254 lines
9.9 KiB
Python
254 lines
9.9 KiB
Python
from PIL import Image
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# 计算两个颜色之间的距离
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def color_distance(color1, color2):
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return np.sqrt(sum((c1 - c2) ** 2 for c1, c2 in zip(color1, color2)))
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# 寻找主要颜色
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def find_dominant_color(block, threshold):
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colors_count = {}
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for color in block:
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found_similar = False
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for dominant_color in colors_count:
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if color_distance(color, dominant_color) < threshold:
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colors_count[dominant_color] += 1
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found_similar = True
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break
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if not found_similar:
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colors_count[tuple(color)] = 1
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# 找出出现最多的颜色
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dominant_color = max(colors_count, key=colors_count.get)
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return np.mean([color for color in block if color_distance(color, dominant_color) < threshold], axis=0)
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# 加载颜色卡
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def load_color_card(color_card_image):
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color_card_pixels = np.array(color_card_image)
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color_palette = color_card_pixels.reshape(-1, color_card_pixels.shape[-1])
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return color_palette
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# 将颜色匹配到颜色卡中的颜色
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def match_color_to_palette(color, palette):
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closest_colors = sorted(palette, key=lambda c: color_distance(c, color))
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return tuple(closest_colors[0])
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# 规格化原图片
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def regular_image(original_image, output_size=(512, 512), fill_color=(255, 255, 255)):
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# 计算等比缩放的尺寸
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original_width, original_height = original_image.size
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ratio = min(output_size[0] / original_width, output_size[1] / original_height)
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new_width = int(original_width * ratio)
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new_height = int(original_height * ratio)
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# 等比缩放图片
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try:
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original_image = original_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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except Exception as e:
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original_image = original_image.resize((new_width, new_height), Image.ANTIALIAS)
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# 创建一个新的白色背景图片
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new_img = Image.new("RGB", output_size, fill_color)
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# 计算居中位置
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left = (output_size[0] - new_width) // 2
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top = (output_size[1] - new_height) // 2
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# 将缩放后的图片粘贴到白色背景图片上
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new_img.paste(original_image, (left, top))
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original_pixels = np.array(new_img)
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return original_pixels
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def to_pixel(original_image, threshold, pix, tile_size, color_card=None):
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regular_size = tile_size*pix
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original_pixels = regular_image(original_image,output_size=(regular_size,regular_size))
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# 创建新的图像，用于存储像素化的结果
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pixelated_image = Image.new('RGB', (pix, pix))
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if color_card!=None:
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color_palette = load_color_card(color_card)
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# 遍历每个8x8的方块
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for i in range(0, regular_size, tile_size):
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for j in range(0, regular_size, tile_size):
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# 获取当前方块
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block = original_pixels[i:i+tile_size, j:j+tile_size].reshape(-1, 3)
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# 找到主要颜色
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dominant_color = find_dominant_color(block, threshold)
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# 匹配到颜色卡中的颜色
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matched_color = match_color_to_palette(dominant_color, color_palette)
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# 将匹配的颜色赋给对应的像素点
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pixelated_image.putpixel((j // tile_size, i // tile_size), matched_color)
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else:
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# 遍历每个8x8的方块
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for i in range(0, regular_size, tile_size):
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for j in range(0, regular_size, tile_size):
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# 获取当前方块
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block = original_pixels[i:i+tile_size, j:j+tile_size].reshape(-1, 3)
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# 找到主要颜色
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dominant_color = find_dominant_color(block, threshold)
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# 将主要颜色的平均值赋给对应的像素点
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pixelated_image.putpixel((j // tile_size, i // tile_size), tuple(dominant_color.astype(int)))
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return pixelated_image
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def map_colors_to_palette(image, palette):
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# 将图像和调色板转换为PyTorch张量
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image_tensor = torch.tensor(np.array(image), dtype=torch.float32).cuda()
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palette_tensor = torch.tensor(np.array(palette), dtype=torch.float32).cuda()
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# 重塑张量以便进行计算
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image_reshaped = image_tensor.view(-1, 3)
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palette_reshaped = palette_tensor.view(-1, 3)
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# 计算图像中每个像素与调色板中每种颜色的欧几里得距离
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distances = torch.cdist(image_reshaped, palette_reshaped)
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# 找到每个像素的最近颜色
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nearest_color_indices = torch.argmin(distances, dim=1)
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# 使用最近的颜色索引创建新图像
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new_image_flat = palette_reshaped[nearest_color_indices]
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new_image = new_image_flat.view(image_tensor.shape)
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# 将结果转换回PIL图像并保存
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result_image = Image.fromarray(new_image.byte().cpu().numpy())
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return result_image
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def reduce_colors(img, n_colors=16):
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# 将图片转换为numpy数组
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img_array = np.array(img)
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# 将图片reshape为二维数组
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original_shape = img_array.shape
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pixels = img_array.reshape((-1, 3))
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# 使用K-means算法来减少颜色
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from sklearn.cluster import KMeans
