906 lines
35 KiB
Python
906 lines
35 KiB
Python
"""Minimal image processing pipeline adapted for the Qt frontend."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import colorsys
|
|
from dataclasses import dataclass
|
|
from pathlib import Path
|
|
from typing import Dict, Iterable, Tuple
|
|
|
|
import numpy as np
|
|
from PIL import Image, ImageDraw
|
|
from PySide6 import QtGui
|
|
|
|
from app.logic import PREVIEW_MAX_SIZE
|
|
|
|
|
|
@dataclass
|
|
class Stats:
|
|
matches_all: int = 0
|
|
total_all: int = 0
|
|
matches_keep: int = 0
|
|
total_keep: int = 0
|
|
matches_excl: int = 0
|
|
total_excl: int = 0
|
|
brightness_score: float = 0.0
|
|
grouping_score: float = 0.0
|
|
continuity_score: float = 0.0
|
|
border_score: float = 0.0
|
|
prefer_dark: bool = False
|
|
|
|
@property
|
|
def effective_brightness(self) -> float:
|
|
"""Returns inverted brightness when prefer_dark is on."""
|
|
return (100.0 - self.brightness_score) if self.prefer_dark else self.brightness_score
|
|
|
|
def composite_score(self, weights: dict[str, int]) -> float:
|
|
"""Calculates weighted composite based on provided weights (0-100)."""
|
|
pct_all = (self.matches_all / self.total_all * 100) if self.total_all else 0.0
|
|
pct_keep = (self.matches_keep / self.total_keep * 100) if self.total_keep else 0.0
|
|
|
|
# weights keys: match_all, match_keep, brightness, grouping, continuity, border
|
|
w_all = weights.get("match_all", 30) / 100.0
|
|
w_keep = weights.get("match_keep", 30) / 100.0
|
|
w_bright = weights.get("brightness", 10) / 100.0
|
|
w_group = weights.get("grouping", 10) / 100.0
|
|
w_cont = weights.get("continuity", 10) / 100.0
|
|
w_bord = weights.get("border", 10) / 100.0
|
|
|
|
return (w_all * pct_all +
|
|
w_keep * pct_keep +
|
|
w_bright * self.effective_brightness +
|
|
w_group * self.grouping_score +
|
|
w_cont * self.continuity_score +
|
|
w_bord * self.border_score)
|
|
|
|
def summary(self, translate, weights: dict[str, int]) -> str:
|
|
if self.total_all == 0:
|
|
return translate("stats.placeholder")
|
|
with_pct = (self.matches_keep / self.total_keep * 100) if self.total_keep else 0.0
|
|
without_pct = (self.matches_all / self.total_all * 100) if self.total_all else 0.0
|
|
excluded_pct = (self.total_excl / self.total_all * 100) if self.total_all else 0.0
|
|
brightness_label = translate("stats.darkness_label") if self.prefer_dark else translate("stats.brightness_label")
|
|
score = self.composite_score(weights)
|
|
return translate(
|
|
"stats.summary",
|
|
score=score,
|
|
with_pct=with_pct,
|
|
without_pct=without_pct,
|
|
brightness_label=brightness_label,
|
|
brightness=self.effective_brightness,
|
|
grouping=self.grouping_score,
|
|
continuity=self.continuity_score,
|
|
border=self.border_score,
|
|
excluded_pct=excluded_pct,
|
|
)
|
|
|
|
|
|
def _rgb_to_hsv_numpy(arr: np.ndarray) -> np.ndarray:
|
|
"""Vectorized RGB→HSV conversion. arr shape: (H, W, 3), dtype float32, range [0,1].
|
|
Returns array of same shape with channels [H(0-360), S(0-100), V(0-100)].
|
|
"""
|
|
r = arr[..., 0]
|
|
g = arr[..., 1]
|
|
b = arr[..., 2]
|
|
|
|
cmax = np.maximum(np.maximum(r, g), b)
|
|
cmin = np.minimum(np.minimum(r, g), b)
|
|
delta = cmax - cmin
|
|
|
|
# Value
|
|
v = cmax
|
|
|
|
# Saturation
|
|
s = np.zeros_like(r)
|
|
np.divide(delta, cmax, out=s, where=cmax > 0)
|
|
|
|
# Hue
|
|
h = np.zeros_like(r)
|
|
mask_r = (delta > 0) & (cmax == r)
|
|
mask_g = (delta > 0) & (cmax == g)
|
|
mask_b = (delta > 0) & (cmax == b)
|
|
h[mask_r] = (60.0 * ((g[mask_r] - b[mask_r]) / delta[mask_r])) % 360.0
|
|
h[mask_g] = (60.0 * ((b[mask_g] - r[mask_g]) / delta[mask_g]) + 120.0) % 360.0
|
|
h[mask_b] = (60.0 * ((r[mask_b] - g[mask_b]) / delta[mask_b]) + 240.0) % 360.0
|
|
|
|
return np.stack([h, s * 100.0, v * 100.0], axis=-1)
|
|
|
|
|
|
def _calculate_border_score(mask: np.ndarray, val: np.ndarray, alpha_ch: np.ndarray, prefer_dark: bool, excl_mask: np.ndarray | None = None) -> float:
|
|
"""Measure border cleanliness: penalizes extremely dark (or bright) pixels along the match perimeter.
|
|
Uses Top-10% percentile to ensure local artifacts (halos) aren't diluted by clean edges.
