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aac350d 892fa81 aac350d 892fa81 aac350d 892fa81 23d337e aac350d 892fa81 23d337e aac350d 892fa81 23d337e 892fa81 23d337e aac350d 23d337e aac350d 23d337e aac350d 23d337e aac350d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | """
Preprocessing — decode, resize, color-convert.
Uses cores.vision for all image operations — no duplicated decode,
resize, or format-sniffing logic. Preserves the original bytes so
metadata / forensics providers can use them.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional
import numpy as np
from cores.vision import bytes_to_numpy, resize_with_aspect, sniff_format
@dataclass
class PreprocessedImage:
"""Output of preprocessing — the canonical image object passed downstream."""
image: np.ndarray # BGR uint8 HxWx3
width: int
height: int
channels: int = 3
source: str = "" # "url" | "base64" | "bytes"
resized: bool = False
original_bytes: Optional[bytes] = None
original_format: Optional[str] = None
class ImagePreprocessor:
"""Decodes + resizes inbound images."""
def __init__(self, max_dim: int = 1024) -> None:
self._max_dim = max_dim
def from_bytes(self, data: bytes, source: str = "bytes") -> PreprocessedImage:
img = bytes_to_numpy(data)
fmt = sniff_format(data)
return self._finalize(img, source, original_bytes=data, original_format=fmt)
def from_url(self, url: str, timeout: int = 15) -> PreprocessedImage:
from cores.vision import url_to_bytes
data = url_to_bytes(url, timeout=timeout)
img = bytes_to_numpy(data)
fmt = sniff_format(data)
return self._finalize(img, "url", original_bytes=data, original_format=fmt)
def from_numpy(self, img: np.ndarray, source: str = "in_memory") -> PreprocessedImage:
return self._finalize(img, source)
def _finalize(
self,
img: np.ndarray,
source: str,
original_bytes: Optional[bytes] = None,
original_format: Optional[str] = None,
) -> PreprocessedImage:
# Ensure 3 channels
if img.ndim == 2:
import cv2
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif img.shape[2] == 4:
import cv2
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
resized = False
h, w = img.shape[:2]
if max(h, w) > self._max_dim:
img = resize_with_aspect(img, max_dim=self._max_dim)
resized = True
h, w = img.shape[:2]
return PreprocessedImage(
image=img,
width=w,
height=h,
channels=img.shape[2] if img.ndim == 3 else 1,
source=source,
resized=resized,
original_bytes=original_bytes,
original_format=original_format,
)
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