Add application file

app.py

@@ -1,7 +1,81 @@
 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 greet(name):
-    return "Hello " + name + "!!"
+# XDoGフィルターを適用する関数
+def XDoG_filter(image, kernel_size=0, sigma=1.4, k_sigma=1.6, epsilon=0, phi=10, gamma=0.98):
+    epsilon /= 255
+    g1 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)
+    g2 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma * k_sigma)
+    dog = g1 - gamma * g2
+    dog /= dog.max()
+    e = 1 + np.tanh(phi * (dog - epsilon))
+    e[e >= 1] = 1
+    return (e * 255).astype('uint8')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 画像を二値化する関数
+def binarize_image(image):
+    _, binarized = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    return binarized
+
+# XDoGフィルターを画像に適用し、その後二値化する関数
+def process_XDoG(image_path):
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    xdog_image = XDoG_filter(image)
+    binarized_image = binarize_image(xdog_image)
+    return Image.fromarray(binarized_image)
+
+# 主要な色を取得する関数
+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
+
+# 色を統合する関数
+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
+
+# 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)
+    lineart = process_XDoG(image_path).convert('L')
+    processed_image = ImageOps.invert(lineart)
+    return processed_image
+
+# Gradioアプリを設定し、起動する
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.inputs.Image(type='pil', label="Original Image"),
+    outputs=gr.outputs.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()