Refine texture metrics and update default weights for premium pattern detection

app/i18n.py

@@ -40,13 +40,14 @@ def _load_translations(lang: str) -> Dict[str, str]:
             data = tomllib.load(handle)
     except (OSError, AttributeError, ValueError, TypeError):  # type: ignore[arg-type]
         return {}
-    translations = data.get("translations")
+    
-    if not isinstance(translations, dict):
+    # Merge all dictionaries found in the TOML (e.g. [translations] and [tooltip])
-        return {}
     out: Dict[str, str] = {}
-    for key, value in translations.items():
+    for section_name, section_data in data.items():
-        if isinstance(key, str) and isinstance(value, str):
+        if isinstance(section_data, dict):
-            out[key] = value
+            for key, value in section_data.items():
+                if isinstance(key, str) and isinstance(value, str):
+                    out[key] = value
     return out
 
 

app/lang/de.toml

@@ -46,10 +46,12 @@
 "sliders.val_max" = "Helligkeit Max (%)"
 "sliders.alpha" = "Overlay Alpha"
 "stats.placeholder" = "Markierungen (mit Ausschlüssen): —"
-"stats.summary" = "Wertung: {score:.2f}%    |    Treffer (mit Exkl.): {with_pct:.2f}%    |    Treffer: {without_pct:.2f}%    |    {brightness_label}: {brightness:.1f}%    |    Gruppierung: {grouping:.1f}%    |    Ausgeschlossen: {excluded_pct:.2f}%"
+"stats.summary" = "Gesamtwertung: {score:.2f}%    |    Treffer (m. Ausschl.): {with_pct:.2f}%    |    Treffer: {without_pct:.2f}%    |    {brightness_label}: {brightness:.1f}%    |    Gruppierung: {grouping:.1f}%    |    Kontinuität: {continuity:.1f}%    |    Rand: {border:.1f}%"
 "stats.brightness_label" = "Helligkeit"
 "stats.darkness_label" = "Dunkelheit"
 "stats.grouping_label" = "Gruppierung"
+"stats.continuity_label" = "Kontinuität"
+"stats.border_label" = "Rand"
 "menu.copy" = "Kopieren"
 "dialog.info_title" = "Info"
 "dialog.error_title" = "Fehler"
@@ -101,6 +103,8 @@
 "dialog.weight_match_keep" = "Treffer (Behalten) %"
 "dialog.weight_brightness" = "Helligkeit/Dunkelheit %"
 "dialog.weight_grouping" = "Gruppierung %"
+"dialog.weight_continuity" = "Kontinuität %"
+"dialog.weight_border" = "Rand Sauberkeit %"
 "dialog.total_weight" = "Gesamt:"
 "dialog.weight_error" = "Gewichtungen müssen exakt 100% ergeben (aktuell {total}%)."
 

app/lang/en.toml

@@ -46,10 +46,12 @@
 "sliders.val_max" = "Value max (%)"
 "sliders.alpha" = "Overlay alpha"
 "stats.placeholder" = "Matches (with exclusions): —"
-"stats.summary" = "Score: {score:.2f}%    |    Matches (w/ excl.): {with_pct:.2f}%    |    Matches: {without_pct:.2f}%    |    {brightness_label}: {brightness:.1f}%    |    Grouping: {grouping:.1f}%    |    Excluded: {excluded_pct:.2f}%"
+"stats.summary" = "Composite Score: {score:.2f}%    |    Matches (w/ excl.): {with_pct:.2f}%    |    Matches: {without_pct:.2f}%    |    {brightness_label}: {brightness:.1f}%    |    Grouping: {grouping:.1f}%    |    Continuity: {continuity:.1f}%    |    Border: {border:.1f}%"
 "stats.brightness_label" = "Brightness"
 "stats.darkness_label" = "Darkness"
 "stats.grouping_label" = "Grouping"
+"stats.continuity_label" = "Continuity"
+"stats.border_label" = "Border"
 "menu.copy" = "Copy"
 "dialog.info_title" = "Info"
 "dialog.error_title" = "Error"
@@ -101,6 +103,8 @@
 "dialog.weight_match_keep" = "Match (Keep) %"
 "dialog.weight_brightness" = "Brightness/Darkness %"
 "dialog.weight_grouping" = "Grouping %"
+"dialog.weight_continuity" = "Continuity %"
+"dialog.weight_border" = "Border Cleanliness %"
 "dialog.total_weight" = "Total:"
 "dialog.weight_error" = "Weights must sum exactly to 100% (currently {total}%)."
 

