Please use the following script and update my script so that the individually calculated timestamp_offset is used throughout all calculations (e.g., in filtering_data), we need to modify the way timestamps are handled in the data matching process. The main challenge here is that timestamp_offset depends on the line_number (which we obtain from tip_2d_y), but tip_2d_y comes from the coordinate data that hasn't been matched yet. This creates a circular dependency. Here is the script: import os
import re
import imageio
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
import argparse
import glob

from src.camera_param import CameraParameters
from src.transformation.projective_geometry import project_points

from src.transformation.coordinates_loader import (
    add_fiducial_coordinates,
    filter_blurry,
    filter_quality_recorded_trackers,
    match_images_dataframes,
    pre_filter_unrecorded_nir,
)

from src.selector_configs import SelectedData, SelectorParameters
from src.raw_dataset_configs import MetadataFile, RawDataFolder
from src.transformation.camera_loader import load_camera_parameters
from src.transformation.projective_geometry import add_tip_coordinates
from src.data_loader  import _join_2d_positions
from src.raw_dataset_configs import CoordinateDataKeys

TOOL_TIP_ESTIMATION = np.array([300, 110, 0])

def create_csv(filename, image_id, x1, y1, x2, y2):
    columns = ['image_id', 'tip_x', 'tip_y',  'center_x', 'center_y']
    df = pd.DataFrame(columns=columns)
    df.loc[0] = [image_id, x1, y1, x2, y2]

    csvFilePath = filename
    if not os.path.isfile(csvFilePath):
        df.to_csv(csvFilePath, sep=',', index=False)
    else:
        df.to_csv(csvFilePath, mode='a', sep=',', header=False, index=False)

import glob

def load_raw_data(
    calibration_data,
    data_folder: str,
    offset: float = 0.0,
    verbose: bool = True,
    image_list: list[str] | None = None,
) -> SelectedData:
    image_folder = os.path.join(data_folder, RawDataFolder.IMAGE_FOLDER)
    camera_configuration_file = os.path.join(
        data_folder, RawDataFolder.CAMERA_CONFIGURATION_FILE
    )

    # Define the path to the logs folder where the coordinate files are located
    logs_folder = os.path.join(data_folder, "logs")

    # Find all coordinate files within the 'logs' subfolder
    coordinate_files = glob.glob(os.path.join(logs_folder, "coord_log_led_based_*.txt"))

    # Check if any files were found
    if not coordinate_files:
        raise FileNotFoundError(f"No coordinate files found in {logs_folder} with pattern 'coord_log_led_based_*.txt'.")

    # Load each file, skip if empty
    raw_coordinates_dataframe_list = []
    for coord_file in coordinate_files:
        df = pd.read_csv(coord_file, delimiter=r"\s+")
        if not df.empty:
            raw_coordinates_dataframe_list.append(df)
        else:
            print(f"Warning: {coord_file} is empty and was skipped.")

    # Check if any data was loaded
    if not raw_coordinates_dataframe_list:
        raise ValueError("No data loaded from any coordinate files; all files are empty.")

    # Concatenate all dataframes into one
    raw_coordinates_dataframe = pd.concat(raw_coordinates_dataframe_list, ignore_index=True)

    if image_list is None:
        image_list = os.listdir(image_folder)
        image_list = pre_filter_unrecorded_nir(image_list, raw_coordinates_dataframe)

    metadata = MetadataFile.parse_obj({
        'date': '2023-10-19:14-45-32', 
        'bur_id': '107-030', 
        'bur_name': 'match head 3.0', 
        'bur_level': '1', 
        'attachment_id': '284', 
        'tracker_id': '0', 
        'bur_offset_mm': [0, 0, -1.5]
    })

    tip_coordinates = np.array([
        [1, 0, 0, 0],
        [0, 1, 0, 0],
        [0, 0, 1, 0],
        [calibration_data[2], calibration_data[0], calibration_data[1], 1]
    ])
    camera_parameters = load_camera_parameters(camera_configuration_file)

    selected_data = SelectedData(
        data_folder=data_folder,
        image_folder=image_folder,
        coordinate_dataframe=match_images_dataframes(
            image_list, raw_coordinates_dataframe, offset=offset
        ),
        camera_parameters=camera_parameters,
        metadata=metadata,
        tip_coordinates=tip_coordinates,
    )

    if verbose:
        print(
            f"Found {selected_data.coordinate_dataframe.shape[0]} matches in {data_folder} across {len(coordinate_files)} files."
        )

    return selected_data


def filtering_data(
    calibration_data,
    data_folder: str,
    selector_parameters: SelectorParameters,
    verbose: bool = True,
    image_list: list[str] | None = None,
) -> SelectedData:
    selected_data = load_raw_data(
        calibration_data,
        data_folder,
        offset=selector_parameters.timestamp_offset,
        verbose=verbose,
        image_list=image_list,
    )

