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diff --git a/static/pages/Detect-people-with-a-RaspberryPi-a-thermal-camera-Platypush-and-a-pinch-of-machine-learning.md b/static/pages/Detect-people-with-a-RaspberryPi-a-thermal-camera-Platypush-and-a-pinch-of-machine-learning.md
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+++ b/static/pages/Detect-people-with-a-RaspberryPi-a-thermal-camera-Platypush-and-a-pinch-of-machine-learning.md
@@ -166,3 +166,272 @@ 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.
+
+
+
+You can also monitor the camera directly outside of the webpanel by pointing your browser to
+`http://your-host:8008/camera/ir/mlx90640/stream?rotate=270&scale_factor=20`.
+
+Now add a cronjob to your `config.yaml` to take snapshots every minute:
+
+```yaml
+cron.ThermalCameraSnapshotCron:
+ cron_expression: '* * * * *'
+ actions:
+ - action: camera.ir.mlx90640.capture
+ args:
+ output_file: "${__import__(’datetime’).datetime.now().strftime(’/your/img/folder/%Y-%m-%d_%H-%M-%S.jpg’)}"
+ grayscale: true
+```
+
+Or directly as a Python script under e.g. `~/.config/platypush/thermal.py` (make sure that `~/.config/platypush/__init__.py` also exists so the folder is recognized as a Python module):
+
+```python
+from datetime import datetime
+
+from platypush.config import Config
+from platypush.cron import cron
+from platypush.utils import run
+
+
+@cron('* * * * *')
+def take_thermal_picture(**context):
+ run('camera.ir.mlx90640.capture', grayscale=True,
+ output_file=datetime.now().strftime('/your/img/folder/%Y-%m-%d_%H-%m-%S.jpg'))
+```
+
+The images will be stored under `/your/img/folder` in the format
+`YYYY-mm-dd_HH-MM-SS.jpg`. No scale factor is applied — even if the images will
+be tiny we’ll only need them to train our model. Also, we’ll convert the images
+to grayscale — the neural network will be lighter and actually more accurate,
+as it will only have to rely on one variable per pixel without being tricked by
+RGB combinations.
+
+Restart Platypush and verify that every minute a new picture is created under
+your images directory. Let it run for a few hours or days until you’re happy
+with the number of samples. Try to balance the numbers of pictures with no
+people in the room and those with people in the room, trying to cover as many
+cases as possible — e.g. sitting, standing in different points of the room etc.
+As I mentioned earlier, in my case I only needed less than 1000 pictures with
+enough variety to achieve accuracy levels above 99%.
+
+## Labelling phase
+
+Once you’re happy with the number of samples you’ve taken, copy the images over
+to the machine you’ll be using to train your model (they should be all small
+JPEG files weighing under 500 bytes each). Copy them to the folder where you
+have cloned my `imgdetect-utils` repository:
+
+```shell
+BASEDIR=~/git_tree/imgdetect-utils
+
+# This directory will contain your raw images
+IMGDIR=$BASEDIR/datasets/ir/images
+
+# This directory will contain the raw numpy training
+# data parsed from the images
+DATADIR=$BASEDIR/datasets/ir/data
+
+mkdir -p $IMGDIR
+mkdir -p $DATADIR
+
+# Copy the images
+scp pi@raspberry:/your/img/folder/*.jpg $IMGDIR
+
+# Create the labels for the images. Each label is a
+# directory under $IMGDIR
+mkdir $IMGDIR/negative
+mkdir $IMGDIR/positive
+```
+
+Once the images have been copied and the directories for the labels created,
+run the `label.py` script provided in the repository to interactively label the
+images:
+
+```shell
+cd $BASEDIR
+python utils/label.py -d $IMGDIR --scale-factor 10
+```
+
+Each image will open in a new window and you can label it by typing either 1
+(negative) or 2 (positive) - the label names are gathered from the names of the
+directories you created at the previous step:
+
+
+
+At the end of the procedure the `negative` and `positive` directories under the
+images directory should have been populated.
+
+## Training phase
+
+Once we’ve got all the labelled images it’s time to train our model. A
+[`train.ipynb`](https://github.com/BlackLight/imgdetect-utils/blob/master/notebooks/ir/train.ipynb)
+Jupyter notebook is provided under `notebooks/ir` and it should be
+relatively self-explanatory:
+
+```python
+### Import stuff
+
+import os
+import sys
+
+import numpy as np
+
+import tensorflow as tf
+from tensorflow import keras
+
+######
+# Change this with the directory where you cloned the imgdetect-utils repo
+basedir = os.path.join(os.path.expanduser('~'), 'git_tree', 'imgdetect-utils')
+sys.path.append(os.path.join(basedir))
+
+from src.image_helpers import plot_images_grid, create_dataset_files
+from src.train_helpers import load_data, plot_results, export_model
+
+# Define the dataset directory - replace it with the path on your local
+# machine where you have stored the previously labelled dataset.
