miner.py

from pathlib import Path
import math
import logging

import cv2
import numpy as np
import onnxruntime as ort
from numpy import ndarray
from pydantic import BaseModel

logger = logging.getLogger(__name__)

# ─── Petrol miner v1.1 ───────────────────────────────────────────────
# Improvements over auto-generated baseline:
# 1. Fix end-to-end ONNX decode (model outputs [1,300,6] post-NMS)
# 2. Spatial co-occurrence scoring (pump+canopy boost, isolated suppress)
# 3. Geometric validation (aspect ratio + size checks per class)
# ──────────────────────────────────────────────────────────────────────

# Class IDs
CLS_HOSE = 0
CLS_PUMP = 1
CLS_PRICEBOARD = 2
CLS_CANOPY = 3

# ── Geometric validation thresholds (derived from 2000-label analysis) ──
# Canopy: wide/flat, aspect(w/h) mean=2.96. Suppress if aspect < 0.8 (too tall)
CANOPY_MIN_ASPECT = 0.8
# Pump: roughly square/tall, aspect mean=0.91. Suppress if aspect > 4.0 (too wide)
PUMP_MAX_ASPECT = 4.0
# Price board: small. Suppress if area > 15% of image
PRICEBOARD_MAX_AREA_FRAC = 0.15
# Hose: variable. Suppress if area < 0.05% of image (tiny FP)
HOSE_MIN_AREA_FRAC = 0.0005

# ── Spatial co-occurrence boost/suppress amounts ──
COOCCUR_BOOST_PUMP_CANOPY = 0.05
COOCCUR_BOOST_PUMP_HOSE = 0.08
COOCCUR_BOOST_CANOPY_HOSE = 0.05
COOCCUR_SUPPRESS_ISOLATED = 0.03  # per missing expected neighbor
# Proximity threshold: normalized distance between box centers
COOCCUR_PROXIMITY = 0.5  # half of image dimension

# ── Geometric suppress penalty ──
GEOMETRIC_SUPPRESS_PENALTY = 0.10


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:
    VERSION = "petrol-v1.1"

    def __init__(self, path_hf_repo: Path) -> None:
        self.path_hf_repo = path_hf_repo
        self.class_names = ['petrol hose', 'petrol pump', 'price board', 'roof canopy']
        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
        self.input_h = int(input_shape[2])
        self.input_w = int(input_shape[3])
        self.conf_threshold = 0.25
        self.iou_threshold = 0.45

        # Detect output format: end-to-end [1,N,6] vs raw [1,C,N]
        out_shape = self.session.get_outputs()[0].shape
        # End-to-end: [1, max_dets, 6] where max_dets is small (100-300)
        # Raw: [1, 4+nc, N] where N is large (8400+)
        if len(out_shape) == 3 and out_shape[2] == 6 and (out_shape[1] or 0) <= 1000:
            self._end2end = True
            logger.info("[init] End-to-end ONNX output detected")
        else:
            self._end2end = False
            logger.info("[init] Raw ONNX output detected")

        logger.info(f"[init] {self.VERSION} loaded, input={self.input_w}x{self.input_h}, "
                     f"end2end={self._end2end}")

    def __repr__(self) -> str:
        return f"Petrol Miner {self.VERSION} end2end={self._end2end}"

    # ─── Preprocessing ────────────────────────────────────────────────

    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)

    # ─── NMS (only needed for raw output format) ─────────────────────

    def _nms(self, dets):
        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]

    # ─── Decode: handles both end-to-end and raw formats ─────────────

    def _decode_end2end(self, out, orig_h, orig_w):
        """Decode end-to-end [1, N, 6] output: [x1,y1,x2,y2,conf,cls_id] in input coords."""
        pred = out[0]  # [N, 6]
        if pred.ndim != 2 or pred.shape[1] != 6:
            return []

        confs = pred[:, 4]
        keep = confs >= self.conf_threshold
        pred = pred[keep]
        if pred.shape[0] == 0:
            return []

        sx = orig_w / float(self.input_w)
        sy = orig_h / float(self.input_h)

        results = []
        for i in range(pred.shape[0]):
            x1 = pred[i, 0] * sx
            y1 = pred[i, 1] * sy
            x2 = pred[i, 2] * sx
            y2 = pred[i, 3] * sy
            conf = float(pred[i, 4])
            cls_id = int(pred[i, 5])
            results.append((x1, y1, x2, y2, conf, cls_id))
        return results

    def _decode_raw(self, out, orig_h, orig_w):
        """Decode raw [1, 4+nc, N] or [1, N, 4+nc] output."""
        pred = out[0]
        if pred.ndim != 2:
            return []
        if pred.shape[0] < pred.shape[1]:
            pred = pred.T
        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, confs, cls_ids = boxes[keep], confs[keep], cls_ids[keep]
        if boxes.shape[0] == 0:
            return []

        sx = orig_w / float(self.input_w)
        sy = orig_h / float(self.input_h)

        dets = []
        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 self._nms(dets)

    # ─── Geometric validation ────────────────────────────────────────

    def _geometric_validate(self, dets, orig_h, orig_w):
        """Suppress detections that fail basic geometric expectations.

