Added RSS feeds and 3rd article [WIP]

app/__init__.py

@@ -5,7 +5,7 @@ import re
 from glob import glob
 from typing import Optional
 
-from flask import Flask, abort, send_from_directory, render_template
+from flask import Flask, Response, abort, send_from_directory, render_template
 from markdown import markdown
 
 basedir = os.path.abspath(os.path.join(os.path.realpath(__file__), '..', '..'))
@@ -66,10 +66,11 @@ def get_page(page: str, title: Optional[str] = None):
                                content=markdown(f.read(), extensions=['fenced_code', 'codehilite']))
 
 
-def get_pages() -> list:
+def get_pages(with_content: bool = False) -> list:
     return sorted([
         {
             'path': path,
+            'content': get_page(path) if with_content else '',
             **get_page_metadata(os.path.basename(path)),
         }
         for path in glob(os.path.join(pages_dir, '*.md'))
@@ -99,3 +100,50 @@ def css_route(style: str):
 @app.route('/article/<article>', methods=['GET'])
 def article_route(article: str):
     return get_page(article)
+
+
+@app.route('/rss', methods=['GET'])
+def rss_route():
+    pages = get_pages(with_content=True)
+
+    return Response('''<?xml version="1.0" encoding="UTF-8" ?>
+<rss version="2.0">
+    <channel>
+        <title>Platypush blog feeds</title> 
+        <link>http://blog.platypush.tech</link>
+        <description>Insights and inspirational projects using Platypush as an automation platform</description> 
+        <category>Programming, automation, Python, machine learning, IoT, smart home</category>
+        <image>
+            <url>https://git.platypush.tech/uploads/-/system/appearance/header_logo/1/icon-256.png</url>
+            <title>Platypush</title>
+            <link>https://git.platypush.tech</link>
+        </image> 
+        <pubDate>{last_pub_date}</pubDate> 
+        <language>en-us</language>
+
+        {items}
+    </channel>
+</rss>'''.format(
+        last_pub_date=pages[0]['published'].strftime('%a, %d %b %Y %H:%M:%S GMT'),
+        items='\n\n'.join([
+            '''
+            <item>
+                <title>{title}</title>
+                <link>https://blog.platypush.tech{link}</link>
+                <pubDate>{published}</pubDate>
+                <description><![CDATA[{content}]]></description>
+                <image>
+                    <url>https://blog.platypush.tech/img{image}</url>
+                </image>
+            </item>
+            '''.format(
+                title=page.get('title', '[No Title]'),
+                link=page.get('uri', ''),
+                published=page['published'].strftime('%a, %d %b %Y %H:%M:%S GMT') if 'published' in page else '',
+                # description=page.get('description', ''),
+                content=page.get('content', ''),
+                image=page.get('image', ''),
+            )
+            for page in pages
+        ]),
+    ), mimetype='application/rss+xml')

static/css/blog.css

@@ -1,9 +1,15 @@
 main .content {
   display: flex;
   flex-direction: column;
-  text-align: justify;
   line-height: 1.5em;
-  letter-spacing: .04em;
+}
+
+main .content p,
+main .content ul {
+  font-family: Avenir, Palatino, charter, Georgia, Cambria, "Times New Roman", Times, serif;
+  text-align: justify;
+  overflow-wrap: break-word;
+  word-break: break-word;
 }
 
 main .content code, .codehilite {

static/css/common.css

@@ -1,11 +1,9 @@
 html {
-    font-size: calc(1em + 1vw);
+  height: 100%;
-}
-
-@media screen and (min-width: 1024px) {
-    html {
   font-size: 20px;
-    }
+  font-family: BlinkMacSystemFont, -apple-system, Segoe UI, Roboto, Oxygen, Ubuntu, Cantarell, Fira Sans, Droid Sans, Helvetica Neue, Helvetica, Arial, sans-serif;
+  font-weight: 400;
+  text-rendering: optimizeLegibility;
 }
 
 body {
@@ -23,43 +21,58 @@ a, a:visited {
 header {
   display: flex;
   align-items: center;
-    height: 3em;
+  height: 8%;
   padding: 0 .5em;
   box-shadow: 1px 3px 3px 0 #bbb;
 }
 
