Upload miner.py

miner.py

@@ -0,0 +1,190 @@
+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 = ['numberplate']
+        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 = 0.15
+        self.iou_threshold = 0.3
+        self.use_tta = True
+
+    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:
+        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 _decode(self, image_bgr: ndarray) -> list[tuple[float, float, float, float, float, int]]:
+        """Run model and return raw detections before NMS."""
+        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])))
+
+        return dets
+
+    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
+        orig_h, orig_w = image_bgr.shape[:2]
+
+        # Original pass
+        all_dets = self._decode(image_bgr)
+
+        # TTA: horizontal flip pass
+        if self.use_tta:
+            flipped = cv2.flip(image_bgr, 1)
+            flip_dets = self._decode(flipped)
+            for x1, y1, x2, y2, conf, cls_id in flip_dets:
+                all_dets.append((orig_w - x2, y1, orig_w - x1, y2, conf, cls_id))
+
+        # NMS
+        all_dets = self._nms(all_dets)
+
+        out_boxes: list[BoundingBox] = []
+        for x1, y1, x2, y2, conf, cls_id in all_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