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kmeans = KMeans(n_clusters=n_colors, random_state=42)
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kmeans.fit(pixels)
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# 替换每个像素的颜色为其最近的中心点
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new_pixels = kmeans.cluster_centers_[kmeans.labels_]
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# 将新的像素值reshape回原来的形状
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new_img_array = new_pixels.reshape(original_shape).astype(np.uint8)
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# 创建新的图片
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new_img = Image.fromarray(new_img_array)
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return new_img
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def convert_image_to_tensor(img):
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img = img.convert("RGB")
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img_np = np.array(img).astype(np.float32)
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img_np = np.transpose(img_np, axes=[2, 0, 1])[np.newaxis, :, :, :]
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img_pt = torch.from_numpy(img_np)
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return img_pt
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def convert_tensor_to_image(img_pt):
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img_pt = img_pt[0, ...].permute(1, 2, 0)
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result_rgb_np = img_pt.cpu().numpy().astype(np.uint8)
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return Image.fromarray(result_rgb_np)
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class PixelEffectModule(nn.Module):
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def __init__(self):
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super(PixelEffectModule, self).__init__()
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def create_mask_by_idx(self, idx_z, max_z):
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h, w = idx_z.shape
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idx_x = torch.arange(h).view([h, 1]).repeat([1, w])
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idx_y = torch.arange(w).view([1, w]).repeat([h, 1])
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mask = torch.zeros([h, w, max_z])
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mask[idx_x, idx_y, idx_z] = 1
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return mask
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def select_by_idx(self, data, idx_z):
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h, w = idx_z.shape
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idx_x = torch.arange(h).view([h, 1]).repeat([1, w])
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idx_y = torch.arange(w).view([1, w]).repeat([h, 1])
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return data[idx_x, idx_y, idx_z]
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def forward(self, rgb, param_num_bins, param_kernel_size, param_pixel_size):
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r, g, b = rgb[:, 0:1, :, :], rgb[:, 1:2, :, :], rgb[:, 2:3, :, :]
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intensity_idx = torch.mean(rgb, dim=[0, 1]) / 256. * param_num_bins
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intensity_idx = intensity_idx.long()
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intensity = self.create_mask_by_idx(intensity_idx, max_z=param_num_bins)
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intensity = torch.permute(intensity, dims=[2, 0, 1]).unsqueeze(dim=0)
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r, g, b = r * intensity, g * intensity, b * intensity
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kernel_conv = torch.ones([param_num_bins, 1, param_kernel_size, param_kernel_size])
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r = F.conv2d(input=r, weight=kernel_conv, padding=(param_kernel_size - 1) // 2, stride=param_pixel_size, groups=param_num_bins, bias=None)[0, :, :, :]
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g = F.conv2d(input=g, weight=kernel_conv, padding=(param_kernel_size - 1) // 2, stride=param_pixel_size, groups=param_num_bins, bias=None)[0, :, :, :]
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b = F.conv2d(input=b, weight=kernel_conv, padding=(param_kernel_size - 1) // 2, stride=param_pixel_size, groups=param_num_bins, bias=None)[0, :, :, :]
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intensity = F.conv2d(input=intensity, weight=kernel_conv, padding=(param_kernel_size - 1) // 2, stride=param_pixel_size, groups=param_num_bins,
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bias=None)[0, :, :, :]
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intensity_max, intensity_argmax = torch.max(intensity, dim=0)
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r = torch.permute(r, dims=[1, 2, 0])
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g = torch.permute(g, dims=[1, 2, 0])
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b = torch.permute(b, dims=[1, 2, 0])
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r = self.select_by_idx(r, intensity_argmax)
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g = self.select_by_idx(g, intensity_argmax)
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b = self.select_by_idx(b, intensity_argmax)
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r = r / intensity_max
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g = g / intensity_max
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b = b / intensity_max
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result_rgb = torch.stack([r, g, b], dim=-1)
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result_rgb = torch.permute(result_rgb, dims=[2, 0, 1]).unsqueeze(dim=0)
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result_rgb_scale = F.interpolate(result_rgb, scale_factor=param_pixel_size)
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return result_rgb,result_rgb_scale
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class Photo2PixelModel(nn.Module):
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def __init__(self):
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super(Photo2PixelModel, self).__init__()
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self.module_pixel_effect = PixelEffectModule()
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def forward(self, rgb,
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param_kernel_size=10,
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param_pixel_size=16):
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rgb,rgb_scale = self.module_pixel_effect(rgb, 4, param_kernel_size, param_pixel_size)
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return rgb,rgb_scale
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def resize_image(image, max_size, is_pixel=False):
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# Image.LANCZOS,Image.NEAREST
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width, height = image.size
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if width > height:
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new_width = max_size
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new_height = int(height * (max_size / width))
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else:
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new_height = max_size
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new_width = int(width * (max_size / height))
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if is_pixel:
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sample_type = Image.NEAREST
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else:
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sample_type = Image.LANCZOS
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resized_image = image.resize((new_width, new_height), sample_type)
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return resized_image
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def convert_photo_to_pixel(img,abstraction,pixel_size,pixel_tile_size,preview_size):
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img = resize_image(img,pixel_size*pixel_tile_size)
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img_tensor = convert_image_to_tensor(img)
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model = Photo2PixelModel()
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model.eval()
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with torch.no_grad():
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rgb,rgb_scale = model(img_tensor,param_kernel_size = abstraction,param_pixel_size = pixel_tile_size)
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img_output = convert_tensor_to_image(rgb)
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img_preview = convert_tensor_to_image(rgb_scale)
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img_preview = resize_image(img_preview,preview_size,is_pixel=True)
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return img_output,img_preview |