File size: 7,488 Bytes
ebeed67 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """SSD / EfficientDet-Lite postprocessor (TFLite_Detection_PostProcess op).
Used by the EdgeTPU (Coral USB Edge TPU) runtime with the Coral model-zoo
SSD MobileNet V2 and EfficientDet-Lite detectors. Those models embed the
``TFLite_Detection_PostProcess`` op, so the 4 output tensors are ALREADY
decoded (boxes + per-box class + score, post-NMS):
output 0 boxes [1, N, 4] (ymin, xmin, ymax, xmax) normalised 0..1
output 1 classes [1, N] float class indices (COCO 90-class map)
output 2 scores [1, N] float 0..1
output 3 count [1] number of valid detections
The inference_pool ``edgetpu`` predict closure returns them keyed by their
positional output index ("0".."3"). Output matches the yolo postprocessor:
``{"kind": "detections", "detections": [{"class", "score", "bbox":[x1,y1,x2,y2]}]}``
with pixel bboxes in ORIGINAL frame coordinates.
The same layout, verified per model on the upstream graph (2026-09-26):
ssd-mobilenet-v2-coco-edgetpu 300x300 uint8, N=20
efficientdet-lite0-edgetpu 320x320 uint8, N=25
ssdlite-mobiledet-coco-edgetpu 320x320 uint8, N=100, 91 score columns
(background + COCO-90), so the emitted
class index is already 0-based: 0 = person
The decode is plain Python over nested lists. Each tensor is turned into a list
once (``ndarray.tolist()``), so this module needs no numpy: CI's self-hosted
runner has none, and the MobileDet fixture test (``test_ssd.py``) must run
there, not be skipped there. Measured: ~85 µs per frame against ~14 µs for
the old numpy decode (about 6×), under 1% of a core at 100 fps and small next
to a ~9 ms Coral invoke.
"""
from __future__ import annotations
from typing import Any
# Coral COCO 90-class label map (github.com/google-coral/test_data coco_labels.txt).
# The "n/a" placeholders keep the raw model class indices aligned and are skipped.
COCO_90 = [
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "n/a", "stop sign", "parking meter",
"bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear",
"zebra", "giraffe", "n/a", "backpack", "umbrella", "n/a", "n/a", "handbag",
"tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "n/a", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
"banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
"pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "n/a",
"dining table", "n/a", "n/a", "toilet", "n/a", "tv", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink",
"refrigerator", "n/a", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush",
]
def _as_list(value: Any) -> Any:
"""A tensor as nested Python lists (a scalar stays a scalar)."""
return value.tolist() if hasattr(value, "tolist") else value
def _shape(value: Any) -> tuple:
"""numpy-style shape of a nested list; ``()`` for a scalar."""
dims: list[int] = []
while isinstance(value, (list, tuple)):
dims.append(len(value))
if not value:
break
value = value[0]
return tuple(dims)
def _flatten(value: Any) -> list:
"""Every scalar of a nested list, in row-major order (``reshape(-1)``)."""
if not isinstance(value, (list, tuple)):
return [value]
flat: list = []
for item in value:
flat.extend(_flatten(item))
return flat
def _rows_of_4(value: Any) -> list:
"""``reshape(-1, 4)`` of a nested list; refuses a size numpy would refuse."""
flat = _flatten(value)
if len(flat) % 4 != 0:
raise ValueError(f"cannot reshape {len(flat)} box values into rows of 4")
return [flat[i:i + 4] for i in range(0, len(flat), 4)]
def _split_outputs(predictions: dict) -> tuple:
"""Resolve (boxes, classes, scores, count) from the raw prediction dict.
Primary path: positional keys "0".."3" (the edgetpu predict closure).
Fallback: shape-based identification for name-keyed / reordered exports.
"""
if all(k in predictions for k in ("0", "1", "2", "3")):
return (
_as_list(predictions["0"]),
_as_list(predictions["1"]),
_as_list(predictions["2"]),
_as_list(predictions["3"]),
)
vals = [_as_list(v) for v in predictions.values()]
boxes = count = None
two_d: list = []
for v in vals:
shape = _shape(v)
ndim = len(shape)
if ndim >= 2 and shape[-1] == 4:
boxes = v
elif ndim == 1 or (ndim == 2 and shape[-1] == 1):
count = v
elif ndim == 2:
two_d.append(v)
# TFLite_Detection_PostProcess emits classes BEFORE scores.
classes = two_d[0] if len(two_d) > 0 else None
scores = two_d[1] if len(two_d) > 1 else None
return boxes, classes, scores, count
def label_for_class(cls: int) -> str:
"""COCO-90 label of a raw class index; ``"n/a"`` for a gap, ``str`` if outside."""
return COCO_90[cls] if 0 <= cls < len(COCO_90) else str(cls)
def postprocess_ssd(
predictions: dict,
config: dict,
orig_w: int,
orig_h: int,
scale: float,
pad: tuple,
) -> dict:
"""Decode SSD/EfficientDet postprocess outputs to original-frame detections."""
conf_threshold = float(config.get("confidence", 0.4) or 0.4)
input_size = int(config.get("inputSize", 300) or 300)
boxes, classes, scores, count = _split_outputs(predictions)
if boxes is None or scores is None:
return {"kind": "detections", "detections": []}
boxes = _rows_of_4(boxes)
scores = _flatten(scores)
classes = _flatten(classes) if classes is not None else [0.0] * len(scores)
n = len(scores)
count_flat = _flatten(count) if count is not None else []
if count_flat:
n = min(n, int(count_flat[0]))
n = min(n, len(boxes), len(classes))
pad_x, pad_y = pad
is_letterbox = not (abs(scale - 1.0) < 1e-6 and pad_x == 0 and pad_y == 0)
detections = []
for i in range(n):
score = float(scores[i])
if score < conf_threshold:
continue
ymin, xmin, ymax, xmax = (float(v) for v in boxes[i])
if is_letterbox:
# Invert letterbox: normalised -> input px -> remove pad -> unscale.
x1 = (xmin * input_size - pad_x) / scale
y1 = (ymin * input_size - pad_y) / scale
x2 = (xmax * input_size - pad_x) / scale
y2 = (ymax * input_size - pad_y) / scale
else:
# Plain resize: normalised coords map linearly to the original frame.
x1, y1, x2, y2 = xmin * orig_w, ymin * orig_h, xmax * orig_w, ymax * orig_h
x1 = max(0.0, min(x1, orig_w))
x2 = max(0.0, min(x2, orig_w))
y1 = max(0.0, min(y1, orig_h))
y2 = max(0.0, min(y2, orig_h))
if x2 <= x1 or y2 <= y1:
continue
label = label_for_class(int(classes[i]))
if label == "n/a":
continue
detections.append({
"class": label,
"score": round(score, 4),
"bbox": [round(x1, 1), round(y1, 1), round(x2, 1), round(y2, 1)],
})
return {"kind": "detections", "detections": detections}
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