+dataset_dir = os.path.join(basedir, 'datasets', 'ir')
+
+# Define the size of the input images. In the case of an
+# MLX90640 it will be (24, 32) for horizontal images and
+# (32, 24) for vertical images
+image_size = (32, 24)
+
+# Image generator batch size
+batch_size = 64
+
+# Number of training epochs
+epochs = 5
+######
+
+# The Tensorflow model and properties file will be stored here
+tf_model_dir = os.path.join(basedir, 'models', 'ir', 'tensorflow')
+tf_model_file = os.path.join(tf_model_dir, 'ir.pb')
+tf_properties_file = os.path.join(tf_model_dir, 'ir.json')
+
+# Base directory that contains your training images and dataset files
+dataset_base_dir = os.path.join(basedir, 'datasets', 'ir')
+dataset_dir = os.path.join(dataset_base_dir, 'data')
+
+# Store your thermal camera images here
+img_dir = os.path.join(dataset_base_dir, 'images')
+
+### Create model directories
+
+os.makedirs(tf_model_dir, mode=0o775, exist_ok=True)
+
+### Create a dataset files from the available images
+
+dataset_files = create_dataset_files(img_dir, dataset_dir,
+ split_size=1000,
+ num_threads=1,
+ resize=input_size)
+
+### Or load existing .npz dataset files
+
+
+
+dataset_files = [os.path.join(dataset_dir, f)
+ for f in os.listdir(dataset_dir)
+ if os.path.isfile(os.path.join(dataset_dir, f))
+ and f.endswith('.npz')]
+
+### Get the training and test set randomly out of the dataset with a split of 70/30
+
+train_set, test_set, classes = load_data(*dataset_files, split_percentage=0.7)
+print('Loaded {} training images and {} test images. Classes: {}'.format(
+ train_set.shape[0], test_set.shape[0], classes))
+
+# Example output:
+# Loaded 623 training images and 267 test images. Classes: ['negative' 'positive']
+
+# Extract training set and test set images and labels
+train_images = np.asarray([item[0] for item in train_set])
+train_labels = np.asarray([item[1] for item in train_set])
+test_images = np.asarray([item[0] for item in test_set])
+test_labels = np.asarray([item[1] for item in test_set])
+
+### Inspect the first 25 images in the training set
+
+plot_images_grid(images=train_images, labels=train_labels,
+ classes=classes, rows=5, cols=5)
+
+### Declare the model
+
+# - Flatten input
+# - Layer 1: 50% the number of pixels per image (RELU activation)
+# - Layer 2: 20% the number of pixels per image (RELU activation)
+# - Layer 3: as many neurons as the output labels
+# (in this case 2: negative, positive) (Softmax activation)
+
+model = keras.Sequential([
+ keras.layers.Flatten(input_shape=train_images[0].shape),
+ keras.layers.Dense(int(0.5 * train_images.shape[1] * train_images.shape[2]),
+ activation=tf.nn.relu),
+ keras.layers.Dense(int(0.2 * train_images.shape[1] * train_images.shape[2]),
+ activation=tf.nn.relu),
+ keras.layers.Dense(len(classes), activation=tf.nn.softmax)
+])
+
+### Compile the model
+
+# - Loss function:This measures how accurate the model is during training. We
+# want to minimize this function to "steer" the model in the right direction.
+# - Optimizer: This is how the model is updated based on the data it sees and
+# its loss function.
+# - Metrics: Used to monitor the training and testing steps. The following
+# example uses accuracy, the fraction of the images that are correctly classified.
+
+model.compile(optimizer='adam',
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy'])
+
+### Train the model
+
+model.fit(train_images, train_labels, epochs=3)
+
+# Example output:
+# Epoch 1/3 623/623 [======] - 0s 487us/sample - loss: 0.2672 - acc: 0.8860
+# Epoch 2/3 623/623 [======] - 0s 362us/sample - loss: 0.0247 - acc: 0.9936
+# Epoch 3/3 623/623 [======] - 0s 373us/sample - loss: 0.0083 - acc: 0.9984
+
+### Evaluate accuracy against the test set
+
+test_loss, test_acc = model.evaluate(test_images, test_labels)
+print('Test accuracy:', test_acc)
+
+# Example output:
+# 267/267 [======] - 0s 243us/sample - loss: 0.0096 - acc: 0.9963
+# Test accuracy: 0.9962547
+
+### Make predictions on the test set
+
+predictions = model.predict(test_images)
+
+# Plot a grid of 36 images and show expected vs. predicted values
+plot_results(images=test_images, labels=test_labels,
+ classes=classes, predictions=predictions,
+ rows=9, cols=4)
+
+### Export as a Tensorflow model
+
+export_model(model, tf_model_file,
+ properties_file=tf_properties_file,
+ classes=classes,
+ input_size=input_size)
+```
+
+If you managed to execute the whole notebook correctly you’ll have a file named
+`ir.pb` under `models/ir/tensorflow`. That’s your Tensorflow model file, you can
+now copy it over to the RaspberryPi and use it to do predictions:
+
+```shell
+scp $BASEDIR/models/ir/tensorflow/ir.pb pi@raspberry:/home/pi/models
+```
+
+## Detect people in the room
+
+Once the Tensorflow model has been deployed to the RaspberryPi you can replace the
+previous cronjob that stores pictures at regular intervals with a cronjob that captures
+pictures and feeds them to the previously trained model
+