Bruno Madeira
69ab4c1c52
unsure if the function is working properly, I don't recall testing it. will leave it as it is, likely a rare thing to be used anyway... I don't anticipate any issues soon.
583 lines
22 KiB
Python
583 lines
22 KiB
Python
import itertools
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import cv2
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import numpy as np
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from shapely.geometry import Polygon
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from shapely.validation import make_valid
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class MangaPanelExtractor:
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def __init__(self, threshold_value: int = 240, min_rel_panel_area: float = 0.01,
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expansion_pixels: int = 50, max_vertices: int = 6):
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"""
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Initialize the manga panel extractor.
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Args:
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threshold_value: Threshold for converting to binary (higher = more selective for white)
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min_rel_panel_area: Minimum panel area as percentage of total page area (0.01 = 1%)
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expansion_pixels: Pixels to expand image borders for edge panel detection
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max_vertices: Maximum vertices allowed in simplified polygon (default 6)
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"""
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self.threshold_value = threshold_value
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self.min_rel_panel_area = min_rel_panel_area
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self.expansion_pixels = expansion_pixels
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self.max_vertices = max_vertices
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self.min_panel_area = None # Will be computed from image size
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def find_empty_corner(self, binary_img: np.ndarray, corner_size: int = 50) -> tuple[int, int]:
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"""
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Find an empty corner (white area) to start flood fill.
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Checks all four corners and returns coordinates of the emptiest one.
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"""
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h, w = binary_img.shape
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corners = [
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(0, 0), # Top-left
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(0, w - 1), # Top-right
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(h - 1, 0), # Bottom-left
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(h - 1, w - 1) # Bottom-right
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]
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best_corner = corners[0]
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max_white_pixels = 0
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for corner in corners:
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y, x = corner
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# Define sample region around corner
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y_start = max(0, y - corner_size // 2)
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y_end = min(h, y + corner_size // 2)
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x_start = max(0, x - corner_size // 2)
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x_end = min(w, x + corner_size // 2)
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sample_region = binary_img[y_start:y_end, x_start:x_end]
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white_pixels = np.sum(sample_region == 255)
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if white_pixels > max_white_pixels:
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max_white_pixels = white_pixels
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best_corner = corner
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return best_corner
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def expand_image(self, img: np.ndarray, pixels: int) -> tuple[np.ndarray, tuple[int, int]]:
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"""
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Expand image by adding white border around it.
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Returns:
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Expanded image and offset coordinates (for coordinate correction later)
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"""
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if len(img.shape) == 3: # Color image
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expanded = cv2.copyMakeBorder(
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img, pixels, pixels, pixels, pixels,
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cv2.BORDER_CONSTANT, value=[255, 255, 255]
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)
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else: # Grayscale
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expanded = cv2.copyMakeBorder(
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img, pixels, pixels, pixels, pixels,
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cv2.BORDER_CONSTANT, value=255
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)
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return expanded, (pixels, pixels)
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def simplify_contour(self, contour: np.ndarray, max_vertices: int = None) -> np.ndarray:
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"""
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Simplify contour to reduce vertices, preferring rectangular shapes.
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"""
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if max_vertices is None:
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max_vertices = self.max_vertices
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# Calculate contour perimeter for epsilon calculation
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perimeter = cv2.arcLength(contour, True)
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# Start with a small epsilon and gradually increase until we get desired vertex count
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epsilon_factor = 0.01
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simplified = contour
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while len(simplified) > max_vertices and epsilon_factor < 0.1:
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epsilon = epsilon_factor * perimeter
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simplified = cv2.approxPolyDP(contour, epsilon, True)
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epsilon_factor += 0.005
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# If still too many vertices, try more aggressive simplification
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if len(simplified) > max_vertices:
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epsilon = 0.1 * perimeter
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simplified = cv2.approxPolyDP(contour, epsilon, True)
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return simplified
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def contour_to_shapely_polygon(self, contour: np.ndarray, offset: tuple[int, int] = (0, 0)) -> Polygon:
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"""
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Convert OpenCV contour to Shapely polygon, adjusting for image expansion offset.