-@media screen and (max-width: 767px) {
+header a {
-    header {
-        height: 4em;
-    }
-}
-
-header > a {
   display: flex;
   align-items: center;
   border-bottom: none;
 }
 
+header .left,
+header .left a,
+header .right {
+  display: inline-flex;
+  align-items: center;
+  width: 50%;
+}
+
+header .right {
+  display: inline-flex;
+  justify-content: right;
+  text-align: right;
+  opacity: .8;
+}
+
 header .icon {
-    background: url(/img/icon.png);
+  background-size: 40px !important;
-    background-size: 40px;
   width: 40px;
   height: 40px;
   display: inline-flex;
+}
+
+header .left .icon {
+  background: url(/img/icon.png);
   margin-right: 1em;
 }
 
+header .right .icon {
+  background: url(/img/rss.png);
+}
+
 header .title {
   display: inline-flex;
 }
 
 main {
-    height: calc(100% - 3em);
+  height: 92%;
   overflow: auto;
   display: flex;
   flex-direction: column;
   align-items: center;
-    font-family: Avenir, Palatino, Georgia, Verdana, Helvetica, Arial, sans-serif;
   padding: 0 2em;
 }
 
@@ -72,3 +85,13 @@ h2 {
   font-size: 1.5em;
   line-height: 1.1em;
 }
+
+@media screen and (min-width: 768px) {
+  header {
+    height: 6%;
+  }
+
+  main {
+    height: 94%;
+  }
+}

static/css/home.css

@@ -4,11 +4,16 @@ main {
 
 .articles {
   width: 100%;
+  height: 100%;
   display: flex;
+  flex-wrap: wrap;
+  overflow: auto;
 }
 
 .article {
   display: block;
+  width: 100%;
+  max-height: 80%;
   box-shadow: 0 1px 3px 1px #ddd;
   overflow: hidden;
   text-overflow: ellipsis;
@@ -19,31 +24,37 @@ main {
    box-shadow: 0 1px 4px 2px #bcbcbc;
 }
 
-@media screen and (max-width: 767px) {
+@media screen and (min-width: 767px) {
   .article {
-    width: 100%;
+    max-height: 65%;
   }
 }
 
-@media screen and (min-width: 768px) and (max-width: 990px) {
+@media screen and (min-width: 640px) and (max-width: 767px) {
+  .article {
+    padding: 0 calc(1em + 7vw);
+  }
+}
+
+@media screen and (min-width: 768px) and (max-width: 979px) {
   .article {
     width: 50%;
   }
 }
 
-@media screen and (min-width: 990px) and (max-width: 1023px) {
+@media screen and (min-width: 980px) and (max-width: 1279px) {
   .article {
     width: 33%;
   }
 }
 
-@media screen and (min-width: 1024px) and (max-width: 1279px) {
+@media screen and (min-width: 1280px) and (max-width: 1599px) {
   .article {
     width: 25%;
   }
 }
 
-@media screen and (min-width: 1280px) {
+@media screen and (min-width: 1600px) {
   .article {
     width: 20%;
   }

static/pages/Detect-people-with-a-RaspberryPi-a-thermal-camera-Platypush-and-a-pinch-of-machine-learning.md

