File size: 2,706 Bytes
699f3cd | 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 86 87 88 89 90 91 92 93 94 95 96 97 | from __future__ import annotations
import re
from typing import Iterable
def extract_frame_index(frame_url: str) -> int:
"""URL veya kimlik icinden deterministik frame indeksi cikarir."""
numbers = re.findall(r"\d+", frame_url)
if not numbers:
return 0
return int(numbers[-1])
def clamp(value: float, low: float, high: float) -> float:
return max(low, min(high, value))
def compute_iou(box_a: tuple[float, float, float, float], box_b: tuple[float, float, float, float]) -> float:
ax1, ay1, ax2, ay2 = box_a
bx1, by1, bx2, by2 = box_b
ix1 = max(ax1, bx1)
iy1 = max(ay1, by1)
ix2 = min(ax2, bx2)
iy2 = min(ay2, by2)
if ix2 <= ix1 or iy2 <= iy1:
return 0.0
intersection = (ix2 - ix1) * (iy2 - iy1)
area_a = max(ax2 - ax1, 0.0) * max(ay2 - ay1, 0.0)
area_b = max(bx2 - bx1, 0.0) * max(by2 - by1, 0.0)
union = area_a + area_b - intersection
if union <= 0:
return 0.0
return intersection / union
def average_pair(values: Iterable[float]) -> float:
collected = list(values)
if not collected:
return 0.0
return sum(collected) / len(collected)
def infer_modality(
video_name: str | None = None,
*,
width: int | None = None,
height: int | None = None,
camera_mode: str | None = None,
) -> str:
if camera_mode:
lowered = camera_mode.lower()
if "term" in lowered or "thermal" in lowered:
return "thermal"
if "rgb" in lowered:
return "rgb"
if width == 640 and height == 512:
return "thermal"
if video_name:
lowered = video_name.lower()
if "term" in lowered or "thermal" in lowered:
return "thermal"
if "rgb" in lowered:
return "rgb"
return "rgb"
def percentile(values: Iterable[float], value: float) -> float:
collected = sorted(float(item) for item in values)
if not collected:
return 0.0
if len(collected) == 1:
return collected[0]
rank = clamp(value, 0.0, 100.0) / 100.0 * (len(collected) - 1)
low_index = int(rank)
high_index = min(low_index + 1, len(collected) - 1)
weight = rank - low_index
return collected[low_index] * (1.0 - weight) + collected[high_index] * weight
def bbox_area(box: tuple[float, float, float, float]) -> float:
return max(box[2] - box[0], 0.0) * max(box[3] - box[1], 0.0)
def normalize_box(box: tuple[float, float, float, float], *, width: float, height: float) -> tuple[float, float, float, float]:
return (
clamp(box[0], 0.0, width),
clamp(box[1], 0.0, height),
clamp(box[2], 0.0, width),
clamp(box[3], 0.0, height),
)
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