Since I used a lot of pictures (and also very large ones in the beginning), this had a huge impact on the speed of the page.
Since PageSpeed ​​is a not unimportant ranking factor for search engines like Google and Co, you should of course make a page as fast as possible.

The images used here are mainly satellite images from [ESA](https://www.esa.int "ESA"), which are licensed under the [CC BY-SA 3.0 IGO](https:/https://creativecommons.org.org/licenses/by-sa/3.0/igo/) IGO) and may therefore also be used for your own purposes under certain conditions.

These pictures are often ~ 30MB, which is a bit too big for a website.
Since I didn't want to crop all pictures manually, I decided to solve this problem with Python or Pillow.

## Pillow library
[Pillow](/pillow/en/stable/) is a Python library for image processing that can be installed on with (assuming you have Python already installed)
```py
pip install Pillow
```

Finally you can import the library with
```py
from PIL import Image
```
into your Python script.

All images for posts are in a separate "images /" folder in the root directory of the project.

First, all ".jpg" files in a certain directory are opened with Pillow and all file names are saved in an array. In addition, a variable is required and can later access any name in the array.
```py 
count = 0
image_list = []

for file in glob.iglob('path/to/images/*.jpg'):
    im=Image.open(file)
    image_list.append(os.path.basename(file))
```

## Define sizes and calculate aspect ratio

Now you should know to which sizes the pictures should be cut and whether, for example, the proportions should be retained.
For all "PostCover" (pictures in posts) the aspect ratio is ignored and the picture is simply cut to a certain size, which is declared in a global variable.
```py
size = (1903,453) #(width,height)
```
With all "PostThumbnails" (picture preview) the aspect ratio should be retained and only scaled smaller. A global standard width is defined for this.
```py
basewidth = 500
```
Then the original width and height of the images are determined, as we need them to be able to calculate and maintain the aspect ratio.
Only the new height is needed here, as the standard width has already been predefined.
```py
    width, height = im.size
    wpercent = (basewidth / float(im.size[0]))
    hsize = int((float(im.size[1]) * float(wpercent)))
```

## Cropping and Rescaling

Now you can cut the images with ```Image.crop``` or scale them with ```Image.resize```. The new width "basewidth" and the calculated height "hsize" are now used as parameters for scaling.

```py
    imThumbnail = im.resize((basewidth, hsize), Image.LANCZOS)
    imCover = im.crop(((width-size[0])//2, (height-size[1])//2, (width+size[0])//2, (height+size[1])//2))
```

Then I renamed the thumbnail and saved both new files under ```static/assets``` with a quality of "85" . 
With the additional parameter "optimize = True" a few KB can be saved.
```py
    newCover = 'static/assets/{}'.format(image_list[count])
    newThumbnail = 'static/assets/{}_thumbnail.jpg'.format(image_list[count].replace(".jpg", ""))
    imCover.save(newCover,optimize=True,quality=85)
    imThumbnail.save(newThumbnail,optimize=True,quality=90)
    count +=1 
```

Complete script:
```py
from PIL import Image
import glob, os

count = 0
image_list = []
basewidth = 500
size = (1903,453)
 

for file in glob.iglob('path/to/images/*.jpg'):
    im=Image.open(file)
    image_list.append(os.path.basename(file))
    width, height = im.size
    wpercent = (basewidth / float(im.size[0]))
    hsize = int((float(im.size[1]) * float(wpercent)))
    imThumbnail = im.resize((basewidth, hsize), Image.LANCZOS)
    imCover = im.crop(((width-size[0])//2, (height-size[1])//2, (width+size[0])//2, (height+size[1])//2))
    newCover = 'static/assets/{}'.format(image_list[count])
    newThumbnail = 'static/assets/{}_thumbnail.jpg'.format(image_list[count].replace(".jpg", ""))
    imCover.save(newCover,optimize=True,quality=85)
    imThumbnail.save(newThumbnail,optimize=True,quality=90)
    count +=1 
```

## Run script automatically

In order not to have to run the script manually every time, you can add the following to "package.json".
```json
    "img-optimize": "py ./src/utils/resize_images.py"
```
So you can optimize all images automatically with ```npm run img-optimize```.


This articles shows you how to edit, crop and resize your pictures with a little Python script.

Scaling and Cropping images using Python

[PyQGIS](https://docs.qgis.org/2.18/de/docs/pyqgis_developer_cookbook/index.html "PyGIS") is a powerful tool that enables the automation of various processes, including the seamless export of images for all layers from a map.

To start the automation, you'll need one or more layers containing raster and/or vector data.

## Add layers with PyQGIS

In the initial step, if all your files reside in the same folder, you can use a ["for .. in loop"](https://www.w3schools.com/python/python_for_loops.asp "for .. in loop") to read them in. By adding ```.endswith(".gpkg")```, for instance, you can specifically target files with the ".gpkg" extension. The layer names are then stored in an array for future reference.

```py
    import os, sys
    from PyQt5.QtCore import QTimer

    # path to look for files
    path = "ordner/nocheinordner/"
    # set path
    dirs = os.listdir( path )
    # array for storing layer_names
    layer_list = []
    # variable for further processing
    count = 0
    #look for files inpath
    for file in dirs:
    	# search for ".gpkg" files 
        if file.endswith(".gpkg"):
    		#add vectorlayers
            vlayer = iface.addVectorLayer(path + file, "Layername", "ogr")
            layer_list.append(vlayer.name())
```

The newly added vector layers will then appear in the QGIS layer tree.

## Image Export for Each Layer

Once you are satisfied with the display, you can use two functions to export a georeferenced image for each layer.

```py
    def prepareMap():
        # make all layers invisible
    	iface.actionHideAllLayers().trigger()
        # get layer by layer_name
    	layer_name = QgsProject.instance().mapLayersByName(layer_list[count])[0]
        # select layer
    	iface.layerTreeView().setCurrentLayer(layer_name)
        # set selected layer visible
    	iface.actionShowSelectedLayers().trigger()
        # Wait a second and export the map
    	QTimer.singleShot(1000, exportMap) 
```

The "prepareMap ()" function first deactivates all layers. A layer is then selected from the "layer_list" array using its layer name and then displayed again. The QTimer class is particularly important here. Before an image is created, there must always be a short wait before the selected layer is really visible. Without QTimer, the script would run so quickly that the result would be loud images with the same content. After waiting a second, the "exportMap" function is called.

```py
    def exportMap(): 
        global count
    	# save current view as image
        iface.mapCanvas().saveAsImage( path + layer_list[count] + ".png" )
    	# feedback for printed map
        print('{}.png exported sucessfully'.format(layer_list[count]))
    	# get map for every layer in layer_list
        if count < len(layer_list)-1:
    		# Wait a second and prepare next map (timer is needed because otherwise all images have the samec content 
    		# the script excecutes faster then the mapCanvas can be reloaded
            QTimer.singleShot(1000, prepareMap) 
        count += 1
```
Now the current map, in which only one level is shown, is saved as a PNG image in the source directory. Ultimately, you "land" in a loop that goes through all the layers that are in the "layer_list" array and calls the "prepareMap" function for each layer again.

With the help of PyQGIS processes such as the export of images for all layers from a map can be automated.

Python

Scaling and Cropping images using Python

Export QGIS layers as images with PyQGIS