miner.py

"""TurboVision crime-detection miner.

YOLO11s @ 1280x1280, 6-class detection (balaclava, bat, glove, graffiti, hoodie,
spray paint), ONNX with end-to-end NMS baked in.

Output of weights.onnx: [1, 300, 6] = x1, y1, x2, y2, conf, cls (post-NMS).

Inference pipeline:
  1) Primary forward pass on the full image.
  2) Hflip TTA: forward on horizontally-flipped image, transform boxes back.
  3) Per-class hard-NMS to merge primary + flip outputs.
  4) Cross-class IoU dedup (suppresses same physical object getting two class labels).
  5) Consensus-confidence boost: when both views agree on a cluster, take max score.
  6) Sanity filter (min size, aspect ratio).

Class taxonomy (must match the validator manifest's `objects` list for this element):
  0 balaclava   1 bat   2 glove   3 graffiti   4 hoodie   5 spray paint
"""

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:
    def __init__(self, path_hf_repo: Path) -> None:
        model_path = path_hf_repo / "weights.onnx"

        # Validator manifest order (from spec.json `objects`):
        #   0=balaclava 1=hoodie 2=glove 3=bat 4="spray paint" 5=graffiti
        # v5 weights.onnx was trained with this exact order, so cls_remap is identity.
        cn_path = model_path.with_name("class_names.txt")
        if cn_path.is_file():
            self.class_names = [
                ln.strip()
                for ln in cn_path.read_text(encoding="utf-8").splitlines()
                if ln.strip() and not ln.strip().startswith("#")
            ]
        else:
            self.class_names = ["balaclava", "hoodie", "glove", "bat", "spray paint", "graffiti"]
        self.cls_remap = np.arange(len(self.class_names), dtype=np.int32)

        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())

        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())

        inp = self.session.get_inputs()[0]
        self.input_name = inp.name
        self.output_names = [o.name for o in self.session.get_outputs()]
        self.input_shape = inp.shape
        self.input_dtype = np.float16 if "float16" in inp.type else np.float32

        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
        self.input_width = self._safe_dim(self.input_shape[3], default=1280)

        # Tuning matched to alfred's deployed model — bias toward precision to dodge
        # the false_positive pillar penalty (validator weights FP heavily on this element).
        # v13 sweet spot on starter (true GT): uniform conf=0.50.
        # Tuning per-class on 7 images overfits — leave-one-out CV showed it
        # collapsed to 0.314 on held-out shards. Uniform 0.50 is robust.
        self.conf_thres = 0.50
        self.conf_thres_per_class = np.array([0.50] * 6, dtype=np.float32)
        self.iou_thres = 0.4
        self.cross_iou_thresh = 0.7
        self.max_det = 100
        self.use_tta = False

        # Sanity filter — reject obviously bad boxes
        self.min_box_area = 14 * 14
        self.min_side = 8
        self.max_aspect_ratio = 8.0
        self.max_box_area_ratio = 0.95

        print(f"✅ ONNX loaded: {model_path}")
        print(f"✅ providers: {self.session.get_providers()}")
        print(f"✅ input: name={self.input_name}, shape={self.input_shape}, dtype={self.input_dtype}")
        print(f"✅ classes: {self.class_names}")
        print(f"✅ config: conf={self.conf_thres}, iou={self.iou_thres}, "
              f"cross_iou={self.cross_iou_thresh}, TTA={self.use_tta}")

    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

    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):
        orig_h, orig_w = image.shape[:2]
        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = img.astype(self.input_dtype) / 255.0
        img = np.transpose(img, (2, 0, 1))[None, ...]
        img = np.ascontiguousarray(img)
        return img, ratio, pad, (orig_w, orig_h)

    @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

    def _filter_sane_boxes(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        orig_size: tuple[int, int],
    ):
        if len(boxes) == 0:
            return boxes, scores, cls_ids
        orig_w, orig_h = orig_size
        image_area = float(orig_w * orig_h)
        keep = []
        for i, box in enumerate(boxes):
            x1, y1, x2, y2 = box.tolist()
            bw = x2 - x1
            bh = y2 - y1
            if bw <= 0 or bh <= 0:
                continue
            if bw < self.min_side or bh < self.min_side:
                continue
            area = bw * bh
            if area < self.min_box_area:
                continue
            if area > self.max_box_area_ratio * image_area:
                continue
            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
            if ar > self.max_aspect_ratio:
                continue
            keep.append(i)
        if not keep:
            return (
                np.empty((0, 4), dtype=np.float32),
                np.empty((0,), dtype=np.float32),
                np.empty((0,), dtype=np.int32),
            )
        k = np.array(keep, dtype=np.intp)
        return boxes[k], scores[k], cls_ids[k]

