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:
    def __init__(self,
        path_hf_repo: Path
    ) -> None:
        model_path = path_hf_repo / "weights.onnx"
        self.class_names = ["fire","smoke","fire extinguisher"]
        model_class_order = ["fire","smoke","fire extinguisher"]
        self.cls_remap = np.array(
            [self.class_names.index(n) for n in model_class_order], 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())

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

        # Your export is fixed-size 1280, but we still read actual ONNX input shape first.
        self.input_height = self._safe_dim(self.input_shape[2], default=960)
        self.input_width = self._safe_dim(self.input_shape[3], default=960)

        # Tuned for validator scoring: reduce FP (FALSE_POSITIVE pillar),
        # preserve recall (MAP50, RECALL), improve precision.
        self.conf_thres = 0.36  # Higher = fewer FP, slightly lower recall
        self.iou_thres = 0.43   # Lower = suppress duplicate detections (FP)
        self.max_det = 150      # Cap detections per image
        self.use_tta = True

        # Box sanity: filter tiny/spurious detections (common FP source)
        self.min_box_area = 14 * 14  # ~144 px²
        self.min_side = 8
        self.max_aspect_ratio = 8.0

        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

    def _letterbox(
        self,
        image: ndarray,
        new_shape: tuple[int, int],
        color=(114, 114, 114),
    ) -> tuple[ndarray, float, tuple[float, float]]:
        """
        Resize with unchanged aspect ratio and pad to target shape.
        Returns:
            padded_image,
            ratio,
            (pad_w, pad_h)  # half-padding
        """
        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
        dh = new_h - resized_h
        dw /= 2.0
        dh /= 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
    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
        """
        Preprocess for fixed-size ONNX export:
        - enhance image quality (CLAHE, denoise, sharpen)
        - letterbox to model input size
        - BGR -> RGB
        - normalize to [0,1]
        - HWC -> NCHW float32
        """
        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(np.float32) / 255.0
        img = np.transpose(img, (2, 0, 1))[None, ...]
        img = np.ascontiguousarray(img, dtype=np.float32)

        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

    @staticmethod
    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
        out = np.empty_like(boxes)
        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
        return out

    def _soft_nms(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        sigma: float = 0.5,
        score_thresh: float = 0.01,
    ) -> tuple[np.ndarray, np.ndarray]:
        """
        Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
        Returns (kept_original_indices, updated_scores).
        """
        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 = max(0.0, 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 _hard_nms(
        boxes: np.ndarray,
        scores: np.ndarray,
        iou_thresh: float,
    ) -> np.ndarray:
        """
        Standard NMS: keep one box per overlapping cluster (the one with highest score).
        Returns indices of kept boxes (into the boxes/scores arrays).
        """
        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(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)

    def _per_class_hard_nms(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        iou_thresh: float,
    ) -> np.ndarray:
        """Hard NMS applied independently per class."""
        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)

    def _per_class_soft_nms(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        sigma: float = 0.5,
        score_thresh: float = 0.01,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Soft NMS applied independently per class."""
        if len(boxes) == 0:
            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
        all_keep: list[int] = []
        all_scores: list[float] = []
        for c in np.unique(cls_ids):
            mask = cls_ids == c
            indices = np.where(mask)[0]
            keep, updated = self._soft_nms(boxes[mask], scores[mask], sigma, score_thresh)
            for k, s in zip(keep, updated):
                all_keep.append(int(indices[k]))
                all_scores.append(float(s))
        if not all_keep:
            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
        return np.array(all_keep, dtype=np.intp), np.array(all_scores, dtype=np.float32)

    def _filter_sane_boxes(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        cls_ids: np.ndarray,
        orig_size: tuple[int, int],
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Filter out tiny, degenerate, or implausible boxes (common FP)."""
        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 > 0.95 * 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 _max_score_per_cluster(
        coords: np.ndarray,
        scores: np.ndarray,
        keep_indices: np.ndarray,
        iou_thresh: float,
    ) -> np.ndarray:
        """
        For each kept box, return the max original score among itself and any
        box that overlaps it with IOU >= iou_thresh (so TTA cluster keeps best conf).
        """
        n_keep = len(keep_indices)
        if n_keep == 0:
            return np.array([], dtype=np.float32)
        out = np.empty(n_keep, dtype=np.float32)
        coords = np.asarray(coords, dtype=np.float32)
        scores = np.asarray(scores, 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_final_dets(
        self,
        preds: np.ndarray,
        ratio: float,
        pad: tuple[float, float],
        orig_size: tuple[int, int],
        apply_optional_dedup: bool = False,
    ) -> list[BoundingBox]:
        """
        Primary path:
        expected output rows like [x1, y1, x2, y2, conf, cls_id]
        in letterboxed input coordinates.
        """
        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 final-det output shape: {preds.shape}")

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

        keep = scores >= self.conf_thres
        boxes = boxes[keep]
        scores = scores[keep]
        cls_ids = cls_ids[keep]

        if len(boxes) == 0:
            return []

        pad_w, pad_h = pad
        orig_w, orig_h = orig_size

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

        # Box sanity filter (reduces FP)
        boxes, scores, cls_ids = self._filter_sane_boxes(
            boxes, scores, cls_ids, orig_size
        )
        if len(boxes) == 0:
            return []

