# Auto-generated ONNX runner. This file is self-contained for a single model.
import json
import os
import sys
from typing import Any, Dict, List, Tuple

import cv2
import numpy as np
import onnxruntime as ort
from PIL import Image


def read_json(path: str) -> Dict[str, Any]:
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def read_text_lines(path: str) -> List[str]:
    with open(path, "r", encoding="utf-8") as f:
        return [line.strip() for line in f.readlines() if line.strip()]


def load_environment(data_dir: str) -> Dict[str, Any]:
    env_path = os.path.join(data_dir, "environment.json")
    if not os.path.exists(env_path):
        return {}
    env = read_json(env_path)
    preproc = env.get("PREPROCESSING")
    if isinstance(preproc, str):
        try:
            env["PREPROCESSING"] = json.loads(preproc)
        except json.JSONDecodeError:
            env["PREPROCESSING"] = {}
    return env


def load_class_names(data_dir: str, environment: Dict[str, Any]) -> List[str]:
    class_path = os.path.join(data_dir, "class_names.txt")
    if os.path.exists(class_path):
        return read_text_lines(class_path)
    class_map = environment.get("CLASS_MAP")
    if isinstance(class_map, dict):
        class_names = []
        for i in range(len(class_map.keys())):
            class_names.append(class_map[str(i)])
        return class_names
    return []


def load_keypoints_metadata(data_dir: str) -> List[Dict[str, Any]]:
    meta_path = os.path.join(data_dir, "keypoints_metadata.json")
    if not os.path.exists(meta_path):
        return []
    return read_json(meta_path)


def load_image(value: Any) -> Tuple[np.ndarray, bool]:
    if isinstance(value, np.ndarray):
        return value, True
    if isinstance(value, Image.Image):
        return np.asarray(value.convert("RGB")), False
    if isinstance(value, (bytes, bytearray)):
        image = cv2.imdecode(np.frombuffer(value, np.uint8), cv2.IMREAD_COLOR)
        return image, True
    if isinstance(value, str):
        image = cv2.imread(value, cv2.IMREAD_COLOR)
        if image is None:
            raise ValueError(f"Could not read image: {value}")
        return image, True
    raise ValueError(f"Unsupported image input type: {type(value)}")


def static_crop_should_be_applied(preprocessing_config: dict) -> bool:
    cfg = preprocessing_config.get("static-crop")
    return bool(cfg and cfg.get("enabled"))


def take_static_crop(image: np.ndarray, crop_parameters: Dict[str, int]) -> np.ndarray:
    height, width = image.shape[:2]
    x_min = int(crop_parameters["x_min"] / 100 * width)
    y_min = int(crop_parameters["y_min"] / 100 * height)
    x_max = int(crop_parameters["x_max"] / 100 * width)
    y_max = int(crop_parameters["y_max"] / 100 * height)
    return image[y_min:y_max, x_min:x_max, :]


def apply_grayscale_conversion(image: np.ndarray) -> np.ndarray:
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)


def apply_contrast_stretching(image: np.ndarray) -> np.ndarray:
    p2, p98 = np.percentile(image, (2, 98))
    image = np.clip(image, p2, p98)
    if p98 - p2 > 0:
        image = (image - p2) * (255.0 / (p98 - p2))
    return image.astype(np.uint8)


def apply_histogram_equalisation(image: np.ndarray) -> np.ndarray:
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    image = cv2.equalizeHist(image)
    return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)


def apply_adaptive_equalisation(image: np.ndarray) -> np.ndarray:
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    clahe = cv2.createCLAHE(clipLimit=0.03, tileGridSize=(8, 8))
    image = clahe.apply(image)
    return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)


