mirror of https://github.com/docusealco/docuseal
Pete Matsyburka
1 week ago
parent
b46ced2b6f
commit
1c8a7b6a7c
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# frozen_string_literal: true
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class TemplatesDetectFieldsController < ApplicationController
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include ActionController::Live
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load_and_authorize_resource :template
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def create
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response.headers['Content-Type'] = 'text/event-stream'
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sse = SSE.new(response.stream)
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documents = @template.schema_documents.preload(:blob)
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documents.each do |document|
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io = StringIO.new(document.download)
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Templates::DetectFields.call(io, attachment: document) do |(attachment_uuid, page, fields)|
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sse.write({ attachment_uuid:, page:, fields: })
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end
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end
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sse.write({ completed: true })
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ensure
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response.stream.close
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end
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end
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# frozen_string_literal: true
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module Templates
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module DetectFields
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module_function
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TextFieldBox = Struct.new(:x, :y, :w, :h, keyword_init: true)
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# rubocop:disable Metrics
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def call(io, attachment: nil, confidence: 0.3, temperature: 1,
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nms: 0.1, split_page: false, aspect_ratio: true, padding: 20, &)
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if attachment&.image?
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process_image_attachment(io, attachment:, confidence:, nms:, split_page:,
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temperature:, aspect_ratio:, padding:, &)
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else
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process_pdf_attachment(io, attachment:, confidence:, nms:, split_page:,
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temperature:, aspect_ratio:, padding:, &)
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end
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end
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def process_image_attachment(io, attachment:, confidence:, nms:, temperature: 1,
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split_page: false, aspect_ratio: false, padding: nil)
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image = Vips::Image.new_from_buffer(io.read, '')
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fields = Templates::ImageToFields.call(image, confidence:, nms:, split_page:,
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temperature:, aspect_ratio:, padding:)
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fields = fields.map do |f|
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{
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uuid: SecureRandom.uuid,
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type: f.type,
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required: true,
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preferences: {},
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areas: [{
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x: f.x,
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y: f.y,
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w: f.w,
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h: f.h,
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page: 0,
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attachment_uuid: attachment&.uuid
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}]
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}
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end
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yield [attachment&.uuid, 0, fields] if block_given?
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fields
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end
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def process_pdf_attachment(io, attachment:, confidence:, nms:, temperature: 1,
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split_page: false, aspect_ratio: false, padding: nil)
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doc = Pdfium::Document.open_bytes(io.read)
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doc.page_count.times.flat_map do |page_number|
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page = doc.get_page(page_number)
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data, width, height = page.render_to_bitmap(width: ImageToFields::RESOLUTION * 1.5)
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image = Vips::Image.new_from_memory(data, width, height, 4, :uchar)
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fields = Templates::ImageToFields.call(image, confidence: 0.05, nms:, split_page:,
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temperature:, aspect_ratio:, padding:)
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text_fields = extract_text_fields_from_page(page)
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line_fields = extract_line_fields_from_page(page)
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fields = increase_confidence_for_overlapping_fields(fields, text_fields)
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fields = increase_confidence_for_overlapping_fields(fields, line_fields)
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fields = fields.filter_map do |f|
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next if f.confidence < confidence
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{
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uuid: SecureRandom.uuid,
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type: f.type,
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required: true,
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preferences: {},
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areas: [{
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x: f.x, y: f.y,
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w: f.w, h: f.h,
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page: page_number,
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attachment_uuid: attachment&.uuid
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}]
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}
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end
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yield [attachment&.uuid, page_number, fields] if block_given?
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fields
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end
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end
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def extract_line_fields_from_page(page)
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line_thickness = 5.0 / page.height
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vertical_lines, all_horizontal_lines = page.line_nodes.partition { |line| line.tilt == 90 }
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horizontal_lines = all_horizontal_lines.reject do |h_line|
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next true if h_line.w > 0.7 && (h_line.h < 0.1 || h_line.h < 0.9)
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next false if vertical_lines.blank?