|
|
"""
|
|
if not mask.any():
|
|
return 100.0
|
|
|
|
dilated = mask.copy()
|
|
# Manual morphological 1-pixel dilation
|
|
dilated[:-1, :] |= mask[1:, :]
|
|
dilated[1:, :] |= mask[:-1, :]
|
|
dilated[:, :-1] |= mask[:, 1:]
|
|
dilated[:, 1:] |= mask[:, :-1]
|
|
|
|
dil2 = dilated.copy()
|
|
dil2[:-1, :] |= dilated[1:, :]
|
|
dil2[1:, :] |= dilated[:-1, :]
|
|
dil2[:, :-1] |= dilated[:, 1:]
|
|
dil2[:, 1:] |= dilated[:, :-1]
|
|
|
|
# Target exterior pixels that aren't transparent and NOT excluded
|
|
outer = dil2 & ~mask & (alpha_ch >= 128)
|
|
if excl_mask is not None:
|
|
outer &= ~excl_mask
|
|
|
|
if not outer.any():
|
|
return 100.0
|
|
|
|
border_vals = val[outer]
|
|
if prefer_dark:
|
|
# Penalize super bright edges (white/silver > 60)
|
|
penalties = np.clip(border_vals - 60.0, 0, None)
|
|
else:
|
|
# Penalize super dark edges (black/heavy shadows < 40)
|
|
penalties = np.clip(40.0 - border_vals, 0, None)
|
|
|
|
# Hammer down harsh cuts: focus on the 'worst' parts of the border
|
|
if not penalties.any():
|
|
return 100.0
|
|
|
|
# Using 4th power penalty for 'catastrophic' edge detection.
|
|
# A single pitch-black line (high diff) is now exponentially worse than a gray transition.
|
|
total_penalty = np.sum(penalties ** 4)
|
|
# Collector's Grade: only 20 pixels at full intensity (40^4)
|
|
# are required for a 1% drop in the Border Score.
|
|
max_penalty_sum = 20.0 * (40.0 ** 4)
|
|
|
|
score = 100.0 * (1.0 - (total_penalty / max_penalty_sum))
|
|
return max(0.0, float(score))
|
|
|
|
|
|
def _export_worker(args: tuple) -> tuple:
|
|
"""Standalone worker for ProcessPoolExecutor batch export.
|
|
|
|
Receives ``(image_path, params)`` where *params* is the dict produced by
|
|
``QtImageProcessor.get_export_params()``. Opens the image, runs the
|
|
full stats pipeline, and returns a plain results tuple. No processor
|
|
instance is needed so nothing has to be pickled.
|
|
"""
|
|
image_path, params = args
|
|
from pathlib import Path
|
|
|
|
try:
|
|
img_path = Path(image_path)
|
|
hue_min = params["hue_min"]
|
|
hue_max = params["hue_max"]
|
|
sat_min = params["sat_min"]
|
|
sat_max = params["sat_max"]
|
|
val_min = params["val_min"]
|
|
val_max = params["val_max"]
|
|
exclude_bg = params["exclude_bg"]
|
|
exclude_bg_rgb = tuple(params["exclude_bg_rgb"])
|
|
exclude_bg_tolerance = params["exclude_bg_tolerance"]
|
|
prefer_dark = params["prefer_dark"]
|
|
exclude_shapes = params["exclude_shapes"]
|
|
exclude_ref_size = params["exclude_ref_size"]
|
|
|
|
img = Image.open(img_path).convert("RGBA")
|
|
arr = np.asarray(img, dtype=np.float32)
|
|
|
|
rgb = arr[..., :3] / 255.0
|
|
alpha_ch = arr[..., 3].copy()
|
|
|
|
if exclude_bg:
|
|
r_bg, g_bg, b_bg = exclude_bg_rgb
|
|
tol = exclude_bg_tolerance
|
|
bg_mask = (
|
|
(np.abs(arr[..., 0] - r_bg) <= tol) &
|
|
(np.abs(arr[..., 1] - g_bg) <= tol) &
|
|
(np.abs(arr[..., 2] - b_bg) <= tol)
|
|
)
|
|
alpha_ch[bg_mask] = 0
|
|
|
|
hsv = _rgb_to_hsv_numpy(rgb)
|
|
hue = hsv[..., 0]
|
|
sat = hsv[..., 1]
|
|
val = hsv[..., 2]
|
|
|
|
if hue_min <= hue_max:
|
|
hue_ok = (hue >= hue_min) & (hue <= hue_max)
|
|
else:
|
|
hue_ok = (hue >= hue_min) | (hue <= hue_max)
|
|
|
|
match_mask = (
|
|
hue_ok
|
|
& (sat >= sat_min)
|
|
& (sat <= sat_max)
|
|
& (val >= val_min)
|
|
& (val <= val_max)
|
|
& (alpha_ch >= 128)
|
|
)
|
|
|
|
# Build exclusion mask
|
|
w, h = img.size
|
|
if not exclude_shapes:
|
|
excl_mask = np.zeros((h, w), dtype=bool)
|
|
else:
|
|
target_w, target_h = w, h
|
|
ref_w, ref_h = exclude_ref_size or (w, h)
|
|
sx = target_w / ref_w if ref_w > 0 else 1.0
|
|
sy = target_h / ref_h if ref_h > 0 else 1.0
|
|
mask_img = Image.new("L", (w, h), 0)
|
|
draw = ImageDraw.Draw(mask_img)
|
|
for shape in exclude_shapes:
|
|
kind = shape.get("kind")
|
|
if kind == "rect":
|
|
x0, y0, x1, y1 = shape["coords"]
|
|
draw.rectangle([x0 * sx, y0 * sy, x1 * sx, y1 * sy], fill=255)
|
|
elif kind == "polygon":
|
|
points = shape.get("points", [])
|
|
if len(points) >= 3:
|
|
scaled_pts = [(int(px * sx), int(py * sy)) for px, py in points]
|
|
draw.polygon(scaled_pts, fill=255)
|
|
excl_mask = np.asarray(mask_img, dtype=bool)
|
|
|
|
keep_match = match_mask & ~excl_mask
|
|
visible = alpha_ch >= 128
|
|
keep_visible = visible & ~excl_mask
|
|
if keep_visible.any():
|
|
v_vals = val[keep_visible]
|
|
mean_v = float(v_vals.mean())
|
|
std_v = float(v_vals.std())