def apply_preproc(image: np.ndarray, preproc: Dict[str, Any]) -> Tuple[np.ndarray, Tuple[int, int]]:
    h, w = image.shape[:2]
    img_dims = (h, w)
    if static_crop_should_be_applied(preproc):
        image = take_static_crop(image, preproc["static-crop"])
    if preproc.get("contrast", {}).get("enabled"):
        ctype = preproc.get("contrast", {}).get("type")
        if ctype == "Contrast Stretching":
            image = apply_contrast_stretching(image)
        elif ctype == "Histogram Equalization":
            image = apply_histogram_equalisation(image)
        elif ctype == "Adaptive Equalization":
            image = apply_adaptive_equalisation(image)
    if preproc.get("grayscale", {}).get("enabled"):
        image = apply_grayscale_conversion(image)
    return image, img_dims


def resize_image_keeping_aspect_ratio(image: np.ndarray, desired_size: Tuple[int, int]) -> np.ndarray:
    height, width = image.shape[:2]
    ratio = min(desired_size[1] / height, desired_size[0] / width)
    new_width = int(width * ratio)
    new_height = int(height * ratio)
    return cv2.resize(image, (new_width, new_height))


def letterbox_image(image: np.ndarray, desired_size: Tuple[int, int], color: Tuple[int, int, int]) -> np.ndarray:
    resized = resize_image_keeping_aspect_ratio(image, desired_size)
    new_height, new_width = resized.shape[:2]
    top = (desired_size[1] - new_height) // 2
    bottom = desired_size[1] - new_height - top
    left = (desired_size[0] - new_width) // 2
    right = desired_size[0] - new_width - left
    return cv2.copyMakeBorder(resized, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)


def get_resize_method(preproc: Dict[str, Any]) -> str:
    resize = preproc.get("resize")
    if not resize:
        return "Stretch to"
    method = resize.get("format", "Stretch to")
    if method in {"Fit (reflect edges) in", "Fit within", "Fill (with center crop) in"}:
        return "Fit (black edges) in"
    if method not in {"Stretch to", "Fit (black edges) in", "Fit (white edges) in", "Fit (grey edges) in"}:
        return "Stretch to"
    return method


def preprocess_image(image: Any, preproc: Dict[str, Any], input_hw: Tuple[int, int]) -> Tuple[np.ndarray, Tuple[int, int]]:
    np_image, is_bgr = load_image(image)
    processed, img_dims = apply_preproc(np_image, preproc)
    resize_method = get_resize_method(preproc)
    h, w = input_hw
    if resize_method == "Stretch to":
        resized = cv2.resize(processed, (w, h))
    elif resize_method == "Fit (white edges) in":
        resized = letterbox_image(processed, (w, h), (255, 255, 255))
    elif resize_method == "Fit (grey edges) in":
        resized = letterbox_image(processed, (w, h), (114, 114, 114))
    else:
        resized = letterbox_image(processed, (w, h), (0, 0, 0))
    if is_bgr:
        resized = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
    img_in = resized.astype(np.float32)
    img_in = np.transpose(img_in, (2, 0, 1))
    img_in = np.expand_dims(img_in, axis=0)
    return img_in, img_dims


def sigmoid(x: np.ndarray) -> np.ndarray:
    return 1.0 / (1.0 + np.exp(-x))


def non_max_suppression_fast(boxes: np.ndarray, overlap_thresh: float) -> List[np.ndarray]:
    if len(boxes) == 0:
        return []
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")
    pick = []
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    conf = boxes[:, 4]
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = np.argsort(conf)
    while len(idxs) > 0:
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)
        overlap = (w * h) / area[idxs[:last]]
        idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlap_thresh)[0])))
    return boxes[pick].astype("float")


def w_np_non_max_suppression(
    prediction: np.ndarray,
    conf_thresh: float = 0.25,
    iou_thresh: float = 0.45,
    class_agnostic: bool = False,
    max_detections: int = 300,
    max_candidate_detections: int = 3000,
    num_masks: int = 0,
    box_format: str = "xywh",
):
    num_classes = prediction.shape[2] - 5 - num_masks
    if box_format == "xywh":
        pred_view = prediction[:, :, :4]
        x1 = pred_view[:, :, 0] - pred_view[:, :, 2] / 2
        y1 = pred_view[:, :, 1] - pred_view[:, :, 3] / 2
        x2 = pred_view[:, :, 0] + pred_view[:, :, 2] / 2
        y2 = pred_view[:, :, 1] + pred_view[:, :, 3] / 2
        pred_view[:, :, 0] = x1
        pred_view[:, :, 1] = y1
        pred_view[:, :, 2] = x2
        pred_view[:, :, 3] = y2
    elif box_format != "xyxy":
        raise ValueError(f"box_format must be 'xywh' or 'xyxy', got {box_format}")

