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, 

        conf_thres: float = 0.3109, 

        iou_thres: float = 0.5181,

        max_det: int = 150,

        conf_high: float = 0.6062,

        tta_match_iou: float = 0.6772,

        conf_adapt_low: float = 0.4076,

        conf_adapt_high: float = 0.6375,

        count_low: int = 5,

        count_high: int = 23,

        use_tta: bool = True,

        min_box_area: int = 14 * 14,

        min_w: int = 8,

        min_h: int = 22,

        max_aspect_ratio: float = 6.5,

        max_box_area_ratio: float = 0.8,

    ) -> None:
        model_path = path_hf_repo / "weights.onnx"
        self.class_names = ["person"]
        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

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

        # --- Scoring-aware adaptive confidence ---
        # total_score = mAP50 * 0.65 + FP_score * 0.35
        # FP_score = max(0, 1 - n_FP / n_images), typically n_images ≈ 10
        #
        # mAP50 weight is higher for person detection → favor recall slightly more
        # Crossover at ~1.9 GT/image: below → recall wins, above → precision wins
        self.conf_thres = conf_thres       # Base threshold for candidate generation (wide net)
        self.iou_thres = iou_thres         # NMS threshold
        self.max_det = max_det

        # TTA consensus thresholds
        self.conf_high = conf_high        # Boxes above this survive without TTA confirmation
        self.tta_match_iou = tta_match_iou    # TTA cross-view match IoU

        # Adaptive conf curve: lerp between low/high based on raw detection count
        self.conf_adapt_low = conf_adapt_low   # Few objects: favor recall, each TP ≈ 0.065+ of total
        self.conf_adapt_high = conf_adapt_high  # Many objects: favor precision, FP costs 0.035 each
        self.count_low = count_low           # Raw count below this → use conf_adapt_low
        self.count_high = count_high         # Raw count above this → use conf_adapt_high

        self.use_tta = use_tta

        # Box sanity filters
        self.min_box_area = min_box_area
        self.min_w = min_w
        self.min_h = min_h
        self.max_aspect_ratio = max_aspect_ratio
        self.max_box_area_ratio = max_box_area_ratio

        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]]:
        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]]:
        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

    @staticmethod
    def _hard_nms(

        boxes: np.ndarray,

        scores: np.ndarray,

        iou_thresh: float,

    ) -> np.ndarray:
        if len(boxes) == 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 = []

        while len(order) > 0:
            i = 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 = np.maximum(0.0, (boxes[i, 2] - boxes[i, 0])) * np.maximum(0.0, (boxes[i, 3] - boxes[i, 1]))
            area_r = np.maximum(0.0, (boxes[rest, 2] - boxes[rest, 0])) * np.maximum(0.0, (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)

    @staticmethod
    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
        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)

    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]:
        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_w or bh < self.min_h:
                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),
            )

        keep = np.array(keep, dtype=np.intp)
        return boxes[keep], scores[keep], cls_ids[keep]

    def _decode_final_dets(

        self,

        preds: np.ndarray,

        ratio: float,

        pad: tuple[float, float],

        orig_size: tuple[int, int],

    ) -> list[BoundingBox]:
        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)

        # person only
        keep = cls_ids == 0
        boxes = boxes[keep]
        scores = scores[keep]
        cls_ids = cls_ids[keep]

        # candidate threshold
        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

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

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

        boxes = boxes[keep_idx]
        scores = scores[keep_idx]
        cls_ids = cls_ids[keep_idx]

        return [
            BoundingBox(
                x1=int(math.floor(box[0])),
                y1=int(math.floor(box[1])),
                x2=int(math.ceil(box[2])),
                y2=int(math.ceil(box[3])),
                cls_id=int(cls_id),
                conf=float(conf),
            )
            for box, conf, cls_id in zip(boxes, scores, cls_ids)
            if box[2] > box[0] and box[3] > box[1]
        ]

    def _decode_raw_yolo(

        self,

        preds: np.ndarray,

        ratio: float,

        pad: tuple[float, float],

        orig_size: tuple[int, int],

    ) -> list[BoundingBox]:
        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)
        tail = preds[:, 4:].astype(np.float32)

        # Supports:
        # [x,y,w,h,score]                 single-class
        # [x,y,w,h,obj,cls]               YOLO standard single-class
        # [x,y,w,h,obj,cls1,cls2,...]     multi-class
        if tail.shape[1] == 1:
            scores = tail[:, 0]
            cls_ids = np.zeros(len(scores), dtype=np.int32)
        elif tail.shape[1] == 2:
            obj = tail[:, 0]
            cls_prob = tail[:, 1]
            scores = obj * cls_prob
            cls_ids = np.zeros(len(scores), dtype=np.int32)
        else:
            obj = tail[:, 0]
            class_probs = tail[:, 1:]
            cls_ids = np.argmax(class_probs, axis=1).astype(np.int32)
            cls_scores = class_probs[np.arange(len(class_probs)), cls_ids]
            scores = obj * cls_scores

        keep = cls_ids == 0
        boxes_xywh = boxes_xywh[keep]
        scores = scores[keep]
        cls_ids = cls_ids[keep]