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h_x_min = h_line.x
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h_x_max = h_line.x + h_line.w
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h_y_avg = h_line.y + (h_line.h / 2)
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vertical_lines.any? do |v_line|
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v_x_avg = v_line.x + (v_line.w / 2)
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v_y_min = v_line.y
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v_y_max = v_line.y + v_line.h
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h_x_min_expanded = h_x_min - line_thickness
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h_x_max_expanded = h_x_max + line_thickness
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h_y_min_expanded = h_y_avg - line_thickness
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h_y_max_expanded = h_y_avg + line_thickness
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v_x_min_expanded = v_x_avg - line_thickness
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v_x_max_expanded = v_x_avg + line_thickness
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v_y_min_expanded = v_y_min - line_thickness
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v_y_max_expanded = v_y_max + line_thickness
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x_overlap = v_x_min_expanded <= h_x_max_expanded && v_x_max_expanded >= h_x_min_expanded
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y_overlap = h_y_min_expanded <= v_y_max_expanded && h_y_max_expanded >= v_y_min_expanded
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x_overlap && y_overlap
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end
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end
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node_index = 0
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horizontal_lines = horizontal_lines.reject do |line|
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nodes = []
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loop do
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node = page.text_nodes[node_index += 1]
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break unless node
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break if node.y > line.y
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next if node.x + node.w < line.x || line.x + line.w < node.x ||
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node.y + node.h < line.y - node.h || line.y < node.y
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nodes << node
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next if nodes.blank?
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next_node = page.text_nodes[node_index + 1]
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break if next_node.x + next_node.w < line.x || line.x + line.w < next_node.x ||
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next_node.y + next_node.h < line.y - next_node.h || line.y < next_node.y
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end
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next if nodes.blank?
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width = nodes.last.x + nodes.last.w - nodes.first.x
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next true if width > line.w / 2.0
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end
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horizontal_lines.each do |line|
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line.h += 4 * line_thickness
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line.y -= 4 * line_thickness
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end
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end
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def extract_text_fields_from_page(page)
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text_nodes = page.text_nodes
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field_boxes = []
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i = 0
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while i < text_nodes.length
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node = text_nodes[i]
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next i += 1 if node.content != '_'
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x1 = node.x
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y1 = node.y
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x2 = node.x + node.w
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y2 = node.y + node.h
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underscore_count = 1
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j = i + 1
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while j < text_nodes.length
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next_node = text_nodes[j]
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break unless next_node.content == '_'
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distance = next_node.x - x2
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height_diff = (next_node.y - y1).abs
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break if distance > 0.02 || height_diff > node.h * 0.5
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underscore_count += 1
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next_x2 = next_node.x + next_node.w
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next_y2 = next_node.y + next_node.h
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x2 = next_x2
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y2 = [y2, next_y2].max
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y1 = [y1, next_node.y].min
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j += 1
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end
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field_boxes << TextFieldBox.new(x: x1, y: y1, w: x2 - x1, h: y2 - y1) if underscore_count >= 2
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i = j
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end
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field_boxes
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end
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def calculate_iou(box1, box2)
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x1 = [box1.x, box2.x].max
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y1 = [box1.y, box2.y].max
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x2 = [box1.x + box1.w, box2.x + box2.w].min
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y2 = [box1.y + box1.h, box2.y + box2.h].min
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intersection_width = [0, x2 - x1].max
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intersection_height = [0, y2 - y1].max
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intersection_area = intersection_width * intersection_height
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return 0.0 if intersection_area.zero?
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box1_area = box1.w * box1.h
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box2_area = box2.w * box2.h
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union_area = box1_area + box2_area - intersection_area
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intersection_area / union_area
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end
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def boxes_overlap?(box1, box2)
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!(box1.x + box1.w < box2.x || box2.x + box2.w < box1.x ||
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box1.y + box1.h < box2.y || box2.y + box2.h < box1.y)
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end
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def increase_confidence_for_overlapping_fields(image_fields, text_fields, by: 1.0)
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return image_fields if text_fields.blank?
