File size: 6,855 Bytes
4837bb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | """OCR evaluation for outputs produced by controlnet_benchmark.py."""
from __future__ import annotations
import argparse
import json
import math
from pathlib import Path
from PIL import Image, ImageChops, ImageDraw, ImageFilter, ImageFont, ImageOps
from benchmarks.text_accuracy import (
centered_text_crop,
character_error_rate,
normalize_ocr,
run_ocr,
)
def _normalized_outline(
image: Image.Image,
size: tuple[int, int] = (384, 128),
*,
light_ink: bool = False,
) -> Image.Image:
grayscale = image.convert("L")
if light_ink:
ink = grayscale.point(lambda value: 255 if value >= 128 else 0)
else:
ink = grayscale.point(lambda value: 255 if value < 128 else 0)
bbox = ink.getbbox()
if bbox is None:
return Image.new("L", size, 0)
cropped = ImageOps.expand(ink.crop(bbox), border=4, fill=0)
normalized = cropped.resize(size, Image.Resampling.LANCZOS)
normalized = normalized.point(lambda value: 255 if value >= 128 else 0)
outer = normalized.filter(ImageFilter.MaxFilter(3))
inner = normalized.filter(ImageFilter.MinFilter(3))
return ImageChops.subtract(outer, inner)
def glyph_similarity(image: Image.Image, control: Image.Image) -> float:
generated = _normalized_outline(image.crop(centered_text_crop(image)))
expected = _normalized_outline(control, light_ink=True)
generated_dilated = generated.filter(ImageFilter.MaxFilter(7))
expected_dilated = expected.filter(ImageFilter.MaxFilter(7))
generated_pixels = sum(1 for value in generated.getdata() if value)
expected_pixels = sum(1 for value in expected.getdata() if value)
if not generated_pixels or not expected_pixels:
return 0.0
generated_hit = sum(
1
for edge, nearby in zip(generated.getdata(), expected_dilated.getdata())
if edge and nearby
)
expected_hit = sum(
1
for edge, nearby in zip(expected.getdata(), generated_dilated.getdata())
if edge and nearby
)
precision = generated_hit / generated_pixels
recall = expected_hit / expected_pixels
return round(2 * precision * recall / max(1e-9, precision + recall), 4)
def light_ink_margin(image: Image.Image) -> int:
ink = image.convert("L").point(lambda value: 255 if value >= 128 else 0)
bbox = ink.getbbox()
if bbox is None:
return -1
width, height = image.size
return min(bbox[0], bbox[1], width - bbox[2], height - bbox[3])
def write_contact_sheets(
results: list[dict[str, object]],
output_dir: Path,
*,
columns: int = 5,
rows: int = 4,
) -> list[str]:
page_size = columns * rows
thumb_size = 240
label_height = 44
font_path = Path("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf")
font = ImageFont.truetype(str(font_path), 22) if font_path.exists() else ImageFont.load_default()
names: list[str] = []
for page_index in range(math.ceil(len(results) / page_size)):
page_rows = results[page_index * page_size : (page_index + 1) * page_size]
sheet = Image.new(
"RGB",
(columns * thumb_size, rows * (thumb_size + label_height)),
"white",
)
draw = ImageDraw.Draw(sheet)
for cell_index, result in enumerate(page_rows):
image_path = output_dir / str(result["image"])
with Image.open(image_path) as source:
thumbnail = source.convert("RGB")
thumbnail.thumbnail((thumb_size, thumb_size), Image.Resampling.LANCZOS)
x = (cell_index % columns) * thumb_size
y = (cell_index // columns) * (thumb_size + label_height)
sheet.paste(
thumbnail,
(x + (thumb_size - thumbnail.width) // 2, y),
)
draw.text((x + 6, y + thumb_size + 4), str(result["text"]), fill="black", font=font)
name = f"contact-sheet-{page_index + 1:02d}.jpg"
sheet.save(output_dir / name, quality=90)
names.append(name)
return names
def summarize(results: list[dict[str, object]]) -> dict[str, dict[str, float | int]]:
strategies = sorted({str(result["strategy"]) for result in results})
summary: dict[str, dict[str, float | int]] = {}
for strategy in strategies:
rows = [result for result in results if result["strategy"] == strategy]
summary[strategy] = {
"samples": len(rows),
"exact": sum(bool(row["exact_match"]) for row in rows),
"mean_cer": round(sum(float(row["cer"]) for row in rows) / len(rows), 4),
"mean_glyph_similarity": round(
sum(float(row.get("glyph_similarity", 0.0)) for row in rows) / len(rows),
4,
),
"edge_touching_controls": sum(
int(row.get("control_margin_px", 1)) <= 0 for row in rows
),
}
return summary
def run(report_path: Path) -> dict[str, object]:
generation = json.loads(report_path.read_text(encoding="utf-8"))
output_dir = report_path.parent
results: list[dict[str, object]] = []
for row in generation["results"]:
image_path = output_dir / row["image"]
with Image.open(image_path) as image:
crop = centered_text_crop(image)
actual = run_ocr(image_path, "rus+eng", crop=crop)
gold = normalize_ocr(row["text"])
similarity = 0.0
control_margin_px: int | None = None
if row.get("control"):
with Image.open(image_path) as image, Image.open(output_dir / row["control"]) as control:
similarity = glyph_similarity(image, control)
control_margin_px = light_ink_margin(control)
results.append(
{
**row,
"gold": gold,
"ocr": actual,
"exact_match": actual == gold,
"cer": round(character_error_rate(gold, actual), 4),
"glyph_similarity": similarity,
"control_margin_px": control_margin_px,
}
)
contact_sheets = write_contact_sheets(results, output_dir)
report: dict[str, object] = {
"generation_report": report_path.name,
"summary": summarize(results),
"contact_sheets": contact_sheets,
"results": results,
}
(output_dir / "accuracy-report.json").write_text(
json.dumps(report, ensure_ascii=False, indent=2),
encoding="utf-8",
)
return report
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("generation_report", type=Path)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
completed = run(args.generation_report.resolve())
print(json.dumps(completed, ensure_ascii=False))
|