    if selector_parameters.quality_threshold is not None:
        selected_data.coordinate_dataframe = filter_quality_recorded_trackers(
            selected_data.coordinate_dataframe,
            quality_threshold=selector_parameters.quality_threshold,
            verbose=verbose,
        )

    if selector_parameters.movement_threshold_mm is not None:
        selected_data.coordinate_dataframe = filter_blurry(
            selected_data.coordinate_dataframe,
            movement_threshold_mm=selector_parameters.movement_threshold_mm,
            verbose=verbose,
        )

    if selector_parameters.join_fiducials:
        add_fiducial_coordinates(selected_data, verbose=verbose)

    if selector_parameters.join_tip_coordinates:
        if verbose:
            print("Joining tip coordinates...")

        add_tip_coordinates(selected_data, corrected=False)

        if selector_parameters.join_tip_correction:
            add_tip_coordinates(selected_data, corrected=True)

    if selector_parameters.join_2d_positions:
        _join_2d_positions(
            selected_data,
            selector_parameters,
            verbose=verbose,
        )

    return selected_data
    
def plot_metrics(timestamps, metrics, metric_name, output_filename):
    # Convert timestamps to seconds from the first timestamp
    reference_time = min(timestamps)
    x_values = [(t - reference_time) / 1000.0 for t in timestamps]  # Convert ms to seconds

    plt.figure(figsize=(10, 6))
    plt.plot(x_values, metrics, marker='o', linestyle='-', color='b')
    plt.title(f'{metric_name} over Time')
    plt.xlabel('Time (seconds from start)')
    plt.ylabel(metric_name)
    plt.grid(True)
    
    # Save the plot as a PNG image file
    plt.savefig(output_filename)
    plt.close()
    print(f"{metric_name} plot saved to {output_filename}")
    
def extract_timestamp(filename: str) -> int:
    """
    Extracts the timestamp from the filename. Assumes the filename contains a large numeric timestamp in milliseconds.
    Example: '106840846.png' -> 106840846 (milliseconds)
    """
    pattern = re.compile(r'(\d+)')  # Adjust this regex pattern to match the timestamp format
    match = pattern.search(filename)
    if match:
        return int(match.group(1))  # Return timestamp as integer
    return None
    
def get_blur_score(grey_image):

    return cv2.Laplacian(grey_image, cv2.CV_64F).var()

def detect_blur_fft(image,size: int = 60):
    height, width = image.shape
    center_x, center_y = int(width / 2.0), int(height / 2.0)
    fft = np.fft.fft2(image)
    fft_shift = np.fft.fftshift(fft) 
    fft_shift[center_y - size : center_y + size, center_x - size : center_x + size] = 0
    fft_shift = np.fft.ifftshift(fft_shift)
    recon = np.fft.ifft2(fft_shift)
    magnitude = 20 * np.log(np.abs(recon))

    return np.mean(magnitude)                                                


def get_2d_position_estimation(
    tool_center_3d: np.ndarray,
    tool_rotation_matrix: np.ndarray,
    translation_vector: np.ndarray,
    camera_parameters: CameraParameters,
) -> np.ndarray:
    translation_vector = translation_vector[[1, 2, 0]]

    tip_coordinates = tool_rotation_matrix @ translation_vector + tool_center_3d

    return project_points(tip_coordinates, camera_parameters)


# The 10 pixel margin for cropping the ROI. Should be adapted based on the resolution of images.
margin = 10

def get_bounding_box_estimation_center(
    tool_center_2d: np.ndarray,
    tool_center_3d: np.ndarray,
    tool_rotation_matrix: np.ndarray,
    width: int,
    height: int,
    camera_parameters: CameraParameters,
    TOOL_TIP_ESTIMATION,
) -> tuple[int, int, int, int]:
    tip_2d_position_estimation = get_2d_position_estimation(
        tool_center_3d, tool_rotation_matrix, TOOL_TIP_ESTIMATION, camera_parameters
    )


    points = np.stack(
        (
            np.array([tip_2d_position_estimation[0], tip_2d_position_estimation[1]]),
            np.array([tool_center_2d[0], tool_center_2d[1]]),
        )
    )

    x1 = int(np.min(points[:, 0]))
    y1 = int(np.min(points[:, 1]))
    x2 = int(np.max(points[:, 0]))
    y2 = int(np.max(points[:, 1]))