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image_fields.map do |image_field|
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next if image_field.type != 'text'
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field_bottom = image_field.y + image_field.h
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text_fields.each do |text_field|
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break if text_field.y > field_bottom
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next if text_field.y + text_field.h < image_field.y
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next unless boxes_overlap?(image_field, text_field) && calculate_iou(image_field, text_field) > 0.5
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break image_field.confidence += by
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end
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end
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image_fields
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end
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# rubocop:enable Metrics
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end
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end
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@ -0,0 +1,331 @@
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# frozen_string_literal: true
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module Templates
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module ImageToFields
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module_function
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Field = Struct.new(:type, :x, :y, :w, :h, :confidence, keyword_init: true)
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MODEL_PATH = Rails.root.join('tmp/model_704_int8.onnx')
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RESOLUTION = 704
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ID_TO_CLASS = %w[text checkbox].freeze
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MEAN = [0.485, 0.456, 0.406].freeze
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STD = [0.229, 0.224, 0.225].freeze
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CPU_THREADS = Etc.nprocessors
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# rubocop:disable Metrics
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def call(image, confidence: 0.3, nms: 0.1, temperature: 1,
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split_page: false, aspect_ratio: true, padding: nil)
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base_image = image.extract_band(0, n: 3)
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trimmed_base, base_offset_x, base_offset_y = trim_image_with_padding(base_image, padding)
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if split_page && image.height > image.width
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half_h = trimmed_base.height / 2
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top_h = half_h
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bottom_h = trimmed_base.height - half_h
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regions = [
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{ img: trimmed_base.crop(0, 0, trimmed_base.width, top_h), offset_y: 0 },
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{ img: trimmed_base.crop(0, top_h, trimmed_base.width, bottom_h), offset_y: top_h }
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]
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detections = { xyxy: Numo::SFloat[], confidence: Numo::SFloat[], class_id: Numo::Int32[] }
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detections = regions.reduce(detections) do |acc, r|
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next detections if r[:img].height <= 0 || r[:img].width <= 0
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input_tensor, transform_info = preprocess_image(r[:img], RESOLUTION, aspect_ratio:)
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transform_info[:trim_offset_x] = base_offset_x
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transform_info[:trim_offset_y] = base_offset_y + r[:offset_y]
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outputs = model.predict({ 'input' => input_tensor })
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postprocess_outputs(outputs, transform_info, acc, confidence:, temperature:)
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end
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else
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input_tensor, transform_info = preprocess_image(trimmed_base, RESOLUTION, aspect_ratio:)
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transform_info[:trim_offset_x] = base_offset_x
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transform_info[:trim_offset_y] = base_offset_y
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outputs = model.predict({ 'input' => input_tensor })
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detections = postprocess_outputs(outputs, transform_info, confidence:, temperature:)
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end
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detections = apply_nms(detections, nms)
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fields = Array.new(detections[:xyxy].shape[0]) do |i|
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x1 = detections[:xyxy][i, 0]
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y1 = detections[:xyxy][i, 1]
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x2 = detections[:xyxy][i, 2]
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y2 = detections[:xyxy][i, 3]
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class_id = detections[:class_id][i].to_i
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confidence = detections[:confidence][i]
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x0_norm = x1 / image.width.to_f
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y0_norm = y1 / image.height.to_f
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x1_norm = x2 / image.width.to_f
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y1_norm = y2 / image.height.to_f
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type_name = ID_TO_CLASS[class_id]
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Field.new(
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type: type_name,
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x: x0_norm,
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y: y0_norm,
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w: (x1_norm - x0_norm),
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h: (y1_norm - y0_norm),
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confidence:
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)
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end
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sort_fields(fields, y_threshold: 10.0 / image.height)
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end
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def trim_image_with_padding(image, padding = 0)
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return [image, 0, 0] if padding.nil?
