diff --git a/remove_line.py b/remove_line.py deleted file mode 100644 index 3952626..0000000 --- a/remove_line.py +++ /dev/null @@ -1,108 +0,0 @@ -from collections import defaultdict -import numpy as np -from PIL import Image - - -def rgb_to_hex(rgb_colr): - return '{:02x}{:02x}{:02x}'.format(*rgb_colr) - - -def hex_to_rgb(hex_color): - return tuple(int(hex_color[i:i + 2], 16) for i in (0, 2, 4)) - - -def get_colors(PIL_img, n): - color_list = [] - img = PIL_img.convert('RGBA') # 确保图片是RGBA模式 - - # 获取图片尺寸 - width, height = img.size - - for y in range(height): - count = 0 - # 从左到右扫描 - for x in range(width): - r, g, b, a = img.getpixel((x, y)) - if a != 0: - count += 1 - if count <= n: - color = (r, g, b) - color_list.append(rgb_to_hex(color)) - else: - count = 0 - count = 0 - # 从右到左扫描 - for x in range(width - 1, -1, -1): - r, g, b, a = img.getpixel((x, y)) - if a != 0: - count += 1 - if count <= n: - color = (r, g, b) - color_list.append(rgb_to_hex(color)) - else: - count = 0 - - - return color_list - - -def color_distance(c1, c2): - (r1, g1, b1) = c1 - (r2, g2, b2) = c2 - return np.sqrt((r1 - r2) ** 2 + (g1 - g2) ** 2 + (b1 - b2) ** 2) - - -def average_color(colors): - r = int(np.mean([c[0] for c in colors])) - g = int(np.mean([c[1] for c in colors])) - b = int(np.mean([c[2] for c in colors])) - return f"{r:02x}{g:02x}{b:02x}" - - -def fuzzy_color_grouping(colors, threshold): - groups = defaultdict(list) - - for color in colors: - rgb = hex_to_rgb(color) - placed = False - - for group_color in groups: - if color_distance(rgb, hex_to_rgb(group_color)) < threshold: - groups[group_color].append(rgb) - placed = True - break - - if not placed: - groups[color].append(rgb) - - return groups - - -def most_common_fuzzy_color(colors, threshold): - groups = fuzzy_color_grouping(colors, threshold) - largest_group = max(groups, key=lambda k: len(groups[k])) - return average_color(groups[largest_group]) - - -def is_color_similar(color1, color2, threshold): - return all(abs(c1 - c2) <= threshold for c1, c2 in zip(color1, color2)) - - -def find_similar_colors(image, color_string, threshold): - # 转换颜色字符串为RGB元组 - target_color = tuple(int(color_string[i:i + 2], 16) for i in (0, 2, 4)) - - # 创建一个同样大小的黑色背景图像 - output_image = Image.new('RGB', image.size, (0, 0, 0)) - pixels = image.load() - output_pixels = output_image.load() - - # 遍历每个像素点，检查颜色是否接近目标颜色 - for x in range(image.width): - for y in range(image.height): - if is_color_similar(pixels[x, y], target_color, threshold): - # 将接近的颜色设置为白色 - output_pixels[x, y] = (255, 255, 255) - - return output_image - diff --git a/thick_lines_from_canny.py b/thick_lines_from_canny.py deleted file mode 100644 index ac6df21..0000000 --- a/thick_lines_from_canny.py +++ /dev/null @@ -1,129 +0,0 @@ -from PIL import Image -from collections import deque - -def draw_line(pixels, x0, y0, x1, y1): - """Draw a white line from (x0, y0) to (x1, y1) on the provided pixels map.""" - dx = abs(x1 - x0) - dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy - - while True: - pixels[x0, y0] = 255 - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: - err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy - -def fill_white_segments(original_image, low_threshold, high_threshold): - # Load the original image and convert it to grayscale - original_image = original_image.convert('L') - original_pixels = original_image.load() - width, height = original_image.size - - low_threshold = int(width*low_threshold) - high_threshold = int(width*high_threshold) - - # Create a new black image to draw the lines - new_image = Image.new('L', (width, height), 0) - new_pixels = new_image.load() - - # Scan horizontally - for y in range(height): - point_a = None - for x in range(width): - if original_pixels[x, y] == 255: - if point_a is None: - point_a = (x, y) - else: - if x - point_a[0] < high_threshold and x - point_a[0] > low_threshold : - draw_line(new_pixels, point_a[0], point_a[1], x, y) - point_a = (x, y) - else: - point_a = (x, y) - - - # Scan vertically - for x in range(width): - point_a = None - for y in range(height): - if original_pixels[x, y] == 255: - if point_a is None: - point_a = (x, y) - else: - if y - point_a[1] < high_threshold and y - point_a[1] > low_threshold: - draw_line(new_pixels, point_a[0], point_a[1], x, y) - point_a = (x, y) - else: - point_a = (x, y) - - # Scan diagonally (top-left to bottom-right) - for diag in range(-height + 1, width): - point_a = None - for y in range(max(-diag, 0), min(width - diag, height)): - x = y + diag - if original_pixels[x, y] == 255: - if point_a is None: - point_a = (x, y) - else: - if max(abs(x - point_a[0]), abs(y - point_a[1])) < high_threshold and max(abs(x - point_a[0]), abs(y - point_a[1])) > low_threshold: - draw_line(new_pixels, point_a[0], point_a[1], x, y) - point_a = (x, y) - else: - point_a = (x, y) - - # Scan diagonally (top-right to bottom-left) - for diag in range(0, width + height): - point_a = None - for y in range(max(diag - width + 1, 0), min(diag + 1, height)): - x = diag - y - if original_pixels[x, y] == 255: - if point_a is None: - point_a = (x, y) - else: - if max(abs(x - point_a[0]), abs(y - point_a[1])) < high_threshold and max(abs(x - point_a[0]), abs(y - point_a[1])) > low_threshold: - draw_line(new_pixels, point_a[0], point_a[1], x, y) - point_a = (x, y) - else: - point_a = (x, y) - - # Save the new image with only the drawn lines - return new_image - -def find_largest_white_component(image): - width, height = image.size - visited = set() - largest_component = [] - largest_size = 0 - - def bfs(x, y): - queue = deque([(x, y)]) - local_visited = set() - while queue: - x, y = queue.popleft() - if (x, y) not in visited and 0 <= x < width and 0 <= y < height and image.getpixel((x, y)) == 255: - visited.add((x, y)) - local_visited.add((x, y)) - queue.extend([(x+1, y), (x-1, y), (x, y+1), (x, y-1)]) - return local_visited - - for y in range(height): - for x in range(width): - if image.getpixel((x, y)) == 255 and (x, y) not in visited: - component = bfs(x, y) - if len(component) > largest_size: - largest_size = len(component) - largest_component = component - - # 创建一个新的图像来绘制最大的白色像素点整体 - new_image = Image.new('1', image.size) - for x, y in largest_component: - new_image.putpixel((x, y), 255) - - return new_image