feat(fc-cleanup): audits/single_color.py + tests — Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

backend/app/services/audits/single_color.py

@@ -0,0 +1,53 @@
+"""Single-color audit: matches images where one color dominates beyond
+the threshold (within the given Euclidean RGB tolerance). The first
+canonical implementation — the import-side filter (SkipReason.single_color)
+was never wired; FC-Cleanup's audit module is the source of truth and a
+future spec can adopt it on the import path too.
+"""
+
+from PIL import Image
+
+_THUMB_SIZE = (64, 64)
+
+
+def evaluate(
+    pil_image,
+    *,
+    threshold: float,
+    tolerance: int,
+) -> bool:
+    """True iff the fraction of pixels within `tolerance` (Euclidean RGB
+    distance) of the dominant color exceeds `threshold`.
+
+    Downsamples to 64x64 for speed (~4ms regardless of source size).
+    Alpha channels are stripped; only RGB is considered. Animated images
+    use frame 0 (PIL's default after Image.open without seek).
+    """
+    im = pil_image
+    if im.mode == "RGBA":
+        im = im.convert("RGB")
+    elif im.mode not in ("RGB", "L"):
+        im = im.convert("RGB")
+    if im.size != _THUMB_SIZE:
+        im = im.resize(_THUMB_SIZE, Image.Resampling.BILINEAR)
+    pixels = list(im.getdata())
+    if not pixels:
+        return False
+    # Normalize L-mode pixels to RGB tuples for distance math.
+    if isinstance(pixels[0], int):
+        pixels = [(p, p, p) for p in pixels]
+    # Dominant color = mean RGB.
+    n = len(pixels)
+    sum_r = sum(p[0] for p in pixels)
+    sum_g = sum(p[1] for p in pixels)
+    sum_b = sum(p[2] for p in pixels)
+    dom = (sum_r / n, sum_g / n, sum_b / n)
+    tol_sq = tolerance * tolerance
+    within = 0
+    for r, g, b in pixels:
+        dr = r - dom[0]
+        dg = g - dom[1]
+        db = b - dom[2]
+        if dr * dr + dg * dg + db * db <= tol_sq:
+            within += 1
+    return (within / n) > threshold