dodgeball-tracker/dodgeballtracker/logic.py

import json
from pathlib import Path
import logging
import torch
from ultralytics import YOLO
import cv2
from dodgeballtracker.helper import iter_frames
from dodgeballtracker.video_writer import VideoWriter, DetectedObjectBox
import numpy as np
import pandas as pd
import pandera as pa
from boxmot import DeepOcSort as DeepOCSORT
from .typing import VideoFrame, DetectedObject_

logger = logging.getLogger(__name__)

Color = tuple[int, int, int]


def get_best_available_torch_device() -> torch.device:
    if torch.cuda.is_available():
        logger.debug("Using the cuda (gpu) torch device.")
        return torch.device('cuda:0')
    if torch.backends.mps.is_available():
        logger.debug("Using the mps (apple silicon) torch device.")
        return torch.device('mps')
    logger.debug("Default to cpu (no apple silicon or cuda) torch device.")
    return torch.device('cpu')


def get_court_perspective_transform():
    """
    """
    empirical_top_left = np.float32([416, 175])
    empirical_top_right = np.float32([905, 173])
    empirical_bottom_left = np.float32([51, 702])
    empirical_bottom_right = np.float32([1188, 695])

    court_top_left = np.float32([0, 0])
    court_top_right = np.float32([9, 0])
    court_bottom_left = np.float32([0, 18])
    court_bottom_right = np.float32([9, 18])

    empirical_source = np.float32([empirical_top_left,
                                   empirical_top_right,
                                   empirical_bottom_left,
                                   empirical_bottom_right])
    pixel_per_meter = 50
    empirical_target = np.float32([court_top_left,
                                   court_top_right,
                                   court_bottom_left,
                                   court_bottom_right]) * pixel_per_meter
    return cv2.getPerspectiveTransform(empirical_source, empirical_target)





class DodgeballTracker:

    def __init__(self,
                 input_file: Path,
                 output_file: Path,
                 video_writer: VideoWriter,
                 detected_box_objects: list[DetectedObjectBox],
                 transformation_matrix):
        self.input_path = input_file
        self.output_path = output_file
        self.output_json_path = output_file.with_suffix(".json")
        self.video_writer = video_writer
        self.detected_box_objects = sorted(detected_box_objects, reverse=True)
        self.transformation_matrix = transformation_matrix
        self.points = []

    def write_frame(self, writer, frame) -> None:
        cv2.imshow("Frame", frame)
        writer.write(frame)

    def extract_box(self, data) -> tuple[int, int, int, int]:
        return int(data[0]), int(data[1]), int(data[2]), int(data[3])

    def print_box(self, data, frame_idx, frame_ts):
        xmin, ymin, xmax, ymax = self.extract_box(data)
        xavg = (xmax + xmin) / 2
        v = np.array([xavg, ymax, 1])
        ret = self.transformation_matrix @ v
        x, y = ret[0] / ret[2], ret[1] / ret[2]
        object_id = data[-1]
        self.points.append({"x": x,
                            "y": y,
                            "frameIndex": frame_idx,
                            "frameTimestamp": frame_ts,
                            "objectId": int(object_id)
                            })

    def print_tracker_data(self, tracker_data, frame_idx, frame_ts):
        for d in tracker_data:
            self.print_box(d, frame_idx, frame_ts)

    def plot_box_to_frame(self, data, frame: VideoFrame):
        confidence = float(data[4])
        for detected_box_object in self.detected_box_objects:

            if detected_box_object.min_threshold < confidence:
                # xmin, ymin, xmax, ymax = self.extract_box(data)
                # cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), detected_box_object.color, 2)
                return True
        return False

    @pa.check_output(DetectedObject_)
    def run_model_on_frame(self, model, frame: VideoFrame):
        model_outputs = model(frame)[0]
        boxes = model_outputs.boxes.data.cpu().numpy()
        columns=['xmin', 'ymin', 'xmax', 'ymin', 'score', 'category']
        return pd.DataFrame(boxes, columns=columns)

    def persist_points(self):
        with open(self.output_json_path, "wt") as file_:
            json.dump({"points": self.points}, file_, indent=4)

    def _get_tracker(self) -> DeepOCSORT:
        return DeepOCSORT(
            Path('lmbn_n_cuhk03_d.pt'),  # which ReID model to use
            get_best_available_torch_device(),
            True,
        )

    def _get_detection_model(self):
        """
        load the pre-trained YOLOv8n model
        """
        return YOLO("yolo11n.pt", verbose=True)

    def update_tracker(self, tracker: DeepOCSORT, detected_objs, frame: VideoFrame):
        data_ = tracker.update(detected_objs.to_numpy(), frame)
        columns = ['xmin', 'ymin', 'xmax', 'ymin', 'object_id', 'score', 'category', 'index']
        return pd.DataFrame(data_, columns=columns)


    def main(self):
        # define some constants
        video_cap = cv2.VideoCapture(str(self.input_path))
        tracker = self._get_tracker()
        model = self._get_detection_model()

        for i, (frame, ts) in enumerate(iter_frames(video_cap)):
            # run the YOLO model on the frame
            detected_objs = self.run_model_on_frame(model, frame)
            tracked_obj = self.update_tracker(tracker, detected_objs, frame)
            # self.video_writer.plot_boxes_to_frame(detected_objs, frame)
            #
            # self.video_writer.write_frame(frame)

            if self.video_writer.wait():
                break

        video_cap.release()
        self.persist_points()