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"""
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# Adjust coordinates back to original image space
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offset_x, offset_y = offset
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points = []
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for point in contour:
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x, y = point[0]
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# Subtract offset to get original coordinates
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original_x = x - offset_x
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original_y = y - offset_y
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points.append((original_x, original_y))
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# Create polygon and ensure it's valid
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try:
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polygon = Polygon(points)
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if not polygon.is_valid:
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polygon = make_valid(polygon)
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return polygon
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except Exception:
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# Fallback: create a simple polygon from bounding box
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x_coords = [p[0] for p in points]
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y_coords = [p[1] for p in points]
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min_x, max_x = min(x_coords), max(x_coords)
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min_y, max_y = min(y_coords), max(y_coords)
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return Polygon([(min_x, min_y), (max_x, min_y), (max_x, max_y), (min_x, max_y)])
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def calculate_internal_angle(self, p1: np.ndarray, p2: np.ndarray, p3: np.ndarray) -> float:
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"""
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Calculate the internal angle at point p2 formed by p1-p2-p3.
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Returns angle in degrees.
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"""
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# Create vectors
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v1 = p1 - p2
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v2 = p3 - p2
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# Calculate angle using dot product
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cos_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
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cos_angle = np.clip(cos_angle, -1, 1) # Handle floating point errors
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angle_rad = np.arccos(cos_angle)
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angle_deg = np.degrees(angle_rad)
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return angle_deg
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def score_vertex_for_removal(self, contour: np.ndarray, vertex_idx: int) -> tuple[float, float]:
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"""
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Score a vertex for removal based on internal angle and area gain.
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Returns:
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(angle_score, area_score) where higher scores = better candidates for removal
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"""
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n_points = len(contour)
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if n_points <= 3: # Can't remove from triangle or smaller
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return 0.0, 0.0
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# Get the three points for angle calculation
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prev_idx = (vertex_idx - 1) % n_points
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next_idx = (vertex_idx + 1) % n_points
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p1 = contour[prev_idx][0]
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p2 = contour[vertex_idx][0]
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p3 = contour[next_idx][0]
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# Calculate internal angle
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internal_angle = self.calculate_internal_angle(p1, p2, p3)
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# Angle score: higher for angles close to 180° (straight lines/jagged cuts)
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# Peak score at 180°, lower scores for acute angles
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angle_score = 1.0 - abs(internal_angle - 180.0) / 180.0
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# Calculate area gain by removing this vertex
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original_area = cv2.contourArea(contour)
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# Create new contour without this vertex
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new_contour = np.delete(contour, vertex_idx, axis=0)
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if len(new_contour) >= 3:
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new_area = cv2.contourArea(new_contour)
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area_gain = new_area - original_area
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# Normalize area score (higher is better for removal)
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area_score = max(0, area_gain / original_area) if original_area > 0 else 0
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else:
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area_score = 0.0
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return angle_score, area_score
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def convert_nonconvex_to_convex(self, contour: np.ndarray, max_iterations: int = 10) -> np.ndarray:
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"""
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Attempt to convert a non-convex shape to convex by removing problematic vertices.
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Args:
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contour: OpenCV contour
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max_iterations: Maximum number of vertices to remove
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Returns:
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Modified contour (convex if successful)
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"""
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current_contour = contour.copy()
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for iteration in range(max_iterations):
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# Check if already convex
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if self.is_convex(current_contour):
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break
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# Need at least 4 points to continue removing
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if len(current_contour) <= 3:
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break
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# Score all vertices for removal
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vertex_scores = []
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for i in range(len(current_contour)):
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angle_score, area_score = self.score_vertex_for_removal(current_contour, i)
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# Combined score: prioritize high internal angles, then area gain
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combined_score = angle_score * 2.0 + area_score * 1.0
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vertex_scores.append((i, combined_score, angle_score, area_score))
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# Sort by combined score (highest first)
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vertex_scores.sort(key=lambda x: x[1], reverse=True)
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# Remove the vertex with highest score
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if vertex_scores and vertex_scores[0][1] > 0:
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best_vertex_idx = vertex_scores[0][0]
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current_contour = np.delete(current_contour, best_vertex_idx, axis=0)
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else:
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# No good candidates for removal
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break
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return current_contour
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def analyze_nonconvex_recovery(self, results: dict) -> dict:
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"""
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Attempt to recover panels from non-convex shapes using vertex removal.
