161 lines
4.7 KiB
Python
161 lines
4.7 KiB
Python
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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hex_color = hex_color.lstrip('#')
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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 get_neighbors(x, y, width, height):
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neighbors = []
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if x > 0:
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neighbors.append((x - 1, y))
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if x < width - 1:
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neighbors.append((x + 1, y))
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if y > 0:
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neighbors.append((x, y - 1))
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if y < height - 1:
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neighbors.append((x, y + 1))
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return neighbors
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def color_distance(c1, c2):
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return sum((a - b) ** 2 for a, b in zip(c1, c2)) ** 0.5
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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_similar_color(target_color, current_color, threshold):
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return color_distance(target_color, current_color) <= threshold
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def find_similar_colors(image, color_string, threshold):
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color_string = color_string.lstrip('#')
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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_similar_color(target_color,pixels[x, y], 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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def detect_outline(image, target_hex_color, threshold):
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target_color = hex_to_rgb(target_hex_color)
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image = image.convert("RGBA")
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data = np.array(image)
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# Extract color and alpha channels
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color_data = data[:, :, :3]
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alpha_data = data[:, :, 3]
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# Create a mask for the outline
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mask = np.zeros((image.height, image.width), dtype=np.uint8)
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# Start from the edges of the image
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edge_pixels = [(x, y) for x in range(image.width) for y in [0, image.height - 1]] + \
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[(x, y) for x in [0, image.width - 1] for y in range(1, image.height - 1)]
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# Use a queue to perform a breadth-first search from the edges
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queue = edge_pixels[:]
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while queue:
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x, y = queue.pop(0)
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if alpha_data[y, x] > 0 and is_similar_color(target_color, color_data[y, x], threshold) and mask[y, x] == 0:
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# Mark as part of the outline
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mask[y, x] = 1
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# Add neighbors to the queue
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for neighbor in get_neighbors(x, y, image.width, image.height):
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if mask[neighbor[1], neighbor[0]] == 0:
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queue.append(neighbor)
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# Create a new image with a black background
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result_image = Image.new("RGB", (image.width, image.height), "black")
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result_data = np.array(result_image)
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# Draw the white pixels based on the mask
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result_data[mask == 1] = (255, 255, 255)
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# Convert back to PIL image
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result_image = Image.fromarray(result_data)
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return result_image
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