miner.py

from pathlib import Path
from typing import List, Tuple, Dict
import sys
import os

from numpy import ndarray
from pydantic import BaseModel
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from team_cluster import TeamClassifier
from utils import (
    BoundingBox, 
    Constants,
)
from inference import predict_batch, load_model


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:
    SMALL_CONTAINED_IOA = Constants.SMALL_CONTAINED_IOA
    SMALL_RATIO_MAX = Constants.SMALL_RATIO_MAX
    SINGLE_PLAYER_HUE_PIVOT = Constants.SINGLE_PLAYER_HUE_PIVOT
    CORNER_INDICES = Constants.CORNER_INDICES
    KEYPOINTS_CONFIDENCE = Constants.KEYPOINTS_CONFIDENCE
    CORNER_CONFIDENCE = Constants.CORNER_CONFIDENCE
    GOALKEEPER_POSITION_MARGIN = Constants.GOALKEEPER_POSITION_MARGIN
    MIN_SAMPLES_FOR_FIT = 16  # Minimum player crops needed before fitting TeamClassifier
    MAX_SAMPLES_FOR_FIT = 1000  # Maximum samples to avoid overfitting

    def __init__(self, path_hf_repo: Path) -> None:
        print("model laoding")
        self.health = load_model(path_hf_repo)

    def __repr__(self) -> str:
        return self.health

    def predict_batch(self, batch_images: List[ndarray], offset: int, n_keypoints: int) -> List[TVFrameResult]:    
        results = predict_batch(
            batch_images,
            offset,
            n_keypoints,
        )
        return results