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3c2cd23 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | from __future__ import annotations
import cv2
import numpy as np
from .models import (
BitmapExtractor,
BitmapRegion,
Box,
DocumentSpec,
LinearGradientPaint,
ShapeRegion,
SolidPaint,
)
from .shape_recognition import geometry_mask, shape_source_mask
def extract_bitmap_layers(
rgba: np.ndarray,
document: DocumentSpec,
text_mask: np.ndarray,
extractor: BitmapExtractor,
device: str,
) -> tuple[list[BitmapRegion], dict[str, np.ndarray]]:
height, width = rgba.shape[:2]
rgb = rgba[:, :, :3]
text_exclusion = cv2.dilate(text_mask, np.ones((5, 5), np.uint8))
for region in document.regions:
polygon = np.array(
[(point.x, point.y) for point in region.source_quad],
dtype=np.int32,
)
cv2.fillPoly(text_exclusion, [polygon], 255)
vector_foreground = np.zeros((height, width), dtype=np.uint8)
for shape in document.shapes:
if not shape.is_container and shape.include_overlay:
vector_foreground = cv2.bitwise_or(
vector_foreground,
geometry_mask(shape.geometry, width, height),
)
regions: list[BitmapRegion] = []
assets: dict[str, np.ndarray] = {}
for shape in document.shapes:
if not shape.is_container or not shape.include_overlay:
continue
container_mask = geometry_mask(shape.geometry, width, height)
predicted = render_fill(shape, width, height)
source_lab = cv2.cvtColor(rgb, cv2.COLOR_RGB2LAB).astype(np.float32)
predicted_lab = cv2.cvtColor(predicted, cv2.COLOR_RGB2LAB).astype(np.float32)
delta = np.linalg.norm(source_lab - predicted_lab, axis=2)
border_exclusion = cv2.subtract(
container_mask,
cv2.erode(container_mask, np.ones((7, 7), np.uint8)),
)
eligible = (
(container_mask > 0)
& (text_exclusion == 0)
& (vector_foreground == 0)
& (border_exclusion == 0)
)
values = delta[eligible]
if values.size < 20:
continue
baseline = float(np.percentile(values, 35))
threshold = max(9.0, baseline + 6.0)
initial = np.zeros((height, width), dtype=np.uint8)
initial[eligible & (delta >= threshold * 0.65)] = cv2.GC_PR_FGD
initial[eligible & (delta >= threshold * 1.35)] = cv2.GC_FGD
initial[(container_mask > 0) & (initial == 0)] = cv2.GC_BGD
if np.count_nonzero(initial == cv2.GC_FGD) < 8:
continue
mask = refine_opencv(rgb, initial, container_mask)
if extractor == BitmapExtractor.SAM2:
from .sam2 import refine_with_sam2
mask = refine_with_sam2(rgb, mask, container_mask, device)
mask[text_exclusion > 0] = 0
mask[vector_foreground > 0] = 0
mask = filter_components(mask, max(8, int(width * height * 0.00001)))
if cv2.countNonZero(mask) < 12:
continue
alpha = soft_alpha(delta, mask, threshold)
x, y, w, h = cv2.boundingRect((alpha > 0).astype(np.uint8))
if w <= 0 or h <= 0:
continue
padding = 2
x0, y0 = max(0, x - padding), max(0, y - padding)
x1, y1 = min(width, x + w + padding), min(height, y + h + padding)
crop = np.dstack([rgb[y0:y1, x0:x1], alpha[y0:y1, x0:x1]])
asset_name = f"bitmap-{len(regions) + 1:04d}.png"
region = BitmapRegion(
id=f"bitmap-{len(regions) + 1:04d}",
asset_name=asset_name,
box=Box(x=x0, y=y0, width=x1 - x0, height=y1 - y0),
z_index=500,
)
regions.append(region)
assets[region.id] = crop
return regions, assets
def refine_opencv(rgb: np.ndarray, initial: np.ndarray, container_mask: np.ndarray) -> np.ndarray:
mask = initial.copy()
try:
cv2.grabCut(
cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR),
mask,
None,
np.zeros((1, 65), np.float64),
np.zeros((1, 65), np.float64),
2,