|
|
# Collector's Purity: multiply mean by a factor derived from variance
|
|
# A perfectly uniform pattern (std=0) gets 100% of its mean.
|
|
# Blotchy patterns (std > 10) get a significant reduction.
|
|
purity_factor = max(0.0, 1.0 - (std_v / 20.0))
|
|
brightness = mean_v * purity_factor
|
|
else:
|
|
brightness = 0.0
|
|
|
|
# Grouping score (inline for worker isolation)
|
|
if not keep_match.any():
|
|
grouping = 0.0
|
|
else:
|
|
mh, mw = keep_match.shape
|
|
padded = np.pad(keep_match, 5, mode='constant', constant_values=0)
|
|
cumsum = padded.astype(np.int32).cumsum(axis=0).cumsum(axis=1)
|
|
y2, x2 = np.arange(9, 9 + mh)[:, None], np.arange(9, 9 + mw)
|
|
y1_1, x1_1 = np.arange(0, mh)[:, None], np.arange(0, mw)
|
|
window_sums = cumsum[y2, x2] - cumsum[y1_1, x2] - cumsum[y2, x1_1] + cumsum[y1_1, x1_1]
|
|
neighbors = (window_sums - keep_match.astype(np.int32)).clip(min=0)
|
|
match_neighbors = neighbors[keep_match]
|
|
grouping = float(((match_neighbors / 80.0) ** 2).mean() * 100.0)
|
|
|
|
matches_all = int(match_mask[visible].sum())
|
|
total_all = int(visible.sum())
|
|
matches_keep = int(keep_match[visible].sum())
|
|
total_keep = int(keep_visible.sum())
|
|
|
|
# Continuity score (inline for worker isolation)
|
|
continuity = 0.0
|
|
if keep_match.any():
|
|
area = keep_match.sum()
|
|
y_idx, x_idx = np.nonzero(keep_match)
|
|
unvisited = set(zip(y_idx, x_idx))
|
|
max_cc_area = 0
|
|
while unvisited:
|
|
start_node = unvisited.pop()
|
|
queue = [start_node]
|
|
cc_area = 0
|
|
while queue:
|
|
cy, cx = queue.pop()
|
|
cc_area += 1
|
|
for ny, nx in ((cy-1, cx), (cy+1, cx), (cy, cx-1), (cy, cx+1)):
|
|
if (ny, nx) in unvisited:
|
|
unvisited.remove((ny, nx))
|
|
queue.append((ny, nx))
|
|
if cc_area > max_cc_area:
|
|
max_cc_area = cc_area
|
|
continuity = float(max_cc_area / area * 100.0) if area > 0 else 0.0
|
|
|
|
eff_brightness = (100.0 - brightness) if prefer_dark else brightness
|
|
|
|
# Border Cleanliness score calculation using standalone util
|
|
border = _calculate_border_score(keep_match, val, alpha_ch, prefer_dark, excl_mask)
|
|
|
|
pct_all = (matches_all / total_all * 100) if total_all else 0.0
|
|
pct_keep = (matches_keep / total_keep * 100) if total_keep else 0.0
|
|
|
|
weights = params["weights"]
|
|
w_all = weights.get("match_all", 30) / 100.0
|
|
w_keep = weights.get("match_keep", 30) / 100.0
|
|
w_bright = weights.get("brightness", 10) / 100.0
|
|
w_group = weights.get("grouping", 10) / 100.0
|
|
w_cont = weights.get("continuity", 10) / 100.0
|
|
w_bord = weights.get("border", 10) / 100.0
|
|
composite = (w_all * pct_all + w_keep * pct_keep + w_bright * eff_brightness +
|
|
w_group * grouping + w_cont * continuity + w_bord * border)
|
|
|
|
img.close()
|
|
return (img_path.name, pct_all, pct_keep, eff_brightness, grouping, continuity, border, composite)
|
|
except Exception:
|
|
return (img_path.name, None, None, None, None, None, None, None)
|
|
|
|
|
|
class QtImageProcessor:
|
|
"""Process images and build overlays for the Qt UI."""
