改修

app.py

@@ -1,86 +1,118 @@
 import gradio as gr
-import firebase_admin
-from firebase_admin import credentials, storage
-import tempfile
-import os
 import io
 from PIL import Image
 import base64
-from scripts.process_utils import initialize, process_image_as_base64
+from scripts.process_utils import initialize, process_image_as_base64, image_to_base64
 from scripts.anime import init_model
 from scripts.generate_prompt import load_wd14_tagger_model
-import uuid
-import time
 
 # 初期化
-initialize(_use_local=False, use_gpu=True, use_dotenv=False)
+initialize(_use_local=False, use_gpu=True, use_dotenv=True)
 init_model(use_local=False)
 load_wd14_tagger_model()
 
-# Firebase の初期化
-cred = credentials.Certificate("firebase/image2body-demo-firebase-adminsdk-ope1k-e19e1da82c.json")
-firebase_admin.initialize_app(cred, {
-    'storageBucket': 'image2body-demo.appspot.com'
-})
-def save_image_pair_to_firebase(sotai_image_data, sketch_image_data):
-    # 一意の識別子を生成
-    unique_id = str(uuid.uuid4())
-    timestamp = int(time.time())
-    folder_name = f"image_pairs/{timestamp}_{unique_id}"
+def process_image(input_image, mode, weight1=None, weight2=None):
+    print(f"Processing image with mode={mode}, weight1={weight1}, weight2={weight2}")
+    # 既存の画像処理ロジック
+    if mode == "original":
+        sotai_image, sketch_image = process_image_as_base64(input_image, mode, None, None)
+    elif mode == "refine":
+        sotai_image, sketch_image = process_image_as_base64(input_image, mode, weight1, weight2)
+
+    # テスト用に、Base64データを返す
+    sotai_image = image_to_base64(input_image)
+    sketch_image = image_to_base64(input_image)
+
+    return sotai_image, sketch_image
 
-    # Base64 データを PIL Image に変換
+def mix_images(sotai_image_data, sketch_image_data, opacity1, opacity2):
+    # Base64からPILイメージに変換
     sotai_image = Image.open(io.BytesIO(base64.b64decode(sotai_image_data)))
     sketch_image = Image.open(io.BytesIO(base64.b64decode(sketch_image_data)))
-    
-    bucket = storage.bucket()
-    urls = {}
+    # 画像を合成
+    mixed_image = Image.new('RGBA', sotai_image.size, (0, 0, 0, 0))
+    opacity_mask1 = Image.new('L', sotai_image.size, int(opacity1 * 255))
+    opacity_mask2 = Image.new('L', sotai_image.size, int(opacity2 * 255))
+    mixed_image.paste(sotai_image, (0, 0), mask=opacity_mask1)
+    mixed_image.paste(sketch_image, (0, 0), mask=opacity_mask2)
 
-    for image_type, image in [("sotai", sotai_image), ("sketch", sketch_image)]:
-        # 一時ファイルを作成
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-            image.save(temp_file, format="PNG")
-            temp_file_path = temp_file.name
+    return mixed_image
 