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Returns:
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Updated results with recovered panels
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"""
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recovered_panels = []
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remaining_nonconvex = []
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for shape in results['non_convex_shapes']:
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original_contour = shape['contour']
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# Attempt to make it convex
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recovered_contour = self.convert_nonconvex_to_convex(original_contour)
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# Check if we successfully made it convex and it's still a reasonable size
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if (self.is_convex(recovered_contour) and
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cv2.contourArea(recovered_contour) > self.min_panel_area * 0.5): # Allow smaller after recovery
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# Convert to polygon and analyze
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polygon = self.contour_to_shapely_polygon(recovered_contour, results['offset'])
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shape_analysis = self.analyze_panel_shape(polygon)
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recovered_panels.append({
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'contour': recovered_contour,
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'original_contour': original_contour,
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'polygon': polygon,
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'area': cv2.contourArea(recovered_contour),
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'bbox': cv2.boundingRect(recovered_contour),
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'shape_analysis': shape_analysis,
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'type': 'recovered_panel',
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'recovery_info': {
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'original_vertices': len(original_contour),
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'final_vertices': len(recovered_contour),
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'vertices_removed': len(original_contour) - len(recovered_contour)
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}
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})
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else:
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# Keep as non-convex
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remaining_nonconvex.append(shape)
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# Update results
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results['recovered_panels'] = recovered_panels
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results['non_convex_shapes'] = remaining_nonconvex
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results['panels'].extend(recovered_panels) # Add to main panels list
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return results
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def analyze_panel_shape(self, polygon: Polygon) -> dict:
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"""
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Analyze the shape characteristics of a panel polygon.
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"""
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coords = list(polygon.exterior.coords[:-1]) # Remove duplicate last point
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num_vertices = len(coords)
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# Calculate aspect ratio from bounding box
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bounds = polygon.bounds # (minx, miny, maxx, maxy)
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width = bounds[2] - bounds[0]
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height = bounds[3] - bounds[1]
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aspect_ratio = width / height if height > 0 else 1.0
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# Determine likely panel type
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if num_vertices == 4:
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panel_type = "rectangular"
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elif num_vertices == 3:
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panel_type = "triangular"
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elif num_vertices <= 6:
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panel_type = f"{num_vertices}-sided"
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else:
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panel_type = "complex"
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return {
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'vertices': num_vertices,
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'aspect_ratio': aspect_ratio,
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'panel_type': panel_type,
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'area': polygon.area,
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'bounds': bounds
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}
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def is_convex(self, contour: np.ndarray) -> bool:
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"""
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Check if a contour represents a convex shape.
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"""
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hull = cv2.convexHull(contour)
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hull_area = cv2.contourArea(hull)
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contour_area = cv2.contourArea(contour)
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if hull_area == 0:
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return False
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# If the ratio is close to 1, the shape is convex
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convexity_ratio = contour_area / hull_area
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return convexity_ratio > 0.85 # Allow some tolerance
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def extract_panels(self, img: np.ndarray) -> dict:
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"""
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Extract panels from manga page using refined flood fill approach.
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:param img: openCV ready, in BGR format, image.
