Method: PyPi
pip install hivemapper-python
Method: Clone locally and run
pip install -r requirements.txt
- Generate a base64 encoded string like 'my-user-name:{apiKey}' to use as input for
authorization - The Imagery API demo restricts queries to Polygons with a maximum area of 1 km^2
- This wrapper supports automatically breaking up large geometries into smaller geometries behind the scenes
> python -m imagery.query
usage: query.py [-h] -i INPUT_FILE [-s START_DAY] [-e END_DAY] [-L] [-x] [-d MAX_DIST] [-l MAX_LAG] [-z MAX_ANGLE] -o OUTPUT_DIR [-g] [-w WIDTH] [-M]
[-I CUSTOM_ID_FIELD] [-S CUSTOM_MIN_DATE_FIELD] [-k] [-E] [-K SKIP_GEO_FILE] [-P IMAGE_POST_PROCESSING] -a AUTHORIZATION [-c NUM_THREADS] [-v]
[-C] [-b] [-N]
options:
-h, --help show this help message and exit
-i INPUT_FILE, --input_file INPUT_FILE
-sg SEGMENT_IDS, --segment_ids SEGMENT_IDS
-s START_DAY, --start_day START_DAY
-e END_DAY, --end_day END_DAY
-L, --latest
-x, --stitch
-d MAX_DIST, --max_dist MAX_DIST
-l MAX_LAG, --max_lag MAX_LAG
-z MAX_ANGLE, --max_angle MAX_ANGLE
-o OUTPUT_DIR, --output_dir OUTPUT_DIR
-Z, --zip_dirs
-Zio, --zip_images_only
-g, --export_geojson
-w WIDTH, --width WIDTH
-m MOUNT, --mount MOUNT
-M, --merge_metadata
-I CUSTOM_ID_FIELD, --custom_id_field CUSTOM_ID_FIELD
-S CUSTOM_MIN_DATE_FIELD, --custom_min_date_field CUSTOM_MIN_DATE_FIELD
-SF CUSTOM_MIN_DATE_FORMATTING --custom_min_date_formatting CUSTOM_MIN_DATE_FORMATTING
-Io CUSTOM_OUTPUT_DIR_FIELD, --custom_output_dir_field CUSTOM_OUTPUT_DIR_FIELD
-Ib CUSTOM_OUTPUT_SUCCESS_FIELD, --custom_output_success_field CUSTOM_OUTPUT_SUCCESS_FIELD
-Is CUSTOM_OUTPUT_DATE_FIELD, --custom_output_date_field CUSTOM_OUTPUT_DATE_FIELD
-tI, --track_by_custom_id
-p, --passthrough_csv_output
-k, --camera_intrinsics
-E, --update_exif
-K SKIP_GEO_FILE, --skip_geo_file SKIP_GEO_FILE
-P IMAGE_POST_PROCESSING, --image_post_processing IMAGE_POST_PROCESSING
-a AUTHORIZATION, --authorization AUTHORIZATION
-c NUM_THREADS, --num_threads NUM_THREADS
-v, --verbose
-C, --cache
-b, --use_batches
-N, --skip_cached_frames
> python -m burts.query
usage: query.py [-h] -i INPUT_FILE -a AUTHORIZAITON
options:
-h, --help show this help message and exit
-i INPUT_FILE, --input_file INPUT_FILE
-a AUTHORIZATION, --authorization AUTHORIZATION
-v, --verbose
Query and download
from imagery import download_files, query_frames
# make the API call to query available data
frames = query_frames(geojson_file, start_day, end_day, output_dir, authorization)
# download the content into folders grouped by its session id
download_files(frames, output_dir)
Create Honey Burst
from bursts import create_bursts
# make the API call to create new bursts by given geojson polygons
# requires polygon type for each of the location
burst_results = create_burts(geojson_file, authorization)
python -m imagery.query -v -M --input_file "test_feature.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string>
python -m imagery.query -v -M -sg 089283082abbffff0423fcc946ad8fec --start_day "2024-07-28" --end_day "2024-07-28" --output_dir "temp" --authorization <your encoded key string>
python -m imagery.query -v -M -sg 089283082abbffff0423fcc946ad8fec 088283082abfffff0467f4b6b725f9af --start_day "2024-07-28" --end_day "2024-07-28" --output_dir "temp" --authorization <your encoded key string>
python -m imagery.query -v -M -C -b --input_file "test_feature.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string>
