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adjust detection

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This commit is contained in:
Pete Matsyburka
2025-11-03 17:00:52 +02:00
parent 291d869843
commit bf6afc5e61
3 changed files with 50 additions and 35 deletions
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lib/templates/image_to_fields.rb
+33 -24
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@@ -19,49 +19,65 @@ module Templates
# rubocop:disable Metrics
def call(image, confidence: 0.3, nms: 0.1, temperature: 1,
split_page: false, aspect_ratio: true, padding: nil)
split_page: false, aspect_ratio: true, padding: nil, resolution: RESOLUTION)
base_image = image.extract_band(0, n: 3)
trimmed_base, base_offset_x, base_offset_y = trim_image_with_padding(base_image, padding)
if split_page && image.height > image.width
half_h = trimmed_base.height / 2
top_h = half_h
bottom_h = trimmed_base.height - half_h
regions = [
{ img: trimmed_base.crop(0, 0, trimmed_base.width, top_h), offset_y: 0 },
{ img: trimmed_base.crop(0, top_h, trimmed_base.width, bottom_h), offset_y: top_h }
]
regions = build_split_image_regions(trimmed_base)
detections = { xyxy: Numo::SFloat[], confidence: Numo::SFloat[], class_id: Numo::Int32[] }
detections = regions.reduce(detections) do |acc, r|
next detections if r[:img].height <= 0 || r[:img].width <= 0
input_tensor, transform_info = preprocess_image(r[:img], RESOLUTION, aspect_ratio:)
input_tensor, transform_info = preprocess_image(r[:img], resolution, aspect_ratio:)
transform_info[:trim_offset_x] = base_offset_x
transform_info[:trim_offset_y] = base_offset_y + r[:offset_y]
outputs = model.predict({ 'input' => input_tensor })
postprocess_outputs(outputs, transform_info, acc, confidence:, temperature:)
boxes = Numo::SFloat.cast(outputs['dets'])[0, true, true]
logits = Numo::SFloat.cast(outputs['labels'])[0, true, true]
postprocess_outputs(boxes, logits, transform_info, acc, confidence:, temperature:, resolution:)
end
else
input_tensor, transform_info = preprocess_image(trimmed_base, RESOLUTION, aspect_ratio:)
input_tensor, transform_info = preprocess_image(trimmed_base, resolution, aspect_ratio:)
transform_info[:trim_offset_x] = base_offset_x
transform_info[:trim_offset_y] = base_offset_y
outputs = model.predict({ 'input' => input_tensor })
detections = postprocess_outputs(outputs, transform_info, confidence:, temperature:)
boxes = Numo::SFloat.cast(outputs['dets'])[0, true, true]
logits = Numo::SFloat.cast(outputs['labels'])[0, true, true]
detections = postprocess_outputs(boxes, logits, transform_info, confidence:, temperature:, resolution:)
end
detections = apply_nms(detections, nms)
fields = Array.new(detections[:xyxy].shape[0]) do |i|
fields = build_fields_from_detections(detections, image)
sort_fields(fields, y_threshold: 10.0 / image.height)
end
def build_split_image_regions(image)
half_h = image.height / 2
top_h = half_h
bottom_h = image.height - half_h
[
{ img: image.crop(0, 0, image.width, top_h), offset_y: 0 },
{ img: image.crop(0, top_h, image.width, bottom_h), offset_y: top_h }
]
end
def build_fields_from_detections(detections, image)
Array.new(detections[:xyxy].shape[0]) do |i|
x1 = detections[:xyxy][i, 0]
y1 = detections[:xyxy][i, 1]
x2 = detections[:xyxy][i, 2]
@@ -87,8 +103,6 @@ module Templates
confidence:
)
end
sort_fields(fields, y_threshold: 10.0 / image.height)
end
def trim_image_with_padding(image, padding = 0)
@@ -185,13 +199,8 @@ module Templates
Numo::Int32.cast(keep)
end
def postprocess_outputs(outputs, transform_info, detections = nil, confidence: 0.3, temperature: 1)
boxes = Numo::SFloat.cast(outputs['dets'])
logits = Numo::SFloat.cast(outputs['labels'])
boxes = boxes[0, true, true] # [300, 4]
logits = logits[0, true, true] # [300, num_classes]
def postprocess_outputs(boxes, logits, transform_info, detections = nil, confidence: 0.3, temperature: 1,
resolution: RESOLUTION)
scaled_logits = logits / temperature
probs = 1.0 / (1.0 + Numo::NMath.exp(-scaled_logits))
@@ -215,7 +224,7 @@ module Templates
boxes_xyxy[true, 2] = x2
boxes_xyxy[true, 3] = y2
boxes_xyxy *= RESOLUTION
boxes_xyxy *= resolution
pad_x = transform_info[:pad_x]
pad_y = transform_info[:pad_y]