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Last active May 10, 2023 17:58
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require "onnxruntime"
require "mini_magick"
img ="bears.jpg")
pixels = img.get_pixels
model ="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
# draw text
img.combine_options do |c|
c.draw "text #{left},#{top - 5} \"#{label}\""
c.fill "red"
c.pointsize 18
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)
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ankane commented Jul 5, 2022

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.

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great work

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Where can I find tf2onnx.convert to create model.onnx?

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ankane commented May 10, 2023

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