miner.py

"""Plate-detection miner — v2.0 "hermestech + tile s<3".

Base weights: hermestech00/numberplate0 (YOLO26s retrained, fp16, ~19 MB).

Inference pipeline:
  1) Full-image primary pass with alfred001 tuning
     (conf=0.22, iou=0.41, sigma=0.685, soft-NMS + hflip TTA).
  2) If the primary returned fewer than 3 boxes, run a 2x2
     overlapping tile pass (tile_conf=0.40) with novelty-merge.
"""
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]]


SIZE = 1280


class Miner:
    def __init__(self, path_hf_repo: Path) -> None:
        model_path = path_hf_repo / "weights.onnx"
        cn_path = model_path.with_name("class_names.txt")
        if cn_path.is_file():
            lines = cn_path.read_text(encoding="utf-8").splitlines()
            self.class_names = [
                ln.strip()
                for ln in lines
                if ln.strip() and not ln.strip().startswith("#")
            ]
        else:
            self.class_names = ["numberplate"]
        print("ORT version:", ort.__version__)

        try:
            ort.preload_dlls()
            print("onnxruntime.preload_dlls() success")
        except Exception as e:
            print(f"preload_dlls failed: {e}")

        print("ORT available providers BEFORE session:", ort.get_available_providers())

        try:
            import torch
            if torch.cuda.is_available():
                print(f"GPU: {torch.cuda.get_device_name(0)}")
                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
            else:
                print("GPU: CUDA not available via torch")
        except Exception as e:
            print(f"GPU detection failed: {e}")

        sess_options = ort.SessionOptions()
        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

        try:
            self.session = ort.InferenceSession(
                str(model_path),
                sess_options=sess_options,
                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
            )
            print("Created ORT session with preferred CUDA provider list")
        except Exception as e:
            print(f"CUDA session creation failed, falling back to CPU: {e}")
            self.session = ort.InferenceSession(
                str(model_path),
                sess_options=sess_options,
                providers=["CPUExecutionProvider"],
            )

        print("ORT session providers:", self.session.get_providers())

        for inp in self.session.get_inputs():
            print("INPUT:", inp.name, inp.shape, inp.type)
        for out in self.session.get_outputs():
            print("OUTPUT:", out.name, out.shape, out.type)

        self.input_name = self.session.get_inputs()[0].name
        self.output_names = [o.name for o in self.session.get_outputs()]
        self.input_shape = self.session.get_inputs()[0].shape

        self.input_height = self._safe_dim(self.input_shape[2], default=SIZE)
        self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)

        # Primary pass: alfred001 tuning (optimized for hermestech weights)
        self.conf_thres = 0.22
        self.iou_thres = 0.41
        self.sigma = 0.685
        self.max_det = 300

        # Conditional tile-pass (trimmed for latency: no hflip, tighter sparse)
        self.sparse_threshold = 3       # fire tiles only if primary returns < this
        self.tile_conf = 0.40
        self.tile_overlap = 0.20
        self.novelty_iou = 0.10
        self.final_max_det = 22
        self.tile_use_hflip = False     # skip hflip tile pass to save ~4 forwards

        self.use_tta = True

        print(f"ONNX model loaded from: {model_path}")
        print(f"ONNX providers: {self.session.get_providers()}")
        print(f"ONNX input: name={self.input_name}, shape={self.input_shape}")

    def __repr__(self) -> str:
        return (
            f"ONNXRuntime(session={type(self.session).__name__}, "
            f"providers={self.session.get_providers()})"
        )

    @staticmethod
    def _safe_dim(value, default: int) -> int:
        return value if isinstance(value, int) and value > 0 else default

    # ---------- image preprocessing ----------
    def _letterbox(
        self,
        image: ndarray,
        new_shape: tuple[int, int],
        color=(114, 114, 114),
    ) -> tuple[ndarray, float, tuple[float, float]]:
        h, w = image.shape[:2]
        new_w, new_h = new_shape
        ratio = min(new_w / w, new_h / h)
        resized_w = int(round(w * ratio))
        resized_h = int(round(h * ratio))
        if (resized_w, resized_h) != (w, h):
            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
        dw = (new_w - resized_w) / 2.0
        dh = (new_h - resized_h) / 2.0
        left = int(round(dw - 0.1))
        right = int(round(dw + 0.1))
        top = int(round(dh - 0.1))
        bottom = int(round(dh + 0.1))
        padded = cv2.copyMakeBorder(
            image, top, bottom, left, right,
            borderType=cv2.BORDER_CONSTANT, value=color,
        )
        return padded, ratio, (dw, dh)

    def _preprocess(self, image: ndarray):
        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
        img = np.transpose(img, (2, 0, 1))[None, ...]
        return np.ascontiguousarray(img, dtype=np.float32), ratio, pad

