Here I've tried to make my case on why should you start your Software Engineering Journey with Java.
You can develop anything for Android the most widely used Operating System.
Start with: | |
Create a file `index.html` and paste the following code | |
``` | |
<!doctype html> | |
<html> | |
<head> | |
<title>Chat Room</title> | |
</head> |
Install Screen
$ sudo apt install screen
Enter a new Screen Session
$ screen
Detach from current screen session
# this is a comment | |
s = 'Quick brown fox jumps over lazy white dog' | |
scount = 0 | |
for c in s : | |
if c == 'a' or c == 'o' or c == 'u' or c == 'i' or c == 'e': | |
# "count" in the following line is being used for the first time. It is one level deep i.e. inside the loop. So its value will not be available outside the loop. | |
# count += 1 |
Radek has provided good example code that we would use.
<head>
<!-- VideoJs CSS -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.2.4/alt/video-js-cdn.min.css" />
<!-- Necessary libs. Video.js 7, Contrib Quality Levels, Jquery -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.2.4/video.min.js"></script>
In the Tests tab of your request, type this code
let cookie = postman.getResponseHeader("Set-Cookie");
postman.setEnvironmentVariable("cookie", cookie);
This will set an environment vairable. In all the requests that need cookie, add to the Request Header
Key: Cookie
async function takePhoto(quality) { | |
// create html elements | |
const div = document.createElement('div'); | |
const video = document.createElement('video'); | |
video.style.display = 'block'; | |
// request the stream. This will ask for Permission to access | |
// a connected Camera/Webcam | |
const stream = await navigator.mediaDevices.getUserMedia({video: true}); |
import os | |
import sys | |
import random | |
import math | |
import numpy as np | |
import skimage.io | |
import matplotlib | |
import matplotlib.pyplot as plt | |
# Root directory of the project |
# Create model object in inference mode. | |
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config) | |
# Load weights trained on MS-COCO | |
model.load_weights(COCO_MODEL_PATH, by_name=True) |
# Load a random image from the images folder | |
file_names = next(os.walk(IMAGE_DIR))[2] | |
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names))) | |
# Run detection | |
results = model.detect([image], verbose=1) | |
# Visualize results | |
r = results[0] | |
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], |