| import cv2
|
| import numpy as np
|
|
|
|
|
| def is_dark_bubble(image, threshold=100):
|
| """
|
| Determine if a bubble image is dark (black bubble with white text).
|
|
|
| Args:
|
| image: Input bubble image (BGR)
|
| threshold: Intensity threshold (below = dark bubble)
|
|
|
| Returns:
|
| bool: True if dark bubble, False if light bubble
|
| """
|
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
| mean_intensity = np.mean(gray)
|
| return mean_intensity < threshold
|
|
|
|
|
| def process_dark_bubble(image):
|
| """
|
| Processes a dark speech bubble (black with white text).
|
| Fills the bubble contents with black.
|
|
|
| Args:
|
| image (numpy.ndarray): Input dark bubble image.
|
|
|
| Returns:
|
| tuple: (processed_image, largest_contour)
|
| """
|
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
|
|
|
| _, thresh = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY_INV)
|
|
|
| contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
| if not contours:
|
| h, w = image.shape[:2]
|
| largest_contour = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.int32)
|
| image[:] = (0, 0, 0)
|
| return image, largest_contour
|
|
|
| largest_contour = max(contours, key=cv2.contourArea)
|
|
|
| mask = np.zeros_like(gray)
|
| cv2.drawContours(mask, [largest_contour], -1, 255, cv2.FILLED)
|
|
|
|
|
| image[mask == 255] = (0, 0, 0)
|
|
|
| return image, largest_contour
|
|
|
|
|
| def process_bubble(image):
|
| """
|
| Processes the speech bubble in the given image, making its contents white.
|
|
|
| Parameters:
|
| - image (numpy.ndarray): Input image.
|
|
|
| Returns:
|
| - image (numpy.ndarray): Image with the speech bubble content set to white.
|
| - largest_contour (numpy.ndarray): Contour of the detected speech bubble (or None if not found).
|
| """
|
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
| _, thresh = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY)
|
|
|
| contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
|
| if not contours:
|
|
|
| h, w = image.shape[:2]
|
| largest_contour = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.int32)
|
|
|
| image[:] = (255, 255, 255)
|
| return image, largest_contour
|
|
|
| largest_contour = max(contours, key=cv2.contourArea)
|
|
|
| mask = np.zeros_like(gray)
|
| cv2.drawContours(mask, [largest_contour], -1, 255, cv2.FILLED)
|
|
|
| image[mask == 255] = (255, 255, 255)
|
|
|
| return image, largest_contour
|
|
|
|
|
| def process_bubble_auto(image, force_dark=False):
|
| """
|
| Automatically detect bubble type and process accordingly.
|
|
|
| Args:
|
| image: Input bubble image (BGR)
|
| force_dark: If True, treat as dark bubble regardless of detection
|
|
|
| Returns:
|
| tuple: (processed_image, contour, is_dark)
|
| """
|
| if force_dark or is_dark_bubble(image):
|
| processed, contour = process_dark_bubble(image)
|
| return processed, contour, True
|
| else:
|
| processed, contour = process_bubble(image)
|
| return processed, contour, False
|
|
|