|
|
|
|
def __init__(self) -> None:
|
|
self.orig_img: Image.Image | None = None
|
|
self.preview_img: Image.Image | None = None
|
|
self.overlay_img: Image.Image | None = None
|
|
self.preview_paths: list[Path] = []
|
|
self.current_index: int = -1
|
|
self.stats = Stats()
|
|
|
|
# Overlay tint color
|
|
self.overlay_r = 255
|
|
self.overlay_g = 0
|
|
self.overlay_b = 0
|
|
|
|
self.defaults: Dict[str, int] = {
|
|
"hue_min": 0,
|
|
"hue_max": 360,
|
|
"sat_min": 25,
|
|
"sat_max": 100,
|
|
"val_min": 15,
|
|
"val_max": 100,
|
|
"alpha": 120,
|
|
}
|
|
self.hue_min = self.defaults["hue_min"]
|
|
self.hue_max = self.defaults["hue_max"]
|
|
self.sat_min = self.defaults["sat_min"]
|
|
self.sat_max = self.defaults["sat_max"]
|
|
self.val_min = self.defaults["val_min"]
|
|
self.val_max = self.defaults["val_max"]
|
|
self.alpha = self.defaults["alpha"]
|
|
|
|
self.exclude_shapes: list[dict[str, object]] = []
|
|
self.reset_exclusions_on_switch: bool = False
|
|
|
|
# Mask caching
|
|
self._cached_mask: np.ndarray | None = None
|
|
self._cached_mask_size: Tuple[int, int] | None = None
|
|
self.exclude_ref_size: Tuple[int, int] | None = None
|
|
self.prefer_dark: bool = False
|
|
self.exclude_bg: bool = True
|
|
self.exclude_bg_rgb: Tuple[int, int, int] = (31, 41, 55)
|
|
self.exclude_bg_tolerance: int = 5
|
|
self.weights: Dict[str, int] = {
|
|
"match_all": 20,
|
|
"match_keep": 20,
|
|
"brightness": 10,
|
|
"grouping": 10,
|
|
"continuity": 20,
|
|
"border": 20
|
|
}
|
|
|
|
def set_defaults(self, defaults: dict) -> None:
|
|
for key in self.defaults:
|
|
if key in defaults:
|
|
self.defaults[key] = int(defaults[key])
|
|
for key, value in self.defaults.items():
|
|
setattr(self, key, value)
|
|
self._rebuild_overlay()
|
|
|
|
# thresholds -------------------------------------------------------------
|
|
|
|
def set_threshold(self, key: str, value: int) -> None:
|
|
setattr(self, key, value)
|
|
if self.preview_img is not None:
|
|
self._rebuild_overlay()
|
|
|
|
# image handling --------------------------------------------------------
|
|
|
|
def load_single_image(self, path: Path, *, reset_collection: bool = True) -> Path:
|
|
image = Image.open(path).convert("RGBA")
|
|
self.orig_img = image
|
|
if reset_collection:
|
|
self.preview_paths = [path]
|
|
self.current_index = 0
|
|
self._build_preview()
|
|
self._rebuild_overlay()
|
|
return path
|
|
|
|
def load_folder(self, paths: Iterable[Path], start_index: int = 0) -> Path:
|
|
self.preview_paths = list(paths)
|
|
if not self.preview_paths:
|
|
raise ValueError("No images in folder.")
|
|
self.current_index = max(0, min(start_index, len(self.preview_paths) - 1))
|
|
return self._load_image_at_current()
|
|
|
|
def next_image(self) -> Path | None:
|
|
if not self.preview_paths:
|
|
return None
|
|
self.current_index = (self.current_index + 1) % len(self.preview_paths)
|
|
return self._load_image_at_current()
|
|
|
|
def previous_image(self) -> Path | None:
|
|
if not self.preview_paths:
|
|
return None
|
|
self.current_index = (self.current_index - 1) % len(self.preview_paths)
|
|
return self._load_image_at_current()
|
|
|
|
def _load_image_at_current(self) -> Path:
|
|
path = self.preview_paths[self.current_index]
|
|
return self.load_single_image(path, reset_collection=False)
|
|
|
|
# preview/overlay -------------------------------------------------------
|
|
|
|
def _build_preview(self) -> None:
|
|
if self.orig_img is None:
|
|
self.preview_img = None
|
|
return
|
|
|
|
img_to_process = self.orig_img.convert("RGBA")
|
|
if self.exclude_bg:
|
|
# Mask the background color with tolerance on the original image before resizing
|
|
# this prevents interpolation artifacts from leaving a background 'halo'
|
|
arr = np.array(img_to_process)
|
|
r_bg, g_bg, b_bg = self.exclude_bg_rgb
|
|
tol = self.exclude_bg_tolerance
|
|
bg_mask = (
|
|
(np.abs(arr[..., 0].astype(np.int16) - r_bg) <= tol) &
|
|
(np.abs(arr[..., 1].astype(np.int16) - g_bg) <= tol) &
|
|
(np.abs(arr[..., 2].astype(np.int16) - b_bg) <= tol)
|
|
)
|
|
arr[bg_mask, 3] = 0
|
|
img_to_process = Image.fromarray(arr, "RGBA")
|
|
|
|
width, height = img_to_process.size
|
|
max_w, max_h = PREVIEW_MAX_SIZE
|
|
scale = min(max_w / width, max_h / height)
|
|
if scale <= 0:
|
|
scale = 1.0
|
|
size = (max(1, int(width * scale)), max(1, int(height * scale)))
|
|
self.preview_img = img_to_process.resize(size, Image.LANCZOS)
|
|
|
|
def _rebuild_overlay(self) -> None:
|
|
"""Build color-match overlay using vectorized NumPy operations."""
