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Improve OCR on downscaled images

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This commit is contained in:
prescientmoon 2024-09-17 03:23:45 +02:00
parent 50f8db4e2e
commit 18d0f320ab
Signed by: prescientmoon
SSH key fingerprint: SHA256:WFp/cO76nbarETAoQcQXuV+0h7XJsEsOCI0UsyPIy6U
2 changed files with 33 additions and 17 deletions
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39
src/recognition/hyperglass.rs
View file

@ -72,7 +72,7 @@ impl ComponentVec {
if let Some(p) = components.components.get_pixel_checked(x, y)
&& p.0[0] == component
{
count += 1;
count += 255 - components.image[(x, y)].0[0] as u32;
}
}
}
@ -141,6 +141,7 @@ struct ComponentBounds {
}
struct ComponentsWithBounds {
image: ImageBuffer<Luma<u8>, Vec<u8>>,
components: ImageBuffer<Luma<u32>, Vec<u32>>,
// NOTE: the index is (the id of the component) - 1
@ -153,16 +154,17 @@ struct ComponentsWithBounds {
}
impl ComponentsWithBounds {
fn from_image(image: &DynamicImage, binarisation_threshold: u8) -> Result<Self, Error> {
let image = threshold(
&image.to_luma8(),
binarisation_threshold,
ThresholdType::Binary,
);
debug_image_buffer_log(&image);
fn from_image(
image: &DynamicImage,
binarisation_threshold: u8,
max_sizes: (f32, f32),
) -> Result<Self, Error> {
let luma_image = image.to_luma8();
let binarized_image = threshold(&luma_image, binarisation_threshold, ThresholdType::Binary);
debug_image_buffer_log(&binarized_image);
let background = Luma([u8::MAX]);
let components = connected_components(&image, Connectivity::Eight, background);
let components = connected_components(&binarized_image, Connectivity::Eight, background);
let mut bounds: Vec<Option<ComponentBounds>> = Vec::new();
for x in 0..components.width() {
@ -198,7 +200,13 @@ impl ComponentsWithBounds {
// {{{ Remove components that are too large
for bound in &mut bounds {
if bound.map_or(false, |b| (b.x_max - b.x_min) >= 9 * image.width() / 10) {
if bound.map_or(false, |b| {
(b.x_max - b.x_min) as f32 >= max_sizes.0 * image.width() as f32
}) {
*bound = None;
} else if bound.map_or(false, |b| {
(b.y_max - b.y_min) as f32 >= max_sizes.1 * image.height() as f32
}) {
*bound = None;
}
}
@ -210,6 +218,7 @@ impl ComponentsWithBounds {
bounds_by_position.sort_by_key(|i| bounds[*i].unwrap().x_min);
Ok(Self {
image: luma_image,
components,
bounds,
bounds_by_position,
@ -254,7 +263,7 @@ impl CharMeasurements {
debug_image_log(&image);
let components = ComponentsWithBounds::from_image(&image, 100)?;
let components = ComponentsWithBounds::from_image(&image, 100, (1.0, 1.0))?;
// {{{ Compute max width/height
let max_width = components
@ -298,9 +307,13 @@ impl CharMeasurements {
image: &DynamicImage,
whitelist: &str,
binarisation_threshold: Option<u8>,
max_sizes: Option<(f32, f32)>,
) -> Result<String, Error> {
let components =
ComponentsWithBounds::from_image(image, binarisation_threshold.unwrap_or(100))?;
let components = ComponentsWithBounds::from_image(
image,
binarisation_threshold.unwrap_or(100),
max_sizes.unwrap_or((0.9, 1.0)),
)?;
let mut result = String::with_capacity(components.bounds.len());
let max_height = components

11
src/recognition/recognize.rs
View file

@ -150,7 +150,7 @@ impl ImageAnalyzer {
let result = Score(
measurements
.recognise(&image, "0123456789'", None)?
.recognise(&image, "0123456789'", None, None)?
.chars()
.filter(|c| *c != '\'')
.collect::<String>()
@ -218,6 +218,7 @@ impl ImageAnalyzer {
let text = ctx.kazesawa_bold_measurements.recognise(
&image,
"PASTPRESENTFUTUREETERNALBEYOND",
Some(200), // We can afford to be generous with binarization here
None,
)?;
@ -240,7 +241,7 @@ impl ImageAnalyzer {
let image = self.interp_crop(ctx, image, PlayKind)?;
let text = ctx
.kazesawa_measurements
.recognise(&image, "ResultSelectaSong ", None)?;
.recognise(&image, "ResultSelectaSong ", None, None)?;
let result = if edit_distance(&text, "Result") < edit_distance(&text, "SelectaSong") {
ScoreKind::ScoreScreen
@ -342,7 +343,8 @@ impl ImageAnalyzer {
let image = self.interp_crop(ctx, image, ScoreScreen(KINDS[i]))?;
out[i] = ctx
.kazesawa_bold_measurements
.recognise(&image, "0123456789", Some(30))?
// We need to be very strict with binarization here
.recognise(&image, "0123456789", Some(30), Some((0.33, 0.85)))?
.parse()
.unwrap_or(100000); // This will get discarded as making no sense
}
@ -361,7 +363,8 @@ impl ImageAnalyzer {
let image = self.interp_crop(ctx, image, ScoreScreen(ScoreScreenRect::MaxRecall))?;
let max_recall = ctx
.exo_measurements
.recognise(&image, "0123456789", None)?
// We can afford to be generous with binarization here
.recognise(&image, "0123456789", Some(200), None)?
.parse()?;
Ok(max_recall)