    @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)[::-1]
        keep: list[int] = []
        suppressed = np.zeros(N, dtype=bool)
        for i in range(N):
            idx = order[i]
            if suppressed[idx]:
                continue
            keep.append(int(idx))
            bi = boxes[idx]
            for k in range(i + 1, N):
                jdx = order[k]
                if suppressed[jdx]:
                    continue
                bj = boxes[jdx]
                xx1 = max(bi[0], bj[0])
                yy1 = max(bi[1], bj[1])
                xx2 = min(bi[2], bj[2])
                yy2 = min(bi[3], bj[3])
                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
                iou = inter / (area_i + area_j - inter + 1e-7)
                if iou > iou_thresh:
                    suppressed[jdx] = True
        return np.array(keep, dtype=np.intp)

    def _per_class_hard_nms(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        iou_thresh: float,
    ) -> np.ndarray:
        if len(boxes) == 0:
            return np.array([], dtype=np.intp)
        all_keep: list[int] = []
        for c in np.unique(cls_ids):
            mask = cls_ids == c
            indices = np.where(mask)[0]
            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
            all_keep.extend(indices[keep].tolist())
        all_keep.sort()
        return np.array(all_keep, dtype=np.intp)

    @staticmethod
    def _cross_class_dedup(
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        iou_thresh: float,
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        n = len(boxes)
        if n <= 1:
            return boxes, scores, cls_ids
        boxes = np.asarray(boxes, dtype=np.float32)
        scores = np.asarray(scores, dtype=np.float32)
        cls_ids = np.asarray(cls_ids, dtype=np.int32)
        areas = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(
            0.0, boxes[:, 3] - boxes[:, 1]
        )
        # Keep larger boxes first, then higher score.
        order = np.lexsort((-scores, -areas))
        suppressed = np.zeros(n, dtype=bool)
        keep: list[int] = []
        for i in order:
            if suppressed[i]:
                continue
            keep.append(int(i))
            bi = boxes[i]
            xx1 = np.maximum(bi[0], boxes[:, 0])
            yy1 = np.maximum(bi[1], boxes[:, 1])
            xx2 = np.minimum(bi[2], boxes[:, 2])
            yy2 = np.minimum(bi[3], boxes[:, 3])
            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
            area_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
            union = area_i + areas - inter + 1e-7
            iou = inter / union
            dup = iou > iou_thresh
            dup[i] = False
            suppressed |= dup
        keep_idx = np.array(keep, dtype=np.intp)
        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]

    @staticmethod
    def _max_score_per_cluster(
        coords: np.ndarray,
        scores: np.ndarray,
        keep_indices: np.ndarray,
        iou_thresh: float,
    ) -> np.ndarray:
        n_keep = len(keep_indices)
        if n_keep == 0:
            return np.array([], dtype=np.float32)
        coords = np.asarray(coords, dtype=np.float32)
        scores = np.asarray(scores, dtype=np.float32)
        out = np.empty(n_keep, dtype=np.float32)
        for i in range(n_keep):
            idx = keep_indices[i]
            bi = coords[idx]
            xx1 = np.maximum(bi[0], coords[:, 0])
            yy1 = np.maximum(bi[1], coords[:, 1])
            xx2 = np.minimum(bi[2], coords[:, 2])
            yy2 = np.minimum(bi[3], coords[:, 3])
            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
            area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
            areas_j = (coords[:, 2] - coords[:, 0]) * (coords[:, 3] - coords[:, 1])
            iou = inter / (area_i + areas_j - inter + 1e-7)
            in_cluster = iou >= iou_thresh
            out[i] = float(np.max(scores[in_cluster]))
        return out

    def _decode_raw_dets(
        self,
        preds: np.ndarray,
        ratio: float,
        pad: tuple[float, float],
        orig_size: tuple[int, int],
        *,
        apply_conf_thresh: bool = True,
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Decode end2end NMS output and return (boxes, scores, cls_ids)
        in original image coordinates, after conf-threshold + remap + letterbox-reverse + sanity.