        Returns list with penalties applied to conf.
        - Canopy: must be wide (aspect w/h >= 0.8)
        - Pump: must not be extremely wide (aspect w/h <= 4.0)
        - Price board: must be small (area <= 15% of image)
        - Hose: must not be tiny (area >= 0.05% of image)
        """
        img_area = max(orig_h * orig_w, 1)
        result = []
        for x1, y1, x2, y2, conf, cls_id in dets:
            bw = max(x2 - x1, 1)
            bh = max(y2 - y1, 1)
            aspect = bw / bh
            box_area = bw * bh
            area_frac = box_area / img_area
            penalty = 0.0

            if cls_id == CLS_CANOPY:
                if aspect < CANOPY_MIN_ASPECT:
                    penalty = GEOMETRIC_SUPPRESS_PENALTY
            elif cls_id == CLS_PUMP:
                if aspect > PUMP_MAX_ASPECT:
                    penalty = GEOMETRIC_SUPPRESS_PENALTY
            elif cls_id == CLS_PRICEBOARD:
                if area_frac > PRICEBOARD_MAX_AREA_FRAC:
                    penalty = GEOMETRIC_SUPPRESS_PENALTY
            elif cls_id == CLS_HOSE:
                if area_frac < HOSE_MIN_AREA_FRAC:
                    penalty = GEOMETRIC_SUPPRESS_PENALTY

            new_conf = max(0.0, conf - penalty)
            if new_conf >= self.conf_threshold:
                result.append((x1, y1, x2, y2, new_conf, cls_id))
        return result

    # ─── Spatial co-occurrence scoring ───────────────────────────────

    def _spatial_cooccurrence(self, dets, orig_h, orig_w):
        """Adjust confidences based on spatial co-occurrence patterns.

        Boosts:
        - Pump near canopy: both get +0.05
        - Pump near hose: hose gets +0.08
        - Canopy near hose: hose gets +0.05

        Suppresses:
        - Low-conf detection with no neighbors of expected class: -0.03
          (except price boards, which are 91% solo in training data)
        """
        if not dets:
            return dets

        n = len(dets)
        adjustments = [0.0] * n
        diag = math.sqrt(orig_h ** 2 + orig_w ** 2)
        prox = COOCCUR_PROXIMITY * diag  # absolute pixel distance

        # Precompute centers
        centers = []
        for x1, y1, x2, y2, conf, cls_id in dets:
            centers.append(((x1 + x2) / 2, (y1 + y2) / 2))

        # Build per-class index
        cls_map = {}
        for i, (_, _, _, _, _, cls_id) in enumerate(dets):
            cls_map.setdefault(cls_id, []).append(i)

        def near(i, j):
            dx = centers[i][0] - centers[j][0]
            dy = centers[i][1] - centers[j][1]
            return math.sqrt(dx * dx + dy * dy) < prox

        # Pump + Canopy boost
        for pi in cls_map.get(CLS_PUMP, []):
            for ci in cls_map.get(CLS_CANOPY, []):
                if near(pi, ci):
                    adjustments[pi] = max(adjustments[pi], COOCCUR_BOOST_PUMP_CANOPY)
                    adjustments[ci] = max(adjustments[ci], COOCCUR_BOOST_PUMP_CANOPY)

        # Pump + Hose boost (hose gets larger boost)
        for pi in cls_map.get(CLS_PUMP, []):
            for hi in cls_map.get(CLS_HOSE, []):
                if near(pi, hi):
                    adjustments[hi] = max(adjustments[hi], COOCCUR_BOOST_PUMP_HOSE)

        # Canopy + Hose boost
        for ci in cls_map.get(CLS_CANOPY, []):
            for hi in cls_map.get(CLS_HOSE, []):
                if near(ci, hi):
                    adjustments[hi] = max(adjustments[hi], COOCCUR_BOOST_CANOPY_HOSE)

        # Suppress isolated low-confidence detections (not price boards)
        for i, (x1, y1, x2, y2, conf, cls_id) in enumerate(dets):
            if cls_id == CLS_PRICEBOARD:
                continue  # price boards are often solo (91% in training)
            if conf > 0.60:
                continue  # high confidence — don't suppress

            has_neighbor = False
            for j in range(n):
                if i == j:
                    continue
                if near(i, j):
                    has_neighbor = True
                    break
            if not has_neighbor:
                adjustments[i] = min(adjustments[i],
                                     adjustments[i] - COOCCUR_SUPPRESS_ISOLATED)

        # Apply adjustments
        result = []
        for i, (x1, y1, x2, y2, conf, cls_id) in enumerate(dets):
            new_conf = min(1.0, max(0.0, conf + adjustments[i]))
            if new_conf >= self.conf_threshold:
                result.append((x1, y1, x2, y2, new_conf, cls_id))
        return result

    # ─── Main inference ──────────────────────────────────────────────

    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
        inp, (orig_h, orig_w) = self._preprocess(image_bgr)
        out = self.session.run(None, {self.input_name: inp})[0]

        # Decode based on detected output format
        if self._end2end:
            dets = self._decode_end2end(out, orig_h, orig_w)
        else:
            dets = self._decode_raw(out, orig_h, orig_w)

        if not dets:
            return []

        # Post-processing pipeline
        dets = self._geometric_validate(dets, orig_h, orig_w)
        dets = self._spatial_cooccurrence(dets, orig_h, orig_w)

        # Convert to BoundingBox
        out_boxes = []
        for x1, y1, x2, y2, conf, cls_id in 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 = []
        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