    batch_predictions = []
    for np_image_pred in prediction:
        np_conf_mask = np_image_pred[:, 4] >= conf_thresh
        if not np.any(np_conf_mask):
            batch_predictions.append([])
            continue
        np_image_pred = np_image_pred[np_conf_mask]
        if np_image_pred.shape[0] == 0:
            batch_predictions.append([])
            continue
        cls_confs = np_image_pred[:, 5 : num_classes + 5]
        if cls_confs.shape[1] == 0:
            batch_predictions.append([])
            continue
        np_class_conf = np.max(cls_confs, axis=1, keepdims=True)
        np_class_pred = np.argmax(cls_confs, axis=1, keepdims=True)
        if num_masks > 0:
            np_mask_pred = np_image_pred[:, 5 + num_classes :]
            np_detections = np.concatenate(
                [
                    np_image_pred[:, :5],
                    np_class_conf,
                    np_class_pred.astype(np.float32),
                    np_mask_pred,
                ],
                axis=1,
            )
        else:
            np_detections = np.concatenate(
                [np_image_pred[:, :5], np_class_conf, np_class_pred.astype(np.float32)],
                axis=1,
            )
        filtered_predictions = []
        if class_agnostic:
            sorted_indices = np.argsort(-np_detections[:, 4])
            np_detections_sorted = np_detections[sorted_indices]
            filtered_predictions.extend(non_max_suppression_fast(np_detections_sorted, iou_thresh))
        else:
            np_unique_labels = np.unique(np_class_pred)
            for c in np_unique_labels:
                class_mask = np.atleast_1d(np_class_pred.squeeze() == c)
                np_detections_class = np_detections[class_mask]
                if np_detections_class.shape[0] == 0:
                    continue
                sorted_indices = np.argsort(-np_detections_class[:, 4])
                np_detections_sorted = np_detections_class[sorted_indices]
                filtered_predictions.extend(non_max_suppression_fast(np_detections_sorted, iou_thresh))

        if filtered_predictions:
            filtered_np = np.array(filtered_predictions)
            idx = np.argsort(-filtered_np[:, 4])
            filtered_np = filtered_np[idx]
            if len(filtered_np) > max_detections:
                filtered_np = filtered_np[:max_detections]
            batch_predictions.append(list(filtered_np))
        else:
            batch_predictions.append([])
    return batch_predictions


def get_static_crop_dimensions(orig_shape: Tuple[int, int], preproc: dict) -> Tuple[Tuple[int, int], Tuple[int, int]]:
    if not static_crop_should_be_applied(preproc):
        return (0, 0), orig_shape
    crop = preproc["static-crop"]
    x_min, y_min, x_max, y_max = (crop[k] / 100.0 for k in ["x_min", "y_min", "x_max", "y_max"])
    crop_shift_x, crop_shift_y = (round(x_min * orig_shape[1]), round(y_min * orig_shape[0]))
    cropped_percent_x = x_max - x_min
    cropped_percent_y = y_max - y_min
    new_shape = (round(orig_shape[0] * cropped_percent_y), round(orig_shape[1] * cropped_percent_x))
    return (crop_shift_x, crop_shift_y), new_shape