    @staticmethod
    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
        w, h = image_size
        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
        return boxes

    # ---------- NMS primitives ----------
    @staticmethod
    def _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
        N = len(boxes)
        if N == 0:
            return np.array([], dtype=np.intp)
        boxes = np.asarray(boxes, dtype=np.float32)
        scores = np.asarray(scores, dtype=np.float32)
        order = np.argsort(-scores)
        keep: list[int] = []
        while len(order):
            i = int(order[0])
            keep.append(i)
            if len(order) == 1:
                break
            rest = order[1:]
            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
            area_r = (boxes[rest, 2] - boxes[rest, 0]) * (boxes[rest, 3] - boxes[rest, 1])
            iou = inter / (area_i + area_r - inter + 1e-7)
            order = rest[iou <= iou_thresh]
        return np.array(keep, dtype=np.intp)

    def _soft_nms(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        sigma: float,
        score_thresh: float = 0.01,
    ) -> tuple[np.ndarray, np.ndarray]:
        N = len(boxes)
        if N == 0:
            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
        boxes = boxes.astype(np.float32, copy=True)
        scores = scores.astype(np.float32, copy=True)
        order = np.arange(N)
        for i in range(N):
            max_pos = i + int(np.argmax(scores[i:]))
            boxes[[i, max_pos]] = boxes[[max_pos, i]]
            scores[[i, max_pos]] = scores[[max_pos, i]]
            order[[i, max_pos]] = order[[max_pos, i]]
            if i + 1 >= N:
                break
            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
            area_i = float(
                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
            )
            areas_j = (
                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
            )
            iou = inter / (area_i + areas_j - inter + 1e-7)
            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
        mask = scores > score_thresh
        return order[mask], scores[mask]

    @staticmethod
    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
        if len(boxes) == 0:
            return np.zeros(0, dtype=np.float32)
        xx1 = np.maximum(box[0], boxes[:, 0])
        yy1 = np.maximum(box[1], boxes[:, 1])
        xx2 = np.minimum(box[2], boxes[:, 2])
        yy2 = np.minimum(box[3], boxes[:, 3])
        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
        area_a = max(0.0, (box[2] - box[0]) * (box[3] - box[1]))
        area_b = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
        return inter / (area_a + area_b - inter + 1e-7)

    # ---------- raw-dets helper ----------
    def _raw_dets(self, image: ndarray, conf: float) -> np.ndarray:
        """Run a single forward pass and return [N, 5] dets in ORIGINAL image coords."""
        x, ratio, (dw, dh) = self._preprocess(image)
        out = self.session.run(self.output_names, {self.input_name: x})[0]
        if out.ndim == 3:
            out = out[0]
        if out.shape[1] < 5:
            return np.zeros((0, 5), dtype=np.float32)
        boxes = out[:, :4].astype(np.float32)
        scores = out[:, 4].astype(np.float32)
        keep = scores >= conf
        boxes, scores = boxes[keep], scores[keep]
        if len(boxes) == 0:
            return np.zeros((0, 5), dtype=np.float32)
        boxes[:, [0, 2]] -= dw
        boxes[:, [1, 3]] -= dh
        boxes /= ratio
        oh, ow = image.shape[:2]
        boxes = self._clip_boxes(boxes, (ow, oh))
        return np.concatenate([boxes, scores[:, None]], axis=1)

    # ---------- primary pass: soft-NMS + hflip TTA ----------
    def _primary(self, image: ndarray) -> np.ndarray:
        d1 = self._raw_dets(image, self.conf_thres)
        flipped = cv2.flip(image, 1)
        d2 = self._raw_dets(flipped, self.conf_thres)
        if len(d2):
            w = image.shape[1]
            x1 = w - d2[:, 2]
            x2 = w - d2[:, 0]
            d2 = np.stack([x1, d2[:, 1], x2, d2[:, 3], d2[:, 4]], axis=1)
        all_d = np.concatenate([d1, d2], axis=0) if len(d2) else d1
        if len(all_d) == 0:
            return np.zeros((0, 5), dtype=np.float32)
        # soft-NMS, then hard-NMS
        keep_idx, scores = self._soft_nms(all_d[:, :4].copy(), all_d[:, 4].copy(), sigma=self.sigma)
        if len(keep_idx) == 0:
            return np.zeros((0, 5), dtype=np.float32)
        merged = np.concatenate([all_d[keep_idx, :4], scores[:, None]], axis=1)
        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
        merged = merged[keep]
        if len(merged) > self.max_det:
            merged = merged[np.argsort(-merged[:, 4])[: self.max_det]]
        return merged

