All posts by morse2022

chatGPT, colab, python, tests

Visualising Philip Island

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One objective (amongst many) that Chris and I have identified is to first start off looking at islands. The reason behind this is simple: an island presents a defined and constrained geographic area – not too big, not too small (depending on the island, of course) – and I’ve had a lot of recent experience working with island data on my recent Macquarie Island project with my colleague Dr Gina Moore. It provides some practical limits to the amount of data we have to work with, whilst engaging many of the technical issues one might have to address.

With this in mind, we’ve started looking a Philip Island (near Melbourne, Victoria) and Kangaroo Island (South Australia). Both fall under a number of satellite paths, from which we can isolate multispectral and hyperspectral data. I’ll add some details about these in a later post – as there are clearly issues around access and rights that need to be absorbed, explored and understood.

For a first attempt, here is a google colab script for looking at some Philip Island data from the XXX Satellite:

# Import necessary libraries
import os
from osgeo import gdal, gdal_array
from google.colab import drive
import numpy as np
import matplotlib.pyplot as plt
# Mount Google Drive
drive.mount('/content/drive')
#this will ask for permissions - you will need to login through your google account in a pop-up window
# Open the multispectral GeoTIFF file
#set the file path to the folder with the relevant data in it on your google drive (mount this first via the panel to the left of this one - it is called 'drive' and appears as a folder)
file_path = '/content/drive/MyDrive/DATA/XXX_multispectral.tif'
#set a variable for your path and the file you open
src_ds = gdal.Open(file_path)
#use gdal to get some characteristics of the data in the file
print("Projection: ", src_ds.GetProjection())  # get projection
print("Columns:", src_ds.RasterXSize)  # number of columns
print("Rows:", src_ds.RasterYSize)  # number of rows
print("Band count:", src_ds.RasterCount)  # number of bands
print("GeoTransform", src_ds.GetGeoTransform()) #affine transform
# Use gdalinfo command to print and save information about the raster file - this is extracted from the geotiff itself
info = gdal.Info(file_path)
print(info)
if not os.path.exists("/content/drive/MyDrive/DATA/OUTPUT"):
    os.makedirs("/content/drive/MyDrive/DATA/OUTPUT")
info_file = os.path.join("/content/drive/MyDrive/DATA/OUTPUT", "raster_info.txt")
with open(info_file, "w") as f:
    f.write(info)
# Retrieve the band count and band metadata
data_array = src_ds.GetRasterBand(1).ReadAsArray()
data_array.shape
band_count = src_ds.RasterCount
# Loop through each band and display in a matplotlib image
for i in range(1, band_count+1):
    band = src_ds.GetRasterBand(i)
    minval, maxval = band.ComputeRasterMinMax()
    data_array = band.ReadAsArray()
    plt.figure(figsize=(16, 9))
    plt.imshow(data_array, vmin=minval, vmax=maxval)
    plt.colorbar(anchor=(0, 0.3), shrink=0.5)
    plt.title("Band {} Data\n Min value: {} Max value: {}".format(i, minval, maxval))
    plt.suptitle("Raster data information")
    band_description = band.GetDescription()
    metadata = band.GetMetadata_Dict()
    geotransform = src_ds.GetGeoTransform()
    top_left_x = geotransform[0]
    top_left_y = geotransform[3]
    w_e_pixel_res = geotransform[1]
    n_s_pixel_res = geotransform[5]
    x_size = src_ds.RasterXSize
    y_size = src_ds.RasterYSize
    bottom_right_x = top_left_x + (w_e_pixel_res * x_size)
    bottom_right_y = top_left_y + (n_s_pixel_res * y_size)
    coordinates = ["Top left corner: ({},{})".format(top_left_x,top_left_y),"Bottom right corner:({},{})".format(bottom_right_x,bottom_right_y)]
    if band_description:
        metadata_list = ["Band description: {}".format(band_description)]
    else:
        metadata_list = ["Band description is not available"]
    if metadata:
        metadata_list += ["{}: {}".format(k, v) for k, v in metadata.items()]
    else:
        metadata_list += ["Metadata is not available"]
    plt.annotate("\n".join(coordinates+metadata_list), (0,0), (0, -50), xycoords='axes fraction', textcoords='offset points', va='top')
    plt.savefig("/content/drive/MyDrive/DATA/OUTPUT/Band_{}_Data.png".format(i), vmin=minval, vmax=maxval)
    plt.show()

 

This works well enough for a plot – but it’s interesting (a debate) – whether it is best/easiest to use GDAL or the rasterio Mapbox wrapper. Tests will tell. And there is Pandas too. They all have pros and cons. Try it yourself and let us know,

I’m looking into ways of sharing the colabs more directly for those who are interested – that’s the whole point.

