In the past two weeks, we have made good progress on the prototype. I have been working with Ethan to move the camera around to frame the photos while balancing defocus blur and resolution. I also built the preprocessing routine to segment the region of interests (rank and suit) from black-and-white captures. From this, we have some test captures I used to build the preprocessing and segmentation. This preprocessing is incredibly fast since it outputs a binary image and uses aggressive downsampling, so that will help us hit our latency target.
One issue we ran into was camera defocus. While the lens claims that 3cm is the minimum object distance, we found the edges to be out of focus. Thankfully, thresholding on intensity removes any blurred boundaries without any artifacts.
Secondly, I discovered the Nvidia Jetson Nano’s camera interface connector cannot support 120Hz captures (even though the camera sensor does). As such, I’ve been working on 60Hz captures. I have not noticed any issues with motion blur in the captures but will update if that becomes a concern.
This imaging system relies heavily on an accurate trigger. We need the trigger to immediately identify the capture where the rank and suit is within the image boundaries. I am working with Ethan to fine-tune the trigger positioning and timing. I hope to avoid identifying the image with the rank and suit in software since that would add significant processing time.
I am currently on schedule. Since the trigger is far more critical to the product than we initially realized, we may be delayed in building the final prototype next week. I will update in next week’s status report.
Attached are some example captures and their preprocessed outputs. We will save the largest binary blobs that correspond to the suit and rank. Those inputs will be the inputs to our classifier. Note these preprocessed images are currently ~40x15px after downsampling.