@@ -0,0 +1,168 @@
+[//]: # (title: Detect people with a RaspberryPi, a thermal camera, Platypush and Tensorflow)
+[//]: # (description: Use cheap components and open-source software to build a robust presence detector.)
+[//]: # (image: /img/people-detect-1.png)
+[//]: # (published: 2019-09-27)
+
+Triggering events based on the presence of people has been the dream of many geeks and DIY automation junkies for a
+while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting
+application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end
+solutions like the [Philips Hue sensors](https://www2.meethue.com/en-us/p/hue-motion-sensor/046677473389), detect
+motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a
+sloth. The ability to turn off music and/or tv when you exit the room and head to your bedroom, without the hassle of
+switching all the buttons off, is also an interesting corollary. Detecting the presence of people in your room while
+you’re not at home is another interesting application.
+
+Thermal cameras coupled with deep neural networks are a much more robust strategy to actually detect the presence of
+people. Unlike motion sensors, they will detect the presence of people even when they aren’t moving. And unlike optical
+cameras, they detect bodies by measuring the heat that they emit in the form of infrared radiation, and are therefore
+much more robust — their sensitivity doesn’t depend on lighting conditions, on the position of the target, or the
+colour. Before exploring the thermal camera solution, I tried for a while to build a model that instead relied on
+optical images from a traditional webcam. The differences are staggering: I trained the optical model on more than ten
+thousands 640x480 images taken all through a week in different lighting conditions, while I trained the thermal camera
+model on a dataset of 900 24x32 images taken during a single day. Even with more complex network architectures, the
+optical model wouldn’t score above a 91% accuracy in detecting the presence of people, while the thermal model would
+achieve around 99% accuracy within a single training phase of a simpler neural network. Despite the high potential,
+there’s not much out there in the market — there’s been some research work on the topic (if you google “people detection
+thermal camera” you’ll mostly find research papers) and a few high-end and expensive products for professional
+surveillance. In lack of ready-to-go solutions for my house, I decided to take on my duty and build my own solution —
+making sure that it can easily be replicated by anyone.
+
+## Prepare the hardware
+
+For this example we'll use the following hardware:
+
+- A RaspberryPi (cost: around $35). In theory any model should work, but it’s probably not a good idea to use a
+  single-core RaspberryPi Zero for machine learning tasks — the task itself is not very expensive (we’ll only use the
+  Raspberry for doing predictions on a trained model, not to train the model), but it may still suffer some latency on a
+  Zero. Plus, it may be really painful to install some of the required libraries (like Tensorflow or OpenCV) on
+  the `arm6` architecture used by the RaspberryPi Zero. Any better performing model (from RPi3 onwards) should
+  definitely do the job.
+
+- A thermal camera. For this project, I’ve used the
+  [MLX90640](https://shop.pimoroni.com/products/mlx90640-thermal-camera-breakout) Pimoroni breakout camera (cost: $55),
+  as it’s relatively cheap, easy to install, and it provides good results. This camera comes in standard (55°) and
+  wide-angle (110°) versions. I’ve used the wide-angle model as the camera monitors a large living room, but take into
+  account that both have the same resolution (32x24 pixels), so the wider angle comes with the cost of a lower spatial
+  resolution. If you want to use a different thermal camera there’s not much you’ll need to change, as long as it comes
+  with a software interface for RaspberryPi and
+  it’s [compatible with Platypush](https://platypush.readthedocs.io/en/latest/platypush/plugins/camera.ir.mlx90640.html).
+
+Setting up the MLX90640 on your RaspberryPi if you have a Breakout Garden it’s easy as a pie. Fit the Breakout Garden on
+top of your RaspberryPi. Fit the camera breakout into an I2C slot. Boot the RaspberryPi. Done. Otherwise, you can also
+connect the device directly to the [RaspberryPi I2C interface](https://radiostud.io/howto-i2c-communication-rpi/),
+either using the right hardware PINs or the software emulation layer.
+
+## Prepare the software
+
+I tested my code on Raspbian, but with a few minor modifications it should be easily adaptable to any distribution
+installed on the RaspberryPi.
+
+The software support for the thermal camera requires a bit of work. The MLX90640 doesn’t come (yet) with a Python
+ready-to-use interface, but a [C++ open-source driver is provided](https://github.com/pimoroni/mlx90640-library) -
+and that's the driver that is wrapped by the Platypush integration. Instructions to install it:
+
+```shell
+# Install the dependencies
+[sudo] apt-get install libi2c-dev
+
+# Enable the I2C interface
+echo dtparam=i2c_arm=on | sudo tee -a /boot/config.txt
+
+# It's advised to configure the SPI bus baud rate to
+# 400kHz to support the higher throughput of the sensor
+echo dtparam=i2c1_baudrate=400000 | sudo tee -a /boot/config.txt
+
+# A reboot is required here if you didn't have the
+# options above enabled in your /boot/config.txt
+[sudo] reboot
+
+# Clone the driver's codebase
+git clone https://github.com/pimoroni/mlx90640-library
+cd mlx90640-library
+
+# Compile the rawrgb example
+make clean
+make bcm2835
+make I2C_MODE=LINUX examples/rawrgb
+```
+
+If it all went well you should see an executable named `rawrgb` under the `examples` directory. If you run it you should
+see a bunch of binary data — that’s the raw binary representation of the frames captured by the camera. Remember where
+it is located or move it to a custom bin folder, as it’s the executable that platypush will use to interact with the
+camera module.
+
+This post assumes that you have already installed and configured Platypush on your system. If not, head to my post on
+[getting started with Platypush](https://blog.platypush.tech/article/Ultimate-self-hosted-automation-with-Platypush),
+the [readthedocs page](https://platypush.readthedocs.io/en/latest/), the
+[Gitlab page](https://git.platypush.tech/platypush/platypush) or
+[the wiki](https://git.platypush.tech/platypush/platypush/-/wikis/home).
+
+Install also the Python dependencies for the HTTP server, the MLX90640 plugin and Tensorflow:
+
+```shell
+[sudo] pip install 'platypush[http,tensorflow,mlx90640]'
+```
+
+Heading to your computer (we'll be using it for building the model that will be used on the RaspberryPi), install
+OpenCV, Tensorflow and Jupyter and my utilities for handling images:
+
+```shell
+# For image manipulation
+[sudo] pip install opencv
+
+# Install Jupyter notebook to run the training code
+[sudo] pip install jupyterlab
+# Then follow the instructions at https://jupyter.org/install
+
+# Tensorflow framework for machine learning and utilities
+[sudo] pip install tensorflow numpy matplotlib
+
+# Clone my repository with the image and training utilities
+# and the Jupyter notebooks that we'll use for training.
+git clone https://github.com/BlackLight/imgdetect-utils
+```
+
+## Capturing phase
+
+Now that you’ve got all the hardware and software in place, it’s time to start capturing frames with your camera and use
+them to train your model. First, configure
+the [MLX90640 plugin](https://platypush.readthedocs.io/en/latest/platypush/plugins/camera.ir.mlx90640.html) in your
+Platypush configuration file (by default, `~/.config/platypush/config.yaml`):
+
+```yaml
+# Enable the webserver
+backend.http:
+  enabled: True
+
+camera.ir.mlx90640:
+    fps: 16      # Frames per second
+    rotate: 270  # Can be 0, 90, 180, 270
+    rawrgb_path: /path/to/your/rawrgb
+```
+
+Restart the service, and if you haven't already create a user from the web interface at `http://your-rpi:8008`. You
+should now be able to take pictures through the API:
+
+```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:
+
+![Thermal camera snapshot](../img/people-detect-1.png)
+
+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.