        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)

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

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

        boxes = boxes[keep_idx]
        scores = scores[keep_idx]
        cls_ids = cls_ids[keep_idx]

        return [
            BoundingBox(
                x1=int(math.floor(box[0])),
                y1=int(math.floor(box[1])),
                x2=int(math.ceil(box[2])),
                y2=int(math.ceil(box[3])),
                cls_id=int(cls_id),
                conf=float(conf),
            )
            for box, conf, cls_id in zip(boxes, scores, cls_ids)
            if box[2] > box[0] and box[3] > box[1]
        ]

    def _postprocess(

        self,

        output: np.ndarray,

        ratio: float,

        pad: tuple[float, float],

        orig_size: tuple[int, int],

    ) -> list[BoundingBox]:
        if output.ndim == 2 and output.shape[1] >= 6:
            return self._decode_final_dets(output, ratio, pad, orig_size)

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

        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 _merge_tta_consensus(

        self,

        boxes_orig: list[BoundingBox],

        boxes_flip: list[BoundingBox],

    ) -> list[BoundingBox]:
        """

        Keep:

        - any box with conf >= conf_high

        - low/medium-conf boxes only if confirmed across TTA views

        Then run final hard NMS.

        """
        if not boxes_orig and not boxes_flip:
            return []

        coords_o = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0, 4), dtype=np.float32)
        scores_o = np.array([b.conf for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0,), dtype=np.float32)

        coords_f = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0, 4), dtype=np.float32)
        scores_f = np.array([b.conf for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0,), dtype=np.float32)

        accepted_boxes = []
        accepted_scores = []

        # Original view candidates
        for i in range(len(coords_o)):
            score = scores_o[i]
            if score >= self.conf_high:
                accepted_boxes.append(coords_o[i])
                accepted_scores.append(score)
            elif len(coords_f) > 0:
                ious = self._box_iou_one_to_many(coords_o[i], coords_f)
                j = int(np.argmax(ious))
                if ious[j] >= self.tta_match_iou:
                    fused_score = max(score, scores_f[j])
                    accepted_boxes.append(coords_o[i])
                    accepted_scores.append(fused_score)

        # Flipped-view high-confidence boxes that original missed
        for i in range(len(coords_f)):
            score = scores_f[i]
            if score < self.conf_high:
                continue

            if len(coords_o) == 0:
                accepted_boxes.append(coords_f[i])
                accepted_scores.append(score)
                continue

            ious = self._box_iou_one_to_many(coords_f[i], coords_o)
            if np.max(ious) < self.tta_match_iou:
                accepted_boxes.append(coords_f[i])
                accepted_scores.append(score)

        if not accepted_boxes:
            return []

        boxes = np.array(accepted_boxes, dtype=np.float32)
        scores = np.array(accepted_scores, dtype=np.float32)

        keep = self._hard_nms(boxes, scores, self.iou_thres)
        keep = keep[: self.max_det]

        out = []
        for idx in keep:
            x1, y1, x2, y2 = boxes[idx].tolist()
            out.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(scores[idx]),
                )
            )
        return out

    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
        boxes_orig = self._predict_single(image)

        flipped = cv2.flip(image, 1)
        boxes_flip_raw = 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_raw
        ]

        return self._merge_tta_consensus(boxes_orig, boxes_flip)

    def _adaptive_conf_threshold(self, n_raw: int) -> float:
        """

        Dynamic confidence threshold based on raw detection count.



        total_score = mAP50 * 0.65 + FP_score * 0.35

        - Few objects → each TP worth ~0.065/n for mAP50 → keep low conf (maximize recall)

        - Many objects → each TP worth little, FPs dominate → raise conf (minimize FP)

        """
        if n_raw <= self.count_low:
            return self.conf_adapt_low
        if n_raw >= self.count_high:
            return self.conf_adapt_high
        t = (n_raw - self.count_low) / (self.count_high - self.count_low)
        return self.conf_adapt_low + t * (self.conf_adapt_high - self.conf_adapt_low)

    def _apply_adaptive_filter(self, boxes: list[BoundingBox]) -> list[BoundingBox]:
        if not boxes:
            return boxes
        n_raw = len(boxes)
        thresh = self._adaptive_conf_threshold(n_raw)
        return [b for b in boxes if b.conf >= thresh]

    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)
                boxes = self._apply_adaptive_filter(boxes)
            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