Spaces:
Sleeping
Sleeping
File size: 4,939 Bytes
dd85fb6 aaa448c 0a3fef3 dd85fb6 0a3fef3 dd85fb6 aaa448c 0a3fef3 dd85fb6 aaa448c dd85fb6 0a3fef3 dd85fb6 |
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import os
import time
from dataclasses import dataclass, field
from typing import Any, Dict, Literal, Optional
import cv2
import numpy as np
from ..inference.classical import detect_classical
from ..inference.dl import DL_MODELS, detect_dl
DEFAULT_PARAMS: Dict[str, Any] = {
"canny_low": 50,
"canny_high": 150,
"harris_k": 0.05,
"harris_block": 2,
"harris_ksize": 3,
"hough_thresh": 50,
"hough_min_len": 30,
"hough_max_gap": 5,
"ellipse_min_area": 300,
"max_ellipses": 5,
"line_detector": "hough",
"dexined_threshold_mode": "adaptive",
"dexined_threshold_sigma": 1.0,
"dexined_threshold_offset": 0.0,
"dexined_threshold_value": 0.3,
"dexined_use_marching_squares": False,
}
def _to_bool(value: Any) -> bool:
if isinstance(value, bool):
return value
if isinstance(value, str):
return value.strip().lower() in {"1", "true", "yes", "on"}
return bool(value)
PARAM_TYPES: Dict[str, Any] = {
"canny_low": int,
"canny_high": int,
"harris_k": float,
"harris_block": int,
"harris_ksize": int,
"hough_thresh": int,
"hough_min_len": int,
"hough_max_gap": int,
"ellipse_min_area": int,
"max_ellipses": int,
"line_detector": lambda x: str(x).lower(),
"dexined_threshold_mode": lambda x: str(x).lower(),
"dexined_threshold_sigma": float,
"dexined_threshold_offset": float,
"dexined_threshold_value": float,
"dexined_use_marching_squares": _to_bool,
}
CLASSICAL_MODEL_INFO = {"name": "opencv-classical", "version": cv2.__version__}
try:
import onnxruntime as ort # type: ignore
except Exception: # pragma: no cover
ort = None # type: ignore
DL_MODEL_INFO = {
"name": "onnxruntime" if ort is not None else "onnxruntime-missing",
"version": getattr(ort, "__version__", "unknown"),
}
def merge_params(params: Optional[Dict[str, Any]]) -> Dict[str, Any]:
merged = DEFAULT_PARAMS.copy()
if params:
for key, value in params.items():
if value is None or key not in DEFAULT_PARAMS:
continue
caster = PARAM_TYPES.get(key, lambda x: x)
try:
merged[key] = caster(value)
except (TypeError, ValueError):
continue
return merged
@dataclass
class DetectionResult:
overlays: Dict[str, np.ndarray] = field(default_factory=dict)
features: Dict[str, Dict[str, Any]] = field(default_factory=dict)
timings_ms: Dict[str, float] = field(default_factory=dict)
fps_estimate: Optional[float] = None
models: Dict[str, Dict[str, Any]] = field(default_factory=dict)
def run_detection(
image: np.ndarray,
detector: str,
params: Optional[Dict[str, Any]] = None,
mode: Literal["classical", "dl", "both"] = "classical",
dl_choice: Optional[str] = None,
) -> DetectionResult:
merged = merge_params(params)
overlays: Dict[str, np.ndarray] = {}
features: Dict[str, Dict[str, Any]] = {}
timings: Dict[str, float] = {}
models: Dict[str, Dict[str, Any]] = {}
execute_classical = mode in ("classical", "both")
execute_dl = mode in ("dl", "both")
total_ms = 0.0
if execute_classical:
t0 = time.perf_counter()
classical_img, classical_meta = detect_classical(
image,
detector,
merged["canny_low"],
merged["canny_high"],
merged["harris_k"],
merged["harris_block"],
merged["harris_ksize"],
merged["hough_thresh"],
merged["hough_min_len"],
merged["hough_max_gap"],
merged["ellipse_min_area"],
merged["max_ellipses"],
merged["line_detector"],
)
t_ms = (time.perf_counter() - t0) * 1000.0
overlays["classical"] = classical_img
features["classical"] = classical_meta
timings["classical"] = round(t_ms, 2)
models["classical"] = CLASSICAL_MODEL_INFO
total_ms += t_ms
if execute_dl:
t0 = time.perf_counter()
dl_img, dl_meta = detect_dl(image, detector, dl_choice, params=merged)
t_ms = (time.perf_counter() - t0) * 1000.0
overlays["dl"] = dl_img
features["dl"] = dl_meta
timings["dl"] = round(t_ms, 2)
model_name = (
os.path.basename(dl_meta["model_path"]) if "model_path" in dl_meta else DL_MODEL_INFO["name"]
)
models["dl"] = {"name": model_name, "version": DL_MODEL_INFO["version"]}
total_ms += t_ms
timings["total"] = round(total_ms, 2)
fps = round(1000.0 / total_ms, 2) if total_ms > 0 else None
return DetectionResult(
overlays=overlays,
features=features,
timings_ms=timings,
fps_estimate=fps,
models=models,
)
__all__ = [
"DetectionResult",
"DEFAULT_PARAMS",
"DL_MODELS",
"merge_params",
"run_detection",
]
|