chatGPT, colab, Hyperspectral Imaging, python

Visualising Satellite data using Google Colab

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Having spent a few hours reading documentation and having an ongoing conversation with chatGPT, I’m getting the hang of the hdf5 file structure and can now visualise some multispectral data in Google Colab:

from google.colab import drive
drive.mount('/content/drive')

import h5py
import numpy as np
import matplotlib.pyplot as plt

# Get the HDF5 file from your Google Drive
file_id = '/content/drive/MyDrive/DATA/file_name.he5'

with h5py.File(file_id, "r") as f:
# List all groups
print("Keys: %s" % f.keys())
a_group_key = list(f.keys())[0]

# Get the data
data = list(f[a_group_key])

#This gives us some idea about the groups/keys in the hdf file and some idea about the datasets contained therein - but will become more detailed as we go along

# Open the HDF5 file
with h5py.File(file_id, 'r') as f:
# Open the data field
#currently this is hard-coded as I know from hdfView that this is the path I want to look at - but really we want to find this programmatically.
data_field = f['/path_to/Data Fields/Cube']
# Print the shape of the data field
print(f'Shape: {data_field.attrs}')
print(f'Shape: {data_field.dtype}')
print(f'Shape: {data_field.shape}')
#This gives us some idea about the data cube we are examining - such as its attributes, data type and shape (typically rows and columns) - it'll print them to output

# Open the HDF5 file
with h5py.File(file_id, 'r') as f:
# Open the data field
data_field = f['/path_to/Data Fields/Cube']
# Get the data and reshape it to 2D
data = np.array(data_field[:]).reshape(data_field.shape[0], data_field.shape[1])
# Scale the data to fit within an 800x800 pixels
data = np.uint8(np.interp(data, (data.min(), data.max()), (0, 255)))
# Create a figure with the specified size
fig = plt.figure(figsize=(8, 8))
# Add the data to the figure
plt.imshow(data, cmap='viridis')
# Display the figure
plt.show()

Satellite data plotted with viridis colourmap

Next steps involve developing a way of iteratively traversing the hdf5 directory structure, so that I can identify relevant data fields within the file – they’re not explicitly identified as ‘image files’. This can be done using h5py functions. Another thing to explore is GDAL: once I’ve identified the correct data in geolocation fields, it should become possible to output geotiffs or UE-friendly png files with geolocation metadata.

Here’s some other matplotlib colourmaps applied to the same dataset.

It’s all pretty crude at this point – just figuring out how this stuff might work.

chatGPT, colab, Hyperspectral Imaging, python

ChatGPT, Google Colab and Hyperspectral Data

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It occurs to me that, rather than going to all the hassle of figuring out how to write all the code for colab myself, that I can use my chatGPT assistant to do it for me:

Q: write a script for google colab to extract images from an hdf5 file located upon my google drive

Chat GPT:

Here is an example script that demonstrates how to extract images from an HDF5 file located on your Google Drive using the h5py library in Python, and the PyDrive library to interact with Google Drive:

# Install the PyDrive library
!pip install -U PyDrive

# Import the necessary libraries
import h5py
import numpy as np
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

# Get the HDF5 file from your Google Drive
file_id = '<YOUR FILE ID>'
downloaded = drive.CreateFile({'id': file_id})
downloaded.GetContentFile('hyperspectral_image_data.hdf5')

# Open the HDF5 file
with h5py.File('hyperspectral_image_data.hdf5', 'r') as hdf5_file:
# Access the dataset containing the images
images = hdf5_file['images']
# Extract an image by its index
image = np.array(images[0])
print(image)

Make sure to replace <YOUR FILE ID> with the actual file ID of the HDF5 file on your Google Drive. You can find the file ID by opening the file in Google Drive and looking at the URL – the file ID is the string of letters and numbers after the last forward slash.

You can also use the gdal_translate command with the PyDrive library as well, just add the following lines of code to download the file and then use gdal_translate command to convert to tiff or geotiff

# Get the HDF5 file from your Google Drive
file_id = '<YOUR FILE ID>'
downloaded = drive.CreateFile({'id': file_id})
downloaded.GetContentFile('hyperspectral_image_data.hdf ')#Use gdal_translate
!gdal_translate -of GTiff HDF5:"hyperspectral_image_data.hdf5"://dataset output.tif

You can then use any image processing library to open and read the image.

Lots of interesting things to unpack in this advice – and lots of errors as I expect to debug the code.

So, as a first step, let’s see if we can just get access to the file and some basic metadata.

