feat(selection): separate selection geometry from image content processing
- Introduced a new design for selection rasterization that decouples the selection geometry from the image content, ensuring that the SelectionMask is independent of the source image's alpha and color. - Updated the `rasterize_selection_path` function to only accept selection paths and image dimensions, removing parameters related to source alpha and alpha thresholds. - Implemented a new approach for handling selection paths using non-zero winding fill, ensuring overlapping areas are preserved. - Enhanced performance by utilizing a scanline algorithm for rasterization, significantly reducing processing time for high-resolution images. - Updated internal documentation and added comprehensive tests to validate the new behavior and performance improvements. - Ensured backward compatibility by maintaining existing interfaces where necessary while introducing breaking changes in the selection processing logic.
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@@ -13,7 +13,6 @@ class ImageEditWarning(ValueError):
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class SelectionPath:
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points: tuple[tuple[float, float], ...]
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invert: bool = False
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fill_rule: str = "EVEN_ODD"
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def __post_init__(self):
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points = tuple(
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@@ -21,8 +20,6 @@ class SelectionPath:
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for point in self.points
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)
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selection_path_bounds(points)
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if self.fill_rule not in {"EVEN_ODD", "NONZERO"}:
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raise ValueError(f"Unknown Selection Path fill rule: {self.fill_rule}")
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object.__setattr__(self, "points", points)
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object.__setattr__(self, "invert", bool(self.invert))
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@@ -31,7 +28,6 @@ class SelectionPath:
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{
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"points": self.points,
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"invert": self.invert,
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"fill_rule": self.fill_rule,
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},
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separators=(",", ":"),
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)
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@@ -39,12 +35,11 @@ class SelectionPath:
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@classmethod
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def from_json(cls, value):
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data = json.loads(value)
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if not isinstance(data, dict):
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if not isinstance(data, dict) or set(data) != {"points", "invert"}:
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raise ValueError("The Selection Path data is invalid")
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return cls(
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points=tuple(data.get("points", ())),
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invert=bool(data.get("invert", False)),
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fill_rule=str(data.get("fill_rule", "EVEN_ODD")),
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points=tuple(data["points"]),
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invert=bool(data["invert"]),
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)
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@@ -108,45 +103,46 @@ def selection_path_bounds(points):
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return left, top, right, bottom
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def _rasterize_path(image_size, points, fill_rule):
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def _rasterize_path(bounds, points):
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import numpy as np
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left, top, right, bottom = bounds
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width = right - left
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height = bottom - top
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points = np.asarray(points, dtype=np.float64)
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y = np.arange(top, bottom, dtype=np.float64) + 0.5
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changes = np.zeros((height, width + 1), dtype=np.int16)
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previous = points[-1]
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for current in points:
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rows = np.flatnonzero((current[1] > y) != (previous[1] > y))
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if len(rows):
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sample_y = y[rows]
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edge_x = (previous[0] - current[0]) * (sample_y - current[1]) / (
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previous[1] - current[1] + 1e-300
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) + current[0]
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ends = np.ceil(edge_x - left - 0.5).astype(np.int64)
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ends = np.clip(ends, 0, width)
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starts = np.zeros_like(rows)
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winding = 1 if current[1] > previous[1] else -1
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np.add.at(changes, (rows, starts), winding)
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np.add.at(changes, (rows, ends), -winding)
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previous = current
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return np.cumsum(changes[:, :width], axis=1, dtype=np.int16) != 0
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def _selection_canvas_bounds(image_size, selection_path, padding):
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width, height = (int(value) for value in image_size)
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if width <= 0 or height <= 0:
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raise ValueError("The source image has no readable dimensions")
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left, top, right, bottom = selection_path_bounds(points)
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left = max(left, 0)
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top = max(top, 0)
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right = min(right, width)
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bottom = min(bottom, height)
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mask = np.zeros((height, width), dtype=bool)
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if right <= left or bottom <= top:
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return mask
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points = np.asarray(points, dtype=np.float64)
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x = np.arange(left, right, dtype=np.float64) + 0.5
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for row_start in range(top, bottom, 128):
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row_end = min(row_start + 128, bottom)
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y = np.arange(row_start, row_end, dtype=np.float64) + 0.5
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state = np.zeros(
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(len(y), len(x)),
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dtype=bool if fill_rule == "EVEN_ODD" else np.int16,
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)
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previous = points[-1]
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for current in points:
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crosses = (current[1] > y) != (previous[1] > y)
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edge_x = (previous[0] - current[0]) * (y - current[1]) / (
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previous[1] - current[1] + 1e-300
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) + current[0]
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selected = crosses[:, None] & (x[None, :] < edge_x[:, None])
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if fill_rule == "EVEN_ODD":
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state ^= selected
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else:
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winding = 1 if current[1] > previous[1] else -1
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state += selected * winding
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previous = current
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mask[row_start:row_end, left:right] = state != 0
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return mask
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if selection_path.invert:
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return 0, 0, width, height
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left, top, right, bottom = selection_path_bounds(selection_path.points)
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return (
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max(left - padding, 0),
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max(top - padding, 0),
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min(right + padding, width),
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min(bottom + padding, height),
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)
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def _antialias_mask(mask):
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@@ -170,40 +166,29 @@ def rasterize_selection_path(
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image_size,
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selection_path,
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*,
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source_alpha=None,
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alpha_threshold=0.0,
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antialias=False,
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):
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import numpy as np
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width, height = (int(value) for value in image_size)
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mask = _rasterize_path(
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canvas_bounds = _selection_canvas_bounds(
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(width, height),
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selection_path.points,
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selection_path.fill_rule,
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selection_path,
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2 if antialias else 0,
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)
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left, top, right, bottom = canvas_bounds
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if right <= left or bottom <= top:
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raise ImageEditWarning("The selection contains no visible image pixels")
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mask = _rasterize_path(canvas_bounds, selection_path.points)
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if antialias:
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mask = _antialias_mask(mask)
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else:
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mask = mask.astype(np.float32)
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if selection_path.invert:
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mask = 1.0 - mask
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if source_alpha is not None:
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alpha = np.asarray(source_alpha)
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if alpha.shape != (height, width):
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raise ValueError("The source Alpha does not match the image selection")
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mask = np.where(alpha > float(alpha_threshold), mask, 0.0)
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visible_y, visible_x = np.nonzero(mask > float(alpha_threshold))
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if not len(visible_x):
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if not np.any(mask > 0.0):
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raise ImageEditWarning("The selection contains no visible image pixels")
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bounds = (
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int(visible_x.min()),
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int(visible_y.min()),
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int(visible_x.max()) + 1,
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int(visible_y.max()) + 1,
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)
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left, top, right, bottom = bounds
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return SelectionMask(mask[top:bottom, left:right], bounds)
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return SelectionMask(mask, canvas_bounds)
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def crop_pixels_to_alpha(pixels, bounds):
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