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improve contrast norm and sharpness measurement

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This commit is contained in:
Brent Schroeter 2025-12-20 08:58:49 +00:00
parent 3da76d4537
commit d48b672e1b
6 changed files with 133 additions and 340 deletions
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2
diagnostics.py
View file

@ -40,7 +40,7 @@ def main():
t_start = time() t_start = time()
minimal_docs = ( minimal_docs = (
[doc for doc in item.docs if doc.name != ""] [doc for doc in item.docs if doc.name != doc.identifier]
if len(item.docs) > 1 if len(item.docs) > 1
else item.docs else item.docs
) )

274
microqa/engine.py
View file

@ -1,7 +1,7 @@
from sys import stdout from sys import stdout
import numpy as np import numpy as np
from PIL import Image, ImageFilter from PIL import Image, ImageChops, ImageFilter
from .items import ArchiveDoc, ArchiveLeaf from .items import ArchiveDoc, ArchiveLeaf
from .ocr import OcrEngine, TextBlock from .ocr import OcrEngine, TextBlock
@ -30,139 +30,100 @@ def analyze_doc(
analyzed_pages = [] analyzed_pages = []
for leaf in all_leaves: for leaf in all_leaves:
im_cropped = leaf.image.crop( im, is_blank = normalize_contrast_for_text(leaf.image)
im_cropped = im.crop(
( (
leaf.image.size[0] * 0.1, im.size[0] * 0.1,
leaf.image.size[1] * 0.1, im.size[1] * 0.1,
leaf.image.size[0] * 0.9, im.size[0] * 0.9,
leaf.image.size[1] * 0.9, im.size[1] * 0.9,
) )
) )
sharpness = analyze_sharpness(im_cropped)
is_blank = im_cropped.getextrema()[0] > 255 * 0.8 # OCR is computationally expensive, so we try to take advantage of
# the Tesseract data already parsed by the Internet Archive and
if is_blank: # embedded in the PDF, when possible. If there is not sufficient
max_sharpness = 1 # text in the PDF to be confident that the Archive's OCR
text_margin_px = -1 # postprocessing captured it all, then OCR is recomputed locally.
#
# In some instances, the Archive's OCR detects rotated text but
# parses it as gibberish. To partially mitigate this, we ignore all
# precomputed text blocks with a "portrait" aspect ratio. This will
# not necessarily help with text that is rotated 180 degrees, but in
# practice that case is rarely encountered. This will also not work
# well with non-latin scripts that are intended to be oriented
# vertically.
OCR_RECOMPUTE_THRESHOLD_WORDS = 30
if (
sum(
(
len(block.text.split())
for block in leaf.text_blocks
if block.x1 - block.x0 > block.y1 - block.y0
)
)
>= OCR_RECOMPUTE_THRESHOLD_WORDS
):
ocred_leaf = leaf
page_angle = 0 page_angle = 0
else: else:
# Sharpness is determined by percentile of pixels that match some OCR_SCALE = 1
# criteria, so it may vary significantly depending on which portion im_scaled = im.resize(np.int_(np.array(im.size) * OCR_SCALE))
# of the image is analyzed. In an effort to identify the sharpest ocr_result = ocr_engine.process(im_scaled)
# edges, we split up the image into chunks and assume that the ocred_leaf = ArchiveLeaf(
# highest sharpness value obtained across all chunks is image=im,
# representative of the image as a whole. page_number=leaf.page_number,
max_sharpness = 0.0 text_blocks=[
if im_cropped.size[0] < im_cropped.size[1]: TextBlock(
# Page is in portrait orientation. x0=int(block.x0 / OCR_SCALE),
segments_x = 2 y0=int(block.y0 / OCR_SCALE),
segments_y = 3 x1=int(block.x1 / OCR_SCALE),
else: y1=int(block.y1 / OCR_SCALE),
# Page is in landscape orientation. text=block.text,
segments_x = 3
segments_y = 2
for i in range(segments_x):
for j in range(segments_y):
max_sharpness = max(
max_sharpness,
analyze_sharpness(
im_cropped.crop(
(
im_cropped.size[0] / segments_x * i,
im_cropped.size[1] / segments_y * j,
im_cropped.size[0] / segments_x * (i + 1),
im_cropped.size[1] / segments_y * (j + 1),
)
)
),
) )
for block in ocr_result.blocks
],
)
page_angle = ocr_result.page_angle
# OCR is computationally expensive, so we try to take advantage of word_margins_all_directions = (
# the Tesseract data already parsed by the Internet Archive and np.sort(
# embedded in the PDF, when possible. If there is not sufficient np.concat(
# text in the PDF to be confident that the Archive's OCR [
# postprocessing captured it all, then OCR is recomputed locally. np.array(
# [
# In some instances, the Archive's OCR detects rotated text but block.x0,
# parses it as gibberish. To partially mitigate this, we ignore all block.y0,
# precomputed text blocks with a "portrait" aspect ratio. This will im.size[0] - block.x1,
# not necessarily help with text that is rotated 180 degrees, but in im.size[1] - block.y1,
# practice that case is rarely encountered. This will also not work ]
# well with non-latin scripts that are intended to be oriented
# vertically.
OCR_RECOMPUTE_THRESHOLD_WORDS = 30
if (
sum(
(
len(block.text.split())
for block in leaf.text_blocks
if block.x1 - block.x0 > block.y1 - block.y0
)
)
>= OCR_RECOMPUTE_THRESHOLD_WORDS
):
if verbose:
print("Using PDF text.")
ocred_leaf = leaf
page_angle = 0
else:
if verbose:
print("Using OCR.")
OCR_SCALE = 1
im_scaled = leaf.image.resize(
np.int_(np.array(leaf.image.size) * OCR_SCALE)
)
ocr_result = ocr_engine.process(im_scaled)
ocred_leaf = ArchiveLeaf(
image=leaf.image,
page_number=leaf.page_number,
text_blocks=[
TextBlock(
x0=int(block.x0 / OCR_SCALE),
y0=int(block.y0 / OCR_SCALE),
x1=int(block.x1 / OCR_SCALE),
y1=int(block.y1 / OCR_SCALE),
text=block.text,
) )
for block in ocr_result.blocks for block in ocred_leaf.text_blocks
], ]
) ).astype(np.int_)
page_angle = ocr_result.page_angle
word_margins_all_directions = np.sort(
np.int_(
np.concat(
[
np.array(
[
block.x0,
block.y0,
leaf.image.size[0] - block.x1,
leaf.image.size[1] - block.y1,
]
)
for block in ocred_leaf.text_blocks
]
)
)
)
# Skip the n closest words to the edge, to help ignore stray OCR artifacts.
SKIP_WORDS = 2
text_margin_px = int(
word_margins_all_directions[SKIP_WORDS]
if word_margins_all_directions.shape[0] > SKIP_WORDS
else -1
) )
if len(ocred_leaf.text_blocks) > 0
else np.array([])
)
# Skip the n closest words to the edge, to help ignore stray OCR artifacts.
SKIP_WORDS = 2
text_margin_px = int(
word_margins_all_directions[SKIP_WORDS]
if word_margins_all_directions.shape[0] > SKIP_WORDS
else -1
)
# Make sure the OCR engine is running with orientation detection. # Make sure the OCR engine is running with orientation detection.
assert page_angle is not None assert page_angle is not None
analyzed_pages.append( analyzed_pages.append(
{ {
"blank": is_blank, "is_blank": is_blank,
"page_angle": page_angle, "page_angle": page_angle,
"size_analyzed": leaf.image.size, "size_analyzed": im.size,
"sharpness": max_sharpness, "sharpness": sharpness,
"text_margin_px": text_margin_px, "text_margin_px": text_margin_px,
} }
) )
@ -170,35 +131,58 @@ def analyze_doc(
return {"pages": analyzed_pages} return {"pages": analyzed_pages}
def normalize_contrast_for_text(im: Image.Image) -> tuple[Image.Image, bool]:
"""
Most of the pages being analyzed, and virtually all of the pages we care
about for the purposes of QA, primarily contain text on a contrasting
background. We can therefore typically assume that it is reasonable to boost
contrast so that the lightest pixels are nearly white and the darkest pixels
are nearly black. This can help make analysis more consistent across leaves
with varying contrast ratios due to varied scanner settings, contrast ratios
of the original documnets, or weathering/fading of the physical fiche.
Processed leaves usually contain some amount of margin around the edges
where the backlight of the scanning rig is visible through the unexposed
region of the negative, so contrast detection is heavily center-weighted.
Params:
im Scan image as a 2-dimensional numpy array. (Use `np.asarray()` to
convert PIL `Image` objects to an array format.)
Returns:
(normalized_image, is_blank)
"""
pixel_values = np.asarray(
im.crop(
(
im.size[0] * 0.1,
im.size[1] * 0.1,
im.size[0] * 0.9,
im.size[1] * 0.9,
)
)
)
# To avoid extreme outliers, use quantiles instead of absolute extrema.
extrema = (np.quantile(pixel_values, 0.002), np.quantile(pixel_values, 0.998))
if extrema[1] - extrema[0] < 64:
# Assume there is essentially no content here and return the original.
return im, True
# Apply a rudimentary tone curve to the image, with the goal that the
# extrema we just calculated will evaluate to values "pretty close to" 0%
# and 100% of the available range.
return im.point(
lambda x: np.interp(x, (0, extrema[0], extrema[1], 255), (0, 8, 247, 255))
), False
def analyze_sharpness(im: Image.Image): def analyze_sharpness(im: Image.Image):
""" """
Crudely quantifies the "sharpness" of edges in an image, on a scale of 0 to Crudely quantifies the "sharpness" of edges in an image, on a scale of 0 to
1. The scale is not linear with respect to scan quality: anything above 0.1 1, by measuring peak intensity of a high-pass filter.
is usually fine.
""" """
arr = np.asarray(im) blurred = im.filter(ImageFilter.GaussianBlur(8))
diff = ImageChops.difference(im, blurred)
# Normalize contrast based on brightest and darkest pixels. For example, return np.quantile(diff, 0.999) / 255
# NORM_QUANTILE=0.1 will attempt to transform pixel values so that 80% fall
# between 10% brightness and 90% brightness. In practice, a value around
# 0.02 seems to work fairly well.
NORM_QUANTILE = 0.03
pixel_range = np.quantile(arr, 1.0 - NORM_QUANTILE) - np.quantile(
arr, NORM_QUANTILE
)
if pixel_range == 0:
arr_normalized = arr
else:
arr_normalized = arr * (1.0 - NORM_QUANTILE * 2) / pixel_range
arr_normalized = (
arr_normalized - np.quantile(arr_normalized, NORM_QUANTILE) + NORM_QUANTILE
)
arr_normalized = np.uint8(np.clip(arr_normalized, 0, 1) * 255)
# "Sharpness" is determined by measuring the median intensity of pixels
# near edges, after an edge detection filter has been applied to the image.
edges_arr = np.asarray(
Image.fromarray(arr_normalized).filter(ImageFilter.FIND_EDGES)
)
EDGE_THRESHOLD = 8
return np.median(edges_arr[edges_arr > EDGE_THRESHOLD]) / 255

