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| import gradio as gr | |
| import subprocess | |
| import os | |
| from PIL import Image | |
| def resize_image(image_path, target_height, output_path): | |
| # Open the image file | |
| with Image.open(image_path) as img: | |
| # Calculate the ratio to resize the image to the target height | |
| ratio = target_height / float(img.size[1]) | |
| # Calculate the new width based on the aspect ratio | |
| new_width = int(float(img.size[0]) * ratio) | |
| # Resize the image | |
| resized_img = img.resize((new_width, target_height), Image.LANCZOS) | |
| # Save the resized image | |
| resized_img.save(output_path) | |
| return output_path | |
| def generate(image, prompt, seed): | |
| print(image, prompt, seed) | |
| image_path = os.path.splitext(image)[0] | |
| image_name = os.path.basename(image_path) | |
| resized=resize_image(image, 512, f"output/{image_name}.jpg") | |
| print(f"IMAGE NAME: {image_name}") | |
| command = f"python handrefiner.py --input_img {resized} --out_dir output --strength 0.55 --weights models/inpaint_depth_control.ckpt --prompt '{prompt}' --seed {seed}" | |
| try: | |
| result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) | |
| output_path = 'output' | |
| print("Output:", result.stdout) | |
| print(output_path) | |
| # List all files and directories in the given directory | |
| contents = os.listdir("output") | |
| # Print the contents | |
| for item in contents: | |
| print(item) | |
| return f"output/{image_name}_0.jpg" | |
| except subprocess.CalledProcessError as e: | |
| print("Error:", e.stderr) | |
| return None | |
| css=""" | |
| #col-container{ | |
| max-width: 860px; | |
| margin: 0 auto; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(""" | |
| <h2 style="text-align: center;"> | |
| HandRefiner | |
| </h2> | |
| <p style="text-align: center;"> | |
| Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting <br /> | |
| For demo purpose, every input images are resized to 512 height ratio | |
| </p> | |
| <p style="margin:12px auto;display: flex;justify-content: center;"> | |
| <a href="https://huggingface.co/spaces/fffiloni/HandRefiner?duplicate=true"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" alt="Duplicate this Space"></a> | |
| </p> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(type='filepath') | |
| gr.Examples( | |
| examples = [ | |
| "examples/IMG_1050.jpeg", | |
| "examples/IMG_1051.jpeg", | |
| "examples/IMG_1052.jpeg", | |
| "examples/IMG_1053.jpeg" | |
| ], | |
| inputs = [image] | |
| ) | |
| textbox = gr.Textbox(show_label=False, value="a person facing the camera, making a hand gesture, indoor") | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=1000000, value=643534) | |
| button = gr.Button() | |
| output_image = gr.Image(show_label=False, type="filepath", interactive=False) | |
| button.click(fn=generate, inputs=[image, textbox, seed], outputs=[output_image]) | |
| demo.queue(max_size=10).launch(inline=False, debug=True) |