|
|
if self.preview_img is None:
|
|
self.overlay_img = None
|
|
self.stats = Stats()
|
|
return
|
|
|
|
base = self.preview_img.convert("RGBA")
|
|
arr = np.asarray(base, dtype=np.float32) # (H, W, 4)
|
|
|
|
rgb = arr[..., :3] / 255.0
|
|
alpha_ch = arr[..., 3].copy() # alpha channel of the image
|
|
|
|
if self.exclude_bg:
|
|
# Exclude specific background color
|
|
r_bg, g_bg, b_bg = self.exclude_bg_rgb
|
|
tol = self.exclude_bg_tolerance
|
|
bg_mask = (
|
|
(np.abs(arr[..., 0] - r_bg) <= tol) &
|
|
(np.abs(arr[..., 1] - g_bg) <= tol) &
|
|
(np.abs(arr[..., 2] - b_bg) <= tol)
|
|
)
|
|
alpha_ch[bg_mask] = 0
|
|
|
|
hsv = _rgb_to_hsv_numpy(rgb) # (H, W, 3): H°, S%, V%
|
|
|
|
hue = hsv[..., 0]
|
|
sat = hsv[..., 1]
|
|
val = hsv[..., 2]
|
|
|
|
hue_min = float(self.hue_min)
|
|
hue_max = float(self.hue_max)
|
|
if hue_min <= hue_max:
|
|
hue_ok = (hue >= hue_min) & (hue <= hue_max)
|
|
else:
|
|
hue_ok = (hue >= hue_min) | (hue <= hue_max)
|
|
|
|
match_mask = (
|
|
hue_ok
|
|
& (sat >= float(self.sat_min))
|
|
& (sat <= float(self.sat_max))
|
|
& (val >= float(self.val_min))
|
|
& (val <= float(self.val_max))
|
|
& (alpha_ch >= 128)
|
|
)
|
|
|
|
# Exclusion mask (same pixel space as preview)
|
|
excl_mask = self._build_exclusion_mask_numpy(base.size) # bool (H,W)
|
|
|
|
keep_match = match_mask & ~excl_mask
|
|
excl_match = match_mask & excl_mask
|
|
visible = alpha_ch >= 128
|
|
matches_all = int(match_mask[visible].sum())
|
|
total_all = int(visible.sum())
|
|
matches_keep = int(keep_match[visible].sum())
|
|
total_keep = int((visible & ~excl_mask).sum())
|
|
matches_excl = int(excl_match[visible].sum())
|
|
total_excl = int((visible & excl_mask).sum())
|
|
|
|
# Brightness: mean Value (0-100) of ALL non-excluded visible pixels
|
|
keep_visible = visible & ~excl_mask
|
|
if keep_visible.any():
|
|
v_vals = val[keep_visible]
|
|
mean_v = float(v_vals.mean())
|
|
std_v = float(v_vals.std())
|
|
# Purity factor: subtract deviation from mean to punish blotchy patterns
|
|
brightness = max(0.0, mean_v - (std_v * 1.5))
|
|
else:
|
|
brightness = 0.0
|
|
|
|
# Grouping: measure clustering of match_mask
|
|
grouping = self._calculate_grouping_score(keep_match)
|
|
|
|
# Continuity: Measure connectivity of matched area
|
|
continuity = self._calculate_continuity_score(keep_match)
|
|
|
|
# Border Cleanliness: Calculate hard edges based on preference
|
|
border = _calculate_border_score(keep_match, val, alpha_ch, self.prefer_dark, excl_mask)
|
|
|
|
# Build overlay image
|
|
overlay_arr = np.zeros((base.height, base.width, 4), dtype=np.uint8)
|
|
overlay_arr[keep_match, 0] = self.overlay_r
|
|
overlay_arr[keep_match, 1] = self.overlay_g
|
|
overlay_arr[keep_match, 2] = self.overlay_b
|
|
overlay_arr[keep_match, 3] = int(self.alpha)
|
|
|
|
self.overlay_img = Image.fromarray(overlay_arr, "RGBA")
|
|
self.stats = Stats(
|
|
matches_all=matches_all,
|
|
total_all=total_all,
|
|
matches_keep=matches_keep,
|
|
total_keep=total_keep,
|
|
matches_excl=matches_excl,
|
|
total_excl=total_excl,
|
|
brightness_score=brightness,
|
|
grouping_score=grouping,
|
|
continuity_score=continuity,
|
|
border_score=border,
|
|
prefer_dark=self.prefer_dark,
|
|
)
|
|
|
|
def get_stats_headless(self, image: Image.Image) -> Stats:
|
|
"""Calculate color-match statistics natively without building UI elements or scaling."""