cv2.GC_INIT_WITH_MASK,
)
output = np.isin(mask, [cv2.GC_FGD, cv2.GC_PR_FGD]).astype(np.uint8) * 255
except cv2.error:
output = (initial >= cv2.GC_PR_FGD).astype(np.uint8) * 255
output[container_mask == 0] = 0
return output
def filter_components(mask: np.ndarray, minimum_area: int) -> np.ndarray:
count, labels, stats, _ = cv2.connectedComponentsWithStats((mask > 0).astype(np.uint8), 8)
output = np.zeros_like(mask, dtype=np.uint8)
for index in range(1, count):
if stats[index, cv2.CC_STAT_AREA] >= minimum_area:
output[labels == index] = 255
return output
def soft_alpha(delta: np.ndarray, mask: np.ndarray, threshold: float) -> np.ndarray:
strength = np.clip((delta - threshold * 0.45) / max(threshold, 1), 0, 1)
alpha = np.rint(strength * 255).astype(np.uint8)
alpha[mask == 0] = 0
alpha = cv2.GaussianBlur(alpha, (3, 3), 0.65)
alpha[cv2.dilate(mask, np.ones((3, 3), np.uint8)) == 0] = 0
return alpha
def render_fill(shape: ShapeRegion, width: int, height: int) -> np.ndarray:
paint = shape.style.fill
if isinstance(paint, SolidPaint):
color = parse_hex(paint.color)
return np.broadcast_to(color, (height, width, 3)).copy()
assert isinstance(paint, LinearGradientPaint)
first, last = paint.stops[0], paint.stops[-1]
color0 = parse_hex(first.color).astype(np.float32)
color1 = parse_hex(last.color).astype(np.float32)
radians = np.deg2rad(paint.angle)
direction = np.array([np.cos(radians), np.sin(radians)], dtype=np.float32)
yy, xx = np.mgrid[0:height, 0:width]
projection = xx * direction[0] + yy * direction[1]
minimum, maximum = float(projection.min()), float(projection.max())
t = (projection - minimum) / max(maximum - minimum, 1)
result = color0[None, None, :] * (1 - t[:, :, None]) + color1[None, None, :] * t[:, :, None]
return np.clip(np.rint(result), 0, 255).astype(np.uint8)
def reconstruct_background(
rgba: np.ndarray,
document: DocumentSpec,
combined_mask: np.ndarray,
inpaint_callback,
) -> np.ndarray:
height, width = rgba.shape[:2]
working = rgba.copy()
container_union = np.zeros((height, width), dtype=np.uint8)
for shape in sorted(document.shapes, key=lambda item: geometry_area(item), reverse=True):
if not shape.is_container or not shape.remove_source:
continue
mask = shape_source_mask(shape, width, height)
if not cv2.countNonZero(mask):
continue
ring = cv2.subtract(
cv2.dilate(mask, np.ones((15, 15), np.uint8)),
cv2.dilate(mask, np.ones((3, 3), np.uint8)),
)
samples = working[:, :, :3][ring > 0]
fill = np.median(samples, axis=0) if samples.size else np.array([255, 255, 255])
working[:, :, :3][mask > 0] = np.rint(fill).astype(np.uint8)
container_union = cv2.bitwise_or(container_union, mask)
remaining = combined_mask.copy()
remaining[container_union > 0] = 0
if cv2.countNonZero(remaining):
working = inpaint_callback(working, remaining)
return working
def geometry_area(shape: ShapeRegion) -> float:
geometry = shape.geometry
if hasattr(geometry, "box"):
return geometry.box.width * geometry.box.height
if hasattr(geometry, "rx"):
return float(np.pi * geometry.rx * geometry.ry)
points = np.array([(point.x, point.y) for point in geometry.points], dtype=np.float32)
return abs(float(cv2.contourArea(points))) if len(points) >= 3 else 0
def parse_hex(value: str) -> np.ndarray:
value = value.lstrip("#")
if len(value) == 3:
value = "".join(character * 2 for character in value)
return np.array([int(value[index:index + 2], 16) for index in (0, 2, 4)], dtype=np.uint8)
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