        When apply_conf_thresh=False, the conf-threshold filter is skipped (used for
        the no-detection fallback path: take the single top-conf raw box)."""
        if preds.ndim == 3 and preds.shape[0] == 1:
            preds = preds[0]
        if preds.ndim != 2 or preds.shape[1] < 6:
            raise ValueError(f"Unexpected ONNX output shape: {preds.shape}")

        boxes = preds[:, :4].astype(np.float32)
        scores = preds[:, 4].astype(np.float32)
        cls_ids = preds[:, 5].astype(np.int32)

        valid = (cls_ids >= 0) & (cls_ids < len(self.cls_remap)) & (scores > 0)
        boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid]
        cls_ids = self.cls_remap[cls_ids]

        if apply_conf_thresh:
            # Per-class threshold: each box compared against its own class's threshold
            cls_thresh = np.full(len(scores), self.conf_thres, dtype=np.float32)
            valid_cls = (cls_ids >= 0) & (cls_ids < len(self.conf_thres_per_class))
            cls_thresh[valid_cls] = self.conf_thres_per_class[cls_ids[valid_cls]]
            keep = scores >= cls_thresh
            boxes = boxes[keep]
            scores = scores[keep]
            cls_ids = cls_ids[keep]
        if len(boxes) == 0:
            return (
                np.empty((0, 4), dtype=np.float32),
                np.empty((0,), dtype=np.float32),
                np.empty((0,), dtype=np.int32),
            )

        pad_w, pad_h = pad
        orig_w, orig_h = orig_size
        boxes[:, [0, 2]] -= pad_w
        boxes[:, [1, 3]] -= pad_h
        boxes /= ratio
        boxes = self._clip_boxes(boxes, (orig_w, orig_h))

        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
        return boxes, scores, cls_ids

    def _forward(
        self, image: np.ndarray
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        x, ratio, pad, orig_size = self._preprocess(image)
        out = self.session.run(self.output_names, {self.input_name: x})[0]
        return self._decode_raw_dets(out, ratio, pad, orig_size)

    def _forward_with_fallback(
        self, image: np.ndarray
    ) -> tuple[
        tuple[np.ndarray, np.ndarray, np.ndarray],
        tuple[np.ndarray, np.ndarray, np.ndarray],
    ]:
        """Run ONNX once, decode twice: (filtered @ conf_thres, all-survived sanity)."""
        x, ratio, pad, orig_size = self._preprocess(image)
        out = self.session.run(self.output_names, {self.input_name: x})[0]
        primary = self._decode_raw_dets(out, ratio, pad, orig_size, apply_conf_thresh=True)
        fallback = self._decode_raw_dets(out, ratio, pad, orig_size, apply_conf_thresh=False)
        return primary, fallback

    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
        (boxes, scores, cls_ids), (fb_b, fb_s, fb_c) = self._forward_with_fallback(image)
        ih, iw = image.shape[:2]
        if len(boxes) > 0:
            return self._build_results(boxes, scores, cls_ids, image_size=(iw, ih))
        # FALLBACK: nothing passed conf_thres — return single top-conf box
        # (any class, any conf > 0) so the validator's mAP isn't a hard zero.
        if len(fb_b) == 0:
            return []
        i = int(np.argmax(fb_s))
        return self._build_results(
            fb_b[i:i + 1], fb_s[i:i + 1], fb_c[i:i + 1], image_size=(iw, ih)
        )

    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
        """Hflip TTA: merge primary + flipped via per-class hard-NMS,
        then cross-class dedup, with consensus-confidence boost."""
        ow = image.shape[1]
        b1, s1, c1 = self._forward(image)

        flipped = cv2.flip(image, 1)
        b2, s2, c2 = self._forward(flipped)
        if len(b2):
            x1f = ow - b2[:, 2]
            x2f = ow - b2[:, 0]
            b2 = np.stack([x1f, b2[:, 1], x2f, b2[:, 3]], axis=1)

        if len(b1) == 0 and len(b2) == 0:
            return []

        boxes = np.concatenate([b1, b2], axis=0) if len(b2) else b1
        scores = np.concatenate([s1, s2], axis=0) if len(b2) else s1
        cls_ids = np.concatenate([c1, c2], axis=0) if len(b2) else c1

        keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
        if len(keep) == 0:
            return []
        keep = keep[: self.max_det]