# Install the PyDrive library
!pip install -U PyDrive
# Import the necessary libraries
import h5py
import numpy as np
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# Authenticate and create the PyDrive client
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
# Get the HDF5 file from your Google Drive
file_id = '/content/drive/MyDrive/DATA/XXXXXXX.he5'
with h5py.File(file_id, "r") as f:
    # List all groups
    print("Keys: %s" % f.keys())
    a_group_key = list(f.keys())[0]
    # Get the data
    data = list(f[a_group_key])

 

Success! We can see the HDF keys returned below:

Keys: <KeysViewHDF5 ['HDFEOS', 'HDFEOS INFORMATION', 'Info', 'KDP_AUX']>

These match the top-level directory structure revealed by HDFView:

This is the first step in identifying the file contents, before we can drill down into the file structure to identify a whole bunch of parameters about the datacube that we can do interesting things with 🙂

chatGPT, Hyperspectral Imaging

Working with ChatGPT & Hyperspectral Data

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Thus far I have been playing around with HDFView to extract hdf5 image data – but I am very interested in working with advanced machine learning systems and developing processes to interface them with Unreal Engine and on-line data sources. It seems the most tractable way to do this is through the use of a Python API – which is pretty much the lingua franca of scientific computing.

As I am a neophyte with using hyperspectral image data, let’s use ChatGPT to find out what might be an ‘easy’ or ‘best’ way to access image data within an hdf5 file:

Q: what is the best way to extract images from an hdf5 file containing hyperspectral image data?

ChatGPT:

One way to extract images from an HDF5 file containing hyperspectral image data is to use the h5py library in Python. This library allows you to open the HDF5 file and access the data stored within it, including the images. You can then use the numpy library to manipulate and extract the images as needed. Here is an example of how you might extract an image from an HDF5 file using h5py:

import h5py
import numpy as np# Open the HDF5 file
with h5py.File(‘hyperspectral_image_data.hdf5’, ‘r’) as hdf5_file:
# Access the dataset containing the images
images = hdf5_file[‘images’]
# Extract an image by its index
image = np.array(images[0])
print(image)

Another way is to use the gdal library in python, specifically with the gdal_translate utility, which can convert HDF5 to other raster formats like tiff or geotiff, and then use any image processing library to open and read the image.

gdal_translate -of GTiff HDF5:"file.hdf5"://dataset output.tif

In this way you will be able to use any library of your preference to read and extract the images.

I have no idea currently whether this will work, so lets look up the relevant documentation:

Both look promising – and has saved me a heap of time looking for ways to do it!

The first outputs images as a numPy array – meaning that we can examine/export each image by its index – which would be useful for selecting for certain  λ (wavelength)) values and conducting operations upon them.

The second uses GDAL (Geospatial Data Abstraction Library), which provides powerful utilities for the translation of geospatial metadata – enabling correct geolocation of the hyperspectral image data, for instance.

So perhaps a combination of both will be useful as we proceed.

But of course, any code generated by chatGPT or OpenAI Codex or other AI assistants must be taken with several grains of salt. For instance – a recent study by MIT shows that users may write more insecure code when working with an AI code assistant (https://doi.org/10.48550/arXiv.2211.03622). Perhaps there are whole API calls and phrases that are hallucinated? I simply don’t know at this stage.

So – my next step will be to fire up a python environment – probably Google Colab or Anaconda and see what happens.

A nice overview of Codex here:

OpenAI Codex Live Demo

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python, Unreal Engine

Scripting the Unreal Editor Using Python

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An important aspect of our visualisation pipeline involves chaining together Unreal Engine and Python workflows.

The UE 5.1 Python introductory documentation lists some common examples of what might be done:

  • Construct larger-scale asset management pipelines or workflows that tie the Unreal Editor to other 3D applications that you use in your organization.
  • Automate time-consuming Asset management tasks in the Unreal Editor, like generating Levels of Detail (LODs) for Static Meshes.
  • Procedurally lay out content in a Level.
  • Control the Unreal Editor from UIs that you create yourself in Python.

Extensive UE Python API documentation is listed here.

A useful introductory video on Youtube:

discussion, Hyperspectral Imaging, Research

Technical Webinar: Can satellites monitor crop and pasture quality across Australia?

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From the SmartSat CRC Newsletter:

This Technical Presentation hosted by Ha Thanh Nguyen, Research Scientist in Digital Interactions (Agriculture & Food) at CSIRO explores the outcomes of SmartSat Project P3.25: Can satellites monitor crop and pasture quality across Australia?

Knowledge of crop and pasture quality can provide the industry with insights to assist with the grazing management of pastures and input management decisions for crops. Handheld and lab-based spectroscopy have been extensively employed to monitor quality-based plant attributes. The methods employed are time consuming and expensive to implement and do not provide the industry with insights into the temporal trends of the critical variables. High resolution and frequent return time can overcome numerous deficiencies affecting equivalent visible IR and SWIR platforms, that limit the ability to create a viable product around crop and pasture quality. This project conducted a feasibility analysis capitalising on existing and planned satellite missions, including the Aquawatch satellites and precursors to test development of new high frequency products for crop and pasture quality across the Australian landscape. This project is led by Dr Roger Lawes, Principal Research Scientist at CSIRO Agriculture Flagship and includes participants from CSIRO and the Grains Research and Development Corporation. For more information visit https://smartsatcrc.com/research-prog…