dice_detection module

dice_detection.py

DiceDetection is a Python package for detecting and analyzing dice in images or video streams.

This module provides a DiceDetection class that encapsulates functionalities for dice detection, including blob detection, clustering, and result announcement using text-to-speech.

Dependencies:

  • OpenCV (cv2)

  • NumPy (numpy)

  • scikit-learn (sklearn)

  • Calibration (calibration)

  • Configuration Settings (configs)

  • Google Text-to-Speech (gtts)

  • Pygame (pygame)

Usage:

To use this package, create an instance of the DiceDetection class, calibrate the distance parameter using calibrate_distance, and then use the various methods provided for blob detection, result extraction, and display.

Example:

  • Create an instance of DiceDetection

dice_detector = DiceDetection()

  • Calibrate the distance parameter

dice_detector.calibrate_distance(35)

  • Get blobs from a frame

frame = cv2.imread(“dice_image.jpg”) blobs = await dice_detector.get_blobs(frame)

  • Get dice information from blobs

dice_info, num_dice = await dice_detector.get_dice_from_blobs(blobs)

  • Overlay information on the frame

await dice_detector.overlay_info(frame, dice_info, blobs)

  • Stop detection when stable state is reached

sum_list, already_printed = await dice_detector.stop_detection(num_dice, sum_list, already_printed, frame)

  • Announce the result using text-to-speech

await dice_detector.announce_result(num_dice)

  • Display text on the frame

dice_detector.show_on_image(frame, “Dice detection in progress…”)

class dice_detection.DiceDetection

Bases: object

DiceDetection is a Python package for detecting and

analyzing dice in images or video streams.

Attributes:

detector: Instance of cv2.SimpleBlobDetector for blob detection. distance_parameter (float): Parameter used for DBSCAN clustering, calibrated based on camera distance.

async announce_result(number: int) → None

Announce the result using text-to-speech.

Args:

number (int): Result to be announced.

calibrate_distance(distance: float)

Calibrate the distance parameter after instantiation.

Args:

distance (float): Distance between the camera and the white table. Range: 30-40 cm.

async get_blobs(frame: ndarray) → list

Detect blobs in the input frame.

Args:

frame (numpy.ndarray): Input image frame.

Returns:

list: List of detected blobs.

async get_dice_from_blobs(blobs: list) -> (<class 'list'>, <class 'int'>)

Extract dice information from detected blobs.

Args:

blobs (list): List of detected blobs.

Returns:

tuple: A tuple containing a list of dice information and the number of dice.

async overlay_info(frame: ndarray, dice: list, blobs: list)

Overlay information on the input frame.

Args:

frame (numpy.ndarray): Input image frame. dice (list): List of dice information. blobs (list): List of detected blobs.

show_on_image(frame: ndarray, text: str)

Display text on the input frame.

Args:

frame (numpy.ndarray): Input image frame. text (str): Text to be displayed.

async stop_detection(num_dice: int, sum_list: list, already_printed: bool, frame: ~numpy.ndarray, sum: int) -> (<class 'list'>, <class 'bool'>)

Stop dice detection when a stable state is reached.

Args:

num_dice (int): Number of dice detected. sum_list (list): List to track the sum of dice over time. already_printed (bool): Flag to avoid redundant printing. frame (numpy.ndarray): Input image frame.

Returns:

tuple: A tuple containing the updated sum_list and already_printed flags.