def post_process_bboxes(
    predictions: List[List[List[float]]],
    infer_shape: Tuple[int, int],
    img_dims: List[Tuple[int, int]],
    preproc: dict,
    resize_method: str,
) -> List[List[List[float]]]:
    scaled_predictions = []
    for i, batch_predictions in enumerate(predictions):
        if len(batch_predictions) == 0:
            scaled_predictions.append([])
            continue
        np_batch_predictions = np.array(batch_predictions)
        predicted_bboxes = np_batch_predictions[:, :4]
        (crop_shift_x, crop_shift_y), origin_shape = get_static_crop_dimensions(img_dims[i], preproc)
        if resize_method == "Stretch to":
            scale_height = origin_shape[0] / infer_shape[0]
            scale_width = origin_shape[1] / infer_shape[1]
            predicted_bboxes[:, 0] *= scale_width
            predicted_bboxes[:, 2] *= scale_width
            predicted_bboxes[:, 1] *= scale_height
            predicted_bboxes[:, 3] *= scale_height
        else:
            scale = min(infer_shape[0] / origin_shape[0], infer_shape[1] / origin_shape[1])
            inter_h = round(origin_shape[0] * scale)
            inter_w = round(origin_shape[1] * scale)
            pad_x = (infer_shape[1] - inter_w) / 2
            pad_y = (infer_shape[0] - inter_h) / 2
            predicted_bboxes[:, 0] -= pad_x
            predicted_bboxes[:, 2] -= pad_x
            predicted_bboxes[:, 1] -= pad_y
            predicted_bboxes[:, 3] -= pad_y
            predicted_bboxes /= scale
        predicted_bboxes[:, 0] = np.round(np.clip(predicted_bboxes[:, 0], 0, origin_shape[1]))
        predicted_bboxes[:, 2] = np.round(np.clip(predicted_bboxes[:, 2], 0, origin_shape[1]))
        predicted_bboxes[:, 1] = np.round(np.clip(predicted_bboxes[:, 1], 0, origin_shape[0]))
        predicted_bboxes[:, 3] = np.round(np.clip(predicted_bboxes[:, 3], 0, origin_shape[0]))
        predicted_bboxes[:, 0] += crop_shift_x
        predicted_bboxes[:, 2] += crop_shift_x
        predicted_bboxes[:, 1] += crop_shift_y
        predicted_bboxes[:, 3] += crop_shift_y
        np_batch_predictions[:, :4] = predicted_bboxes
        scaled_predictions.append(np_batch_predictions.tolist())
    return scaled_predictions


def post_process_keypoints(
    predictions: List[List[List[float]]],
    keypoints_start_index: int,
    infer_shape: Tuple[int, int],
    img_dims: List[Tuple[int, int]],
    preproc: dict,
    resize_method: str,
) -> List[List[List[float]]]:
    scaled_predictions = []
    for i, batch_predictions in enumerate(predictions):
        if len(batch_predictions) == 0:
            scaled_predictions.append([])
            continue
        np_batch_predictions = np.array(batch_predictions)
        keypoints = np_batch_predictions[:, keypoints_start_index:]
        (crop_shift_x, crop_shift_y), origin_shape = get_static_crop_dimensions(img_dims[i], preproc)
        if resize_method == "Stretch to":
            scale_width = origin_shape[1] / infer_shape[1]
            scale_height = origin_shape[0] / infer_shape[0]
            for k in range(keypoints.shape[1] // 3):
                keypoints[:, k * 3] *= scale_width
                keypoints[:, k * 3 + 1] *= scale_height
        else:
            scale = min(infer_shape[0] / origin_shape[0], infer_shape[1] / origin_shape[1])
            inter_w = int(origin_shape[1] * scale)
            inter_h = int(origin_shape[0] * scale)
            pad_x = (infer_shape[1] - inter_w) / 2
            pad_y = (infer_shape[0] - inter_h) / 2
            for k in range(keypoints.shape[1] // 3):
                keypoints[:, k * 3] -= pad_x
                keypoints[:, k * 3] /= scale
                keypoints[:, k * 3 + 1] -= pad_y
                keypoints[:, k * 3 + 1] /= scale
        for k in range(keypoints.shape[1] // 3):
            keypoints[:, k * 3] = np.round(np.clip(keypoints[:, k * 3], 0, origin_shape[1]))
            keypoints[:, k * 3 + 1] = np.round(np.clip(keypoints[:, k * 3 + 1], 0, origin_shape[0]))
            keypoints[:, k * 3] += crop_shift_x
            keypoints[:, k * 3 + 1] += crop_shift_y
        np_batch_predictions[:, keypoints_start_index:] = keypoints
        scaled_predictions.append(np_batch_predictions.tolist())
    return scaled_predictions