|
|
base = image.convert("RGBA")
|
|
arr = np.asarray(base, dtype=np.float32)
|
|
|
|
rgb = arr[..., :3] / 255.0
|
|
alpha_ch = arr[..., 3].copy()
|
|
|
|
if self.exclude_bg:
|
|
# Exclude background color with tolerance
|
|
r_bg, g_bg, b_bg = self.exclude_bg_rgb
|
|
tol = self.exclude_bg_tolerance
|
|
bg_mask = (
|
|
(np.abs(arr[..., 0] - r_bg) <= tol) &
|
|
(np.abs(arr[..., 1] - g_bg) <= tol) &
|
|
(np.abs(arr[..., 2] - b_bg) <= tol)
|
|
)
|
|
alpha_ch[bg_mask] = 0
|
|
|
|
hsv = _rgb_to_hsv_numpy(rgb)
|
|
|
|
hue = hsv[..., 0]
|
|
sat = hsv[..., 1]
|
|
val = hsv[..., 2]
|
|
|
|
hue_min = float(self.hue_min)
|
|
hue_max = float(self.hue_max)
|
|
if hue_min <= hue_max:
|
|
hue_ok = (hue >= hue_min) & (hue <= hue_max)
|
|
else:
|
|
hue_ok = (hue >= hue_min) | (hue <= hue_max)
|
|
|
|
match_mask = (
|
|
hue_ok
|
|
& (sat >= float(self.sat_min))
|
|
& (sat <= float(self.sat_max))
|
|
& (val >= float(self.val_min))
|
|
& (val <= float(self.val_max))
|
|
& (alpha_ch >= 128)
|
|
)
|
|
|
|
excl_mask = self._build_exclusion_mask_numpy(base.size)
|
|
|
|
keep_match = match_mask & ~excl_mask
|
|
excl_match = match_mask & excl_mask
|
|
|
|
visible = alpha_ch >= 128
|
|
matches_keep_count = int(keep_match[visible].sum())
|
|
keep_visible = visible & ~excl_mask
|
|
if keep_visible.any():
|
|
v_vals = val[keep_visible]
|
|
mean_v = float(v_vals.mean())
|
|
std_v = float(v_vals.std())
|
|
# Collector's Purity: multiply mean by a factor derived from variance
|
|
# A perfectly uniform pattern (std=0) gets 100% of its mean.
|
|
# Blotchy patterns (std > 10) get a significant reduction.
|
|
purity_factor = max(0.0, 1.0 - (std_v / 20.0))
|
|
brightness = mean_v * purity_factor
|
|
else:
|
|
brightness = 0.0
|
|
grouping = self._calculate_grouping_score(keep_match)
|
|
continuity = self._calculate_continuity_score(keep_match)
|
|
border = _calculate_border_score(keep_match, val, alpha_ch, self.prefer_dark, excl_mask)
|
|
|
|
return Stats(
|
|
matches_all=int(match_mask[visible].sum()),
|
|
total_all=int(visible.sum()),
|
|
matches_keep=matches_keep_count,
|
|
total_keep=int((visible & ~excl_mask).sum()),
|
|
matches_excl=int(excl_match[visible].sum()),
|
|
total_excl=int((visible & excl_mask).sum()),
|
|
brightness_score=brightness,
|
|
grouping_score=grouping,
|
|
continuity_score=continuity,
|
|
border_score=border,
|
|
prefer_dark=self.prefer_dark,
|
|
)
|
|
|
|
def _calculate_grouping_score(self, mask: np.ndarray) -> float:
|
|
"""Measure clustering: average density in a 9x9 neighborhood (0-100)."""
|
|
if not mask.any():
|
|
return 0.0
|
|
|
|
h, w = mask.shape
|
|
# Use cumulative sums for O(1) box sum calculation
|
|
padded = np.pad(mask, 5, mode='constant', constant_values=0)
|
|
cumsum = padded.astype(np.int32).cumsum(axis=0).cumsum(axis=1)
|
|
|
|
# Indices for 9x9 windows centered at each mask pixel
|
|
y2, x2 = np.arange(9, 9 + h)[:, None], np.arange(9, 9 + w)
|
|
y1_1, x1_1 = np.arange(0, h)[:, None], np.arange(0, w)
|
|
|
|
# Box sum formula: S(window) = S(x2,y2) - S(x1-1,y2) - S(x2,y1-1) + S(x1-1,y1-1)
|
|
window_sums = cumsum[y2, x2] - cumsum[y1_1, x2] - cumsum[y2, x1_1] + cumsum[y1_1, x1_1]
|
|
|
|
# Max neighbors in 9x9 is 80 (excluding the center pixel itself)
|
|
neighbors = (window_sums - mask.astype(np.int32)).clip(min=0)
|
|
|
|
match_neighbors = neighbors[mask]
|
|
# Square the density to heavily penalize thin bridges and frayed edges
|
|
score = ( (match_neighbors / 80.0) ** 2 ).mean() * 100.0
|
|
return float(score)
|
|
|
|
def _calculate_continuity_score(self, mask: np.ndarray) -> float:
|
|
"""Measure continuity: largest connected component ratio and surface smoothness (0-100).
|
|
Penalizes jaggedness and 'perforated' patterns with many internal holes.