        # Consensus-confidence boost: cluster by IoU and take max score.
        boosted = self._max_score_per_cluster(boxes, scores, keep, self.iou_thres)

        boxes = boxes[keep]
        cls_ids = cls_ids[keep]
        scores = boosted

        boxes, scores, cls_ids = self._cross_class_dedup(
            boxes, scores, cls_ids, self.cross_iou_thresh
        )
        if len(boxes) == 0:
            return []

        ih, iw = image.shape[:2]
        return self._build_results(boxes, scores, cls_ids, image_size=(iw, ih))

    def _filter_balaclava_geometry(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        image_size: tuple[int, int] | None = None,
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        # Real-balaclava prior (from 43 manual GT labels):
        #   aspect ratio max(w/h, h/w):  p5=1.11, median=1.33, p99=1.71
        #   rel area % of image:         p1=0.041, p5=0.070, p10=0.087
        # FP balaclavas frequently violate these (very thin/wide boxes from
        # face-fragment matches, or tiny ~0.01%-area boxes from texture noise).
        BALACLAVA = 0
        ASPECT_MAX = 1.8     # above p99 of real
        REL_AREA_MIN = 0.0004  # below p1 of real (0.04%)
        if len(boxes) == 0:
            return boxes, scores, cls_ids
        is_bal = cls_ids == BALACLAVA
        if not is_bal.any():
            return boxes, scores, cls_ids
        keep = np.ones(len(boxes), dtype=bool)
        if image_size is not None:
            iw, ih = image_size
            img_area = max(1.0, iw * ih)
        else:
            img_area = None
        for i in np.where(is_bal)[0]:
            x1, y1, x2, y2 = boxes[i]
            bw = max(1.0, x2 - x1)
            bh = max(1.0, y2 - y1)
            aspect = max(bw / bh, bh / bw)
            if aspect > ASPECT_MAX:
                keep[i] = False
                continue
            if img_area is not None:
                rel = (bw * bh) / img_area
                if rel < REL_AREA_MIN:
                    keep[i] = False
        return boxes[keep], scores[keep], cls_ids[keep]

    def _suppress_balaclava_under_hoodie(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        # Validator rule: "balaclavas worn under a hoodie hood are IGNORED
        # (a hoodie includes the jacket and its hood)". A small balaclava
        # box can sit fully inside a much larger hoodie box — IoU between
        # them stays low (intersection / large union), but containment
        # (intersection / balaclava_area) is ~1.0. So drop any balaclava
        # whose containment by any hoodie box is >= COVER_THRESH.
        BALACLAVA, HOODIE = 0, 1
        COVER_THRESH = 0.5
        if len(boxes) == 0:
            return boxes, scores, cls_ids
        is_hood = cls_ids == HOODIE
        is_bal  = cls_ids == BALACLAVA
        if not is_hood.any() or not is_bal.any():
            return boxes, scores, cls_ids
        hood_boxes = boxes[is_hood]
        keep = np.ones(len(boxes), dtype=bool)
        for i in np.where(is_bal)[0]:
            bx1, by1, bx2, by2 = boxes[i]
            bal_area = max(1.0, (bx2 - bx1) * (by2 - by1))
            ix1 = np.maximum(bx1, hood_boxes[:, 0])
            iy1 = np.maximum(by1, hood_boxes[:, 1])
            ix2 = np.minimum(bx2, hood_boxes[:, 2])
            iy2 = np.minimum(by2, hood_boxes[:, 3])
            iw = np.clip(ix2 - ix1, 0.0, None)
            ih = np.clip(iy2 - iy1, 0.0, None)
            inter = iw * ih
            cover = inter / bal_area
            if (cover >= COVER_THRESH).any():
                keep[i] = False
        return boxes[keep], scores[keep], cls_ids[keep]

    def _build_results(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        image_size: tuple[int, int] | None = None,
    ) -> list[BoundingBox]:
        boxes, scores, cls_ids = self._filter_balaclava_geometry(
            boxes, scores, cls_ids, image_size
        )
        boxes, scores, cls_ids = self._suppress_balaclava_under_hoodie(
            boxes, scores, cls_ids
        )
        results: list[BoundingBox] = []
        for box, conf, cls_id in zip(boxes, scores, cls_ids):
            x1, y1, x2, y2 = box.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=int(cls_id),
                    conf=float(conf),
                )
            )
        return results

    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):
            if image is None or not isinstance(image, np.ndarray) or image.ndim != 3:
                results.append(
                    TVFrameResult(
                        frame_id=offset + frame_number_in_batch,
                        boxes=[],
                        keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
                    )
                )
                continue
            if image.dtype != np.uint8:
                image = image.astype(np.uint8)
            try:
                if self.use_tta:
                    boxes = self._predict_tta(image)
                else:
                    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