删除旧文件
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AbyssYuan0
-108
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from collections import defaultdict
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import numpy as np
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from PIL import Image
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def rgb_to_hex(rgb_colr):
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return '{:02x}{:02x}{:02x}'.format(*rgb_colr)
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def hex_to_rgb(hex_color):
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return tuple(int(hex_color[i:i + 2], 16) for i in (0, 2, 4))
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def get_colors(PIL_img, n):
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color_list = []
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img = PIL_img.convert('RGBA') # 确保图片是RGBA模式
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# 获取图片尺寸
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width, height = img.size
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for y in range(height):
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count = 0
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# 从左到右扫描
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for x in range(width):
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r, g, b, a = img.getpixel((x, y))
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if a != 0:
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count += 1
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if count <= n:
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color = (r, g, b)
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color_list.append(rgb_to_hex(color))
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else:
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count = 0
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count = 0
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# 从右到左扫描
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for x in range(width - 1, -1, -1):
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r, g, b, a = img.getpixel((x, y))
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if a != 0:
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count += 1
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if count <= n:
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color = (r, g, b)
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color_list.append(rgb_to_hex(color))
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else:
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count = 0
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return color_list
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def color_distance(c1, c2):
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(r1, g1, b1) = c1
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(r2, g2, b2) = c2
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return np.sqrt((r1 - r2) ** 2 + (g1 - g2) ** 2 + (b1 - b2) ** 2)
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def average_color(colors):
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r = int(np.mean([c[0] for c in colors]))
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g = int(np.mean([c[1] for c in colors]))
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b = int(np.mean([c[2] for c in colors]))
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return f"{r:02x}{g:02x}{b:02x}"
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def fuzzy_color_grouping(colors, threshold):
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groups = defaultdict(list)
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for color in colors:
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rgb = hex_to_rgb(color)
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placed = False
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for group_color in groups:
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if color_distance(rgb, hex_to_rgb(group_color)) < threshold:
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groups[group_color].append(rgb)
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placed = True
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break
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if not placed:
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groups[color].append(rgb)
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return groups
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def most_common_fuzzy_color(colors, threshold):
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groups = fuzzy_color_grouping(colors, threshold)
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largest_group = max(groups, key=lambda k: len(groups[k]))
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return average_color(groups[largest_group])
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def is_color_similar(color1, color2, threshold):
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return all(abs(c1 - c2) <= threshold for c1, c2 in zip(color1, color2))
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def find_similar_colors(image, color_string, threshold):
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# 转换颜色字符串为RGB元组
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target_color = tuple(int(color_string[i:i + 2], 16) for i in (0, 2, 4))
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# 创建一个同样大小的黑色背景图像
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output_image = Image.new('RGB', image.size, (0, 0, 0))
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pixels = image.load()
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output_pixels = output_image.load()
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# 遍历每个像素点，检查颜色是否接近目标颜色
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for x in range(image.width):
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for y in range(image.height):
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if is_color_similar(pixels[x, y], target_color, threshold):
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# 将接近的颜色设置为白色
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output_pixels[x, y] = (255, 255, 255)
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return output_image
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@@ -1,129 +0,0 @@
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from PIL import Image
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from collections import deque
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def draw_line(pixels, x0, y0, x1, y1):
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"""Draw a white line from (x0, y0) to (x1, y1) on the provided pixels map."""
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dx = abs(x1 - x0)
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dy = abs(y1 - y0)
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sx = 1 if x0 < x1 else -1
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sy = 1 if y0 < y1 else -1
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err = dx - dy
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while True:
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pixels[x0, y0] = 255
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if x0 == x1 and y0 == y1:
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break
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e2 = 2 * err
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if e2 > -dy:
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err -= dy
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x0 += sx
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if e2 < dx:
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err += dx
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y0 += sy
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def fill_white_segments(original_image, low_threshold, high_threshold):
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# Load the original image and convert it to grayscale
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original_image = original_image.convert('L')
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original_pixels = original_image.load()
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width, height = original_image.size
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low_threshold = int(width*low_threshold)
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high_threshold = int(width*high_threshold)
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# Create a new black image to draw the lines
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new_image = Image.new('L', (width, height), 0)
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new_pixels = new_image.load()
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# Scan horizontally
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for y in range(height):
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point_a = None
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for x in range(width):
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if original_pixels[x, y] == 255:
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if point_a is None:
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point_a = (x, y)
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else:
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if x - point_a[0] < high_threshold and x - point_a[0] > low_threshold :
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draw_line(new_pixels, point_a[0], point_a[1], x, y)
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point_a = (x, y)
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else:
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point_a = (x, y)
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# Scan vertically
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for x in range(width):
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point_a = None
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for y in range(height):
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if original_pixels[x, y] == 255:
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if point_a is None:
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point_a = (x, y)
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else:
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if y - point_a[1] < high_threshold and y - point_a[1] > low_threshold:
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draw_line(new_pixels, point_a[0], point_a[1], x, y)
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point_a = (x, y)
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else:
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point_a = (x, y)
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# Scan diagonally (top-left to bottom-right)
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for diag in range(-height + 1, width):
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point_a = None
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for y in range(max(-diag, 0), min(width - diag, height)):
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x = y + diag
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if original_pixels[x, y] == 255:
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if point_a is None:
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point_a = (x, y)
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else:
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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:
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draw_line(new_pixels, point_a[0], point_a[1], x, y)
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point_a = (x, y)
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else:
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point_a = (x, y)
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# Scan diagonally (top-right to bottom-left)
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for diag in range(0, width + height):
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point_a = None
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for y in range(max(diag - width + 1, 0), min(diag + 1, height)):
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x = diag - y
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if original_pixels[x, y] == 255:
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if point_a is None:
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point_a = (x, y)
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else:
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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:
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draw_line(new_pixels, point_a[0], point_a[1], x, y)
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point_a = (x, y)
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else:
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point_a = (x, y)
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# Save the new image with only the drawn lines
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return new_image
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def find_largest_white_component(image):
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width, height = image.size
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visited = set()
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largest_component = []
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largest_size = 0
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def bfs(x, y):
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queue = deque([(x, y)])
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local_visited = set()
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while queue:
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x, y = queue.popleft()
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if (x, y) not in visited and 0 <= x < width and 0 <= y < height and image.getpixel((x, y)) == 255:
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visited.add((x, y))
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local_visited.add((x, y))
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queue.extend([(x+1, y), (x-1, y), (x, y+1), (x, y-1)])
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return local_visited
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for y in range(height):
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for x in range(width):
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if image.getpixel((x, y)) == 255 and (x, y) not in visited:
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component = bfs(x, y)
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if len(component) > largest_size:
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largest_size = len(component)
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largest_component = component
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# 创建一个新的图像来绘制最大的白色像素点整体
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new_image = Image.new('1', image.size)
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for x, y in largest_component:
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new_image.putpixel((x, y), 255)
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return new_image
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