Added RSS feeds and 3rd article [WIP]
This commit is contained in:
parent
d89725f8ab
commit
60ab950093
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@ -5,7 +5,7 @@ import re
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from glob import glob
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from typing import Optional
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from flask import Flask, abort, send_from_directory, render_template
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from flask import Flask, Response, abort, send_from_directory, render_template
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from markdown import markdown
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basedir = os.path.abspath(os.path.join(os.path.realpath(__file__), '..', '..'))
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@ -63,13 +63,14 @@ def get_page(page: str, title: Optional[str] = None):
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description=metadata.get('description'),
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published=(metadata['published'].strftime('%b %d, %Y')
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if metadata.get('published') else None),
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content=markdown(f.read(),extensions=['fenced_code', 'codehilite']))
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content=markdown(f.read(), extensions=['fenced_code', 'codehilite']))
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def get_pages() -> list:
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def get_pages(with_content: bool = False) -> list:
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return sorted([
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{
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'path': path,
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'content': get_page(path) if with_content else '',
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**get_page_metadata(os.path.basename(path)),
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}
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for path in glob(os.path.join(pages_dir, '*.md'))
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@ -99,3 +100,50 @@ def css_route(style: str):
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@app.route('/article/<article>', methods=['GET'])
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def article_route(article: str):
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return get_page(article)
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@app.route('/rss', methods=['GET'])
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def rss_route():
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pages = get_pages(with_content=True)
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return Response('''<?xml version="1.0" encoding="UTF-8" ?>
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<rss version="2.0">
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<channel>
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<title>Platypush blog feeds</title>
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<link>http://blog.platypush.tech</link>
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<description>Insights and inspirational projects using Platypush as an automation platform</description>
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<category>Programming, automation, Python, machine learning, IoT, smart home</category>
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<image>
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<url>https://git.platypush.tech/uploads/-/system/appearance/header_logo/1/icon-256.png</url>
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<title>Platypush</title>
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<link>https://git.platypush.tech</link>
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</image>
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<pubDate>{last_pub_date}</pubDate>
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<language>en-us</language>
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{items}
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</channel>
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</rss>'''.format(
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last_pub_date=pages[0]['published'].strftime('%a, %d %b %Y %H:%M:%S GMT'),
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items='\n\n'.join([
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'''
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<item>
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<title>{title}</title>
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<link>https://blog.platypush.tech{link}</link>
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<pubDate>{published}</pubDate>
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<description><![CDATA[{content}]]></description>
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<image>
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<url>https://blog.platypush.tech/img{image}</url>
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</image>
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</item>
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'''.format(
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title=page.get('title', '[No Title]'),
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link=page.get('uri', ''),
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published=page['published'].strftime('%a, %d %b %Y %H:%M:%S GMT') if 'published' in page else '',
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# description=page.get('description', ''),
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content=page.get('content', ''),
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image=page.get('image', ''),
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)
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for page in pages
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]),
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), mimetype='application/rss+xml')
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@ -1,9 +1,15 @@
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main .content {
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display: flex;
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flex-direction: column;
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text-align: justify;
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line-height: 1.5em;
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letter-spacing: .04em;
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}
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main .content p,
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main .content ul {
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font-family: Avenir, Palatino, charter, Georgia, Cambria, "Times New Roman", Times, serif;
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text-align: justify;
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overflow-wrap: break-word;
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word-break: break-word;
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}
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main .content code, .codehilite {
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@ -1,74 +1,97 @@
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html {
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font-size: calc(1em + 1vw);
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}
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@media screen and (min-width: 1024px) {
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html {
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font-size: 20px;
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}
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height: 100%;
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font-size: 20px;
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font-family: BlinkMacSystemFont, -apple-system, Segoe UI, Roboto, Oxygen, Ubuntu, Cantarell, Fira Sans, Droid Sans, Helvetica Neue, Helvetica, Arial, sans-serif;
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font-weight: 400;
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text-rendering: optimizeLegibility;
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}
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body {
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margin: 0;
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width: 100%;
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height: 100%;
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margin: 0;
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width: 100%;
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height: 100%;
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}
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a, a:visited {
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color: #555;
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border-bottom: 1px dashed #999;
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text-decoration: none;
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color: #555;
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border-bottom: 1px dashed #999;
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text-decoration: none;
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}
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header {
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display: flex;
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align-items: center;
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height: 3em;
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padding: 0 .5em;
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box-shadow: 1px 3px 3px 0 #bbb;
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display: flex;
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align-items: center;
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height: 8%;
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padding: 0 .5em;
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box-shadow: 1px 3px 3px 0 #bbb;
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}