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:return: Dictionary containing panels, non-convex shapes, and debug images
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"""
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# Load and preprocess image
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# img = cv2.imread(image_path)
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#if img is None:
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# raise ValueError(f"Could not load image: {image_path}")
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# (image[0].cpu().numpy()[..., ::-1] * 255).astype(np.uint8)
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# Store original dimensions for reference
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original_shape = img.shape
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page_area = original_shape[0] * original_shape[1] # height * width
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self.min_panel_area = page_area * self.min_rel_panel_area
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Expand image to detect edge panels
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expanded_img, offset = self.expand_image(img, self.expansion_pixels)
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expanded_gray, _ = self.expand_image(gray, self.expansion_pixels)
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# Create binary image (white background, black content)
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_, binary = cv2.threshold(expanded_gray, self.threshold_value, 255, cv2.THRESH_BINARY)
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# Find empty corner for flood fill (now in expanded image)
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start_point = self.find_empty_corner(binary)
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start_point = list(start_point)[::-1] # swap x and y for floodfill =/
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# Create flood fill mask
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h, w = binary.shape
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mask = np.zeros((h + 2, w + 2), np.uint8)
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# Perform flood fill from empty corner
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flood_filled = binary.copy()
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cv2.floodFill(flood_filled, mask, start_point, 128) # Fill with gray (128)
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# Create mask of non-filled areas (panels and content)
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panel_mask = (flood_filled != 128).astype(np.uint8) * 255
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# Find contours in the panel mask
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contours, _ = cv2.findContours(panel_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# Classify and process contours
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panels = []
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non_convex_shapes = []
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for contour in contours:
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area = cv2.contourArea(contour)
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# Filter out very small areas (noise)
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if area < self.min_panel_area:
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continue
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# Simplify contour
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simplified_contour = self.simplify_contour(contour)
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# Convert to Shapely polygon
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polygon = self.contour_to_shapely_polygon(simplified_contour, offset)
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# Analyze shape
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shape_analysis = self.analyze_panel_shape(polygon)
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# Check if convex and classify
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if self.is_convex(simplified_contour):
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panels.append({
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'contour': simplified_contour,
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'original_contour': contour,
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'polygon': polygon,
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'area': area,
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'bbox': cv2.boundingRect(simplified_contour),
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'shape_analysis': shape_analysis,
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'type': 'panel'
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})
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else:
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non_convex_shapes.append({
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'contour': simplified_contour,
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'original_contour': contour,
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'polygon': polygon,
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'area': area,
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'bbox': cv2.boundingRect(simplified_contour),
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'shape_analysis': shape_analysis,
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'type': 'non_convex'
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})
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return {
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'original': img,
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'expanded': expanded_img,
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'binary': binary,
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'flood_filled': flood_filled,
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'panel_mask': panel_mask,
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'panels': panels,
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'non_convex_shapes': non_convex_shapes,
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'recovered_panels': [], # Will be populated if recovery is run
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'start_point': start_point,
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'offset': offset,
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'original_shape': original_shape
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}
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# optional user "enforced" panel shape related funcs
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def simplify_panels(self, results: dict, method: str) -> dict:
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for i, r in enumerate(results["panels"]):
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results["panels"][i]["polygon"] = \
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MangaPanelExtractor.simplify_polygon_to_rectangle(results["panels"][i]["polygon"], method)
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return results
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@staticmethod
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def simplify_polygon_to_rectangle(polygon: Polygon, method: str = 'max_area_combination') -> Polygon:
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"""
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Simplify a polygon with >4 vertices to exactly 4 vertices (rectangle) by
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maximizing the area of the resulting shape.
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Args:
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polygon: Shapely polygon with >4 vertices
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method: 'max_area_combination' or 'bounding_box'
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Returns:
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Simplified 4-vertex polygon with maximum possible area
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"""
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coords = list(polygon.exterior.coords[:-1]) # Remove duplicate last point
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if method == 'bounding_box':
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return MangaPanelExtractor._simplify_to_bounding_box(polygon)
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if len(coords) <= 4:
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return polygon
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return MangaPanelExtractor.simplify_maximize_area(polygon)
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@staticmethod
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def simplify_maximize_area(polygon: Polygon) -> Polygon:
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"""
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Simplify polygon to 4 vertices by finding the combination that maximizes area.