    # ---------- conditional tile pass ----------
    def _tile_augment(self, image: ndarray, primary: np.ndarray) -> np.ndarray:
        """Run 2x2 overlapping tiles + hflip, novelty-merge into primary."""
        oh, ow = image.shape[:2]
        tw, th = ow // 2, oh // 2
        ox, oy = int(tw * self.tile_overlap), int(th * self.tile_overlap)
        tiles = [
            (0, 0, min(ow, tw + ox), min(oh, th + oy)),
            (max(0, tw - ox), 0, ow, min(oh, th + oy)),
            (0, max(0, th - oy), min(ow, tw + ox), oh),
            (max(0, tw - ox), max(0, th - oy), ow, oh),
        ]
        collected: list[np.ndarray] = []
        for x1, y1, x2, y2 in tiles:
            crop = image[y1:y2, x1:x2]
            if crop.size == 0:
                continue
            d = self._raw_dets(crop, self.tile_conf)
            if len(d):
                d[:, 0] += x1
                d[:, 1] += y1
                d[:, 2] += x1
                d[:, 3] += y1
                collected.append(d)

        # hflip tile pass (skipped when tile_use_hflip=False — saves 4 ONNX forwards)
        if self.tile_use_hflip:
            flipped = cv2.flip(image, 1)
            for x1, y1, x2, y2 in tiles:
                fx1 = ow - x2
                fx2 = ow - x1
                if fx2 <= fx1:
                    continue
                crop = flipped[y1:y2, fx1:fx2]
                if crop.size == 0:
                    continue
                d = self._raw_dets(crop, self.tile_conf)
                if len(d):
                    d_un = d.copy()
                    d_un[:, 0] = (ow - (d[:, 2] + fx1))
                    d_un[:, 2] = (ow - (d[:, 0] + fx1))
                    d_un[:, 1] = d[:, 1] + y1
                    d_un[:, 3] = d[:, 3] + y1
                    collected.append(d_un)

        if not collected:
            return primary

        tile_dets = np.concatenate(collected, axis=0)
        keep = self._hard_nms(tile_dets[:, :4], tile_dets[:, 4], 0.5)
        tile_dets = tile_dets[keep]

        # Novelty: drop tile boxes that overlap any primary box at IoU >= novelty_iou
        if len(primary) > 0 and len(tile_dets) > 0:
            mask = np.ones(len(tile_dets), dtype=bool)
            for i in range(len(tile_dets)):
                ious = self._box_iou_one_to_many(tile_dets[i, :4], primary[:, :4])
                if len(ious) and np.max(ious) >= self.novelty_iou:
                    mask[i] = False
            tile_dets = tile_dets[mask]

        if len(tile_dets) == 0:
            return primary

        # Sanity filter: min/max size, aspect ratio
        w = tile_dets[:, 2] - tile_dets[:, 0]
        h = tile_dets[:, 3] - tile_dets[:, 1]
        area = w * h
        ar = np.maximum(w / np.maximum(h, 1e-6), h / np.maximum(w, 1e-6))
        img_area = float(ow * oh)
        ok = (w >= 6) & (h >= 6) & (area >= 36) & (area <= 0.5 * img_area) & (ar <= 10.0)
        tile_dets = tile_dets[ok]
        if len(tile_dets) == 0:
            return primary

        merged = np.concatenate([primary, tile_dets], axis=0)
        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
        merged = merged[keep]
        if len(merged) > self.final_max_det:
            merged = merged[np.argsort(-merged[:, 4])[: self.final_max_det]]
        return merged

    # ---------- single-image predict ----------
    def _predict_single(self, image: ndarray) -> list[BoundingBox]:
        if image is None or not isinstance(image, np.ndarray) or image.ndim != 3:
            return []
        if image.shape[0] <= 0 or image.shape[1] <= 0 or image.shape[2] != 3:
            return []
        if image.dtype != np.uint8:
            image = image.astype(np.uint8)

        primary = self._primary(image)
        if len(primary) < self.sparse_threshold:
            dets = self._tile_augment(image, primary)
        else:
            dets = primary

        results: list[BoundingBox] = []
        for row in dets:
            x1, y1, x2, y2, conf = row.tolist()
            if x2 <= x1 or y2 <= y1:
                continue
            results.append(
                BoundingBox(
                    x1=int(math.floor(x1)),
                    y1=int(math.floor(y1)),
                    x2=int(math.ceil(x2)),
                    y2=int(math.ceil(y2)),
                    cls_id=0,
                    conf=float(conf),
                )
            )
        return results

    # ---------- chute entrypoint ----------
    def predict_batch(
        self,
        batch_images: list[ndarray],
        offset: int,
        n_keypoints: int,
    ) -> list[TVFrameResult]:
        results: list[TVFrameResult] = []
        for frame_number_in_batch, image in enumerate(batch_images):
            try:
                boxes = self._predict_single(image)
            except Exception as e:
                print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
                boxes = []
            results.append(
                TVFrameResult(
                    frame_id=offset + frame_number_in_batch,
                    boxes=boxes,
                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
                )
            )
        return results