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@media screen and (max-width: 767px) {
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header {
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height: 4em;
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}
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header a {
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display: flex;
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align-items: center;
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border-bottom: none;
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}
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header > a {
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display: flex;
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align-items: center;
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border-bottom: none;
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header .left,
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header .left a,
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header .right {
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display: inline-flex;
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align-items: center;
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width: 50%;
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}
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header .right {
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display: inline-flex;
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justify-content: right;
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text-align: right;
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opacity: .8;
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}
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header .icon {
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background: url(/img/icon.png);
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background-size: 40px;
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width: 40px;
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height: 40px;
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display: inline-flex;
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margin-right: 1em;
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background-size: 40px !important;
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width: 40px;
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height: 40px;
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display: inline-flex;
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}
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header .left .icon {
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background: url(/img/icon.png);
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margin-right: 1em;
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}
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header .right .icon {
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background: url(/img/rss.png);
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}
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header .title {
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display: inline-flex;
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display: inline-flex;
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}
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main {
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height: calc(100% - 3em);
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overflow: auto;
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display: flex;
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flex-direction: column;
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align-items: center;
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font-family: Avenir, Palatino, Georgia, Verdana, Helvetica, Arial, sans-serif;
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padding: 0 2em;
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height: 92%;
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overflow: auto;
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display: flex;
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flex-direction: column;
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align-items: center;
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padding: 0 2em;
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}
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h1 {
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font-size: 2em;
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line-height: 1.2em;
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font-size: 2em;
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line-height: 1.2em;
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}
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h2 {
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font-size: 1.5em;
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line-height: 1.1em;
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font-size: 1.5em;
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line-height: 1.1em;
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}
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@media screen and (min-width: 768px) {
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header {
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height: 6%;
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}
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main {
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height: 94%;
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}
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}
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@ -4,11 +4,16 @@ main {
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.articles {
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width: 100%;
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height: 100%;
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display: flex;
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flex-wrap: wrap;
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overflow: auto;
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}
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.article {
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display: block;
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width: 100%;
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max-height: 80%;
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box-shadow: 0 1px 3px 1px #ddd;
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overflow: hidden;
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text-overflow: ellipsis;
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@ -17,33 +22,39 @@ main {
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.article:hover {
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box-shadow: 0 1px 4px 2px #bcbcbc;
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}
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}
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@media screen and (max-width: 767px) {
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@media screen and (min-width: 767px) {
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.article {
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width: 100%;
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max-height: 65%;
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}
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}
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@media screen and (min-width: 768px) and (max-width: 990px) {
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@media screen and (min-width: 640px) and (max-width: 767px) {
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.article {
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padding: 0 calc(1em + 7vw);
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}
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}
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@media screen and (min-width: 768px) and (max-width: 979px) {
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.article {
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width: 50%;
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}
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}
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@media screen and (min-width: 990px) and (max-width: 1023px) {
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@media screen and (min-width: 980px) and (max-width: 1279px) {
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.article {
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width: 33%;
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}
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}
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@media screen and (min-width: 1024px) and (max-width: 1279px) {
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@media screen and (min-width: 1280px) and (max-width: 1599px) {
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.article {
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width: 25%;
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}
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}
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@media screen and (min-width: 1280px) {
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@media screen and (min-width: 1600px) {
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.article {
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width: 20%;
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}
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Binary file not shown.
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After Width: | Height: | Size: 110 KiB |
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After Width: | Height: | Size: 1.7 KiB |
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@ -0,0 +1,168 @@
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[//]: # (title: Detect people with a RaspberryPi, a thermal camera, Platypush and Tensorflow)
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[//]: # (description: Use cheap components and open-source software to build a robust presence detector.)