|
|
"""
|
|
if not mask.any():
|
|
return 0.0
|
|
|
|
area = mask.sum()
|
|
|
|
# 1. Connectivity Ratio
|
|
y_idx, x_idx = np.nonzero(mask)
|
|
unvisited = set(zip(y_idx, x_idx))
|
|
max_cc_area = 0
|
|
while unvisited:
|
|
start_node = unvisited.pop()
|
|
queue = [start_node]
|
|
cc_area = 0
|
|
while queue:
|
|
cy, cx = queue.pop()
|
|
cc_area += 1
|
|
for ny, nx in ((cy-1, cx), (cy+1, cx), (cy, cx-1), (cy, cx+1)):
|
|
if (ny, nx) in unvisited:
|
|
unvisited.remove((ny, nx))
|
|
queue.append((ny, nx))
|
|
if cc_area > max_cc_area:
|
|
max_cc_area = cc_area
|
|
|
|
connectivity = max_cc_area / area
|
|
|
|
# 2. Smoothness / Jaggedness (Perimeter-to-Area)
|
|
# Theoretically perfect smoothness (circle) has perimeter 2*sqrt(pi*area)
|
|
# We penalize departure from 'ideal' shape density
|
|
eroded = mask.copy()
|
|
eroded[:-1, :] &= mask[1:, :]
|
|
eroded[1:, :] &= mask[:-1, :]
|
|
eroded[:, :-1] &= mask[:, 1:]
|
|
eroded[:, 1:] &= mask[:, :-1]
|
|
perimeter = np.count_nonzero(mask ^ eroded)
|
|
|
|
# min_perim for a circle
|
|
min_perim = 2.0 * np.sqrt(np.pi * area)
|
|
# Jaggedness factor (0 is perfect, higher is messier)
|
|
# We normalize by the expected complexity of the item (e.g. 15 for Karambit)
|
|
# but here we use a general sensitivity factor
|
|
jaggedness = max(0.0, (perimeter / min_perim) - 1.0)
|
|
|
|
# Penalty increases as jaggedness goes up.
|
|
# For Urban Masked, we are more lenient (factor of 40 instead of 20)
|
|
smoothness_factor = 1.0 / (1.0 + (jaggedness / 40.0))
|
|
|
|
# 3. Island Count Penalty
|
|
# Premium patterns should be unified. Each separate piece (island)
|
|
# adds a small deduction to the continuity score.
|
|
y, x = np.nonzero(mask)
|
|
unvisited = set(zip(y, x))
|
|
islands = 0
|
|
while unvisited:
|
|
islands += 1
|
|
node = unvisited.pop()
|
|
q = [node]
|
|
while q:
|
|
cy, cx = q.pop()
|
|
for ny, nx in ((cy-1, cx), (cy+1, cx), (cy, cx-1), (cy, cx+1)):
|
|
if (ny, nx) in unvisited:
|
|
unvisited.remove((ny, nx))
|
|
q.append((ny, nx))
|
|
|
|
# Collector's factor: 2000 is now the baseline for Karambits.
|
|
island_factor = max(0.0, 1.0 - (islands / 2000.0))
|
|
|
|
score = connectivity * smoothness_factor * island_factor * 100.0
|
|
return float(score)
|
|
|
|
# helpers ----------------------------------------------------------------
|
|
|
|
def _matches(self, r: int, g: int, b: int) -> bool:
|
|
"""Single-pixel match — kept for compatibility / eyedropper use."""
|
|
h, s, v = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)
|
|
hue = (h * 360.0) % 360.0
|
|
if self.hue_min <= self.hue_max:
|
|
hue_ok = self.hue_min <= hue <= self.hue_max
|
|
else:
|
|
hue_ok = hue >= self.hue_min or hue <= self.hue_max
|
|
sat_ok = self.sat_min <= s * 100.0 <= self.sat_max
|
|
val_ok = self.val_min <= v * 100.0 <= self.val_max
|
|
return hue_ok and sat_ok and val_ok
|
|
|
|
def pick_color(self, x: int, y: int) -> Tuple[float, float, float] | None:
|
|
"""Return (hue°, sat%, val%) of the preview pixel at (x, y), or None."""
|
|
if self.preview_img is None:
|
|
return None
|
|
img = self.preview_img.convert("RGBA")
|
|
try:
|
|
r, g, b, a = img.getpixel((x, y))
|
|
except IndexError:
|
|
return None
|
|
if a == 0:
|
|
return None
|
|
h, s, v = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)
|
|
return (h * 360.0) % 360.0, s * 100.0, v * 100.0
|
|
|
|
# exported data ----------------------------------------------------------
|
|
|
|
def preview_pixmap(self) -> QtGui.QPixmap:
|
|
return self._to_pixmap(self.preview_img)
|
|
|
|
def overlay_pixmap(self) -> QtGui.QPixmap:
|
|
if self.preview_img is None:
|
|
return QtGui.QPixmap()
|
|
if self.overlay_img is None:
|
|
return self.preview_pixmap()
|
|
merged = Image.alpha_composite(self.preview_img.convert("RGBA"), self.overlay_img)
|
|
return self._to_pixmap(merged)
|
|
|
|
@staticmethod
|
|
def _to_pixmap(image: Image.Image | None) -> QtGui.QPixmap:
|
|
if image is None:
|
|
return QtGui.QPixmap()
|
|
buffer = image.tobytes("raw", "RGBA")
|
|
qt_image = QtGui.QImage(buffer, image.width, image.height, QtGui.QImage.Format_RGBA8888)
|
|
return QtGui.QPixmap.fromImage(qt_image)
|
|
|
|
# exclusions -------------------------------------------------------------
|
