improve contrast norm and sharpness measurement
This commit is contained in:
Brent Schroeter
parent
3da76d4537
commit
d48b672e1b
6 changed files with 133 additions and 340 deletions
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@ -40,7 +40,7 @@ def main():
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t_start = time()
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minimal_docs = (
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[doc for doc in item.docs if doc.name != ""]
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[doc for doc in item.docs if doc.name != doc.identifier]
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if len(item.docs) > 1
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else item.docs
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)
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@ -1,7 +1,7 @@
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from sys import stdout
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import numpy as np
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from PIL import Image, ImageFilter
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from PIL import Image, ImageChops, ImageFilter
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from .items import ArchiveDoc, ArchiveLeaf
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from .ocr import OcrEngine, TextBlock
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@ -30,139 +30,100 @@ def analyze_doc(
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analyzed_pages = []
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for leaf in all_leaves:
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im_cropped = leaf.image.crop(
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im, is_blank = normalize_contrast_for_text(leaf.image)
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im_cropped = im.crop(
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(
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leaf.image.size[0] * 0.1,
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leaf.image.size[1] * 0.1,
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leaf.image.size[0] * 0.9,
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leaf.image.size[1] * 0.9,
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im.size[0] * 0.1,
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im.size[1] * 0.1,
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im.size[0] * 0.9,
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im.size[1] * 0.9,
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)
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)
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sharpness = analyze_sharpness(im_cropped)
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is_blank = im_cropped.getextrema()[0] > 255 * 0.8
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if is_blank:
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max_sharpness = 1
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text_margin_px = -1
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# OCR is computationally expensive, so we try to take advantage of
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# the Tesseract data already parsed by the Internet Archive and
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# embedded in the PDF, when possible. If there is not sufficient
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# text in the PDF to be confident that the Archive's OCR
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# postprocessing captured it all, then OCR is recomputed locally.
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#
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# In some instances, the Archive's OCR detects rotated text but
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# parses it as gibberish. To partially mitigate this, we ignore all
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# precomputed text blocks with a "portrait" aspect ratio. This will
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# not necessarily help with text that is rotated 180 degrees, but in
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# practice that case is rarely encountered. This will also not work
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# well with non-latin scripts that are intended to be oriented
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# vertically.
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OCR_RECOMPUTE_THRESHOLD_WORDS = 30
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if (
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sum(
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(
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len(block.text.split())
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for block in leaf.text_blocks
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if block.x1 - block.x0 > block.y1 - block.y0
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)
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)
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>= OCR_RECOMPUTE_THRESHOLD_WORDS
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):
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ocred_leaf = leaf
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page_angle = 0
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else:
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# Sharpness is determined by percentile of pixels that match some
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# criteria, so it may vary significantly depending on which portion
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# of the image is analyzed. In an effort to identify the sharpest
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# edges, we split up the image into chunks and assume that the
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# highest sharpness value obtained across all chunks is
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# representative of the image as a whole.
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max_sharpness = 0.0
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if im_cropped.size[0] < im_cropped.size[1]:
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# Page is in portrait orientation.
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segments_x = 2
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segments_y = 3
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else:
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# Page is in landscape orientation.
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segments_x = 3
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segments_y = 2
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for i in range(segments_x):
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for j in range(segments_y):
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max_sharpness = max(
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max_sharpness,
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analyze_sharpness(
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im_cropped.crop(
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(
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im_cropped.size[0] / segments_x * i,
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im_cropped.size[1] / segments_y * j,
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im_cropped.size[0] / segments_x * (i + 1),
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im_cropped.size[1] / segments_y * (j + 1),
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)
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)
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),
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OCR_SCALE = 1
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im_scaled = im.resize(np.int_(np.array(im.size) * OCR_SCALE))
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ocr_result = ocr_engine.process(im_scaled)
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ocred_leaf = ArchiveLeaf(
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image=im,
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page_number=leaf.page_number,
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text_blocks=[
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TextBlock(
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x0=int(block.x0 / OCR_SCALE),
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y0=int(block.y0 / OCR_SCALE),
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x1=int(block.x1 / OCR_SCALE),
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y1=int(block.y1 / OCR_SCALE),
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text=block.text,
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)
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for block in ocr_result.blocks
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],
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)
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page_angle = ocr_result.page_angle
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# OCR is computationally expensive, so we try to take advantage of
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# the Tesseract data already parsed by the Internet Archive and
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# embedded in the PDF, when possible. If there is not sufficient
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# text in the PDF to be confident that the Archive's OCR
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# postprocessing captured it all, then OCR is recomputed locally.
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#
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# In some instances, the Archive's OCR detects rotated text but
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# parses it as gibberish. To partially mitigate this, we ignore all
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# precomputed text blocks with a "portrait" aspect ratio. This will
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# not necessarily help with text that is rotated 180 degrees, but in
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# practice that case is rarely encountered. This will also not work
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# well with non-latin scripts that are intended to be oriented
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# vertically.
