Update README.md

Author: MMathisLab

README.md

@@ -5,9 +5,11 @@
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/deeplabcut-live?color=purple)
 ![Python package](https://github.com/DeepLabCut/DeepLabCut-live/workflows/Python%20package/badge.svg)
 
-This package contains a DeepLabCut inference pipeline that has minimal (software) dependencies. Thus, it is as easy to install as possible (in particular, on atypical systems like NVIDIA Jetson boards).
+This package contains a DeepLabCut inference pipeline for real-time applications that has minimal (software) dependencies. Thus, it is as easy to install as possible (in particular, on atypical systems like NVIDIA Jetson boards).
 
-This package provides a `DLCLive` class which enables pose estimation online to provide feedback. This object loads and prepares a DeepLabCut network for inference, and will return the predicted pose for single images.
+**Performance:** If you would like to see estimates on how your model might perform given a video size, neural network type, and hardware, please see: https://deeplabcut.github.io/DLC-inferencespeed-benchmark/ And, consider submitting your results too! https://github.com/DeepLabCut/DLC-inferencespeed-benchmark
+
+**What this SDK provides:** This package provides a `DLCLive` class which enables pose estimation online to provide feedback. This object loads and prepares a DeepLabCut network for inference, and will return the predicted pose for single images.
 
 To perform processing on poses (such as predicting the future pose of an animal given it's current pose, or to trigger external hardware like send TTL pulses to a laser for optogenetic stimulation), this object takes in a `Processor` object. Processor objects must contain two methods: process and save.
 
@@ -22,6 +24,8 @@ For examples, please see the [processor directory](processor)
 
 Please see our instruction manual to install on a [Windows or Linux machine](docs/install_desktop.md) or on a [NVIDIA Jetson Development Board](docs/install_jetson.md)
 
+- available on pypi as: `pip install deeplabcut-live`
+
 
 ### Quick Start: instructions for use:
 
@@ -52,12 +56,47 @@ dlc_live.get_pose(<your image>)
   - `resize` = float, optional; factor by which to resize image (resize=0.5 downsizes both width and height of image by half). Can be used to downsize large images for faster inference
   - `processor` = dlc pose processor object, optional
   - `display` = bool, optional; display processed image with DeepLabCut points? Can be used to troubleshoot cropping and resizing parameters, but is very slow
-  
+
 `DLCLive` **inputs:**
 
   - `<path to exported model directory>` = path to the folder that has the `.pb` files that you acquire after running `deeplabcut.export_model`
   - `<your image>` = is a numpy array of each frame
-  
+
+
+### Benchmarking/Analyzing Exported DeepLabCut Models
+
+DeepLabCut-live offers some analysis tools that allow users to peform the following operations on videos, from python or from the command line: 
+1. Test inference speed across a range of image sizes, downsizing images by specifying the `resize` or `pixels` parameter. Using the `pixels` parameter will resize images to the desired number of `pixels`, without changing the aspect ratio. Results will be saved (along with system info) to a pickle file if you specify an output directory.
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', ['/path/to/video1', '/path/to/video2'], output='/path/to/output', resize=[1.0, 0.75, '0.5'])
+```
+##### command line
+```
+dlc-live-analyze /path/to/exported/model /path/to/video1 /path/to/video2 -o /path/to/output -r 1.0 0.75 0.5
+```
+
+2. Display keypoints to visually inspect the accuracy of exported models on different image sizes (note, this is slow and only for testing purposes):
+
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=[0.5], display=True, pcutoff=0.5, display_radius=4, cmap='bmy')
+```
+##### command line
+```
+dlc-live-analyze /path/to/exported/model /path/to/video -r 0.5 --display --pcutoff 0.5 --display-radius 4 --cmap bmy
+```
+
+3. Analyze and create a labeled video using the exported model and desired resize parameters. This option functions similar to `deeplabcut.analyze_videos` and `deeplabcut.create_labeled_video` (note, this is slow and only for testing purposes).
+
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=[1.0, 0.75, 0.5], pcutoff=0.5, display_radius=4, cmap='bmy', save_poses=True, save_video=True)
+```
+##### command line
+```
+dlc-live-analyze /path/to/exported/model /path/to/video -r 0.5 --pcutoff 0.5 --display-radius 4 --cmap bmy --save_poses --save_video
+```
 
 ### Citation: