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https://github.com/out-of-cheese-error/mars2020api
Unofficial Python API for NASA's Mars rover images and metadata (with a touch of image processing)
https://github.com/out-of-cheese-error/mars2020api
mars-rover nasa nasa-api perseverance rover
Last synced: 3 months ago
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Unofficial Python API for NASA's Mars rover images and metadata (with a touch of image processing)
- Host: GitHub
- URL: https://github.com/out-of-cheese-error/mars2020api
- Owner: out-of-cheese-error
- License: bsd-3-clause
- Created: 2021-02-24T11:04:59.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-10T14:25:22.000Z (almost 4 years ago)
- Last Synced: 2023-03-06T14:48:43.485Z (almost 2 years ago)
- Topics: mars-rover, nasa, nasa-api, perseverance, rover
- Language: Python
- Homepage:
- Size: 134 MB
- Stars: 7
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Perseverance Rover Image API (with a touch of image processing)
Python API for downloading [NASA's Perseverance Rover](https://mars.nasa.gov) images and metadata.
Combines JSON metadata about the camera and image with
[image filename metadata](https://mastcamz.asu.edu/decoding-the-raw-publicly-released-mastcam-z-image-filenames/) about the instrument.
## Installation
```sh
pip install git+https://github.com/out-of-cheese-error/mars2020api
```
## Usage
Some imports and a helper function to plot a grid of images:
```python
import PIL
import numpy as np
from mars2020 import image_api
```
Fetch all of NASA's Mars data (this just gets all the image metadata, the actual images are downloaded lazily when requested)
```python
all_data = image_api.ImageDataCollection.fetch_all_mars2020_imagedata()
```
## Collage
During the descent, the EDL_RDCAM camera continously took a ton of pictures that were perfect for collaging together.
```python
images = [
x for x in all_data.images
if x.camera_type.instrument == "EDL_RDCAM"
and not x.instrument_metadata.thumbnail # Not a thumbnail pic
and x.instrument_metadata.filter_number == "E"
]
```
We used Photoshop's Photomerge algorithm (had to subsample to a 100 images to keep Photoshop from crashing) to get this absolute beauty:
[comment]: <> ()
[comment]: <> (And similarly for `filter_number = F`:)
## Panorama
NASA released a beautiful [360-degree panorama](https://mars.nasa.gov/resources/25640/mastcam-zs-first-360-degree-panorama/) shot by the Mastcam-Z cameras on board. We tried to replicate this by getting the same images and running it through Photomerge again.
[NASA's claims to have used](https://www.nasa.gov/offices/oct/home/tech_life_gigapan.html) the [GigaPan software](http://gigapan.com/) for this but we couldn't really get this to work probably because of the ordering of the images.
```python
images = [
x
for x in all_data.images
if x.camera_type.instrument == "MCZ_LEFT" # MastCam Z - Left
and not x.instrument_metadata.thumbnail # Not a thumbnail picture
and x.instrument_metadata.filter_number == "F"
and x.date_received_on_earth_utc.day == 24 # Received on 24th Feb 2021
]
```
Here are some results from Photomerge!
## RGB
A bunch of the cameras took separate R, G, and B channels for each image. We matched these together with the `camera_vector` information and composited them.
```python
def match_rgb(r_image: image_api.ImageData, g_images: [image_api.ImageData], b_images: [image_api.ImageData]):
vector = r_image.camera_type.camera_vector
g_image = [x for x in g_images if x.camera_type.camera_vector == vector]
if len(g_image) == 0:
return None
b_image = [x for x in b_images if x.camera_type.camera_vector == vector]
if len(b_image) == 0:
return None
return (r_image, g_image[0], b_image[0])
```
```python
rgb_matches = []
for cam_type in all_data.instrument_names:
cam_images = [x for x in all_data.images if x.camera_type.instrument == cam_type and not x.instrument_metadata.thumbnail]
filters = set(x.instrument_metadata.filter_number for x in cam_images)
if not set("RGB").difference(filters):
print(cam_type)
r_images = [x for x in cam_images if x.instrument_metadata.filter_number == "R"]
g_images = [x for x in cam_images if x.instrument_metadata.filter_number == "G"]
b_images = [x for x in cam_images if x.instrument_metadata.filter_number == "B"]
rgb_matches += [match_rgb(r_image, g_images, b_images) for r_image in r_images]
rgb_images = []
for m in rgb_matches:
rgb_image = np.zeros((m[0].dimension[1], m[0].dimension[0], 3), dtype=np.int8)
for i in range(3):
rgb_image[:, :, i] = np.asarray(m[i].image_data.split()[i])
rgb_images.append(PIL.Image.fromarray(rgb_image, "RGB"))
```
An example of what that looks like:
# Demosaicing/DeBayering
* Implemented! example soon
# Grid images
* Implemented! example soon
* GUI app in releases to form grid images with debayering
See the rest, as well as high-res versions of all of the above in [this Flickr album](https://flic.kr/s/aHsmUybm5N)
Visit our [blog post](https://out-of-cheese-error.netlify.app/perseverance) for more details .