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https://github.com/agentmorris/megadetector
MegaDetector is an AI model that helps conservation folks spend less time doing boring things with camera trap images.
https://github.com/agentmorris/megadetector
camera-traps cameratraps computer-vision conservation ecology machine-learning megadetector wildlife
Last synced: about 20 hours ago
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MegaDetector is an AI model that helps conservation folks spend less time doing boring things with camera trap images.
- Host: GitHub
- URL: https://github.com/agentmorris/megadetector
- Owner: agentmorris
- License: mit
- Created: 2023-05-20T01:46:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-29T20:10:15.000Z (16 days ago)
- Last Synced: 2024-10-29T22:43:37.691Z (16 days ago)
- Topics: camera-traps, cameratraps, computer-vision, conservation, ecology, machine-learning, megadetector, wildlife
- Language: Jupyter Notebook
- Homepage:
- Size: 185 MB
- Stars: 109
- Watchers: 9
- Forks: 24
- Open Issues: 1
-
Metadata Files:
- Readme: README-package.md
- License: LICENSE
- Citation: citation.cff
Awesome Lists containing this project
README
# MegaDetector
This package is a pip-installable version of the support/inference code for [MegaDetector](https://github.com/agentmorris/MegaDetector/?tab=readme-ov-file#megadetector), an object detection model that helps conservation biologists spend less time doing boring things with camera trap images. Complete documentation for this Python package is available at [megadetector.readthedocs.io](https://megadetector.readthedocs.io).
If you aren't looking for the Python package specifically, and you just want to learn more about what MegaDetector is all about, head over to the [MegaDetector repo](https://github.com/agentmorris/MegaDetector/?tab=readme-ov-file#megadetector).
## Reasons you might not be looking for this package
### If you are an ecologist...
If you are an ecologist looking to use MegaDetector to help you get through your camera trap images, you probably don't want this package, or at least you probably don't want to start at this page. We recommend starting with our "[Getting started with MegaDetector](https://github.com/agentmorris/MegaDetector/blob/main/getting-started.md)" page, then digging in to the [MegaDetector User Guide](https://github.com/agentmorris/MegaDetector/blob/main/megadetector.md), which will walk you through the process of using MegaDetector.
### If you are a computer-vision-y type...
If you are a computer-vision-y person looking to run or fine-tune MegaDetector programmatically, you probably don't want this package. MegaDetector is just a fine-tuned version of [YOLOv5](https://github.com/ultralytics/yolov5), and the [ultralytics](https://github.com/ultralytics/ultralytics/) package (from the developers of YOLOv5) has a zillion bells and whistles for both inference and fine-tuning that this package doesn't.
## Reasons you might want to use this package
If you want to programmatically interact with the postprocessing tools from the MegaDetector repo, or programmatically run MegaDetector in a way that produces [Timelapse](https://saul.cpsc.ucalgary.ca/timelapse)-friendly output (i.e., output in the standard [MegaDetector output format](https://github.com/agentmorris/MegaDetector/tree/main/megadetector/api/batch_processing#megadetector-batch-output-format)), this package might be for you.
## If I haven't talked you out of using this package...
To install:
`pip install megadetector`
MegaDetector model weights aren't downloaded at pip-install time, but they will be (optionally) automatically downloaded the first time you run the model.
## Package reference
See [megadetector.readthedocs.io](https://megadetector.readthedocs.io).
## Examples of things you can do with this package
### Run MegaDetector on one image and count the number of detections
```
from megadetector.utils import url_utils
from megadetector.visualization import visualization_utils as vis_utils
from megadetector.detection import run_detector
# This is the image at the bottom of this page, it has one animal in it
image_url = 'https://github.com/agentmorris/MegaDetector/raw/main/images/orinoquia-thumb-web.jpg'
temporary_filename = url_utils.download_url(image_url)
image = vis_utils.load_image(temporary_filename)
# This will automatically download MDv5a; you can also specify a filename.
model = run_detector.load_detector('MDV5A')
result = model.generate_detections_one_image(image)
detections_above_threshold = [d for d in result['detections'] if d['conf'] > 0.2]
print('Found {} detections above threshold'.format(len(detections_above_threshold)))
```
### Run MegaDetector on a folder of images
```
from megadetector.detection.run_detector_batch import \
load_and_run_detector_batch, write_results_to_file
from megadetector.utils import path_utils
import os
# Pick a folder to run MD on recursively, and an output file
image_folder = os.path.expanduser('~/megadetector_test_images')
output_file = os.path.expanduser('~/megadetector_output_test.json')
# Recursively find images
image_file_names = path_utils.find_images(image_folder,recursive=True)
# This will automatically download MDv5a; you can also specify a filename.
results = load_and_run_detector_batch('MDV5A', image_file_names)
# Write results to a format that Timelapse and other downstream tools like.
write_results_to_file(results,
output_file,
relative_path_base=image_folder,
detector_file=detector_filename)
```
## Contact
Contact [email protected] with questions.
## Gratuitous animal picture
Image credit University of Minnesota, from the [Orinoquía Camera Traps](http://lila.science/datasets/orinoquia-camera-traps/) data set.