    h = y2 - y1
    w = x2 - x1

    if w > h:
        x1 = x1 - margin
        x2 = x2 + margin
        ym = int((y2 + y1)*0.5)
        y1 = ym - int(w/2) - margin
        y2 = ym + int(w / 2) + margin
    else:
        y1 = y1 - margin
        y2 = y2 + margin
        xm = int((x2 + x1) * 0.5)
        x1 = xm - int(h / 2) - margin
        x2 = xm + int(h / 2) + margin

    return (
             max(int(x1), 0),
             max(int(y1), 0),
             min(int(x2), width),
             min(int(y2), height)), (tip_2d_position_estimation[0], tip_2d_position_estimation[1])

def crop_frame(frame_array, bounding_box):
    return frame_array[
        bounding_box[1] : bounding_box[3], bounding_box[0] : bounding_box[2]
    ], (bounding_box[0], bounding_box[1])


def resize_frame(
    frame: np.ndarray, size: tuple[int, int]
) -> tuple[np.ndarray, tuple[float, float]]:
    return cv2.resize(frame, size), (size[1] / frame.shape[1], size[0] / frame.shape[0])

def _reduce_point_scale(
    point: np.ndarray | None,
    x_min: int,
    y_min: int,
    scale: tuple[float, float]
) -> np.ndarray | None:
    return (point - np.array([x_min, y_min])) * np.array(scale) if point is not None else None


def reduce_labels_scale(point: np.ndarray, bouding_box: tuple[int, int, int, int], scale: tuple[float, float]):
    x_min, y_min, _, _ = bouding_box
    return _reduce_point_scale(point, x_min, y_min, scale)


import cv2

def save_cropped_frame(size, width, height, tool_center_2d, tool_center_3d, tool_center_rotation, bur_tip_x, bur_tip_y, camera_parameters, frame_array, image_name, quality, movement, output_subfolder):
    bounding_box, tip_2d_position_estimation = get_bounding_box_estimation_center(
        tool_center_2d, tool_center_3d, tool_center_rotation, width, height, camera_parameters, TOOL_TIP_ESTIMATION
    )
    input_size = (size, size)
    cropped_frame, offset = crop_frame(frame_array, bounding_box)
    resized_frame, scale = resize_frame(cropped_frame, input_size)

    # Paint tip_2d_position_estimation and tool_center_2d on the resized frame
    tip_color = (0, 255, 0)  # Green for tip
    center_color = (255, 0, 0)  # Blue for center
    radius = 3
    thickness = 2

    # Adjust coordinates for cropping and resizing
    tip_point = reduce_labels_scale(np.array(tip_2d_position_estimation), bounding_box, scale)
    center_point = reduce_labels_scale(tool_center_2d, bounding_box, scale)

    # Draw circles for tip and center
    cv2.circle(resized_frame, tuple(map(int, tip_point)), radius, tip_color, thickness)
    cv2.circle(resized_frame, tuple(map(int, center_point)), radius, center_color, thickness)

    grey_image = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
    laplacian_blur = cv2.Laplacian(grey_image, cv2.CV_64F).var()
    fft_blur = detect_blur_fft(grey_image)
    print(f"Laplacian Blur: {laplacian_blur:.2f}, FFT Blur: {fft_blur:.2f}")

    # Prepare output path with subfolder
    os.makedirs(output_subfolder, exist_ok=True)
    new_image_name = f"{os.path.splitext(image_name)[0]}_q{quality:.2f}_m{movement:.2f}_lap{laplacian_blur:.2f}_fft{fft_blur:.2f}{os.path.splitext(image_name)[1]}"
    imageio.v2.imwrite(os.path.join(output_subfolder, new_image_name), resized_frame)

    return laplacian_blur, fft_blur


selector_parameters = SelectorParameters(
    timestamp_offset= 0.059,
    quality_threshold=0.5,
    movement_threshold_mm=10000,
    join_fiducials=False,
    join_tip_coordinates=True,
    join_tip_correction=True,
    join_2d_positions=True,
    interest_region_radius=400,
    tip_inclusion_radius=200,
    final_tool_radius=100,
    small_component_threshold=0,
    cropping_margin=5,
    pixel_margin=50,
)


def main(file_path, UHD, output_size, output_folder, framerate, exposure_time):
    file_list = os.listdir(file_path)
    file_list.sort(key=lambda f: extract_timestamp(f))
    timestamps = []
    laplacian_blurs = []
    fft_blurs = []
    movements = []

    total_lines = 2160 if UHD else 1080  # Set total lines based on UHD flag

    for file in file_list:
        data_folder = os.path.join(file_path, file)
        raw_data_folder = data_folder
        camera_parameters = load_camera_parameters(
            os.path.join(data_folder, RawDataFolder.CAMERA_CONFIGURATION_FILE)
        )
        solution = [149.421361, 0, 200.342416]
        width, height = (3840, 2160) if UHD else (1920, 1080)
        selected_data = filtering_data(solution, raw_data_folder, selector_parameters, verbose=True)

