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54a7b7b 2ab378c 54a7b7b ef0641f 54a7b7b ef0641f 54a7b7b ef0641f 2ab378c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | 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)
# For dark bubbles, find the dark region (invert threshold)
_, 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) # Fill with black
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)
# Fill with black instead of white
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)
# Handle case when no contours found
if not contours:
# Return original image with a simple rectangular contour
h, w = image.shape[:2]
largest_contour = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.int32)
# Fill with white anyway
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
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