (optionally) Enable password login in /etc/ssh/sshd_config by setting PasswordAuthentication yes and sudo passwd $USER.
5. Enable GatewayPorts yes and restart sshd. E.g. sudo nano /etc/ssh/sshd_config then sudo systemctl restart ssh.service.

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/*
  The most basic example to capture matching RGB and Depth images from an Azure Kinect DK device.
  
  CImg is used to visualize the matching RGB and Depth.
*/

#pragma comment(lib, "gdi32.lib")
#pragma comment(lib, "user32.lib")
#pragma comment(lib, "shell32.lib")

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// Note this is just a workaround until the offical support of web

// We would need to add these as well
//// ignore_for_file: avoid_web_libraries_in_flutter
//import 'dart:html';
//import 'package:mime/mime.dart';
//import 'package:firebase/firebase.dart' as fb;

// Some upload button is pressed
onPressed: () async {          

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import tensorflow as tf
import keras.backend as K
from keras.layers import Lambda

#
# Keras + TensorFlow
# Resize 'tensorA' to be of the same size as 'tensorB' using 'tf.image.resize_bilinear'
# Very useful for skip-connections and 'Concatenate' layers that might complain about being off by one column
# Only works when dimensions are provided since we use ' K.int_shape' that returns the static shape
#

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from OpenGL import GL, GLU
from OpenGL.arrays import vbo

# Prepare point cloud once
def makeCloud(self):    
    width, height = self.rgb.shape[1], self.rgb.shape[0] 
    
    # ...
    # Compute 3D positions from depth
    # ...

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from joblib import Parallel, delayed

import numpy as np
from skimage.transform import resize

files = list(data.keys())[1:]

def coords(filename):
    filename = filename.replace('11_1024/', '').replace('.jpg', '').replace('11_', '')
    filename = filename.replace('[','').replace(']','').split('_')

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# Original Matlab code https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
#
#
# Python port of depth filling code from NYU toolbox
# Speed needs to be improved
#
# Uses 'pypardiso' solver 
#
import scipy
import skimage