Preserve full opaque HD backgrounds and source-supported small cues

tools/hd_frame_policy.py

@@ -0,0 +1,68 @@
+"""Strict optional HD presentation metadata; source fidelity requires peer review."""
+import struct
+
+FRAME_KEYS = {"frame", "file", "logical_width", "logical_height", "origin",
+              "source_sha256", "crop", "preserve_reserved_coverage", "native_nearest_cues"}
+
+def _indices(payload):
+    if len(payload) < 20:
+        raise ValueError("truncated original picture")
+    kind, _, _, width, height = struct.unpack_from("<iiiii", payload)
+    if not (1 <= width <= 4096 and 1 <= height <= 4096 and width * height <= 1_048_576):
+        raise ValueError("invalid original canvas")
+    if kind == 1:
+        if len(payload) < 20 + width * height:
+            raise ValueError("truncated original raw picture")
+        return width, height, payload[20:20 + width * height]
+    if kind != 0:
+        raise ValueError("unsupported original picture type")
+    out = [256] * (width * height)
+    off = 20
+    runs = 0
+    while True:
+        if off + 16 > len(payload):
+            raise ValueError("truncated original picture span")
+        x, y, dest, length = struct.unpack_from("<iiii", payload, off)
+        off += 16
+        if dest == -1:
+            break
+        if not (x >= 0 and 0 <= y < height and length > 0 and x + length <= width
+                and off + length <= len(payload)):
+            raise ValueError("invalid original picture span")
+        start = y * width + x
+        out[start:start + length] = payload[off:off + length]
+        off += length
+        runs += 1
+        if runs > len(out):
+            raise ValueError("too many original picture spans")
+    return width, height, out
+
+def validate_frame_policy(frame, source_payload=None):
+    if set(frame) - FRAME_KEYS:
+        raise ValueError("unknown HD frame fields")
+    if type(frame.get("preserve_reserved_coverage", False)) is not bool:
+        raise ValueError("preserve_reserved_coverage must be a boolean")
+    cues = frame.get("native_nearest_cues", [])
+    if not isinstance(cues, list) or len(cues) > 64:
+        raise ValueError("native_nearest_cues must contain at most64 cells")
+    width, height = frame["logical_width"], frame["logical_height"]
+    if type(width) is not int or type(height) is not int or not (0 < width < 2**32 and 0 < height < 2**32):
+        raise ValueError("logical dimensions must be nonzero u32")
+    previous = None
+    for cue in cues:
+        if not isinstance(cue, list) or len(cue) != 2 or any(type(n) is not int or not 0 <= n < 2**32 for n in cue):
+            raise ValueError("native nearest cue must be a pair of u32 coordinates")
+        if cue[0] >= width or cue[1] >= height:
+            raise ValueError("native nearest cue outside original logical canvas")
+        key = tuple(cue)
+        if previous is not None and key <= previous:
+            raise ValueError("native nearest cues must be sorted and unique")
+        previous = key
+    if source_payload is not None and cues:
+        source_width, source_height, indices = _indices(source_payload)
+        if (source_width, source_height) != (width, height):
+            raise ValueError("logical canvas differs from original")
+        if any(index == 256 for index in indices):
+            raise ValueError("native nearest cues require an opaque original canvas")
+        if any(indices[y * width + x] >= 240 for x, y in cues):
+            raise ValueError("native nearest cue must be an ordinary original source cell")