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left, top, trim_width, trim_height = image.find_trim(threshold: 10, background: [255, 255, 255])
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padded_left = [left - padding, 0].max
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padded_top = [top - padding, 0].max
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padded_right = [left + trim_width + padding, image.width].min
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padded_bottom = [top + trim_height + padding, image.height].min
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width = padded_right - padded_left
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height = padded_bottom - padded_top
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trimmed_image = image.crop(padded_left, padded_top, width, height)
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[trimmed_image, padded_left, padded_top]
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end
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def preprocess_image(image, resolution, aspect_ratio: false)
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scale_x = resolution.to_f / image.width
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scale_y = resolution.to_f / image.height
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if aspect_ratio
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scale = [scale_x, scale_y].min
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new_width = (image.width * scale).round
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new_height = (image.height * scale).round
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resized = image.resize(scale, vscale: scale, kernel: :lanczos3)
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pad_x = ((resolution - new_width) / 2.0).round
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pad_y = ((resolution - new_height) / 2.0).round
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image = resized.embed(pad_x, pad_y, resolution, resolution, background: [255, 255, 255])
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transform_info = { scale_x: scale, scale_y: scale, pad_x: pad_x, pad_y: pad_y }
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else
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image = image.resize(scale_x, vscale: scale_y, kernel: :lanczos3)
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transform_info = { scale_x: scale_x, scale_y: scale_y, pad_x: 0, pad_y: 0 }
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end
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image /= 255.0
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image = (image - MEAN) / STD
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pixel_data = image.write_to_memory
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img_array = Numo::SFloat.from_binary(pixel_data, [resolution, resolution, 3])
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img_array = img_array.transpose(2, 0, 1)
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[img_array.reshape(1, 3, resolution, resolution), transform_info]
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end
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def nms(boxes, scores, iou_threshold = 0.5)
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return Numo::Int32[] if boxes.shape[0].zero?
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x1 = boxes[true, 0]
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y1 = boxes[true, 1]
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x2 = boxes[true, 2]
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y2 = boxes[true, 3]
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areas = (x2 - x1) * (y2 - y1)
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order = scores.sort_index.reverse
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keep = []
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while order.size.positive?
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i = order[0]
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keep << i
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break if order.size == 1
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xx1 = Numo::SFloat.maximum(x1[i], x1[order[1..]])
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yy1 = Numo::SFloat.maximum(y1[i], y1[order[1..]])
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xx2 = Numo::SFloat.minimum(x2[i], x2[order[1..]])
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yy2 = Numo::SFloat.minimum(y2[i], y2[order[1..]])
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w = Numo::SFloat.maximum(0.0, xx2 - xx1)
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h = Numo::SFloat.maximum(0.0, yy2 - yy1)
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intersection = w * h
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iou = intersection / (areas[i] + areas[order[1..]] - intersection)
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inds = iou.le(iou_threshold).where
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order = order[inds + 1]
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end
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Numo::Int32.cast(keep)
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end
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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]
|
||||
|
||||
scaled_logits = logits / temperature
|
||||
|
||||
probs = 1.0 / (1.0 + Numo::NMath.exp(-scaled_logits))
|
||||
|
||||
scores = probs.max(axis: 1)
|
||||
labels = probs.argmax(axis: 1)
|
||||
|
||||
cx = boxes[true, 0]
|
||||
cy = boxes[true, 1]
|
||||
w = boxes[true, 2]
|
||||
h = boxes[true, 3]
|
||||
|
||||
x1 = cx - (w / 2.0)
|
||||
y1 = cy - (h / 2.0)
|
||||
x2 = cx + (w / 2.0)
|
||||
y2 = cy + (h / 2.0)
|
||||
|
||||
boxes_xyxy = Numo::SFloat.zeros(boxes.shape[0], 4)
|
||||
boxes_xyxy[true, 0] = x1
|
||||
boxes_xyxy[true, 1] = y1
|
||||
boxes_xyxy[true, 2] = x2
|
||||
boxes_xyxy[true, 3] = y2
|
||||
|
||||
boxes_xyxy *= RESOLUTION
|
||||
|
||||
pad_x = transform_info[:pad_x]
|
||||
pad_y = transform_info[:pad_y]
|
||||
boxes_xyxy[true, 0] -= pad_x
|
||||
boxes_xyxy[true, 1] -= pad_y
|
||||
boxes_xyxy[true, 2] -= pad_x
|
||||
boxes_xyxy[true, 3] -= pad_y
|
||||
|
||||
scale_x = transform_info[:scale_x]
|
||||
scale_y = transform_info[:scale_y]
|
||||
boxes_xyxy[true, 0] /= scale_x
|
||||
boxes_xyxy[true, 1] /= scale_y
|
||||
boxes_xyxy[true, 2] /= scale_x
|
||||
boxes_xyxy[true, 3] /= scale_y
|
||||
|
||||
trim_offset_x = transform_info[:trim_offset_x]
|
||||
trim_offset_y = transform_info[:trim_offset_y]
|
||||
boxes_xyxy[true, 0] += trim_offset_x
|
||||
boxes_xyxy[true, 1] += trim_offset_y
|
||||
boxes_xyxy[true, 2] += trim_offset_x
|
||||
boxes_xyxy[true, 3] += trim_offset_y
|
||||
|
||||
keep_mask = scores.gt(confidence)
|
||||
|
||||
keep_indices = keep_mask.where
|
||||
|
||||
if keep_indices.empty?