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[//]: # (image: /img/people-detect-1.png)
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[//]: # (published: 2019-09-27)
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Triggering events based on the presence of people has been the dream of many geeks and DIY automation junkies for a
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while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting
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application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end
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solutions like the [Philips Hue sensors](https://www2.meethue.com/en-us/p/hue-motion-sensor/046677473389), detect
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motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a
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sloth. The ability to turn off music and/or tv when you exit the room and head to your bedroom, without the hassle of
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switching all the buttons off, is also an interesting corollary. Detecting the presence of people in your room while
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you’re not at home is another interesting application.
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Thermal cameras coupled with deep neural networks are a much more robust strategy to actually detect the presence of
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people. Unlike motion sensors, they will detect the presence of people even when they aren’t moving. And unlike optical
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cameras, they detect bodies by measuring the heat that they emit in the form of infrared radiation, and are therefore
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much more robust — their sensitivity doesn’t depend on lighting conditions, on the position of the target, or the
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colour. Before exploring the thermal camera solution, I tried for a while to build a model that instead relied on
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optical images from a traditional webcam. The differences are staggering: I trained the optical model on more than ten
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thousands 640x480 images taken all through a week in different lighting conditions, while I trained the thermal camera
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model on a dataset of 900 24x32 images taken during a single day. Even with more complex network architectures, the
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optical model wouldn’t score above a 91% accuracy in detecting the presence of people, while the thermal model would
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achieve around 99% accuracy within a single training phase of a simpler neural network. Despite the high potential,
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there’s not much out there in the market — there’s been some research work on the topic (if you google “people detection
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thermal camera” you’ll mostly find research papers) and a few high-end and expensive products for professional
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surveillance. In lack of ready-to-go solutions for my house, I decided to take on my duty and build my own solution —
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making sure that it can easily be replicated by anyone.
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## Prepare the hardware
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For this example we'll use the following hardware:
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- A RaspberryPi (cost: around $35). In theory any model should work, but it’s probably not a good idea to use a
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single-core RaspberryPi Zero for machine learning tasks — the task itself is not very expensive (we’ll only use the
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Raspberry for doing predictions on a trained model, not to train the model), but it may still suffer some latency on a
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Zero. Plus, it may be really painful to install some of the required libraries (like Tensorflow or OpenCV) on
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the `arm6` architecture used by the RaspberryPi Zero. Any better performing model (from RPi3 onwards) should
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definitely do the job.
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- A thermal camera. For this project, I’ve used the
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[MLX90640](https://shop.pimoroni.com/products/mlx90640-thermal-camera-breakout) Pimoroni breakout camera (cost: $55),
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as it’s relatively cheap, easy to install, and it provides good results. This camera comes in standard (55°) and
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wide-angle (110°) versions. I’ve used the wide-angle model as the camera monitors a large living room, but take into
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account that both have the same resolution (32x24 pixels), so the wider angle comes with the cost of a lower spatial
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resolution. If you want to use a different thermal camera there’s not much you’ll need to change, as long as it comes
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with a software interface for RaspberryPi and
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it’s [compatible with Platypush](https://platypush.readthedocs.io/en/latest/platypush/plugins/camera.ir.mlx90640.html).
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Setting up the MLX90640 on your RaspberryPi if you have a Breakout Garden it’s easy as a pie. Fit the Breakout Garden on
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top of your RaspberryPi. Fit the camera breakout into an I2C slot. Boot the RaspberryPi. Done. Otherwise, you can also
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connect the device directly to the [RaspberryPi I2C interface](https://radiostud.io/howto-i2c-communication-rpi/),
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either using the right hardware PINs or the software emulation layer.
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## Prepare the software
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I tested my code on Raspbian, but with a few minor modifications it should be easily adaptable to any distribution
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installed on the RaspberryPi.
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The software support for the thermal camera requires a bit of work. The MLX90640 doesn’t come (yet) with a Python
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ready-to-use interface, but a [C++ open-source driver is provided](https://github.com/pimoroni/mlx90640-library) -
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and that's the driver that is wrapped by the Platypush integration. Instructions to install it:
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```shell
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# Install the dependencies
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[sudo] apt-get install libi2c-dev
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# Enable the I2C interface
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echo dtparam=i2c_arm=on | sudo tee -a /boot/config.txt
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# It's advised to configure the SPI bus baud rate to
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# 400kHz to support the higher throughput of the sensor
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echo dtparam=i2c1_baudrate=400000 | sudo tee -a /boot/config.txt
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# A reboot is required here if you didn't have the
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# options above enabled in your /boot/config.txt
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[sudo] reboot
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# Clone the driver's codebase
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git clone https://github.com/pimoroni/mlx90640-library
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cd mlx90640-library
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# Compile the rawrgb example
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make clean
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make bcm2835
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make I2C_MODE=LINUX examples/rawrgb
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```
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If it all went well you should see an executable named `rawrgb` under the `examples` directory. If you run it you should
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see a bunch of binary data — that’s the raw binary representation of the frames captured by the camera. Remember where
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it is located or move it to a custom bin folder, as it’s the executable that platypush will use to interact with the
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camera module.
