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require "onnxruntime" | |
require "mini_magick" | |
img = MiniMagick::Image.open("bears.jpg") | |
pixels = img.get_pixels | |
model = OnnxRuntime::Model.new("model.onnx") | |
result = model.predict({"inputs" => [pixels]}) | |
p result["num_detections"] | |
p result["detection_classes"] | |
coco_labels = { | |
23 => "bear", | |
88 => "teddy bear" | |
} | |
def draw_box(img, label, box) | |
width, height = img.dimensions | |
thickness = 2 | |
top = (box[0] * height).round - thickness | |
left = (box[1] * width).round - thickness | |
bottom = (box[2] * height).round + thickness | |
right = (box[3] * width).round + thickness | |
# draw box | |
img.combine_options do |c| | |
c.draw "rectangle #{left},#{top} #{right},#{bottom}" | |
c.fill "none" | |
c.stroke "red" | |
c.strokewidth thickness | |
end | |
# draw text | |
img.combine_options do |c| | |
c.draw "text #{left},#{top - 5} \"#{label}\"" | |
c.fill "red" | |
c.pointsize 18 | |
end | |
end | |
result["num_detections"].each_with_index do |n, idx| | |
n.to_i.times do |i| | |
label = result["detection_classes"][idx][i].to_i | |
label = coco_labels[label] || label | |
box = result["detection_boxes"][idx][i] | |
draw_box(img, label, box) | |
end | |
end | |
img.write("labeled.jpg") |
Make sure you're using the latest version of tf2onnx to convert the model. Also, the blog post has info on how to check the input names.
great work
Where can I find tf2onnx.convert to create model.onnx?
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I'm not aware of any Ruby libraries that can convert TensorFlow models to ONNX.