4
microqa/items.py
View file

@ -66,8 +66,8 @@ class ArchiveDoc:
identifier archive.org identifier string, for example identifier archive.org identifier string, for example
`"micro_IA40386007_0012"`. `"micro_IA40386007_0012"`.
name Document name, with the item identifier, leading whitespace, name Document name, with the item identifier intact but file
and file extension stripped. extension stripped.
title Optional `title` metadata field assigned to the `_jp2.zip` title Optional `title` metadata field assigned to the `_jp2.zip`
file, usually indicating that this file represents a subset file, usually indicating that this file represents a subset

17
microqa/ocr/tesseract.py
View file

@ -47,23 +47,6 @@ class TesseractOcrEngine(OcrEngine):
# TODO: Will this work for non-Latin scripts? Probably not all. # TODO: Will this work for non-Latin scripts? Probably not all.
df = df[(df["width"] / df["height"]) > 0.8] df = df[(df["width"] / df["height"]) > 0.8]
print(
[
TextBlock(
# Rotate X and Y coordinates back to match the original image.
*_box_after_rotation(
int(row["left"]),
int(row["top"]),
int(row["left"] + row["width"]),
int(row["top"] + row["height"]),
*rotated_image.size,
angle,
),
text=row["text"],
)
for _, row in df.iterrows()
]
)
if angle_best is None or df.shape[0] > len(blocks_best): if angle_best is None or df.shape[0] > len(blocks_best):
angle_best = angle angle_best = angle
blocks_best = [ blocks_best = [

2
pyproject.toml
View file

@ -18,7 +18,5 @@ dependencies = [
[dependency-groups] [dependency-groups]
dev = [ dev = [
"jedi>=0.19.2",
"python-lsp-server>=1.13.0",
"ruff>=0.12.8", "ruff>=0.12.8",
] ]

174
uv.lock generated
View file

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