|
||||
detections || {
|
||||
xyxy: Numo::SFloat[],
|
||||
confidence: Numo::SFloat[],
|
||||
class_id: Numo::Int32[]
|
||||
}
|
||||
else
|
||||
scores = scores[keep_indices]
|
||||
labels = labels[keep_indices]
|
||||
boxes_xyxy = boxes_xyxy[keep_indices, true]
|
||||
|
||||
if detections
|
||||
existing_n = detections[:xyxy].shape[0]
|
||||
new_n = boxes_xyxy.shape[0]
|
||||
total = existing_n + new_n
|
||||
|
||||
xyxy = Numo::SFloat.zeros(total, 4)
|
||||
conf = Numo::SFloat.zeros(total)
|
||||
cls = Numo::Int32.zeros(total)
|
||||
|
||||
if existing_n.positive?
|
||||
xyxy[0...existing_n, true] = detections[:xyxy]
|
||||
conf[0...existing_n] = detections[:confidence]
|
||||
cls[0...existing_n] = detections[:class_id]
|
||||
end
|
||||
|
||||
xyxy[existing_n...(existing_n + new_n), true] = boxes_xyxy
|
||||
conf[existing_n...(existing_n + new_n)] = scores
|
||||
cls[existing_n...(existing_n + new_n)] = Numo::Int32.cast(labels)
|
||||
|
||||
{ xyxy: xyxy, confidence: conf, class_id: cls }
|
||||
else
|
||||
{
|
||||
xyxy: boxes_xyxy,
|
||||
confidence: scores,
|
||||
class_id: Numo::Int32.cast(labels)
|
||||
}
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
def sort_fields(fields, y_threshold: 0.01)
|
||||
sorted_fields = fields.sort { |a, b| a.y == b.y ? a.x <=> b.x : a.y <=> b.y }
|
||||
|
||||
lines = []
|
||||
current_line = []
|
||||
|
||||
sorted_fields.each do |field|
|
||||
if current_line.blank? || (field.y - current_line.first.y).abs < y_threshold
|
||||
current_line << field
|
||||
else
|
||||
lines << current_line.sort_by(&:x)
|
||||
|
||||
current_line = [field]
|
||||
end
|
||||
end
|
||||
|
||||
lines << current_line.sort_by(&:x) if current_line.present?
|
||||
|
||||
lines.flatten
|
||||
end
|
||||
|
||||
def apply_nms(detections, threshold = 0.5)
|
||||
return detections if detections[:xyxy].shape[0].zero?
|
||||
|
||||
keep_indices = nms(detections[:xyxy], detections[:confidence], threshold)
|
||||
|
||||
{
|
||||
xyxy: detections[:xyxy][keep_indices, true],
|
||||
confidence: detections[:confidence][keep_indices],
|
||||
class_id: detections[:class_id][keep_indices]
|
||||
}
|
||||
end
|
||||
|
||||
def model
|
||||
@model ||= OnnxRuntime::Model.new(
|
||||
MODEL_PATH.to_s,
|
||||
inter_op_num_threads: CPU_THREADS,
|
||||
intra_op_num_threads: CPU_THREADS,
|
||||
enable_mem_pattern: false,
|
||||
enable_cpu_mem_arena: false,
|
||||
providers: ['CPUExecutionProvider']
|
||||
)
|
||||
end
|
||||
# rubocop:enable Metrics
|
||||
end
|
||||
end
|
||||
Loading…
Reference in new issue