app/logic/__init__.py

@@ -8,6 +8,7 @@ from .constants import (
     OVERLAY_COLOR,
     EXCLUDE_BG_COLOR,
     EXCLUDE_BG_TOLERANCE,
+    WEIGHTS,
     PREVIEW_MAX_SIZE,
     RESET_EXCLUSIONS_ON_IMAGE_CHANGE,
     SUPPORTED_IMAGE_EXTENSIONS,
@@ -20,7 +21,7 @@ __all__ = [
     "LANGUAGE",
     "OVERLAY_COLOR",
     "EXCLUDE_BG_COLOR",
-    "EXCLUDE_BG_TOLERANCE",
+    "WEIGHTS",
     "PREVIEW_MAX_SIZE",
     "RESET_EXCLUSIONS_ON_IMAGE_CHANGE",
     "SUPPORTED_IMAGE_EXTENSIONS",

app/logic/constants.py

@@ -101,6 +101,15 @@ _OPTION_DEFAULTS = {
     "exclude_bg_tolerance": 5,
 }
 
+_WEIGHT_DEFAULTS = {
+    "match_all": 20,
+    "match_keep": 20,
+    "brightness": 10,
+    "grouping": 10,
+    "continuity": 20,
+    "border": 20,
+}
+
 
 def _extract_options(data: dict[str, Any]) -> dict[str, Any]:
     section = data.get("options")
@@ -122,6 +131,18 @@ def _extract_options(data: dict[str, Any]) -> dict[str, Any]:
     return result
 
 
+def _extract_weights(data: dict[str, Any]) -> dict[str, int]:
+    section = data.get("weights")
+    if not isinstance(section, dict):
+        return {}
+    result: dict[str, int] = {}
+    for key in _WEIGHT_DEFAULTS:
+        value = section.get(key)
+        if isinstance(value, int):
+            result[key] = max(0, min(100, value))
+    return result
+
+
 DEFAULTS = {**_DEFAULTS_BASE, **_extract_default_overrides(_CONFIG_DATA)}
 LANGUAGE = _extract_language(_CONFIG_DATA)
 OPTIONS = {**_OPTION_DEFAULTS, **_extract_options(_CONFIG_DATA)}
@@ -129,3 +150,4 @@ RESET_EXCLUSIONS_ON_IMAGE_CHANGE = OPTIONS["reset_exclusions_on_image_change"]
 OVERLAY_COLOR = OPTIONS["overlay_color"]
 EXCLUDE_BG_COLOR = OPTIONS["exclude_bg_color"]
 EXCLUDE_BG_TOLERANCE = OPTIONS["exclude_bg_tolerance"]
+WEIGHTS = {**_WEIGHT_DEFAULTS, **_extract_weights(_CONFIG_DATA)}

app/qt/app.py

@@ -46,11 +46,12 @@ def create_application() -> QtWidgets.QApplication:
 def run() -> int:
     """Run the PySide6 GUI."""
     app = create_application()
-    from app.logic import OVERLAY_COLOR, EXCLUDE_BG_COLOR, EXCLUDE_BG_TOLERANCE
+    from app.logic import OVERLAY_COLOR, EXCLUDE_BG_COLOR, EXCLUDE_BG_TOLERANCE, WEIGHTS
     window = MainWindow(
         language=LANGUAGE,
         defaults=DEFAULTS.copy(),
         reset_exclusions=RESET_EXCLUSIONS_ON_IMAGE_CHANGE,
+        weights=WEIGHTS.copy(),
         overlay_color=OVERLAY_COLOR,
         exclude_bg_color=EXCLUDE_BG_COLOR,
         exclude_bg_tolerance=EXCLUDE_BG_TOLERANCE,

app/qt/image_processor.py

@@ -24,6 +24,8 @@ class Stats:
     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
@@ -36,16 +38,20 @@ class Stats:
         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
+        # 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", 50) / 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_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:
@@ -63,6 +69,8 @@ class Stats:
             brightness_label=brightness_label,
             brightness=self.effective_brightness,
             grouping=self.grouping_score,
+            continuity=self.continuity_score,
+            border=self.border_score,
             excluded_pct=excluded_pct,
         )
 
@@ -98,6 +106,57 @@ def _rgb_to_hsv_numpy(arr: np.ndarray) -> np.ndarray:
     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.
 
