File size: 6,319 Bytes
4a132e1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | """TurboVision public-track miner for manak0/Detect-worksite-ppe-compliance-detection-probe-set.
Model: YOLO26n exported to ONNX (input 1x3x704x704, NMS embedded, output 1x300x6
rows of x1, y1, x2, y2, score, class in letterboxed pixel space).
cls_id in the response is the index into the element's manifest `objects` list.
"""
from pathlib import Path
import cv2
import numpy as np
import onnxruntime as ort
from numpy import ndarray
from pydantic import BaseModel
MANIFEST_CLASSES = [
"safety helmet",
"high-visibility safety vest",
"protective gloves",
"protective eyewear",
]
# Per-class confidence thresholds, indexed like MANIFEST_CLASSES.
CLASS_CONF = np.array([0.40, 0.40, 0.40, 0.30], dtype=np.float32)
MODEL_FILENAME = "weights.onnx"
LETTERBOX_FILL = 114
class BoundingBox(BaseModel):
x1: int
y1: int
x2: int
y2: int
cls_id: int
conf: float
class Polygon(BaseModel):
cls_id: int
conf: float
points: list[tuple[int, int]]
class TVFrameResult(BaseModel):
frame_id: int
boxes: list[BoundingBox] | None = None
polygons: list[Polygon] | None = None
keypoints: list[tuple[int, int]] | None = None
def _letterbox(image: ndarray, size: int) -> tuple[ndarray, float, int, int]:
"""Ultralytics-style centred letterbox to a size x size canvas."""
height, width = image.shape[:2]
ratio = min(size / height, size / width)
new_w = int(round(width * ratio))
new_h = int(round(height * ratio))
if (new_w, new_h) != (width, height):
image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
pad_w = (size - new_w) / 2
pad_h = (size - new_h) / 2
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
canvas = cv2.copyMakeBorder(
image,
top,
bottom,
left,
right,
cv2.BORDER_CONSTANT,
value=(LETTERBOX_FILL, LETTERBOX_FILL, LETTERBOX_FILL),
)
return canvas, ratio, left, top
class Miner:
def __init__(self, path_hf_repo: Path) -> None:
model_path = Path(path_hf_repo) / MODEL_FILENAME
if not model_path.is_file():
raise FileNotFoundError(f"{MODEL_FILENAME} not found in {path_hf_repo}")
options = ort.SessionOptions()
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.intra_op_num_threads = 2
options.inter_op_num_threads = 1
providers = ["CPUExecutionProvider"]
if "CUDAExecutionProvider" in ort.get_available_providers():
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
try:
self.session = ort.InferenceSession(
str(model_path), sess_options=options, providers=providers
)
except Exception as exc:
print(f"CUDA session failed, falling back to CPU: {exc}")
self.session = ort.InferenceSession(
str(model_path), sess_options=options, providers=["CPUExecutionProvider"]
)
model_input = self.session.get_inputs()[0]
self.input_name = model_input.name
self.input_size = int(model_input.shape[2])
self.output_name = self.session.get_outputs()[0].name
self._check_class_order()
warmup = np.zeros((self.input_size, self.input_size, 3), dtype=np.uint8)
for _ in range(2):
self._infer_one(warmup)
print(f"Miner ready: providers={self.session.get_providers()} input={self.input_size}")
def __repr__(self) -> str:
return (
f"PPE YOLO26n ONNX input={self.input_size} "
f"providers={self.session.get_providers()}"
)
def _check_class_order(self) -> None:
names = self.session.get_modelmeta().custom_metadata_map.get("names", "")
for idx, name in enumerate(MANIFEST_CLASSES):
if f"{idx}: '{name}'" not in names:
raise ValueError(f"ONNX class order does not match manifest: {names}")
def _infer_one(self, image: ndarray) -> list[BoundingBox]:
canvas, ratio, pad_x, pad_y = _letterbox(image, self.input_size)
rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
tensor = np.ascontiguousarray(rgb.transpose(2, 0, 1)[None], dtype=np.float32) / 255.0
raw = self.session.run([self.output_name], {self.input_name: tensor})[0][0]
scores = raw[:, 4]
cls_ids = raw[:, 5].astype(np.int64)
known = (cls_ids >= 0) & (cls_ids < len(MANIFEST_CLASSES))
keep = known & (scores >= CLASS_CONF[np.clip(cls_ids, 0, len(CLASS_CONF) - 1)])
if not keep.any():
return []
boxes = raw[keep, :4].astype(np.float64)
boxes[:, [0, 2]] = (boxes[:, [0, 2]] - pad_x) / ratio
boxes[:, [1, 3]] = (boxes[:, [1, 3]] - pad_y) / ratio
height, width = image.shape[:2]
boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, width)
boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, height)
results: list[BoundingBox] = []
for (x1, y1, x2, y2), score, cls_id in zip(boxes, scores[keep], cls_ids[keep]):
x1, y1, x2, y2 = (int(round(v)) for v in (x1, y1, x2, y2))
if x2 <= x1 or y2 <= y1:
continue
results.append(
BoundingBox(
x1=x1, y1=y1, x2=x2, y2=y2, cls_id=int(cls_id), conf=float(score)
)
)
return results
def predict_batch(
self,
batch_images: list[ndarray],
offset: int,
n_keypoints: int,
) -> list[TVFrameResult]:
results: list[TVFrameResult] = []
for index, image in enumerate(batch_images):
try:
boxes = self._infer_one(image)
except Exception as exc:
print(f"Inference failed on frame {offset + index}: {exc}")
boxes = []
results.append(
TVFrameResult(
frame_id=offset + index,
boxes=boxes,
polygons=[],
keypoints=[(0, 0)] * max(0, int(n_keypoints)),
)
)
return results
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