def masks2poly(masks: np.ndarray) -> List[np.ndarray]:
    segments = []
    for mask in masks:
        if mask.dtype == np.bool_:
            m_uint8 = mask
            if not m_uint8.flags.c_contiguous:
                m_uint8 = np.ascontiguousarray(m_uint8)
            m_uint8 = m_uint8.view(np.uint8)
        elif mask.dtype == np.uint8:
            m_uint8 = mask if mask.flags.c_contiguous else np.ascontiguousarray(mask)
        else:
            m_bool = mask > 0
            if not m_bool.flags.c_contiguous:
                m_bool = np.ascontiguousarray(m_bool)
            m_uint8 = m_bool.view(np.uint8)
        if not np.any(m_uint8):
            segments.append(np.zeros((0, 2), dtype=np.float32))
            continue
        contours = cv2.findContours(m_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
        if contours:
            contours = np.array(contours[np.array([len(x) for x in contours]).argmax()]).reshape(-1, 2)
        else:
            contours = np.zeros((0, 2))
        segments.append(contours.astype("float32"))
    return segments


def post_process_polygons(
    origin_shape: Tuple[int, int],
    polys: List[List[Tuple[float, float]]],
    infer_shape: Tuple[int, int],
    preproc: dict,
    resize_method: str,
) -> List[List[Tuple[float, float]]]:
    (crop_shift_x, crop_shift_y), origin_shape = get_static_crop_dimensions(origin_shape, preproc)
    new_polys = []
    if resize_method == "Stretch to":
        width_ratio = origin_shape[1] / infer_shape[1]
        height_ratio = origin_shape[0] / infer_shape[0]
        for poly in polys:
            new_polys.append([(p[0] * width_ratio, p[1] * height_ratio) for p in poly])
    else:
        scale = min(infer_shape[0] / origin_shape[0], infer_shape[1] / origin_shape[1])
        inter_w = int(origin_shape[1] * scale)
        inter_h = int(origin_shape[0] * scale)
        pad_x = (infer_shape[1] - inter_w) / 2
        pad_y = (infer_shape[0] - inter_h) / 2
        for poly in polys:
            new_polys.append([((p[0] - pad_x) / scale, (p[1] - pad_y) / scale) for p in poly])
    shifted_polys = []
    for poly in new_polys:
        shifted_polys.append([(p[0] + crop_shift_x, p[1] + crop_shift_y) for p in poly])
    return shifted_polys


def preprocess_segmentation_masks(protos: np.ndarray, masks_in: np.ndarray, shape: Tuple[int, int]) -> np.ndarray:
    c, mh, mw = protos.shape
    masks = protos.astype(np.float32)
    masks = masks.reshape((c, -1))
    masks = masks_in @ masks
    masks = sigmoid(masks)
    masks = masks.reshape((-1, mh, mw))
    gain = min(mh / shape[0], mw / shape[1])
    pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2
    top, left = int(pad[1]), int(pad[0])
    bottom, right = int(mh - pad[1]), int(mw - pad[0])
    return masks[:, top:bottom, left:right]


def crop_mask(masks: np.ndarray, boxes: np.ndarray) -> np.ndarray:
    n, h, w = masks.shape
    x1, y1, x2, y2 = np.split(boxes[:, :, None], 4, 1)
    r = np.arange(w, dtype=x1.dtype)[None, None, :]
    c = np.arange(h, dtype=x1.dtype)[None, :, None]
    masks = masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
    return masks


def process_mask_accurate(protos: np.ndarray, masks_in: np.ndarray, bboxes: np.ndarray, shape: Tuple[int, int]) -> np.ndarray:
    masks = preprocess_segmentation_masks(protos, masks_in, shape)
    if len(masks.shape) == 2:
        masks = np.expand_dims(masks, axis=0)
    masks = masks.transpose((1, 2, 0))
    masks = cv2.resize(masks, (shape[1], shape[0]), cv2.INTER_LINEAR)
    if len(masks.shape) == 2:
        masks = np.expand_dims(masks, axis=2)
    masks = masks.transpose((2, 0, 1))
    masks = crop_mask(masks, bboxes)
    masks[masks < 0.5] = 0
    return masks