-        try:
-            # Firebase Storage にアップロード
-            blob_path = f'{folder_name}/{image_type}.png'
-            blob = bucket.blob(blob_path)
-            blob.upload_from_filename(temp_file_path)
-            
-            # URLを取得
-            blob.make_public()
-            urls[image_type] = blob.public_url
-        finally:
-            # 一時ファイルを削除
-            os.unlink(temp_file_path)
-    
-    return urls
+with gr.Blocks() as demo:
+    # title
+    gr.HTML("<h1>Image2Body demo</h1>")
+    # description
+    gr.HTML("<p>Upload an image and select processing options to generate body and sketch images.</p>")
+    # interface
+    submit = None
+    with gr.Row():
+        with gr.Column() as input_col:
+            with gr.Tab("original"):
+                original_input = [
+                    gr.Image(type="pil", label="Input Image"),
+                    gr.Text("original", label="Mode", visible=False),
+                ]
+                original_submit = gr.Button("Submit", variant="primary")
+            with gr.Tab("refine"):
+                refine_input = [
+                    gr.Image(type="pil", label="Input Image"),
+                    gr.Text("refine", label="Mode", visible=False),
+                    gr.Slider(0, 2, value=0.6, step=0.05, label="Weight 1 (Sketch)"),
+                    gr.Slider(0, 1, value=0.05, step=0.025, label="Weight 2 (Body)")
+                ]
+                refine_submit = gr.Button("Submit", variant="primary")
+        with gr.Column() as output_col:
+            sotai_image_data = gr.Text(label="Sotai Image data", visible=False)
+            sketch_image_data = gr.Text(label="Sketch Image data", visible=False)
+            mixed_image = gr.Image(label="Output Image", elem_id="output_image")
+            opacity_slider1 = gr.Slider(0, 1, value=0.5, step=0.05, label="Opacity (Sotai)")
+            opacity_slider2 = gr.Slider(0, 1, value=0.5, step=0.05, label="Opacity (Sketch)")
+
+    original_submit.click(
+        process_image,
+        inputs=original_input,
+        outputs=[sotai_image_data, sketch_image_data]
+    )
+    refine_submit.click(
+        process_image,
+        inputs=refine_input,
+        outputs=[sotai_image_data, sketch_image_data]
+    )
+    sotai_image_data.change(
+        mix_images,
+        inputs=[sotai_image_data, sketch_image_data, opacity_slider1, opacity_slider2],
+        outputs=mixed_image
+    )
+    opacity_slider1.change(
+        mix_images,
+        inputs=[sotai_image_data, sketch_image_data, opacity_slider1, opacity_slider2],
+        outputs=mixed_image
+    )
+    opacity_slider2.change(
+        mix_images,
+        inputs=[sotai_image_data, sketch_image_data, opacity_slider1, opacity_slider2],
+        outputs=mixed_image
+    )
+
+    demo.launch()
 
-def process_image(input_image, mode, weight1, weight2):
-    # 既存の画像処理ロジック
-    sotai_image, sketch_image = process_image_as_base64(input_image, mode, weight1, weight2)
-    
-    # Firebase に画像ペアを保存し、URLを取得
-    urls = save_image_pair_to_firebase(sotai_image, sketch_image)
-    
-    return urls['sotai'], urls['sketch']
 
-# Gradio インターフェースの定義
-iface = gr.Interface(
-    fn=process_image,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Radio(["original", "refine"], label="Mode", value="original"),
-        gr.Slider(0, 2, value=0.6, step=0.05, label="Weight 1 (Sketch)"),
-        gr.Slider(0, 1, value=0.05, step=0.025, label="Weight 2 (Body)")
-    ],
-    outputs=[
-        gr.Text(label="Sotai Image URL"),
-        gr.Text(label="Sketch Image URL")
-    ],
-    title="Image2Body API",
-    description="Upload an image and select processing options to generate body and sketch images."
-)
+# # Gradio インターフェースの定義
+# iface = gr.Interface(
+#     fn=process_image,
+#     inputs=[
+#         gr.Image(type="pil", label="Input Image"),
+#         gr.Radio(["original", "refine"], label="Mode", value="original"),
+#         gr.Slider(0, 2, value=0.6, step=0.05, label="Weight 1 (Sketch)"),
+#         gr.Slider(0, 1, value=0.05, step=0.025, label="Weight 2 (Body)")
+#     ],
+#     outputs=[
+#         gr.Text(label="Sotai Image URL"),
+#         gr.Text(label="Sketch Image URL")
+#     ],
+#     title="Image2Body API",
+#     description="Upload an image and select processing options to generate body and sketch images."
+# )
 
-# Hugging Face Spacesでデプロイする場合
-iface.queue().launch()
+# # Hugging Face Spacesでデプロイする場合
+# iface.queue().launch()

requirements.txt

@@ -7,8 +7,8 @@ diffusers==0.27.0 # pth file cannot be loaded in the latest version
 Flask==3.0.3
 Flask-Cors==4.0.0
 Flask-SocketIO==5.3.6
-gradio==4.36.1
-huggingface_hub==0.23.2
+gradio==5.5.0
+huggingface-hub==0.25.2
 kornia==0.7.1
 numpy==1.23.5
 opencv-python==4.9.0.80