Detecto

Adds support for live webcam detection (14) with the `visualize.detect_live` function.

Given that custom-trained Detecto models often output predictions with scores of 0.6 or 0.7, the default score_filter parameter for visualize_detect_video and visualize.plot_prediction_grid has been lowered to 0.6.

To prevent this change in behavior, you can explicitly set score_filter=0.8 as follows:

python
from detecto import visualize

visualize.detect_video(model, 'input_vid.mp4', 'output_vid.avi', score_filter=0.8)
visualize.plot_prediction_grid(model, images, score_filter=0.8)

Summary of changes (coming from pull request 19):

* Changes behavior of detect_video and plot_prediction_grid to show all predictions above a certain threshold (optional parameter) rather than the top prediction per class
* Allows show_labeled_image to accept labels to display along with boxes
* Fixes bug when running detect_video on a default model

All changes are non-breaking.

Major updates to the core module make it even easier to use Detecto. In addition, a new utils.split_video function helps users generate image data from video footage.

* Split video function 12
* Increased flexibility of classes in core module 15
* Support for default pre-trained model 18

Note: for using the default model, it's recommended to wait until v1.0.1 is released, which will fix a few minor bugs and improve existing visualization methods to better support the default model's predictions.

* Removes Pillow version dependency 8
* Adds automatic reverse normalization to visualize.show_labeled_image 7
* Fixes excessive memory usage build error for Read the Docs