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bird_bar [2024/02/01 00:17] – [bird bar] align image qlyoungbird_bar [2024/07/03 19:16] (current) – change ai tag to machine learning qlyoung
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 {{ :me-with-phone-yolo-detection.png?200|Screen capture of webcam feed after applying YOLOv5's out-of-box 'small' model to a scene of me holding up my cell phone. Picture is heavily blurred}} {{ :me-with-phone-yolo-detection.png?200|Screen capture of webcam feed after applying YOLOv5's out-of-box 'small' model to a scene of me holding up my cell phone. Picture is heavily blurred}}
  
-I’d read about [[https://pjreddie.com/darknet/yolo/|YOLO] some years before and began to reacquaint myself. It’s come quite far and seems to be more or less the state of the art for realtime computer vision object detection and classification. I downloaded the latest version ([[https://github.com/ultralytics/yolov5|YOLOv5]] at time of writing) and ran the webcam demo. It ran well over 30fps with good accuracy on my RTX3080, correctly picking out myself as “person”, my phone as “cell phone”, and my light switch as “clock”.+I’d read about [[https://pjreddie.com/darknet/yolo/|YOLO]] some years before and began to reacquaint myself. It’s come quite far and seems to be more or less the state of the art for realtime computer vision object detection and classification. I downloaded the latest version ([[https://github.com/ultralytics/yolov5|YOLOv5]] at time of writing) and ran the webcam demo. It ran well over 30fps with good accuracy on my RTX3080, correctly picking out myself as “person”, my phone as “cell phone”, and my light switch as “clock”.
  
 Out of the box YOLOv5 is trained on COCO, which is a dataset of _co_mmon objects in _co_ntext. This dataset is able to identify a picture of a Carolina chickadee as “bird”. Tufted titmice are also identified as “bird”. All birds are “bird” to COCO (at least the ones I tried). Out of the box YOLOv5 is trained on COCO, which is a dataset of _co_mmon objects in _co_ntext. This dataset is able to identify a picture of a Carolina chickadee as “bird”. Tufted titmice are also identified as “bird”. All birds are “bird” to COCO (at least the ones I tried).
  
-{{ :chickadee-2.jpg?200|Image of chickadee with a poorly sized bounding box drawn around it with the label "bird" and a confidence rating of 0.31}}+{{:chickadee-2.jpg?200|Image of chickadee with a poorly sized bounding box drawn around it with the label "bird" and a confidence rating of 0.31}}
  
 Pretty good, but not exactly what I was going for. YOLO needed to be trained to recognize specific bird species. Pretty good, but not exactly what I was going for. YOLO needed to be trained to recognize specific bird species.
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 After deciding that I wanted to maintain this as a long term installation I ponied up for a NUC and an eGPU enclosure. I initially tried to use the enclosure with an RTX 3070, but I couldn’t get it working with that card so I used a spare 1070 instead which worked flawlessly. The 1070 runs at about 25fps when inferencing with my bird model which is more than enough to look snappy overlaid on a video feed. The whole thing sits on my kitchen floor and is relatively unobtrusive. After deciding that I wanted to maintain this as a long term installation I ponied up for a NUC and an eGPU enclosure. I initially tried to use the enclosure with an RTX 3070, but I couldn’t get it working with that card so I used a spare 1070 instead which worked flawlessly. The 1070 runs at about 25fps when inferencing with my bird model which is more than enough to look snappy overlaid on a video feed. The whole thing sits on my kitchen floor and is relatively unobtrusive.
  
-====== 60fps ======+==== 60fps ====
  
 Up to this point I was streaming the window with annotated frames displayed by YOLO’s detect.py convenience script. However, this window updates only as often as an inferencing run completes, so around 25fps. It doesn't look good on a livestream. It would be better to stream video straight from the camera at native framerates (ideally 60fps) and overlay the labels on top of it. Up to this point I was streaming the window with annotated frames displayed by YOLO’s detect.py convenience script. However, this window updates only as often as an inferencing run completes, so around 25fps. It doesn't look good on a livestream. It would be better to stream video straight from the camera at native framerates (ideally 60fps) and overlay the labels on top of it.
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 This was stable for over a year, until I decided to install Windows 11. What could go wrong? This was stable for over a year, until I decided to install Windows 11. What could go wrong?
 +
 +==== Camera ====
 +
 +The original setup used an off-brand 720p webcam wrapped in a righteous amount of plastic wrap for weatherproofing. Surprisingly the weatherproofing worked well and there was never a major failure while using the first camera. However, the quality and color on that camera wasn’t good and an upgrade was due. I already had a Logitech Brio 4k webcam intended for remote work, but it ended up largely unused so it was repurposed for birdwatching.
 +
 +While the plastic wrap method never had any major failures it wasn’t ideal either. Heavy humidity created fogging inside the plastic that could take a few hours to wear off. It needed replacing anytime the camera was adjusted. Due to these problems and the higher cost of the Brio I decided to build a weatherproof enclosure.
 +
 +The feeder is constructed of acrylic. My initial plan was to use acrylic sheeting build out an extension to the feeder big enough to house the camera. I picked up some acrylic sheeting from Amazon and began researching appropriate adhesives. It turns out most adhesives don’t work very well on acrylic, at least not for my use case – the load bearing joints between the sheets were thin and I needed the construction to be rigid enough to support its own weight and the weight of the camera without sagging. Since the enclosure would be suspended over air relying on its inherent rigidity for structure the adhesive needed to be strong.
 +
 +The best way to adhere acrylic to itself is using acrylic cement. Acrylic cement dissolves the surfaces of the two pieces to be bonded, allowing them to mingle, and then evaporates away. This effectively fuses the two pieces together with a fairly strong bond (though not as strong as if the piece had been manufactured that way).
 +
 +{{:acrylic-cement.jpg?400 |}}
 +
 +{{:assembled-box.jpg?400 |}}
 +
 +Three sides were opaque to prevent sunlight reflections within the box. Joints were caulked and taped the joints to increase weather resistance. I played around with using magnets to secure the enclosure to the main feeder body but didn’t come up with anything I liked, so I glued it to the feeder with more acrylic cement, threw my camera in there and called it a day.
 +
 +{{ :full-setup-with-first-weather-shield.jpg?600 |}}
 +
 +This weatherproofing solution turned out great. It successfully protected the camera from all inclement weather until I retired that feeder, surviving rain, snow, and high winds over the course of the year.
  
 ====== Switching to Linux ====== ====== Switching to Linux ======
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-{{tag>from_blog technology ai nature}}+{{tag>from_blog technology machine_learning nature}}
  
  
  
  
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bird_bar.1706746674.txt.gz · Last modified: 2024/02/01 00:17 by qlyoung
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