miner.py

from pathlib import Path
import math

import cv2
import numpy as np
import onnxruntime as ort
from numpy import ndarray
from pydantic import BaseModel


class BoundingBox(BaseModel):
    x1: int
    y1: int
    x2: int
    y2: int
    cls_id: int
    conf: float


class TVFrameResult(BaseModel):
    frame_id: int
    boxes: list[BoundingBox]
    keypoints: list[tuple[int, int]]


class Miner:
    """
    Auto-generated by subnet_bridge from a Manako element repo.
    This miner is intentionally self-contained for chute import restrictions.
    """

    def __init__(self, path_hf_repo: Path) -> None:
        self.path_hf_repo = path_hf_repo
        self.class_names = ['person']
        self.session = ort.InferenceSession(
            str(path_hf_repo / "weights.onnx"),
            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
        )
        self.input_name = self.session.get_inputs()[0].name
        input_shape = self.session.get_inputs()[0].shape
        # expected [N, C, H, W]
        self.input_h = int(input_shape[2])
        self.input_w = int(input_shape[3])
        self.conf_threshold = 0.70  # sweep-optimised: max composite 0.65×mAP+0.35×FP_score
        self.iou_threshold = 0.45

    def __repr__(self) -> str:
        return f"ONNX Miner session={type(self.session).__name__} classes={len(self.class_names)}"

    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, tuple[int, int]]:
        h, w = image_bgr.shape[:2]
        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
        resized = cv2.resize(rgb, (self.input_w, self.input_h))
        x = resized.astype(np.float32) / 255.0
        x = np.transpose(x, (2, 0, 1))[None, ...]
        return x, (h, w)

    def _normalize_predictions(self, raw: np.ndarray) -> np.ndarray:
        # Common ultralytics export shapes:
        # - [1, C, N] where C=4+num_classes
        # - [1, N, C]
        pred = raw[0]
        if pred.ndim != 2:
            raise ValueError(f"Unexpected prediction shape: {raw.shape}")
        if pred.shape[0] < pred.shape[1]:
            pred = pred.transpose(1, 0)
        return pred

    def _nms(self, dets: list[tuple[float, float, float, float, float, int]]) -> list[tuple[float, float, float, float, float, int]]:
        if not dets:
            return []

        boxes = np.array([[d[0], d[1], d[2], d[3]] for d in dets], dtype=np.float32)
        scores = np.array([d[4] for d in dets], dtype=np.float32)
        order = scores.argsort()[::-1]
        keep = []

        while order.size > 0:
            i = order[0]
            keep.append(i)

            xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
            yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
            xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
            yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])

            w = np.maximum(0.0, xx2 - xx1)
            h = np.maximum(0.0, yy2 - yy1)
            inter = w * h

            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
            area_rest = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
            union = np.maximum(area_i + area_rest - inter, 1e-6)
            iou = inter / union

            remaining = np.where(iou <= self.iou_threshold)[0]
            order = order[remaining + 1]

        return [dets[idx] for idx in keep]

    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
        inp, (orig_h, orig_w) = self._preprocess(image_bgr)
        out = self.session.run(None, {self.input_name: inp})[0]
        pred = self._normalize_predictions(out)

        if pred.shape[1] < 5:
            return []

        boxes = pred[:, :4]
        cls_scores = pred[:, 4:]

        if cls_scores.shape[1] == 0:
            return []

        cls_ids = np.argmax(cls_scores, axis=1)
        confs = np.max(cls_scores, axis=1)
        keep = confs >= self.conf_threshold

        boxes = boxes[keep]
        confs = confs[keep]
        cls_ids = cls_ids[keep]

        if boxes.shape[0] == 0:
            return []

        sx = orig_w / float(self.input_w)
        sy = orig_h / float(self.input_h)

        dets: list[tuple[float, float, float, float, float, int]] = []
        for i in range(boxes.shape[0]):
            cx, cy, bw, bh = boxes[i].tolist()
            x1 = (cx - bw / 2.0) * sx
            y1 = (cy - bh / 2.0) * sy
            x2 = (cx + bw / 2.0) * sx
            y2 = (cy + bh / 2.0) * sy
            dets.append((x1, y1, x2, y2, float(confs[i]), int(cls_ids[i])))

        dets = self._nms(dets)

        out_boxes: list[BoundingBox] = []
        for x1, y1, x2, y2, conf, cls_id in dets:
            ix1 = max(0, min(orig_w, math.floor(x1)))
            iy1 = max(0, min(orig_h, math.floor(y1)))
            ix2 = max(0, min(orig_w, math.ceil(x2)))
            iy2 = max(0, min(orig_h, math.ceil(y2)))
            out_boxes.append(
                BoundingBox(
                    x1=ix1,
                    y1=iy1,
                    x2=ix2,
                    y2=iy2,
                    cls_id=cls_id,
                    conf=max(0.0, min(1.0, conf)),
                )
            )
        return out_boxes

    def predict_batch(
        self,
        batch_images: list[ndarray],
        offset: int,
        n_keypoints: int,
    ) -> list[TVFrameResult]:
        results: list[TVFrameResult] = []
        for idx, image in enumerate(batch_images):
            boxes = self._infer_single(image)
            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
            results.append(
                TVFrameResult(
                    frame_id=offset + idx,
                    boxes=boxes,
                    keypoints=keypoints,
                )
            )
        return results