|
|
|
def set_exclusions(self, shapes: list[dict[str, object]], ref_size: Tuple[int, int] | None = None) -> None:
|
|
copied: list[dict[str, object]] = []
|
|
for shape in shapes:
|
|
kind = shape.get("kind")
|
|
if kind == "rect":
|
|
coords = tuple(shape.get("coords", (0, 0, 0, 0))) # type: ignore[assignment]
|
|
copied.append({"kind": "rect", "coords": tuple(int(c) for c in coords)})
|
|
elif kind == "polygon":
|
|
pts = shape.get("points", [])
|
|
copied.append({"kind": "polygon", "points": [(int(x), int(y)) for x, y in pts]})
|
|
self.exclude_shapes = copied
|
|
|
|
if ref_size:
|
|
self.exclude_ref_size = ref_size
|
|
elif self.preview_img:
|
|
self.exclude_ref_size = self.preview_img.size
|
|
else:
|
|
self.exclude_ref_size = None
|
|
|
|
self._cached_mask = None # Invalidate cache
|
|
self._cached_mask_size = None
|
|
self._rebuild_overlay()
|
|
|
|
def _build_exclusion_mask(self, size: Tuple[int, int]) -> Image.Image | None:
|
|
if not self.exclude_shapes:
|
|
return None
|
|
|
|
target_w, target_h = size
|
|
ref_w, ref_h = self.exclude_ref_size or size
|
|
sx = target_w / ref_w if ref_w > 0 else 1.0
|
|
sy = target_h / ref_h if ref_h > 0 else 1.0
|
|
|
|
mask = Image.new("L", size, 0)
|
|
draw = ImageDraw.Draw(mask)
|
|
for shape in self.exclude_shapes:
|
|
kind = shape.get("kind")
|
|
if kind == "rect":
|
|
x0, y0, x1, y1 = shape["coords"] # type: ignore[index]
|
|
draw.rectangle([x0 * sx, y0 * sy, x1 * sx, y1 * sy], fill=255)
|
|
elif kind == "polygon":
|
|
points = shape.get("points", [])
|
|
if len(points) >= 3:
|
|
scaled_pts = [(int(x * sx), int(y * sy)) for x, y in points]
|
|
draw.polygon(scaled_pts, fill=255)
|
|
return mask
|
|
|
|
def set_overlay_color(self, hex_code: str) -> None:
|
|
"""Set the RGB channels for the match overlay from a hex string."""
|
|
if not hex_code.startswith("#") or len(hex_code) not in (7, 9):
|
|
return
|
|
try:
|
|
self.overlay_r = int(hex_code[1:3], 16)
|
|
self.overlay_g = int(hex_code[3:5], 16)
|
|
self.overlay_b = int(hex_code[5:7], 16)
|
|
if self.preview_img:
|
|
self._rebuild_overlay()
|
|
except ValueError:
|
|
pass
|
|
|
|
def _build_exclusion_mask_numpy(self, size: Tuple[int, int]) -> np.ndarray:
|
|
"""Return a boolean (H, W) mask — True where pixels are excluded."""
|
|
if self._cached_mask is not None and self._cached_mask_size == size:
|
|
return self._cached_mask
|
|
|
|
w, h = size
|
|
if not self.exclude_shapes:
|
|
mask = np.zeros((h, w), dtype=bool)
|
|
else:
|
|
pil_mask = self._build_exclusion_mask(size)
|
|
if pil_mask is None:
|
|
mask = np.zeros((h, w), dtype=bool)
|
|
else:
|
|
mask = np.asarray(pil_mask, dtype=bool)
|
|
|
|
self._cached_mask = mask
|
|
self._cached_mask_size = size
|
|
return mask
|
|
|
|
def set_exclude_bg_color(self, hex_code: str, tolerance: int = 5) -> None:
|
|
"""Set the RGB channels for background exclusion from a hex string."""
|
|
self.exclude_bg_tolerance = tolerance
|
|
if not hex_code.startswith("#") or len(hex_code) not in (7, 9):
|
|
return
|
|
try:
|
|
r = int(hex_code[1:3], 16)
|
|
g = int(hex_code[3:5], 16)
|
|
b = int(hex_code[5:7], 16)
|
|
self.exclude_bg_rgb = (r, g, b)
|
|
except ValueError:
|
|
pass
|
|
|
|
def get_export_params(self) -> dict:
|
|
"""Extract all parameters needed for headless batch processing.
|
|
|
|
Called once before a batch export so that each worker receives
|
|
a plain dict instead of re-reading instance attributes.
|
|
"""
|
|
return {
|
|
"hue_min": float(self.hue_min),
|
|
"hue_max": float(self.hue_max),
|
|
"sat_min": float(self.sat_min),
|
|
"sat_max": float(self.sat_max),
|
|
"val_min": float(self.val_min),
|
|
"val_max": float(self.val_max),
|
|
"exclude_bg": self.exclude_bg,
|
|
"exclude_bg_rgb": self.exclude_bg_rgb,
|
|
"exclude_bg_tolerance": self.exclude_bg_tolerance,
|
|
"prefer_dark": self.prefer_dark,
|
|
"exclude_shapes": self.exclude_shapes,
|
|
"exclude_ref_size": self.exclude_ref_size,
|
|
"weights": self.weights,
|
|
}
|
|
|
|
@property
|
|
def exclude_bg_color_hex(self) -> str:
|
|
r, g, b = self.exclude_bg_rgb
|
|
return f"#{r:02x}{g:02x}{b:02x}"
|
|
|
|
@property
|
|
def overlay_color_hex(self) -> str:
|
|
return f"#{self.overlay_r:02x}{self.overlay_g:02x}{self.overlay_b:02x}"
|