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This post assumes that you have already installed and configured Platypush on your system. If not, head to my post on
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[getting started with Platypush](https://blog.platypush.tech/article/Ultimate-self-hosted-automation-with-Platypush),
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the [readthedocs page](https://platypush.readthedocs.io/en/latest/), the
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[Gitlab page](https://git.platypush.tech/platypush/platypush) or
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[the wiki](https://git.platypush.tech/platypush/platypush/-/wikis/home).
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Install also the Python dependencies for the HTTP server, the MLX90640 plugin and Tensorflow:
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```shell
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[sudo] pip install 'platypush[http,tensorflow,mlx90640]'
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```
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Heading to your computer (we'll be using it for building the model that will be used on the RaspberryPi), install
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OpenCV, Tensorflow and Jupyter and my utilities for handling images:
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```shell
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# For image manipulation
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[sudo] pip install opencv
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# Install Jupyter notebook to run the training code
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[sudo] pip install jupyterlab
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# Then follow the instructions at https://jupyter.org/install
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# Tensorflow framework for machine learning and utilities
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[sudo] pip install tensorflow numpy matplotlib
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# Clone my repository with the image and training utilities
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# and the Jupyter notebooks that we'll use for training.
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git clone https://github.com/BlackLight/imgdetect-utils
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```
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## Capturing phase
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Now that you’ve got all the hardware and software in place, it’s time to start capturing frames with your camera and use
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them to train your model. First, configure
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the [MLX90640 plugin](https://platypush.readthedocs.io/en/latest/platypush/plugins/camera.ir.mlx90640.html) in your
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Platypush configuration file (by default, `~/.config/platypush/config.yaml`):
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```yaml
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# Enable the webserver
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backend.http:
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enabled: True
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camera.ir.mlx90640:
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fps: 16 # Frames per second
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rotate: 270 # Can be 0, 90, 180, 270
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rawrgb_path: /path/to/your/rawrgb
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```
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Restart the service, and if you haven't already create a user from the web interface at `http://your-rpi:8008`. You
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should now be able to take pictures through the API:
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||||
|
||||
```yaml
|
||||
curl -XPOST -H 'Content-Type: application/json' -d '
|
||||
{
|
||||
"type":"request",
|
||||
"action":"camera.ir.mlx90640.capture",
|
||||
"args": {
|
||||
"output_file":"~/snap.png",
|
||||
"scale_factor":20
|
||||
}
|
||||
}' -a 'username:password' http://localhost:8008/execute
|
||||
```
|
||||
|
||||
If everything went well, the thermal picture should be stored under `~/snap.png`. In my case it looks like this while
|
||||
I’m in standing front of the sensor:
|
||||
|
||||
|
||||
|
||||
Notice the glow at the bottom-right corner — that’s actually the heat from my RaspberryPi 4 CPU. It’s there in all the
|
||||
images I take, and you may probably see similar results if you mounted your camera on top of the Raspberry itself, but
|
||||
it shouldn’t be an issue for your model training purposes.
|
||||
|
||||
If you open the web panel (`http://your-host:8008`) you’ll also notice a new tab, represented by the sun icon, that you
|
||||
can use to monitor your camera from a web interface.