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OCR_RECOMPUTE_THRESHOLD_WORDS = 30
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if (
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sum(
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(
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len(block.text.split())
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for block in leaf.text_blocks
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if block.x1 - block.x0 > block.y1 - block.y0
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)
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)
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>= OCR_RECOMPUTE_THRESHOLD_WORDS
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):
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if verbose:
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print("Using PDF text.")
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ocred_leaf = leaf
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page_angle = 0
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else:
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if verbose:
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print("Using OCR.")
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OCR_SCALE = 1
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im_scaled = leaf.image.resize(
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np.int_(np.array(leaf.image.size) * OCR_SCALE)
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)
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ocr_result = ocr_engine.process(im_scaled)
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ocred_leaf = ArchiveLeaf(
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image=leaf.image,
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page_number=leaf.page_number,
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text_blocks=[
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TextBlock(
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x0=int(block.x0 / OCR_SCALE),
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y0=int(block.y0 / OCR_SCALE),
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x1=int(block.x1 / OCR_SCALE),
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y1=int(block.y1 / OCR_SCALE),
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text=block.text,
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word_margins_all_directions = (
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np.sort(
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np.concat(
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[
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np.array(
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[
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block.x0,
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block.y0,
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im.size[0] - block.x1,
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im.size[1] - block.y1,
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]
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)
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for block in ocr_result.blocks
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],
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)
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page_angle = ocr_result.page_angle
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word_margins_all_directions = np.sort(
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np.int_(
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np.concat(
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[
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np.array(
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[
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block.x0,
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block.y0,
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leaf.image.size[0] - block.x1,
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leaf.image.size[1] - block.y1,
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]
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)
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for block in ocred_leaf.text_blocks
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]
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)
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)
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)
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# Skip the n closest words to the edge, to help ignore stray OCR artifacts.
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SKIP_WORDS = 2
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text_margin_px = int(
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word_margins_all_directions[SKIP_WORDS]
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if word_margins_all_directions.shape[0] > SKIP_WORDS
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else -1
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for block in ocred_leaf.text_blocks
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]
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).astype(np.int_)
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)
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if len(ocred_leaf.text_blocks) > 0
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else np.array([])
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)
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# Skip the n closest words to the edge, to help ignore stray OCR artifacts.
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SKIP_WORDS = 2
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text_margin_px = int(
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word_margins_all_directions[SKIP_WORDS]
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if word_margins_all_directions.shape[0] > SKIP_WORDS
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else -1
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)
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# Make sure the OCR engine is running with orientation detection.
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assert page_angle is not None
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analyzed_pages.append(
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{
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"blank": is_blank,
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"is_blank": is_blank,
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"page_angle": page_angle,
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"size_analyzed": leaf.image.size,
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"sharpness": max_sharpness,
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"size_analyzed": im.size,
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"sharpness": sharpness,
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"text_margin_px": text_margin_px,
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}
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)
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@ -170,35 +131,58 @@ def analyze_doc(
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return {"pages": analyzed_pages}
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def normalize_contrast_for_text(im: Image.Image) -> tuple[Image.Image, bool]:
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"""
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Most of the pages being analyzed, and virtually all of the pages we care
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about for the purposes of QA, primarily contain text on a contrasting
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background. We can therefore typically assume that it is reasonable to boost
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contrast so that the lightest pixels are nearly white and the darkest pixels
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are nearly black. This can help make analysis more consistent across leaves
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with varying contrast ratios due to varied scanner settings, contrast ratios
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of the original documnets, or weathering/fading of the physical fiche.
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Processed leaves usually contain some amount of margin around the edges
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where the backlight of the scanning rig is visible through the unexposed
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region of the negative, so contrast detection is heavily center-weighted.
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Params:
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im Scan image as a 2-dimensional numpy array. (Use `np.asarray()` to
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convert PIL `Image` objects to an array format.)
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Returns:
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(normalized_image, is_blank)
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"""
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pixel_values = np.asarray(
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im.crop(
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(
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im.size[0] * 0.1,
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im.size[1] * 0.1,
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im.size[0] * 0.9,
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im.size[1] * 0.9,
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)
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)
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)
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# To avoid extreme outliers, use quantiles instead of absolute extrema.
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extrema = (np.quantile(pixel_values, 0.002), np.quantile(pixel_values, 0.998))
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if extrema[1] - extrema[0] < 64:
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# Assume there is essentially no content here and return the original.
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return im, True
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# Apply a rudimentary tone curve to the image, with the goal that the
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# extrema we just calculated will evaluate to values "pretty close to" 0%
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# and 100% of the available range.
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return im.point(
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lambda x: np.interp(x, (0, extrema[0], extrema[1], 255), (0, 8, 247, 255))
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), False
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def analyze_sharpness(im: Image.Image):
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"""
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Crudely quantifies the "sharpness" of edges in an image, on a scale of 0 to
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1. The scale is not linear with respect to scan quality: anything above 0.1
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is usually fine.