Query imagery for a GeoJSON Polygon FeatureCollection; stitch together; save a GeoJSON of LineStrings
python -m imagery.query -v -M -x -g --input_file "test_feature_col.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string>
python -m imagery.query -v -M -g --input_file "test_feature_col.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string>
python -m imagery.query -v -M -k -E --input_file "test_feature.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string>
Focal Length is encoded in pixel units (i.e., not mm)
Lens is encoded as <k1> <k2>
Note: exiftool is required to be installed (see https://exiftool.org/)
> python -m util.geo -h
usage: geo.py [-h] [-s SHAPEFILE] [-c CSVFILE] -o OUTPUT_JSON [-w WIDTH] [-I CUSTOM_ID_FIELD] [-S CUSTOM_MIN_DATE_FIELD] [-q]
options:
-h, --help show this help message and exit
-s SHAPEFILE, --shapefile SHAPEFILE
-c CSVFILE, --csvfile CSVFILE
-o OUTPUT_JSON, --output_json OUTPUT_JSON
-w WIDTH, --width WIDTH
-I CUSTOM_ID_FIELD, --custom_id_field CUSTOM_ID_FIELD
-S CUSTOM_MIN_DATE_FIELD, --custom_min_date_field CUSTOM_MIN_DATE_FIELD
-q, --quiet
> python -m util.geo -h
python -m imagery.query -v -M -g --input_file "test_feature_col.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string> -K last_out/frames.geojson
> python -m util.geo -h
python -m imagery.query -v -M -g --input_file "test_feature_col.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string> -K last_out/frames.geojson,another_out/frames.geojson
usage: info.py [-h] -a AUTHORIZATION [-b] [-l LIMIT] [-t] [-v]
options:
-h, --help show this help message and exit
-a AUTHORIZATION, --authorization AUTHORIZATION
-b, --balance
-l LIMIT, --limit LIMIT
-t, --history
-v, --verbose
python -m account.info -ba <your encoded key string>
python -m account.info -ta <your encoded key string>
usage: stitching.py [-h] [-R RESTITCH] [-o OUT] [-d MAX_DIST] [-l MAX_LAG] [-z MAX_ANGLE] [-m MIN_SEQ_SIZE] [-v]
options:
-h, --help show this help message and exit
-R RESTITCH, --restitch RESTITCH
-o OUT, --out OUT
-d MAX_DIST, --max_dist MAX_DIST
-l MAX_LAG, --max_lag MAX_LAG
-z MAX_ANGLE, --max_angle MAX_ANGLE
-m MIN_SEQ_SIZE, --min_seq_size MIN_SEQ_SIZE
-v, --verbose
python -m util.stitching -R out -o out2 -v
python -m util.stitching -R out -o out2 -v -m 100
- Install ImageMagick >=7.0.0
- Use Python >=3.7
python -m imagery.query -v -M -x -g --input_file "test_feature_col.json" --start_day "2023-07-28" --end_day "2023-07-28" --output_dir "temp" --authorization <your encoded key string> -P clahe-smart-clip
Default Usage:
python optical_flow.py input_dir
All Options:
python optical_flow.py input_dir --unzip --max_corners MAX_CORNERS --num_random_checks NUM_RANDOM_CHECKS --threshold_dxdy_ratio THRESHOLD_DXDY_RATIO --turn_threshold TURN_THRESHOLD
- https://en.wikipedia.org/wiki/Adaptive_histogram_equalization#Contrast_Limited_AHE
- https://imagemagick.org/script/clahe.php
- https://www.mdpi.com/1424-8220/22/22/8967
It's highly recommended to use the module directly in order to preserve the original imagery, as well as to tune values for your own purposes.
By default, settings are naively configured to sacrifice aesthetics to improve unsupervised feature detection. Some general deep learning inference use cases and human in the loop use cases may also see benefits from these default settings.
