integrated finger process pipeline on remapped 2d image
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ykcheungab
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"""2D UV flatten remap tables and runtime frame remapping.
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Stores precomputed map_x/map_y/valid_mask and applies cv2.remap at runtime to
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convert source camera frames into the UV flatten domain.
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"""
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from dataclasses import dataclass, field
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from typing import Any, Dict, Optional, Tuple
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import cv2
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import numpy as np
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@dataclass
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class UVRemap:
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"""Precomputed UV flatten remap tables."""
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map_x: np.ndarray
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map_y: np.ndarray
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valid_mask: np.ndarray
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metadata: Dict[str, Any] = field(default_factory=dict)
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_map1_fixed: Optional[np.ndarray] = field(default=None, repr=False, compare=False)
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_map2_fixed: Optional[np.ndarray] = field(default=None, repr=False, compare=False)
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_border_only_invalid: bool = field(default=False, repr=False, compare=False)
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@property
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def out_height(self) -> int:
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return int(self.map_x.shape[0])
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@property
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def out_width(self) -> int:
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return int(self.map_x.shape[1])
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@classmethod
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def from_dict(cls, remap_dict: dict) -> "UVRemap":
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"""Build UVRemap from a flat dict (e.g. np.load result)."""
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meta_keys = {
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"source_width",
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"source_height",
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"out_width",
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"out_height",
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"flip_v",
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"edge_band",
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"edge_expand",
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"projection_K",
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"method",
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}
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metadata = {k: remap_dict[k] for k in meta_keys if k in remap_dict}
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remap = cls(
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map_x=np.asarray(remap_dict["map_x"], dtype=np.float32),
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map_y=np.asarray(remap_dict["map_y"], dtype=np.float32),
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valid_mask=np.asarray(remap_dict["valid_mask"], dtype=bool),
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metadata=metadata,
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)
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remap.ensure_fast_maps()
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return remap
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@classmethod
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def load(cls, path: str) -> "UVRemap":
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"""Load cached remap from .npz file."""
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data = np.load(path)
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return cls.from_dict({key: data[key] for key in data.files})
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def to_dict(self) -> dict:
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"""Serialize to dict suitable for np.savez_compressed."""
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out = {
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"map_x": self.map_x.astype(np.float32),
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"map_y": self.map_y.astype(np.float32),
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"valid_mask": self.valid_mask.astype(bool),
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}
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out.update(self.metadata)
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return out
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def save(self, path: str) -> None:
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"""Save remap cache to .npz file."""
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np.savez_compressed(path, **self.to_dict())
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def ensure_fast_maps(self) -> None:
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"""Build OpenCV CV_16SC2 remap tables once for faster cv2.remap."""
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if self._map1_fixed is not None:
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return
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self._map1_fixed, self._map2_fixed = cv2.convertMaps(
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self.map_x,
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self.map_y,
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cv2.CV_16SC2,
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)
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invalid = ~self.valid_mask
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if not np.any(invalid):
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self._border_only_invalid = True
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else:
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self._border_only_invalid = bool(
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np.all(self.map_x[invalid] <= 0.0) and np.all(self.map_y[invalid] <= 0.0)
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)
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def flatten(self, frame_bgr, border_value=0, dst=None):
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"""Remap a source BGR frame into UV flatten coordinates.
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frame_bgr: (H_src, W_src, 3) source camera image
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border_value: fill value for invalid/out-of-domain pixels
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returns: (H_out, W_out, 3) flattened BGR image
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"""
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return flatten_frame(frame_bgr, self, border_value=border_value, dst=dst)
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def remap_valid_ratio(remap) -> float:
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"""Fraction of output pixels covered by the valid UV domain."""
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if isinstance(remap, UVRemap):
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mask = remap.valid_mask
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else:
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mask = remap["valid_mask"]
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return float(np.mean(mask))
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def _cache_matches(
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remap,
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source_K,
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source_width,
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source_height,
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out_width,
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out_height,
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flip_v,
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edge_band,
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edge_expand,
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) -> bool:
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"""True if cached remap matches the requested build parameters."""
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if isinstance(remap, UVRemap):
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rd = remap.to_dict()
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else:
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rd = remap
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required = {"map_x", "map_y", "valid_mask", "source_width", "source_height", "out_width", "out_height"}
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if not required.issubset(rd.keys()):
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return False
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if rd["map_x"].shape != (out_height, out_width):
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return False
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if int(rd["source_width"]) != source_width or int(rd["source_height"]) != source_height:
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return False
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if int(rd["out_width"]) != out_width or int(rd["out_height"]) != out_height:
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return False
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if "flip_v" in rd and int(rd["flip_v"]) != int(flip_v):
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return False
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if "edge_band" in rd and not np.isclose(float(rd["edge_band"]), float(edge_band), rtol=1e-6, atol=1e-6):
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return False
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if "edge_expand" in rd and not np.isclose(float(rd["edge_expand"]), float(edge_expand), rtol=1e-6, atol=1e-6):
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return False
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if "projection_K" in rd and not np.allclose(rd["projection_K"], source_K, rtol=1e-6, atol=1e-6):
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return False
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return True
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def _remap_maps(remap) -> Tuple[np.ndarray, np.ndarray, np.ndarray, bool]:
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if isinstance(remap, UVRemap):
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remap.ensure_fast_maps()
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valid_mask = remap.valid_mask
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skip_invalid_fill = remap._border_only_invalid
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if remap._map1_fixed is not None:
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return remap._map1_fixed, remap._map2_fixed, valid_mask, skip_invalid_fill
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return remap.map_x, remap.map_y, valid_mask, skip_invalid_fill
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return (
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remap["map_x"].astype(np.float32),
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remap["map_y"].astype(np.float32),
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remap["valid_mask"].astype(bool),
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False,
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)
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def flatten_frame(frame, remap, border_value=0, dst=None):
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"""Remap frame using map_x/map_y; zeros invalid UV pixels.
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frame: (H, W, 3) or (H, W) source image
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remap: UVRemap or dict with map_x, map_y, valid_mask
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returns: flattened image, same channels as input
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"""
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map1, map2, valid_mask, skip_invalid_fill = _remap_maps(remap)
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flat = cv2.remap(
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frame,
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map1,
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map2,
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interpolation=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=border_value,
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dst=dst,
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)
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if skip_invalid_fill and border_value == 0:
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return flat
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if not flat.flags.writeable:
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flat = flat.copy()
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flat[~valid_mask] = border_value
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return flat
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