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1, by measuring peak intensity of a high-pass filter.
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"""
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arr = np.asarray(im)
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# Normalize contrast based on brightest and darkest pixels. For example,
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# NORM_QUANTILE=0.1 will attempt to transform pixel values so that 80% fall
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# between 10% brightness and 90% brightness. In practice, a value around
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# 0.02 seems to work fairly well.
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NORM_QUANTILE = 0.03
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pixel_range = np.quantile(arr, 1.0 - NORM_QUANTILE) - np.quantile(
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arr, NORM_QUANTILE
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)
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if pixel_range == 0:
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arr_normalized = arr
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else:
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arr_normalized = arr * (1.0 - NORM_QUANTILE * 2) / pixel_range
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arr_normalized = (
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arr_normalized - np.quantile(arr_normalized, NORM_QUANTILE) + NORM_QUANTILE
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)
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arr_normalized = np.uint8(np.clip(arr_normalized, 0, 1) * 255)
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# "Sharpness" is determined by measuring the median intensity of pixels
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# near edges, after an edge detection filter has been applied to the image.
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edges_arr = np.asarray(
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Image.fromarray(arr_normalized).filter(ImageFilter.FIND_EDGES)
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)
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EDGE_THRESHOLD = 8
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return np.median(edges_arr[edges_arr > EDGE_THRESHOLD]) / 255
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blurred = im.filter(ImageFilter.GaussianBlur(8))
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diff = ImageChops.difference(im, blurred)
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return np.quantile(diff, 0.999) / 255
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@ -66,8 +66,8 @@ class ArchiveDoc:
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identifier archive.org identifier string, for example
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`"micro_IA40386007_0012"`.
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name Document name, with the item identifier, leading whitespace,
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and file extension stripped.
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name Document name, with the item identifier intact but file
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extension stripped.
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title Optional `title` metadata field assigned to the `_jp2.zip`
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file, usually indicating that this file represents a subset
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@ -47,23 +47,6 @@ class TesseractOcrEngine(OcrEngine):
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# TODO: Will this work for non-Latin scripts? Probably not all.
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df = df[(df["width"] / df["height"]) > 0.8]
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print(
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[
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TextBlock(
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# Rotate X and Y coordinates back to match the original image.
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*_box_after_rotation(
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int(row["left"]),
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int(row["top"]),
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int(row["left"] + row["width"]),
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int(row["top"] + row["height"]),
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*rotated_image.size,
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angle,
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),
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text=row["text"],
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)
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for _, row in df.iterrows()
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]
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)
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if angle_best is None or df.shape[0] > len(blocks_best):
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angle_best = angle
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blocks_best = [
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@ -18,7 +18,5 @@ dependencies = [
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[dependency-groups]
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dev = [
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"jedi>=0.19.2",
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"python-lsp-server>=1.13.0",
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"ruff>=0.12.8",
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]
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174
uv.lock
generated
174
uv.lock
generated
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@ -62,43 +62,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/eb/f3/b39e98f6a7eeb8d848cf1cc74766c4783f3d1d6afe50ff7726a2df51f681/bce_python_sdk-0.9.56-py3-none-any.whl", hash = "sha256:432cf3ea4cd4b959dd0185f36ed5c49304a6272a08a17e5f443730b1f437135b", size = 393233, upload-time = "2025-12-16T11:25:22.957Z" },
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]
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[[package]]
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name = "black"
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version = "25.12.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
|
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{ name = "click" },
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{ name = "mypy-extensions" },
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{ name = "packaging" },
|
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{ name = "pathspec" },
|
||||
{ name = "platformdirs" },
|
||||
{ name = "pytokens" },
|
||||
]
|
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sdist = { url = "https://files.pythonhosted.org/packages/c4/d9/07b458a3f1c525ac392b5edc6b191ff140b596f9d77092429417a54e249d/black-25.12.0.tar.gz", hash = "sha256:8d3dd9cea14bff7ddc0eb243c811cdb1a011ebb4800a5f0335a01a68654796a7", size = 659264, upload-time = "2025-12-08T01:40:52.501Z" }
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||||
wheels = [
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||||
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||||
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||||
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|
||||
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||||
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||||
{ name = "docstring-to-markdown" },
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{ name = "pluggy" },
|
||||
{ name = "python-lsp-jsonrpc" },
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{ name = "ujson" },
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[[package]]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/84/25/d9db8be44e205a124f6c98bc0324b2bb149b7431c53877fc6d1038dddaf5/pytokens-0.3.0-py3-none-any.whl", hash = "sha256:95b2b5eaf832e469d141a378872480ede3f251a5a5041b8ec6e581d3ac71bbf3", size = 12195, upload-time = "2025-11-05T13:36:33.183Z" },
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]
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|
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[[package]]
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name = "pytz"
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version = "2025.2"
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|
@ -1729,12 +1566,3 @@ sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc7
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wheels = [
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[[package]]
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wheels = [
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]
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|
|
|
|||
Loading…
Add table
Reference in a new issue