def process_mask_tradeoff(protos: np.ndarray, masks_in: np.ndarray, bboxes: np.ndarray, shape: Tuple[int, int], tradeoff_factor: float) -> np.ndarray:
    c, mh, mw = protos.shape
    masks = preprocess_segmentation_masks(protos, masks_in, shape)
    if len(masks.shape) == 2:
        masks = np.expand_dims(masks, axis=0)
    masks = masks.transpose((1, 2, 0))
    ih, iw = shape
    h = int(mh * (1 - tradeoff_factor) + ih * tradeoff_factor)
    w = int(mw * (1 - tradeoff_factor) + iw * tradeoff_factor)
    if tradeoff_factor != 0:
        masks = cv2.resize(masks, (w, h), cv2.INTER_LINEAR)
    if len(masks.shape) == 2:
        masks = np.expand_dims(masks, axis=2)
    masks = masks.transpose((2, 0, 1))
    c, mh, mw = masks.shape
    scale_x = mw / iw
    scale_y = mh / ih
    bboxes = bboxes.copy()
    bboxes[:, 0] *= scale_x
    bboxes[:, 2] *= scale_x
    bboxes[:, 1] *= scale_y
    bboxes[:, 3] *= scale_y
    masks = crop_mask(masks, bboxes)
    masks[masks < 0.5] = 0
    return masks


def process_mask_fast(protos: np.ndarray, masks_in: np.ndarray, bboxes: np.ndarray, shape: Tuple[int, int]) -> np.ndarray:
    ih, iw = shape
    c, mh, mw = protos.shape
    masks = preprocess_segmentation_masks(protos, masks_in, shape)
    scale_x = mw / iw
    scale_y = mh / ih
    bboxes = bboxes.copy()
    bboxes[:, 0] *= scale_x
    bboxes[:, 2] *= scale_x
    bboxes[:, 1] *= scale_y
    bboxes[:, 3] *= scale_y
    masks = crop_mask(masks, bboxes)
    masks[masks < 0.5] = 0
    return masks


def load_onnx_session(onnx_path: str, providers: List[str] = None) -> ort.InferenceSession:
    if providers is None:
        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
    return ort.InferenceSession(onnx_path, providers=providers)


def find_default_onnx(data_dir: str) -> str:
    candidates = [f for f in os.listdir(data_dir) if f.lower().endswith(".onnx")]
    candidates.sort()
    if not candidates:
        raise FileNotFoundError(f"No .onnx file found in {data_dir}")
    if len(candidates) > 1:
        # Prefer weights.onnx if present.
        for name in candidates:
            if name.lower() == "weights.onnx":
                return os.path.join(data_dir, name)
    return os.path.join(data_dir, candidates[0])


def get_input_hw(session: ort.InferenceSession, preproc: Dict[str, Any]) -> Tuple[int, int]:
    inputs = session.get_inputs()[0]
    shape = inputs.shape
    h, w = shape[2], shape[3]
    if isinstance(h, str) or isinstance(w, str) or h is None or w is None:
        resize = preproc.get("resize") if preproc else None
        if resize:
            h = int(resize.get("height", 640))
            w = int(resize.get("width", 640))
        else:
            h, w = 640, 640
    return int(h), int(w)


def build_meta(data_dir: str, session: ort.InferenceSession) -> Dict[str, Any]:
    environment = load_environment(data_dir)
    preproc = environment.get("PREPROCESSING") or {}
    class_names = load_class_names(data_dir, environment)
    resize_method = get_resize_method(preproc)
    input_hw = get_input_hw(session, preproc)
    keypoints_metadata = load_keypoints_metadata(data_dir)
    return {
        "environment": environment,
        "preproc": preproc,
        "class_names": class_names,
        "resize_method": resize_method,
        "input_hw": input_hw,
        "keypoints_metadata": keypoints_metadata,
    }


def normalize_rgb(img_in: np.ndarray, means: List[float], stds: List[float]) -> np.ndarray:
    img_in = img_in.astype(np.float32)
    img_in /= 255.0
    img_in[:, 0, :, :] = (img_in[:, 0, :, :] - means[0]) / stds[0]
    img_in[:, 1, :, :] = (img_in[:, 1, :, :] - means[1]) / stds[1]
    img_in[:, 2, :, :] = (img_in[:, 2, :, :] - means[2]) / stds[2]
    return img_in


