from dataclasses import asdict from operator import truth from funcy import lmap, flatten from cv_analysis.figure_detection.figure_detection import detect_figures from cv_analysis.table_parsing import parse_tables from cv_analysis.utils.structures import Rectangle from pdf2img.conversion import convert_pages_to_images from pdf2img.default_objects.image import ImagePlus, ImageInfo from pdf2img.default_objects.rectangle import RectanglePlus def get_analysis_pipeline(operation, table_parsing_skip_pages_without_images): if operation == "table": return make_analysis_pipeline( parse_tables, table_parsing_formatter, dpi=200, skip_pages_without_images=table_parsing_skip_pages_without_images, ) elif operation == "figure": return make_analysis_pipeline(detect_figures, figure_detection_formatter, dpi=200) else: raise def make_analysis_pipeline(analysis_fn, formatter, dpi, skip_pages_without_images=False): def analyse_pipeline(pdf: bytes, index=None): def parse_page(page: ImagePlus): image = page.asarray() rects = analysis_fn(image) if not rects: return infos = formatter(rects, page, dpi) return infos pages = convert_pages_to_images(pdf, index=index, dpi=dpi, skip_pages_without_images=skip_pages_without_images) results = map(parse_page, pages) yield from flatten(filter(truth, results)) return analyse_pipeline def table_parsing_formatter(rects, page: ImagePlus, dpi): def format_rect(rect: Rectangle): rect_plus = RectanglePlus.from_pixels(*rect.xyxy(), page.info, alpha=False, dpi=dpi) return rect_plus.asdict(derotate=True) bboxes = lmap(format_rect, rects) return {"pageInfo": page.asdict(natural_index=True), "tableCells": bboxes} def figure_detection_formatter(rects, page, dpi): def format_rect(rect: Rectangle): rect_plus = RectanglePlus.from_pixels(*rect.xyxy(), page.info, alpha=False, dpi=dpi) return asdict(ImageInfo(page.info, rect_plus.asbbox(derotate=False), rect_plus.alpha)) return lmap(format_rect, rects)