@@ -185,7 +244,17 @@ def _export_worker(args: tuple) -> tuple:
         keep_match = match_mask & ~excl_mask
         visible = alpha_ch >= 128
         keep_visible = visible & ~excl_mask
-        brightness = float(val[keep_visible].mean()) if keep_visible.any() else 0.0
+        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():
@@ -206,22 +275,50 @@ def _export_worker(args: tuple) -> tuple:
         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", 50) / 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
-        composite = w_all * pct_all + w_keep * pct_keep + w_bright * eff_brightness + w_group * grouping
+        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, composite)
+        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)
+        return (img_path.name, None, None, None, None, None, None, None)
 
 
 class QtImageProcessor:
@@ -269,10 +366,12 @@ class QtImageProcessor:
         self.exclude_bg_rgb: Tuple[int, int, int] = (31, 41, 55)
         self.exclude_bg_tolerance: int = 5
         self.weights: Dict[str, int] = {
-            "match_all": 30,
+            "match_all": 20,
-            "match_keep": 50,
+            "match_keep": 20,
             "brightness": 10,
-            "grouping": 10
+            "grouping": 10,
+            "continuity": 20,
+            "border": 20
         }
 
     def set_defaults(self, defaults: dict) -> None:
@@ -416,11 +515,24 @@ class QtImageProcessor:
 
         # Brightness: mean Value (0-100) of ALL non-excluded visible pixels
         keep_visible = visible & ~excl_mask
-        brightness = float(val[keep_visible].mean()) if keep_visible.any() else 0.0
+        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
@@ -438,6 +550,8 @@ class QtImageProcessor:
             total_excl=total_excl,
             brightness_score=brightness,
             grouping_score=grouping,
+            continuity_score=continuity,
+            border_score=border,
             prefer_dark=self.prefer_dark,
         )
 
@@ -490,8 +604,20 @@ class QtImageProcessor:
         visible = alpha_ch >= 128
         matches_keep_count = int(keep_match[visible].sum())
         keep_visible = visible & ~excl_mask
-        brightness = float(val[keep_visible].mean()) if keep_visible.any() else 0.0
+        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()),
@@ -502,6 +628,8 @@ class QtImageProcessor:
             total_excl=int((visible & excl_mask).sum()),
             brightness_score=brightness,
             grouping_score=grouping,
+            continuity_score=continuity,
+            border_score=border,
             prefer_dark=self.prefer_dark,
         )
 
@@ -530,6 +658,79 @@ class QtImageProcessor:
         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:

app/qt/main_window.py

@@ -552,6 +552,8 @@ class WeightingDialog(QtWidgets.QDialog):
             ("match_keep", "dialog.weight_match_keep"),
             ("brightness", "dialog.weight_brightness"),
             ("grouping", "dialog.weight_grouping"),
+            ("continuity", "dialog.weight_continuity"),
+            ("border", "dialog.weight_border"),
         ]
         
         for i, (key, label_key) in enumerate(specs):
@@ -608,7 +610,7 @@ class WeightingDialog(QtWidgets.QDialog):
 class MainWindow(QtWidgets.QMainWindow, I18nMixin):
     """Main application window containing all controls."""
 