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"""
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coords, original_area = polygon.exterior.coords, polygon.area
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n_vertices = len(coords)
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if n_vertices <= 4:
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return Polygon(coords)
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# For very large polygons, use a heuristic approach to avoid combinatorial explosion
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if n_vertices > 12:
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return MangaPanelExtractor._simplify_large_polygon_heuristic(coords)
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# Try all combinations of 4 vertices from the original polygon
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max_area = 0
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best_combination = None
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for combination in itertools.combinations(range(n_vertices), 4):
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# Extract the 4 vertices
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selected_coords = [coords[i] for i in combination]
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# Ensure proper ordering (clockwise or counter-clockwise)
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ordered_coords = MangaPanelExtractor._order_vertices_properly(selected_coords)
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try:
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candidate_polygon = Polygon(ordered_coords)
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if not candidate_polygon.is_valid:
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candidate_polygon = make_valid(candidate_polygon)
|
|
|
|
# We want to maximize area
|
|
candidate_area = candidate_polygon.area
|
|
|
|
if candidate_area > max_area:
|
|
max_area = candidate_area
|
|
best_combination = ordered_coords
|
|
|
|
except Exception:
|
|
continue # Skip invalid combinations
|
|
|
|
if best_combination is None:
|
|
# Fallback to bounding box (guaranteed to be large)
|
|
return MangaPanelExtractor._simplify_to_bounding_box(Polygon(coords))
|
|
|
|
return Polygon(best_combination)
|
|
|
|
@staticmethod
|
|
def _order_vertices_properly(vertices: list[tuple[float, float]]) -> list[tuple[float, float]]:
|
|
"""
|
|
Order vertices in proper sequence (clockwise or counter-clockwise) for a valid polygon.
|
|
"""
|
|
# Calculate centroid
|
|
centroid_x = sum(v[0] for v in vertices) / len(vertices)
|
|
centroid_y = sum(v[1] for v in vertices) / len(vertices)
|
|
|
|
# Calculate angle from centroid to each vertex
|
|
def angle_from_centroid(vertex):
|
|
return np.arctan2(vertex[1] - centroid_y, vertex[0] - centroid_x)
|
|
|
|
# Sort vertices by angle
|
|
sorted_vertices = sorted(vertices, key=angle_from_centroid)
|
|
|
|
return sorted_vertices
|
|
|
|
@staticmethod
|
|
def _simplify_to_bounding_box(polygon: Polygon) -> Polygon:
|
|
"""Convert polygon to its bounding box rectangle."""
|
|
bounds = polygon.bounds # (minx, miny, maxx, maxy)
|
|
minx, miny, maxx, maxy = bounds
|
|
|
|
rectangle_coords = [
|
|
(minx, miny),
|
|
(maxx, miny),
|
|
(maxx, maxy),
|
|
(minx, maxy)
|
|
]
|
|
|
|
return Polygon(rectangle_coords)
|
|
|
|
@staticmethod
|
|
def _simplify_large_polygon_heuristic(coords: list[tuple[float, float]]) -> Polygon:
|
|
"""
|
|
Heuristic approach for polygons with high vertex count (>12).
|
|
Selects vertices that are most important for maintaining shape.
|
|
"""
|
|
n = len(coords)
|
|
|
|
# Calculate importance score for each vertex
|
|
vertex_scores = []
|
|
|
|
for i in range(n):
|
|
prev_idx = (i - 1) % n
|
|
next_idx = (i + 1) % n
|
|
|
|
# Calculate the area of triangle formed by this vertex and its neighbors
|
|
p1, p2, p3 = coords[prev_idx], coords[i], coords[next_idx]
|
|
triangle_area = abs((p2[0] - p1[0]) * (p3[1] - p1[1]) - (p3[0] - p1[0]) * (p2[1] - p1[1])) / 2
|
|
|
|
# Calculate distance from centroid (corner vertices are typically further)
|
|
centroid = (sum(c[0] for c in coords) / n, sum(c[1] for c in coords) / n)
|
|
distance_from_centroid = ((coords[i][0] - centroid[0]) ** 2 + (coords[i][1] - centroid[1]) ** 2) ** 0.5
|
|
|
|
# Combined importance score
|
|
importance = triangle_area * 0.7 + distance_from_centroid * 0.3
|
|
vertex_scores.append((i, importance))
|
|
|
|
# Sort by importance and take top 4
|
|
vertex_scores.sort(key=lambda x: x[1], reverse=True)
|
|
top_4_indices = [idx for idx, _ in vertex_scores[:4]]
|
|
top_4_indices.sort() # Maintain original ordering
|
|
|
|
selected_coords = [coords[i] for i in top_4_indices]
|
|
ordered_coords = MangaPanelExtractor._order_vertices_properly(selected_coords)
|
|
|
|
return Polygon(ordered_coords)
|