        # Per-class NMS to remove duplicates without suppressing across classes
        if len(boxes) > 1:
            if apply_optional_dedup:
                keep_idx, scores = self._per_class_soft_nms(boxes, scores, cls_ids)
                boxes = boxes[keep_idx]
                cls_ids = cls_ids[keep_idx]
            else:
                keep_idx = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
                keep_idx = keep_idx[: self.max_det]
                boxes = boxes[keep_idx]
                scores = scores[keep_idx]
                cls_ids = cls_ids[keep_idx]

        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 _decode_raw_yolo(
        self,
        preds: np.ndarray,
        ratio: float,
        pad: tuple[float, float],
        orig_size: tuple[int, int],
    ) -> list[BoundingBox]:
        """
        Fallback path for raw YOLO predictions.
        Supports common layouts:
        - [1, C, N]
        - [1, N, C]
        """
        if preds.ndim != 3:
            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")

        if preds.shape[0] != 1:
            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")

        preds = preds[0]

        # Normalize to [N, C]
        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
            preds = preds.T

        if preds.ndim != 2 or preds.shape[1] < 5:
            raise ValueError(f"Unexpected normalized raw output shape: {preds.shape}")

        boxes_xywh = preds[:, :4].astype(np.float32)
        cls_part = preds[:, 4:].astype(np.float32)

        if cls_part.shape[1] == 1:
            scores = cls_part[:, 0]
            cls_ids = np.zeros(len(scores), dtype=np.int32)
        else:
            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
            scores = cls_part[np.arange(len(cls_part)), cls_ids]
        cls_ids = self.cls_remap[cls_ids]

        keep = scores >= self.conf_thres
        boxes_xywh = boxes_xywh[keep]
        scores = scores[keep]
        cls_ids = cls_ids[keep]

        if len(boxes_xywh) == 0:
            return []

        boxes = self._xywh_to_xyxy(boxes_xywh)

        keep_idx = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
        keep_idx = keep_idx[: self.max_det]
        boxes = boxes[keep_idx]
        scores = scores[keep_idx]
        cls_ids = cls_ids[keep_idx]

        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_w, orig_h)
        )
        if len(boxes) == 0:
            return []

        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 _postprocess(
        self,
        output: np.ndarray,
        ratio: float,
        pad: tuple[float, float],
        orig_size: tuple[int, int],
    ) -> list[BoundingBox]:
        """
        Prefer final detections first.
        Fallback to raw decode only if needed.
        """
        # final detections: [N,6]
        if output.ndim == 2 and output.shape[1] >= 6:
            return self._decode_final_dets(output, ratio, pad, orig_size)

        # final detections: [1,N,6]
        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
            return self._decode_final_dets(output, ratio, pad, orig_size)

        # fallback raw decode
        return self._decode_raw_yolo(output, ratio, pad, orig_size)

    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
        if image is None:
            raise ValueError("Input image is None")
        if not isinstance(image, np.ndarray):
            raise TypeError(f"Input is not numpy array: {type(image)}")
        if image.ndim != 3:
            raise ValueError(f"Expected HWC image, got shape={image.shape}")
        if image.shape[0] <= 0 or image.shape[1] <= 0:
            raise ValueError(f"Invalid image shape={image.shape}")
        if image.shape[2] != 3:
            raise ValueError(f"Expected 3 channels, got shape={image.shape}")

        if image.dtype != np.uint8:
            image = image.astype(np.uint8)

        input_tensor, ratio, pad, orig_size = self._preprocess(image)

        expected_shape = (1, 3, self.input_height, self.input_width)
        if input_tensor.shape != expected_shape:
            raise ValueError(
                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
            )

        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
        det_output = outputs[0]
        return self._postprocess(det_output, ratio, pad, orig_size)

    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
        """
        Horizontal-flip TTA: merge original + flipped via hard NMS.
        Boost confidence for consensus detections (both views agree) to improve
        mAP: validator sorts by confidence, so higher conf for TP helps PR curve.
        """
        boxes_orig = self._predict_single(image)

        flipped = cv2.flip(image, 1)
        boxes_flip = self._predict_single(flipped)

        w = image.shape[1]
        boxes_flip = [
            BoundingBox(
                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
                cls_id=b.cls_id, conf=b.conf,
            )
            for b in boxes_flip
        ]

        all_boxes = boxes_orig + boxes_flip
        if len(all_boxes) == 0:
            return []

        coords = np.array(
            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
        )
        scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
        cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)

        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
        if len(hard_keep) == 0:
            return []

        hard_keep = hard_keep[: self.max_det]

        # Boost confidence when both views agree (overlapping detections)
        boosted = self._max_score_per_cluster(
            coords, scores, hard_keep, self.iou_thres
        )

        return [
            BoundingBox(
                x1=all_boxes[i].x1,
                y1=all_boxes[i].y1,
                x2=all_boxes[i].x2,
                y2=all_boxes[i].y2,
                cls_id=all_boxes[i].cls_id,
                conf=float(boosted[j]),
            )
            for j, i in enumerate(hard_keep)
        ]

    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:
                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