ControlNet-v1-1

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nej-dot commited on Mar 13, 2024

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f61605f

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1 Parent(s): df4c4ab

Update app.py

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app.py +49 -49

app.py CHANGED Viewed

@@ -1,57 +1,57 @@
-#!/usr/bin/env python
-from __future__ import annotations
 import gradio as gr
-import torch
-import uvicorn
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import HTMLResponse
-from io import BytesIO
-import numpy as np
-import PIL.Image
 from preprocessor import Preprocessor
-app = FastAPI()
 preprocessor = Preprocessor()
-def apply_preprocessor(input_image, preprocessor_name):
-    preprocessor.load(preprocessor_name)
-    processed_image = preprocessor(input_image)
-    return processed_image
-@app.post("/preprocess/")
-async def preprocess_image(file: UploadFile = File(...), preprocessor_name: str = 'Canny'):
-    contents = await file.read()
-    image = PIL.Image.open(BytesIO(contents))
-    processed_image = apply_preprocessor(image, preprocessor_name)
-    processed_image_pil = PIL.Image.fromarray(processed_image)
-    buffered = BytesIO()
-    processed_image_pil.save(buffered, format="JPEG")
-    return {"image": buffered.getvalue()}
-@app.get("/")
-async def main():
-    content = """
-    <body>
-    <form action="/preprocess/" enctype="multipart/form-data" method="post">
-      <input name="file" type="file">
-      <select name="preprocessor_name">
-        <option value="Canny">Canny</option>
-        <option value="Midas">Midas</option>
-        <option value="MLSD">MLSD</option>
-      </select>
-      <input type="submit">
-    </form>
-    </body>
-    """
-    return HTMLResponse(content)
-def apply_gradio_preprocessor(input_image, preprocessor_name):
     preprocessor.load(preprocessor_name)
-    processed_image = preprocessor(input_image)
-    return processed_image
 if __name__ == "__main__":
-    gr.Interface(apply_gradio_preprocessor, gr.inputs.Image(), "image", title="Preprocessor GUI")\
-        .launch()

 import gradio as gr
 from preprocessor import Preprocessor
+# Initialize Preprocessor
 preprocessor = Preprocessor()
+# Define processing function with extended options
+def process_image(image, preprocessor_name, preprocess_resolution=None, mlsd_value_threshold=None, mlsd_distance_threshold=None, canny_low_threshold=None, canny_high_threshold=None):
     preprocessor.load(preprocessor_name)
+    kwargs = {}
+    if preprocess_resolution:
+        kwargs['preprocess_resolution'] = preprocess_resolution
+    if mlsd_value_threshold and preprocessor_name == "MLSD":
+        kwargs['mlsd_value_threshold'] = mlsd_value_threshold
+    if mlsd_distance_threshold and preprocessor_name == "MLSD":
+        kwargs['mlsd_distance_threshold'] = mlsd_distance_threshold
+    if canny_low_threshold and preprocessor_name == "Canny":
+        kwargs['canny_low_threshold'] = canny_low_threshold
+    if canny_high_threshold and preprocessor_name == "Canny":
+        kwargs['canny_high_threshold'] = canny_high_threshold
+    return preprocessor(image, **kwargs)
+# UI creation with segmentation options
+def create_ui():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            image_input = gr.Image(type="pil")
+            preprocessor_dropdown = gr.Dropdown(choices=["ContentShuffle", "Openpose", "Midas", "MLSD", "Canny", "Lineart", "DPT", "UPerNet", "HED", "PidiNet"], label="Preprocessor")
+            preprocess_resolution = gr.Slider(128, 512, step=1, label="Preprocess Resolution", visible=False)
+            # Additional options for MLSD and Canny
+            mlsd_value_threshold = gr.Slider(0.01, 2.0, step=0.01, label="MLSD Value Threshold", visible=False)
+            mlsd_distance_threshold = gr.Slider(0.01, 20.0, step=0.01, label="MLSD Distance Threshold", visible=False)
+            canny_low_threshold = gr.Slider(1, 255, step=1, label="Canny Low Threshold", visible=False)
+            canny_high_threshold = gr.Slider(1, 255, step=1, label="Canny High Threshold", visible=False)
+            submit_button = gr.Button("Process")
+            result_image = gr.Image(label="Processed Image")
+        def update_options(preprocessor_name):
+            # Update visibility based on preprocessor choice
+            options_visibility = {
+                'preprocess_resolution': preprocessor_name in ["Openpose", "Midas", "MLSD", "Lineart", "DPT", "UPerNet", "HED", "PidiNet"],
+                'mlsd_value_threshold': preprocessor_name == "MLSD",
+                'mlsd_distance_threshold': preprocessor_name == "MLSD",
+                'canny_low_threshold': preprocessor_name == "Canny",
+                'canny_high_threshold': preprocessor_name == "Canny",
+            }
+            return list(options_visibility.values())
+        preprocessor_dropdown.change(fn=update_options, inputs=[preprocessor_dropdown], outputs=[preprocess_resolution, mlsd_value_threshold, mlsd_distance_threshold, canny_low_threshold, canny_high_threshold])
+        submit_button.click(fn=process_image, inputs=[image_input, preprocessor_dropdown, preprocess_resolution, mlsd_value_threshold, mlsd_distance_threshold, canny_low_threshold, canny_high_threshold], outputs=[result_image])
+    return demo
 if __name__ == "__main__":
+    create_ui().launch()