Real-time imaging and pose estimation

I’ve made substantial progress to read real-time DeepLabCut Live date (see paper by Kane et al here) from Scanbox. As you can see in the example below, I got to the point where we can do two-photon imaging and pose estimation in real time.

As you recall, the behavior cameras in Scanbox are triggered once per frame, which leads to the same number of behavior and microscope frames. The real-time pose data also gets displayed and saved directly in the info structure of Scanbox. Here is how it looks for the case above, where we have the pose for 8 different locations along the iris.

ans = 

  struct with fields:

    pose: [3×8 single]

>> info.dlc_eye_data(1).pose

ans =

  3×8 single matrix

   72.7508   75.4426   84.9112   63.3472   80.4150   68.1809   67.1091   82.9950
   19.3464   72.1103   43.8801   31.7922   23.4480   49.3159   21.9147   51.1657
    0.9999    0.2841    0.9270    0.9900    0.9998    0.6011    1.0000    0.3656


Let me share how I did this, as some of the methods may be useful to others that want to do the same thing with DLC Live outside of Scanbox.

My approach consists in doing image acquisition using the imaq toolbox in Matlab, and to share the data with a Python process via memory-mapped files. One advantage of this mechanism is that it allows all the cameras models supported in the image acquisition toolbox to feed data to DLC Live. This is convenient because, as of today , DLC Live does not support a huge range of models.

To provide a concrete example of how this approach works, consider the following, 16-lines code of Python:

import numpy as np
from dlclive import DLCLive

image_in  = np.memmap('c:/2pdata/sbx2dlc', dtype='uint8',mode='r+',shape=(112,160))	# the image
data_out  = np.memmap('c:/2pdata/dlc2sbx', dtype='float32',mode='r+',shape=(8,3))	# the data

dlc_live = DLCLive('C:/Users/dario/Documents/eye2p-Dario-2021-03-21/exported-models/DLC_eye2p_resnet_50_iteration-0_shuffle-1')

print('DLCLive Ready to process Matlab data stream')

while True:
	if image_in[0,0] != 0:								# wait for a new image_in
		pose = dlc_live.get_pose(image_in)		        # get the pose
		image_in[0,0] = 0								# report we are next for the next one
		data_out[:] = pose[:]

It simply opens two memory mapped files — one to read the incoming images and another to write the results of pose estimation. The handshake is rather primitive. I use the first pixel as rudimentary semaphore. When Matlab writes a new image, it sets that pixel to 1. When DLC Live finishes estimating the pose, it tells Matlab it is done by sharing the estimates and and setting the pixel to 0.

You can run the above script in the DLC-Live environment and it will first print a bunch of CUDA gibberish, and finally you will see “DLCLive Ready to process Matlab data stream”. Now the script is just sitting there waiting for data from Matlab.

On the Matlab side you can have something like:

% Closing the DLC Live loop with Matlab (dlr 3/22/2021)

% memory mapped files

image_out = memmapfile('c:\2pdata\sbx2dlc','Writable',true,'Format',{'uint8', [112 160], 'img'});
data_in   = memmapfile('c:\2pdata\dlc2sbx','Writable',false,'Format',{'single', [3 8], 'pose'});

% I am reading from an old file here, but you can get the data from a
% camera instead

vr = VideoReader('bmi00_063_001_eye.mj2');

% create a Figure 

h_img = image(vr.readFrame);
axis image off
colormap gray
hold on
h_data = plot(zeros(1,8),zeros(1,8),'r.','markersize',12);
hold off;

image_out.Data.img(1,1)=0; % nothing written yet.

while true
    x = vr.readFrame;
    image_out.Data.img = x';
    while(image_out.Data.img(1,1) ~=0)      % wait for it to finish
    h_img.CData = x;
    h_data.XData = data_in.Data.pose(1,:)+1;  % update pose in graph
    h_data.YData = data_in.Data.pose(2,:)+1; 
    fprintf("Frame rate = %.1f\n",1/toc);


In this example, I just send images from an old movie file I have, but you can easily do it by using a hook to the preview function in Matlab, which is what I did the Scanbox video above.

In this example, just for benchmarking, purposes, I am displaying the images and the estimated pose as well. This code runs at 50-60 fps on an old Quadro M6000. One would expect it to do much better on newer cards.

Thanks to the Mathis Lab for producing these tools and answering questions. If you have moments and/or suggestions about the code above let me know (can you think of a solution that does not involve polling?). If you have questions about DeepLabCut the best people to answer them in the Github forums.