import os
import random
import time
from typing import Dict, Optional, Tuple
import numpy as np
# 后视镜折叠场景配置（请按你的4种分辨率填写）
# key: (width, height) value: (x1, y1, x2, y2) 车辆黑色区域在原图上的像素坐标
MIRROR_FOLD_CAR_BOXES: Dict[Tuple[int, int], Tuple[int, int, int, int]] = {
# (960, 1088): (x1, y1, x2, y2),
# (1280, 720): (x1, y1, x2, y2),
# (1920, 1080): (x1, y1, x2, y2),
# (1440, 1080): (x1, y1, x2, y2),
}
# 若分辨率不在上表中，可用比例兜底（0-1），None 表示不启用兜底
MIRROR_FOLD_CAR_BOX_RATIOS: Optional[Tuple[float, float, float, float]] = None
# 是否启用后视镜折叠增强
MIRROR_FOLD_ENABLE = True
MIRROR_FOLD_PROB = 1.0 # 1.0=每次都做，0.5=50%概率
# 粉色色块颜色
MIRROR_FOLD_PINK_COLOR_BGR = (255, 0, 255) # OpenCV/BGR
MIRROR_FOLD_PINK_COLOR_RGB = (255, 0, 255) # PIL/RGB
# 语义分割标签填充值（按你的数据集语义修改）
FSD_PINK_VALUE = 1 # FSD中粉色区域视为可行驶
RM_PINK_VALUE = 0 # RM中粉色区域视为背景
# Debug保存（预处理后可视化）
MIRROR_FOLD_DEBUG_SAVE = False
MIRROR_FOLD_DEBUG_DIR = "runs/mirror_fold_debug"
MIRROR_FOLD_DEBUG_MAX = 200
MIRROR_FOLD_DEBUG_EVERY = 1
MIRROR_FOLD_DEBUG_ALPHA = 0.45
_debug_counts = {"det": 0, "fsd": 0, "rm": 0}
def _clamp_box(x1: int, y1: int, x2: int, y2: int, w: int, h: int) -> Optional[Tuple[int, int, int, int]]:
x1 = int(max(0, min(x1, w)))
x2 = int(max(0, min(x2, w)))
y1 = int(max(0, min(y1, h)))
y2 = int(max(0, min(y2, h)))
if x2 <= x1 or y2 <= y1:
return None
return x1, y1, x2, y2
def get_car_box_for_shape(width: int, height: int) -> Optional[Tuple[int, int, int, int]]:
car_box = MIRROR_FOLD_CAR_BOXES.get((width, height))
if car_box is None and MIRROR_FOLD_CAR_BOX_RATIOS is not None:
x1r, y1r, x2r, y2r = MIRROR_FOLD_CAR_BOX_RATIOS
car_box = (int(x1r * width), int(y1r * height), int(x2r * width), int(y2r * height))
if car_box is None:
return None
return _clamp_box(*car_box, w=width, h=height)
def build_pink_mask(width: int, height: int, car_box: Tuple[int, int, int, int]) -> Optional[np.ndarray]:
x1, y1, x2, y2 = _clamp_box(*car_box, w=width, h=height) or (None, None, None, None)
if x1 is None:
return None
mask = np.zeros((height, width), dtype=bool)
if x1 > 0:
mask[y1:y2, :x1] = True
if x2 < width:
mask[y1:y2, x2:] = True
return mask
def should_apply_mirror_fold() -> bool:
return MIRROR_FOLD_ENABLE and random.random() < MIRROR_FOLD_PROB
def get_debug_save_path(branch: str, img_path: str, suffix: str = "jpg") -> Optional[str]:
if not MIRROR_FOLD_DEBUG_SAVE:
return None
count = _debug_counts.get(branch, 0)
if count >= MIRROR_FOLD_DEBUG_MAX:
return None
if MIRROR_FOLD_DEBUG_EVERY > 1 and (count % MIRROR_FOLD_DEBUG_EVERY) != 0:
_debug_counts[branch] = count + 1
return None
_debug_counts[branch] = count + 1
base = os.path.splitext(os.path.basename(img_path))[0]
out_dir = os.path.join(MIRROR_FOLD_DEBUG_DIR, branch)
os.makedirs(out_dir, exist_ok=True)
ts = int(time.time() * 1000)
return os.path.join(out_dir, f"{base}_{count:06d}_{ts}.{suffix}")