        max_row = selected_data.coordinate_dataframe.shape[0]
        for row in range(max_row):
            image_name = selected_data.coordinate_dataframe.iloc[row]["image_name"]
            image_path = os.path.join(selected_data.image_folder, image_name)

            try:
                image = imageio.v2.imread(image_path)
            except Exception as e:
                print(f"Failed to read {image_name}: {e}")
                continue

            timestamp = extract_timestamp(image_name)
            if timestamp is not None:
                timestamps.append(timestamp)
                quality_value = selected_data.coordinate_dataframe.iloc[row]["quality"]
                movement_value = selected_data.coordinate_dataframe.iloc[row]["estimated_movement"]
                movements.append(movement_value)

                # Get line_number from y-coordinate of tool tip position
                line_number = int(selected_data.coordinate_dataframe.iloc[row]["tip_2d_y"])

                # Calculate timestamp_offset for this image
                timestamp_offset = (1000.0 / framerate - exposure_time / 1000.0) * (line_number / total_lines) + (exposure_time / 1000.0 / 2.0)

                tool_center_x = selected_data.coordinate_dataframe.iloc[row]["center_x"]
                tool_center_y = selected_data.coordinate_dataframe.iloc[row]["center_y"]
                bur_tip_x = selected_data.coordinate_dataframe.iloc[row]["tip_2d_x"]
                bur_tip_y = selected_data.coordinate_dataframe.iloc[row]["tip_2d_y"]
                tool_center_3d = selected_data.coordinate_dataframe.iloc[row][CoordinateDataKeys.TOOL_CENTER_3D].to_numpy()
                rotation_matrix = (
                    selected_data.coordinate_dataframe.iloc[row][CoordinateDataKeys.TOOL_ROTATION_MATRIX].to_numpy().reshape(-1, 3, 3)
                ).transpose(0, 2, 1)[0]
                tool_center_2d = selected_data.coordinate_dataframe.iloc[row][CoordinateDataKeys.TOOL_CENTER_2D].to_numpy()

                # Get relative path to maintain folder structure
                relative_path = os.path.relpath(image_path, file_path)
                output_subfolder = os.path.join(output_folder, os.path.dirname(relative_path))

                try:
                    print(f"Processed row {row+1}/{max_row}: {image_path}")
                    laplacian_blur, fft_blur = save_cropped_frame(
                        output_size,
                        width,
                        height,
                        tool_center_2d,
                        tool_center_3d,
                        rotation_matrix,
                        bur_tip_x,
                        bur_tip_y,
                        camera_parameters,
                        image,
                        image_name,
                        quality_value,
                        movement_value,
                        output_subfolder
                    )
                    laplacian_blurs.append(laplacian_blur)
                    fft_blurs.append(fft_blur)

                    # Store the calculated timestamp_offset if needed for further processing
                    print(f"Timestamp Offset for {image_name}: {timestamp_offset:.4f}")

                except Exception as e:
                    print(f"Failed to write {image_name}: {e}")

    results_df = pd.DataFrame({
        'timestamp': timestamps,
        'laplacian_blur': laplacian_blurs,
        'fft_blur': fft_blurs,
        'movement': movements
    })
    output_csv_path = os.path.join(output_folder, 'blur_analysis_results.csv')
    results_df.to_csv(output_csv_path, index=False)
    print(f"Results saved to {output_csv_path}")

    if timestamps:
        plot_metrics(timestamps, laplacian_blurs, "Laplacian Blur", os.path.join(output_folder, "laplacian_blur_plot.png"))
        plot_metrics(timestamps, fft_blurs, "FFT Blur", os.path.join(output_folder, "fft_blur_plot.png"))
        plot_metrics(timestamps, movements, "Movement", os.path.join(output_folder, "movement_plot.png"))

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Process parameters")
    parser.add_argument('--file_path', type=str, required=True, help='Path to the data files.')
    parser.add_argument('--output_size', type=int, required=True, help='Input size for the MV model.')
    parser.add_argument('--UHD', action='store_true', help='Flag to set UHD (4K resolution).')
    parser.add_argument('--output_folder', type=str, required=True, help='Path to save output images and CSV with the same subfolder structure as file_path.')
    parser.add_argument('--framerate', type=float, required=True, help='Framerate input from user.')
    parser.add_argument('--exposure_time', type=float, required=True, help='Exposure time input from user.')

    args = parser.parse_args()
    main(args.file_path, args.UHD, args.output_size, args.output_folder, args.framerate, args.exposure_time)