-    def __init__(self, language: str, defaults: dict, reset_exclusions: bool, overlay_color: str | None = None, exclude_bg_color: str | None = None, exclude_bg_tolerance: int = 5) -> None:
+    def __init__(self, language: str, defaults: dict, reset_exclusions: bool, weights: dict[str, int], overlay_color: str | None = None, exclude_bg_color: str | None = None, exclude_bg_tolerance: int = 5) -> None:
         super().__init__()
         self.init_i18n(language)
         self.setWindowTitle(self._t("app.title"))
@@ -628,6 +630,7 @@ class MainWindow(QtWidgets.QMainWindow, I18nMixin):
 
         self.content = QtWidgets.QWidget()
         self.processor = QtImageProcessor()
+        self.processor.weights = weights.copy()
         self.processor.set_defaults(defaults)
         self.processor.reset_exclusions_on_switch = reset_exclusions
         # Always use red for the overlay regardless of the target color
@@ -1203,18 +1206,22 @@ class MainWindow(QtWidgets.QMainWindow, I18nMixin):
         decimal = ","
 
         # Weights mapping
-        w_all = self.processor.weights.get("match_all", 30)
+        w_all = self.processor.weights.get("match_all", 20)
-        w_keep = self.processor.weights.get("match_keep", 50)
+        w_keep = self.processor.weights.get("match_keep", 30)
         w_bright = self.processor.weights.get("brightness", 10)
         w_group = self.processor.weights.get("grouping", 10)
+        w_cont = self.processor.weights.get("continuity", 15)
+        w_bord = self.processor.weights.get("border", 15)
 
         brightness_col = self._t("stats.darkness_label") if self.processor.prefer_dark else self._t("stats.brightness_label")
         headers = [
             "Filename",
-            f"Matching Pixels ({w_all}%)",  # Was the non-exclusion match percentage
+            f"Matching Pixels ({w_all}%)",
             f"Matching Pixels w/ Exclusions ({w_keep}%)",
             f"{brightness_col} ({w_bright}%)",
             f"{self._t('stats.grouping_label')} ({w_group}%)",
+            f"{self._t('stats.continuity_label')} ({w_cont}%)",
+            f"{self._t('stats.border_label')} ({w_bord}%)",
             "Composite Score"
         ]
         
@@ -1233,25 +1240,21 @@ class MainWindow(QtWidgets.QMainWindow, I18nMixin):
             for future in concurrent.futures.as_completed(future_to_idx):
                 idx = future_to_idx[future]
                 res = future.result()
-                name, pct_all, pct_keep, eff_brightness, grouping, composite_score = res
+                name, pct_all, pct_keep, eff_brightness, grouping, continuity, border, composite_score = res
                 
                 if pct_keep is None:
                     # Error parsing image
-                    results[idx] = [name, "Error", "Error", "Error", "Error", "Error"]
+                    results[idx] = [name, "Error", "Error", "Error", "Error", "Error", "Error", -1.0]
                 else:
-                    pct_all_str = f"{pct_all:.2f}".replace(".", decimal)
-                    pct_keep_str = f"{pct_keep:.2f}".replace(".", decimal)
-                    brightness_str = f"{eff_brightness:.2f}".replace(".", decimal)
-                    grouping_str = f"{grouping:.2f}".replace(".", decimal)
-                    composite_str = f"{composite_score:.2f}".replace(".", decimal)
-                    
                     results[idx] = [
                         name,
-                        pct_all_str,
+                        pct_all,
-                        pct_keep_str,
+                        pct_keep,
-                        brightness_str,
+                        eff_brightness,
-                        grouping_str,
+                        grouping,
-                        composite_str
+                        continuity,
+                        border,
+                        composite_score
                     ]
 
                 done_count += 1
@@ -1259,7 +1262,21 @@ class MainWindow(QtWidgets.QMainWindow, I18nMixin):
                     self.set_status(self._t("status.exporting", current=str(done_count), total=str(total)))
                     QtWidgets.QApplication.processEvents()
 
-        rows.extend(results)
+        # Sort results by composite_score (last element) descending
+        results.sort(key=lambda x: x[-1] if isinstance(x[-1], (int, float)) else -1.0, reverse=True)
+
+        # Convert numbers to strings with custom decimal separator for CSV
+        final_rows = []
+        for r in results:
+            str_row = []
+            for item in r:
+                if isinstance(item, (int, float)):
+                    str_row.append(f"{item:.2f}".replace(".", decimal))
+                else:
+                    str_row.append(str(item))
+            final_rows.append(str_row)
+
+        rows.extend(final_rows)
 