|
||||
|
|
@ -1,41 +1,29 @@
|
|||
<html lang="en">
|
||||
<head>
|
||||
<link rel="stylesheet" type="text/css" href="/css/common.css">
|
||||
<link rel="stylesheet" type="text/css" href="/css/blog.css">
|
||||
<link rel="stylesheet" type="text/css" href="/css/code.css">
|
||||
<title>{{ title }}</title>
|
||||
</head>
|
||||
{% with title=title or 'Platypush blog', styles=['/css/blog.css', '/css/code.css'] %}
|
||||
{% include 'common-head.html' %}
|
||||
{% endwith %}
|
||||
|
||||
<body>
|
||||
<header>
|
||||
<a href="/">
|
||||
<div class="icon"></div>
|
||||
<div class="title">PlatyBlog</div>
|
||||
</a>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<div class="container">
|
||||
<div class="title">
|
||||
<h1>{{ title }}</h1>
|
||||
</div>
|
||||
|
||||
{% if description %}
|
||||
<div class="description">
|
||||
<h3>{{ description }}</h3>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if published %}
|
||||
<div class="published-date">
|
||||
Published on {{ published }}
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
<div class="content">
|
||||
{{ content | safe }}
|
||||
</div>
|
||||
<main>
|
||||
<div class="container">
|
||||
<div class="title">
|
||||
<h1>{{ title }}</h1>
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
{% if description %}
|
||||
<div class="description">
|
||||
<h3>{{ description }}</h3>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if published %}
|
||||
<div class="published-date">
|
||||
Published on {{ published }}
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
<div class="content">
|
||||
{{ content | safe }}
|
||||
</div>
|
||||
</div>
|
||||
</main>
|
||||
|
||||
{% include 'common-tail.html' %}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,30 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
|
||||
<link rel="alternate" type="application/rss+xml" title="RSS Feed for the Platypush blog" href="/rss" />
|
||||
<link rel="stylesheet" type="text/css" href="/css/common.css">
|
||||
|
||||
{% if styles %}
|
||||
{% for style in styles %}
|
||||
<link rel="stylesheet" type="text/css" href="{{ style }}">
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
<title>{{ title }}</title>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<div class="left">
|
||||
<a href="/">
|
||||
<div class="icon"></div>
|
||||
<div class="title">PlatyBlog</div>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<div class="right">
|
||||
<a href="/rss" target="_blank">
|
||||
<div class="icon"></div>
|
||||
</a>
|
||||
</div>
|
||||
</header>
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
</body>
|
||||
</html>
|
||||
|
|
@ -1,48 +1,37 @@
|
|||
<html lang="en">
|
||||
<head>
|
||||
<link rel="stylesheet" type="text/css" href="/css/common.css">
|
||||
<link rel="stylesheet" type="text/css" href="/css/home.css">
|
||||
<title>Platypush blog</title>
|
||||
</head>
|
||||
{% with title=title or 'Platypush blog', styles=['/css/home.css'] %}
|
||||
{% include 'common-head.html' %}
|
||||
{% endwith %}
|
||||
|
||||
<body>
|
||||
<header>
|
||||
<a href="/">
|
||||
<div class="icon"></div>
|
||||
<div class="title">PlatyBlog</div>
|
||||
</a>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<div class="articles">
|
||||
{% for page in pages %}
|
||||
<a class="article" href="{{ page['uri'] }}">
|
||||
<div class="container">
|
||||
{% if page['image'] %}
|
||||
<div class="image">
|
||||
<img src="{{ page['image'] }}" alt="">
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
<div class="title">
|
||||
{{ page['title'] }}
|
||||
</div>
|
||||
|
||||
{% if page['published'] %}
|
||||
<div class="published-date">
|
||||
{{ page['published'].strftime('%b %d, %Y') }}
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if page['description'] %}
|
||||
<div class="description">
|
||||
{{ page['description'] }}
|
||||
</div>
|
||||
{% endif %}
|
||||
<main>
|
||||
<div class="articles">
|
||||
{% for page in pages %}
|
||||
<a class="article" href="{{ page['uri'] }}">
|
||||
<div class="container">
|
||||
{% if page['image'] %}
|
||||
<div class="image">
|
||||
<img src="{{ page['image'] }}" alt="">
|
||||
</div>
|
||||
</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
</html>
|
||||
{% endif %}
|
||||
|
||||
<div class="title">
|
||||
{{ page['title'] }}
|
||||
</div>
|
||||
|
||||
{% if page['published'] %}
|
||||
<div class="published-date">
|
||||
{{ page['published'].strftime('%b %d, %Y') }}
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if page['description'] %}
|
||||
<div class="description">
|
||||
{{ page['description'] }}
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</main>
|
||||
|
||||
{% include 'common-tail.html' %}
|
||||
|
|
|
|||
Loading…
Reference in New Issue