MODEL_TASK_TYPE = "object-detection"


def preprocess_for_model(image: Any, meta: Dict[str, Any]) -> Tuple[np.ndarray, Tuple[int, int]]:
    img_in, img_dims = preprocess_image(image, meta["preproc"], meta["input_hw"])
    img_in = img_in.astype(np.float32)
    img_in /= 255.0
    return img_in, img_dims


def pack_predictions(predictions: np.ndarray) -> np.ndarray:
    predictions = predictions.transpose(0, 2, 1)
    boxes = predictions[:, :, :4]
    class_confs = predictions[:, :, 4:]
    confs = np.expand_dims(np.max(class_confs, axis=2), axis=2)
    return np.concatenate([boxes, confs, class_confs], axis=2)


def postprocess_predictions(predictions: np.ndarray, meta: Dict[str, Any], img_dims: List[Tuple[int, int]],
                            confidence: float = 0.4, iou_threshold: float = 0.3, max_detections: int = 300):
    preds = w_np_non_max_suppression(
        predictions,
        conf_thresh=confidence,
        iou_thresh=iou_threshold,
        class_agnostic=False,
        max_detections=max_detections,
        box_format="xywh",
    )
    infer_shape = meta["input_hw"]
    preds = post_process_bboxes(preds, infer_shape, img_dims, meta["preproc"], meta["resize_method"])
    class_names = meta["class_names"]
    results = []
    for batch_preds in preds:
        batch_out = []
        for pred in batch_preds:
            cls_id = int(pred[6])
            batch_out.append({
                "x": (pred[0] + pred[2]) / 2,
                "y": (pred[1] + pred[3]) / 2,
                "width": pred[2] - pred[0],
                "height": pred[3] - pred[1],
                "confidence": float(pred[4]),
                "class_id": cls_id,
                "class": class_names[cls_id] if cls_id < len(class_names) else str(cls_id),
            })
        results.append(batch_out)
    return results


def load_model(onnx_path: str | None = None, data_dir: str | None = None):
    data_dir = data_dir or os.path.dirname(os.path.abspath(__file__))
    onnx_path = onnx_path or find_default_onnx(data_dir)
    session = load_onnx_session(onnx_path)
    meta = build_meta(data_dir, session)
    model_type_fn = globals().get("load_model_type")
    model_type = model_type_fn(data_dir) if callable(model_type_fn) else "unknown"
    return {"session": session, "meta": meta, "model_type": model_type}


def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_dir: str | None = None):
    if image is None:
        image = model
        model = load_model(onnx_path=onnx_path, data_dir=data_dir)
    session = model["session"]
    meta = model["meta"]
    model_type = model["model_type"]

    img_in, img_dims = preprocess_for_model(image, meta)
    input_name = session.get_inputs()[0].name
    outputs = session.run(None, {input_name: img_in})
    predictions = pack_predictions(outputs[0])
    return postprocess_predictions(predictions, meta, [img_dims])


def main():
    if len(sys.argv) < 2:
        print("Usage: main.py <image_path> [onnx_path]", file=sys.stderr)
        sys.exit(1)
    image_path = sys.argv[1]
    data_dir = os.path.dirname(os.path.abspath(__file__))
    onnx_path = sys.argv[2] if len(sys.argv) > 2 else find_default_onnx(data_dir)
    results = run_model(image_path, onnx_path=onnx_path, data_dir=data_dir)
    print(json.dumps(results, indent=2))


if __name__ == "__main__":
    main()