app.py

import gradio as gr
from PIL import Image, ImageFilter, ImageOps
import cv2
import numpy as np
import os
from collections import defaultdict
from skimage.color import deltaE_ciede2000, rgb2lab

def DoG_filter(image, kernel_size=0, sigma=1.0, k_sigma=2.0, gamma=1.5):
    g1 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)
    g2 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma * k_sigma)
    return g1 - gamma * g2

def XDoG_filter(image, kernel_size=0, sigma=1.4, k_sigma=1.6, epsilon=0, phi=10, gamma=0.98):
    epsilon /= 255
    dog = DoG_filter(image, kernel_size, sigma, k_sigma, gamma)
    dog /= dog.max()
    e = 1 + np.tanh(phi * (dog - epsilon))
    e[e >= 1] = 1
    return (e * 255).astype('uint8')

def binarize_image(image):
    _, binarized = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return binarized


def process_XDoG(image_path):
    kernel_size=0
    sigma=1.4
    k_sigma=1.6
    epsilon=0
    phi=10
    gamma=0.98

    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    xdog_image = XDoG_filter(image, kernel_size, sigma, k_sigma, epsilon, phi, gamma)
    binarized_image = binarize_image(xdog_image)
    final_image = Image.fromarray(binarized_image)
    return final_image



def replace_color(image, color_1, blur_radius=2):
    data = np.array(image)
    original_shape = data.shape
    data = data.reshape(-1, 4)
    color_1 = np.array(color_1)
    matches = np.all(data[:, :3] == color_1, axis=1)
    nochange_count = 0
    mask = np.zeros(data.shape[0], dtype=bool)

    while np.any(matches):
        new_matches = np.zeros_like(matches)
        match_num = np.sum(matches)
        for i in tqdm(range(len(data))):
            if matches[i]:
                x, y = divmod(i, original_shape[1])
                neighbors = [
                    (x, y-1), (x, y+1), (x-1, y), (x+1, y)
                ]
                valid_neighbors = []
                for nx, ny in neighbors:
                    if 0 <= nx < original_shape[0] and 0 <= ny < original_shape[1]:
                        ni = nx * original_shape[1] + ny
                        if not np.all(data[ni, :3] == color_1, axis=0):
                            valid_neighbors.append(data[ni, :3])
                if valid_neighbors:
                    new_color = np.mean(valid_neighbors, axis=0).astype(np.uint8)
                    data[i, :3] = new_color
                    data[i, 3] = 255
                    mask[i] = True
                else:
                    new_matches[i] = True
        matches = new_matches
        if match_num == np.sum(matches):
            nochange_count += 1
        if nochange_count > 5:
            break

    data = data.reshape(original_shape)
    mask = mask.reshape(original_shape[:2])

    result_image = Image.fromarray(data, 'RGBA')
    blurred_image = result_image.filter(ImageFilter.GaussianBlur(radius=blur_radius))
    blurred_data = np.array(blurred_image)

    np.copyto(data, blurred_data, where=mask[..., None])

    return Image.fromarray(data, 'RGBA')

def generate_distant_colors(consolidated_colors, distance_threshold):
    consolidated_lab = [rgb2lab(np.array([color], dtype=np.float32) / 255.0).reshape(3) for color, _ in consolidated_colors]
    max_attempts = 10000
    for _ in range(max_attempts):
        random_rgb = np.random.randint(0, 256, size=3)
        random_lab = rgb2lab(np.array([random_rgb], dtype=np.float32) / 255.0).reshape(3)
        if all(deltaE_ciede2000(base_color_lab, random_lab) > distance_threshold for base_color_lab in consolidated_lab):
            return tuple(random_rgb)
    return (128, 128, 128)

def consolidate_colors(major_colors, threshold):
    colors_lab = [rgb2lab(np.array([[color]], dtype=np.float32)/255.0).reshape(3) for color, _ in major_colors]
    i = 0
    while i < len(colors_lab):
        j = i + 1
        while j < len(colors_lab):
            if deltaE_ciede2000(colors_lab[i], colors_lab[j]) < threshold:
                if major_colors[i][1] >= major_colors[j][1]:
                    major_colors[i] = (major_colors[i][0], major_colors[i][1] + major_colors[j][1])
                    major_colors.pop(j)
                    colors_lab.pop(j)
                else:
                    major_colors[j] = (major_colors[j][0], major_colors[j][1] + major_colors[i][1])
                    major_colors.pop(i)
                    colors_lab.pop(i)
                continue
            j += 1
        i += 1
    return major_colors

def get_major_colors(image, threshold_percentage=0.01):
    if image.mode != 'RGB':
        image = image.convert('RGB')
    color_count = defaultdict(int)
    for pixel in image.getdata():
        color_count[pixel] += 1
    total_pixels = image.width * image.height
    major_colors = [(color, count) for color, count in color_count.items() if (count / total_pixels) >= threshold_percentage]
    return major_colors

def line_color(image, mask, new_color):
    data = np.array(image)
    data[mask, :3] = new_color
    return Image.fromarray(data, 'RGBA')


def main(image, lineart):
    lineart = lineart.point(lambda x: 0 if x < 200 else 255)
    lineart = ImageOps.invert(lineart)
    kernel = np.ones((3, 3), np.uint8)
    lineart = cv2.dilate(np.array(lineart), kernel, iterations=1)
    lineart = Image.fromarray(lineart)
    mask = np.array(lineart) == 255
    major_colors = get_major_colors(image, threshold_percentage=0.05)
    major_colors = consolidate_colors(major_colors, 10)
    new_color_1 = generate_distant_colors(major_colors, 100)
    filled_image = line_color(image, mask, new_color_1)
    replace_color_image = replace_color(filled_image, new_color_1, 2).convert('RGB')
    return replace_color_image 


# Gradioインターフェース用のメイン関数
def gradio_interface(image):
    image_path = 'temp_input_image.jpg'
    image.save(image_path)
    image = Image.open(image_path).convert('RGBA')
    lineart = process_XDoG(image_path).convert('L')
    replace_color_image = main(image, lineart)
    return replace_color_image

# Gradioアプリを設定し、起動する
iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Image(type='pil', label="Original Image"),
    outputs=gr.Image(type='pil', label="Processed Image"),
    title="Line Art Removal",
    description="画像をアップロードして線画を除去します。"
)

iface.launch()