         # Compute max width per column for alignment, plus extra space so it's not cramped
         col_widths = [max(len(str(item)) for item in col) + 4 for col in zip(*rows)]

config.toml

@@ -21,14 +21,20 @@ exclude_bg_color = "#1f2937"
 exclude_bg_tolerance = 5
 
 [defaults]
-# Override any of the following keys to tweak the initial slider values whenever
+# Override any of the following to tweak the initial slider values upon application start.
-# the application starts.
+hue_min = 250.0  # (0-360) Starting Hue for the target color range
-# hue_min, hue_max, sat_min, val_min, val_max accept floating point numbers.
+hue_max = 310.0  # (0-360) Ending Hue for the target color range
-# alpha accepts an integer between 0 and 255.
+sat_min = 15.0   # (0-100) Minimum Saturation percentage
-hue_min = 250.0
+sat_max = 100.0  # (0-100) Maximum Saturation percentage
-hue_max = 310.0
+val_min = 15.0   # (0-100) Minimum Value/Brightness percentage
-sat_min = 15.0
+val_max = 100.0  # (0-100) Maximum Value/Brightness percentage
-sat_max = 100.0
+alpha = 150      # (0-255) Opacity of the red overlay in the UI preview
-val_min = 15.0
+
-val_max = 100.0
+[weights]
-alpha = 150
+# Contribution of each measurement to the final Composite Score (0-100%).
+match_all = 20   # % of the total visible image that matches
+match_keep = 30  # % of the non-excluded area that matches (the most important area)
+brightness = 10  # % Importance of Vibrance (or Darkness if "Prefer Darkness" is on)
+grouping = 10    # % Importance of pixel clustering (rewarding solid color blocks)
+continuity = 15  # % Quality of the largest connected surface area
+border = 15      # % Quality of the transition edges (penalizing dark/hard outlines)

tests/test_i18n.py

@@ -0,0 +1,73 @@
+import pytest
+from pathlib import Path
+import re
+
+# Base directory for language files
+LANG_DIR = Path(__file__).resolve().parent.parent / "app" / "lang"
+
+def get_structure(file_path: Path):
+    """
+    Returns a list of (line_number, key/header) for a TOML file.
+    Only captures keys and section headers, ignoring the values.
+    """
+    structure = []
+    # Regex to capture "key" = or [header]
+    key_pattern = re.compile(r'^\s*"?([^"\s=]+)"?\s*=')
+    header_pattern = re.compile(r'^\s*\[([^\]]+)\]')
+    
+    with open(file_path, "r", encoding="utf-8") as f:
+        for i, line in enumerate(f, 1):
+            line = line.strip()
+            if not line:
+                structure.append((i, "<empty>"))
+                continue
+            
+            # Check for header [section]
+            header_match = header_pattern.match(line)
+            if header_match:
+                structure.append((i, f"[{header_match.group(1)}]"))
+                continue
+                
+            # Check for key "name" =
+            key_match = key_pattern.match(line)
+            if key_match:
+                structure.append((i, key_match.group(1)))
+                continue
+                
+            # Comments or anything else
+            structure.append((i, "<other/comment>"))
+            
+    return structure
+
+def test_i18n_files_exist():
+    assert LANG_DIR.exists(), f"Language directory {LANG_DIR} not found"
+    en_file = LANG_DIR / "en.toml"
+    assert en_file.exists(), "English language file (en.toml) must exist as baseline"
+
+def test_i18n_synchronization():
+    """
+    Ensures all language files have the same keys/headers on the same lines
+    as the baseline en.toml.
+    """
+    en_path = LANG_DIR / "en.toml"
+    en_structure = get_structure(en_path)
+    
+    other_files = list(LANG_DIR.glob("*.toml"))
+    other_files.remove(en_path)
+    
+    for lang_file in other_files:
+        lang_name = lang_file.name
+        lang_structure = get_structure(lang_file)
+        
+        # Check line count
+        assert len(lang_structure) == len(en_structure), \
+            f"{lang_name} length mismatch: expected {len(en_structure)} lines, got {len(lang_structure)}"
+            
+        # Check line-by-line sync
+        for (en_line, en_key), (lang_line, lang_key) in zip(en_structure, lang_structure):
+            assert en_key == lang_key, \
+                f"Sync error at {lang_name}:{lang_line}. Expected '{en_key}', found '{lang_key}'"
+
+if __name__ == "__main__":
+    # Allow running directly as a script
+    pytest.main([__file__])