CameraTraps/archive/visualization

Announcement

At the core of our mission is the desire to create a harmonious space where conservation scientists from all over the globe can unite, share, and grow. We are expanding the CameraTraps repo to introduce Pytorch-Wildlife, a Collaborative Deep Learning Framework for Conservation, where researchers can come together to share and use datasets and deep learning architectures for wildlife conservation.

We've been inspired by the potential and capabilities of Megadetector, and we deeply value its contributions to the community. As we forge ahead with Pytorch-Wildlife, under which Megadetector now resides, please know that we remain committed to supporting, maintaining, and developing Megadetector, ensuring its continued relevance, expansion, and utility.

To use the newest version of MegaDetector with all the exisitng functionatlities, you can use our newly developed user interface or simply load the model with Pytorch-Wildlife and the weights will be automatically downloaded:

from PytorchWildlife.models import detection as pw_detection
detection_model = pw_detection.MegaDetectorV5()

If you'd like to learn more about Pytorch-Wildlife, please continue reading.

For those interested in accessing the previous MegaDetector repository, which utilizes the same MegaDetector v5 model weights and was primarily developed by Dan Morris during his time at Microsoft, please visit the archive directory, or you can visit this forked repository that Dan Morris is actively maintaining.

If you have any questions regarding MegaDetector and Pytorch-Wildlife, please email us!

Visualization tools

This directory contains some stand-alone Python utility scripts that you can use to visualize various incoming and predicted labels on images.

Environment setup

Please see the Installation section in the main README for instructions on setting up the environment to run these scripts in.

Visualize detector output

visualize_detector_output.py draws the bounding boxes, their confidence level, and predicted category annotated on top of the original images, and saves the annotated images to another directory. The original images can be in a local directory or in Azure Blob Storage.

If you are not running this on the computer with the original images, the script can download them from Azure Blob Storage using a SAS key to the container (supplied as the --images-dir argument with the --is-azure flag, please surround the SAS URL by double quotes). It takes about 1.5 seconds per image, depending on your location and network speed. The SAS key looks like

https://storageaccountname.blob.core.windows.net/container-name?se=2019-04-06T23%3A38%3A00Z&sp=rl&sv=2018-03-28&sr=c&sig=A_LONG_STRING

You can choose to render a sample of n images by supplying the --sample argument.

Please run python visualize_detector_output.py -h to see the arguments that the script takes.

Example invocations

It is best to call the script from the root dir of this repo so the path to the repo is on the PYTHONPATH.

Example invocation of the script, images stored locally:

python visualize_detector_output.py path_to/requestID_detections.json rendered_images_dir --confidence 0.9 --images_dir path_to_root_dir_of_original_images

Another example, for images stored in Azure Blob Storage and drawing a sample of 20 images:

python visualize_detector_output.py path_to/requestID_detections.json rendered_images_dir --confidence 0.9 --images-dir "https://storageaccountname.blob.core.windows.net/container-name?se=2019-04-06T23%3A38%3A00Z&sp=rl&sv=2018-03-28&sr=c&sig=A_LONG_STRING" --is-azure --sample 20

If you encounter an error where it complains about not finding the module visualization_utils, you need to append the absolute path to the current directory to your PYTHONPATH. At your terminal or command line:

export PYTHONPATH=$PYTHONPATH:/absolute_path/CameraTraps