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Knoten Camera Operations Tutorial (#71) 

* Knoten Camera Operations

* small fixes

* spellcheck fixes

* fixed pds download

* addressed PR feedback
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[codespell] [codespell]
skip = *.po,*.ts,*.js,*.map,*.css,*.bsp,*.cub,*.tsc,*.tf,*.ti,*.pdf skip = *.po,*.ts,*.js,*.map,*.css,*.bsp,*.cub,*.tsc,*.tf,*.ti,*.pdf,*.ipynb
docs/getting-started/csm-stack/knoten-camera-tutorial.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
# Tutorial: Generating a CSM Camera Model from PDS Image in Knoten with Basic Camera Operations
## 1. Prerequisites
### Install Knoten
This tutorial requires Knoten version 0.4 or higher.
`conda install -c conda-forge knoten=0.4`
### Install Matplotlib
This tutorial requires matplotlib.
`conda install -c conda-forge matplotlib`
%% Cell type:markdown id: tags:
## 2. Create a CSM camera for a PDS image
%% Cell type:code id: tags:
``` python
import os
import json
import shapely.wkt
import numpy as np
import matplotlib.pyplot as plt
import urllib.request
from csmapi import csmapi
import pandas as pd
import ale
from knoten import csm, sensor_utils
from knoten.shape import Ellipsoid
from knoten.illuminator import Illuminator
```
%% Cell type:markdown id: tags:
## 3. Download the image from the PDS archives
%% Cell type:code id: tags:
``` python
data_dir = '../data/image_to_ground'
filename = os.path.join(data_dir, "B10_013341_1010_XN_79S172W.IMG")
downloader = urllib.request.URLopener()
downloader.retrieve("https://asc-pds-mars-reconnaissance-orbiter.s3.us-west-2.amazonaws.com/CTX/mrox_0826/data/B10_013341_1010_XN_79S172W.IMG", filename)
```
%% Cell type:markdown id: tags:
## 4. Set Ale SPICE Data Area
If an ale driver is going to be used that leverages SPICE data, it is necessary to set the ALESPICEROOT.
For this example, the necessary SPICE data is preselected. In most cases, you do not know what kernels are needed for a particular image.
To set your spice area, one can do this using normal shell syntax, e.g.:
`export ALESPICEROOT=/path/to/ale/spice`
or inside of a conda environment:
`conda env config vars set ALESPICEROOT=/path/to/ale/spice.`
See instructions for installing SPICE data [here](https://github.com/DOI-USGS/ISIS3?tab=readme-ov-file#full-isis-data-download).
%% Cell type:code id: tags:
``` python
props = {
'kernels': [
os.path.join(data_dir, 'B10_013341_1010_XN_79S172W_0.bsp'),
os.path.join(data_dir, 'B10_013341_1010_XN_79S172W_1.bsp'),
os.path.join(data_dir, 'mro_ctx_v11.ti'),
os.path.join(data_dir, 'mro_sc_psp_090526_090601_0_sliced_-74000.bc'),
os.path.join(data_dir, 'mro_sc_psp_090526_090601_1_sliced_-74000.bc'),
os.path.join(data_dir, 'mro_sclkscet_00082_65536.tsc'),
os.path.join(data_dir, 'mro_v16.tf'),
os.path.join(data_dir, 'naif0012.tls'),
os.path.join(data_dir, 'pck00008.tpc')
]
}
```
%% Cell type:markdown id: tags:
## 5. Generate and Write an Ale ISD
%% Cell type:code id: tags:
``` python
isd_string = ale.loads(filename, props=props)
csm_isd = os.path.splitext(filename)[0] + '.json'
with open(csm_isd, 'w') as isd_file:
isd_file.write(isd_string)
```
%% Cell type:markdown id: tags:
## 6. Create a CSM Camera Model from ISD
%% Cell type:code id: tags:
``` python
camera = csm.create_csm(csm_isd)
```
%% Cell type:markdown id: tags:
## 7. Get the footprint using the camera model and plot it
%% Cell type:code id: tags:
``` python
with open(csm_isd, 'r') as fp:
aledict = json.load(fp)
nlines, nsamples = aledict["image_lines"], aledict["image_samples"]
boundary = csm.generate_boundary((nlines, nsamples))
