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To help with this process, we have developed some open resources that can use a cellphone video as input, and a cluster count as output. We have written a public Google Colaboratory (Colab) that is freely available and can detect, track, and count flower clusters in video files. This makes it possible to get a rough count of clusters over a given distance in a vineyard. Here we’ll walk you through the steps to use our Colab notebook—a free tool from Google—to upload and analyze your own cluster videos using one of our pre-trained models. This Colab notebook is compatible with custom object detection models as well.
Getting Started
To use our video cluster counter, you will need a few things:
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In this tutorial we will be using ‘nighttime-grape-flower-clusters/1’ which is a nighttime cluster counting model. You can explore our other cluster counting models, leverage any Roboflow Universe models, or even train your own model.
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Credit: The data used to train nighttime‘nighttime-grape-flower-clusters/1 1’ was originally captured and trained by Jonathan Jaramillo as part of his PHD research which motivated the creation of this guide. Read the original paper >> |
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Note: ‘nighttime-grape-flower-clusters/1’ is a nighttime-only model. This means you’ll need to capture your video at night using a lighting source. We recommend placing light sources a few feet above and/or below your camera as in the figure below:  |
You will want to experiment with different frame rates and video lengths to see how effectively they work with in our cluster counter. The video used in this tutorial was filmed at 250 fps and is 17 seconds long. We are actively testing much lower frame rates (60 fps) and longer videos and we will update this document as we come up with further recommendations. Here is the video we’ll be using in this example:
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Sign in to drive.google.com using your Google account. Click on ‘My Drive’ to see any existing files/folders in your Google Drive. Click ‘New’ → ‘New folder’ to create a new folder for your project. For the sake of this tutorial, call the folder ‘Computer_Vision’.
Next, double-click into your ‘Computer_Vision’ folder. Inside this folder, we will upload our model weights alongside the video file you have captured with your phone. For this tutorial, download our ‘nighttime-grape-flower-clusters’ model and upload it to your ‘Computer_Vision’ folder. You can download the file here:
View file name nighttime-grape-flower-clusters.pt At this point, you should have a video file and a trained model next to each other in your ‘Computer_Vision’ folder . For the sake of on Google Drive. In this tutorial, we’ll name our file ‘test.MOV’. Don’t worry if your video file is in a different format; our processing script will convert it later.
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At this point, you should be all set to analyze your video and count the clusters! Simply step through one code block at a time pressing the little play button on the left and waiting for a checkbox to appear before moving on to the next block. Some blocks will take a significantly longer time to run. Be sure not to close the Colab or you will have to start at the top again (despite the checkboxes remaining).
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