templates/article.html

@@ -1,18 +1,6 @@
-<html lang="en">
+{% with title=title or 'Platypush blog', styles=['/css/blog.css', '/css/code.css'] %}
-<head>
+    {% include 'common-head.html' %}
-    <link rel="stylesheet" type="text/css" href="/css/common.css">
+{% endwith %}
-    <link rel="stylesheet" type="text/css" href="/css/blog.css">
-    <link rel="stylesheet" type="text/css" href="/css/code.css">
-    <title>{{ title }}</title>
-</head>
-
-<body>
-    <header>
-        <a href="/">
-            <div class="icon"></div>
-            <div class="title">PlatyBlog</div>
-        </a>
-    </header>
 
 <main>
     <div class="container">
@@ -37,5 +25,5 @@
         </div>
     </div>
 </main>
-</body>
+
-</html>
+{% include 'common-tail.html' %}

templates/common-head.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>

templates/common-tail.html

@@ -0,0 +1,2 @@
+</body>
+</html>

templates/index.html

@@ -1,17 +1,6 @@
-<html lang="en">
+{% with title=title or 'Platypush blog', styles=['/css/home.css'] %}
-<head>
+    {% include 'common-head.html' %}
-    <link rel="stylesheet" type="text/css" href="/css/common.css">
+{% endwith %}
-    <link rel="stylesheet" type="text/css" href="/css/home.css">
-    <title>Platypush blog</title>
-</head>
-
-<body>
-    <header>
-        <a href="/">
-            <div class="icon"></div>
-            <div class="title">PlatyBlog</div>
-        </a>
-    </header>
 
 <main>
     <div class="articles">
@@ -44,5 +33,5 @@
         {% endfor %}
     </div>
 </main>
-</body>
+
-</html>
+{% include 'common-tail.html' %}