lons, lats, alts = csm.generate_latlon_boundary(camera, boundary)
plt.rcParams["figure.figsize"] = [5, 5]
plt.axes().set_aspect('equal','datalim')
plt.plot(lons, lats)
plt.xlabel("Longitude (deg)")
plt.ylabel("Latitude (deg)")
plt.title("CSM footprint")
```
%% Output
Text(0.5, 1.0, 'CSM footprint')
%% Cell type:code id: tags:
``` python
bodyfixed_footprint = csm.generate_bodyfixed_footprint(camera, boundary)
csm_wkt = bodyfixed_footprint.ExportToWkt()
csm_geom = shapely.wkt.loads(csm_wkt)
csm_x, csm_y = csm_geom.geoms[0].exterior.coords.xy
plt.xlabel("x")
plt.ylabel("y")
plt.plot(csm_x, csm_y)
plt.xticks(
rotation=45,
horizontalalignment='right',
fontweight='light'
)
plt.title("Body Fixed Footprint")
```
%% Output
Text(0.5, 1.0, 'Body Fixed Footprint')
%% Cell type:markdown id: tags:
## 8. Get Camera Info at an Image Point
%% Cell type:code id: tags:
``` python
image_pt = csmapi.ImageCoord(nlines/2, nsamples/2)
shape = Ellipsoid.from_csm_sensor(camera)
illuminator = Illuminator()
```
%% Cell type:code id: tags:
``` python
caminfo = {
"PhaseAngle": sensor_utils.phase_angle(image_pt, camera, shape, illuminator),
"EmissionAngle": sensor_utils.emission_angle(image_pt, camera, shape),
"SlantDistance": sensor_utils.slant_distance(image_pt, camera, shape),
"TargetCenterDistance": sensor_utils.target_center_distance(image_pt, camera),
"SubSpacecraftLatitude": sensor_utils.sub_spacecraft_point(image_pt, camera).lat,
"SubSpacecraftLongitude": sensor_utils.sub_spacecraft_point(image_pt, camera).lon,
"LocalRadius": sensor_utils.local_radius(image_pt, camera, shape),
"RightAscension": sensor_utils.right_ascension_declination(image_pt, camera)[0],
"Declination": sensor_utils.right_ascension_declination(image_pt, camera)[1],
"LineResolution": sensor_utils.line_resolution(image_pt, camera, shape),
"SampleResolution": sensor_utils.sample_resolution(image_pt, camera, shape),
"PixelResolution": sensor_utils.pixel_resolution(image_pt, camera, shape),
}
caminfo
```
%% Output
129387.64878271958
251123.7546265136
{'PhaseAngle': 58.986916499957154,
'EmissionAngle': 0.10161336902837881,
'SlantDistance': 251124.1495516768,
'TargetCenterDistance': 3628059.1980234315,
'SubSpacecraftLatitude': -78.89842320708418,
'SubSpacecraftLongitude': 187.2644730283536,
'LocalRadius': 3376935.3088204544,
'RightAscension': 6.933803726271131,
'Declination': 78.9463718229981,
'LineResolution': 6.0485566304149945,
'SampleResolution': 5.017404463546378,
'PixelResolution': 5.532980546980687}
mkdocs.yml
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...@@ -41,6 +41,7 @@ nav: ...@@ -41,6 +41,7 @@ nav:
- Home: getting-started/index.md - Home: getting-started/index.md
- CSM Stack: - CSM Stack:
- Generating an ISD, creating a CSM model, and converting coordinates: getting-started/csm-stack/image-to-ground-tutorial.ipynb - Generating an ISD, creating a CSM model, and converting coordinates: getting-started/csm-stack/image-to-ground-tutorial.ipynb
- Knoten - Basic Camera Operations: getting-started/csm-stack/knoten-camera-tutorial.ipynb
- "Using ISIS: First Steps": - "Using ISIS: First Steps":
- Introduction to ISIS: getting-started/using-isis-first-steps/introduction-to-isis.md - Introduction to ISIS: getting-started/using-isis-first-steps/introduction-to-isis.md
- Locating and Ingesting Image Data: getting-started/using-isis-first-steps/locating-and-ingesting-image-data.md - Locating and Ingesting Image Data: getting-started/using-isis-first-steps/locating-and-ingesting-image-data.md
......
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