File size: 1,831 Bytes
1c0c94d | 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 | """Image enhancement for varying conditions (low light, rain, noise). Geometry is
preserved (no resize/crop) so detection boxes stay valid on the original image, which is
what we annotate and hash for evidence.
Prefers OpenCV (CLAHE + denoise + gamma); falls back to a Pillow-only path so the code
runs without cv2 installed. ``enhance`` writes to a temp file and returns its path.
"""
from __future__ import annotations
import os
import tempfile
def _enhance_cv2(src: str, dst: str) -> None:
import cv2 # heavy, lazy
import numpy as np
img = cv2.imread(src)
if img is None:
raise ValueError("cv2 could not read image")
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
lo, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
lo = clahe.apply(lo)
img = cv2.cvtColor(cv2.merge((lo, a, b)), cv2.COLOR_LAB2BGR)
img = cv2.fastNlMeansDenoisingColored(img, None, 5, 5, 7, 21)
if img.mean() < 70: # brighten dark scenes (gamma < 1)
inv = 1.0 / 0.6
table = ((np.arange(256) / 255.0) ** inv * 255).astype("uint8")
img = cv2.LUT(img, table)
cv2.imwrite(dst, img)
def _enhance_pil(src: str, dst: str) -> None:
from PIL import Image, ImageFilter, ImageOps
img = Image.open(src).convert("RGB")
img = ImageOps.autocontrast(img, cutoff=1)
img = img.filter(ImageFilter.MedianFilter(size=3)) # light denoise
img.save(dst, format="JPEG")
def enhance(src: str) -> str:
fd, dst = tempfile.mkstemp(suffix=".jpg")
os.close(fd)
try:
_enhance_cv2(src, dst)
except Exception:
_enhance_pil(src, dst)
return dst
def cleanup(path: str, keep: str) -> None:
if path != keep and os.path.exists(path):
try:
os.remove(path)
except OSError:
pass
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