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#This code is largely based on: https://huggingface.co/spaces/diffusers/controlnet-3d-pose
import gradio as gr
from PIL import Image, ImageFilter, ImageOps
from io import BytesIO
import numpy as np
import base64
import cv2
import os
import uuid
uid=uuid.uuid4()
canvas_html = '<div style="height:50%"><pose-maker/></div>'
low_threshold = 100
high_threshold = 200
load_js = """
async () => {
const url = "https://huggingface.co/datasets/mishig/gradio-components/raw/main/mannequinAll.js"
fetch(url)
.then(res => res.text())
.then(text => {
const script = document.createElement('script');
script.type = "module"
script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
document.head.appendChild(script);
});
}
"""
get_js_image = """
async (canvas, prompt) => {
const poseMakerEl = document.querySelector("pose-maker");
const imgBase64 = poseMakerEl.captureScreenshotDepthMap();
return [imgBase64, prompt]
}
"""
js_change_rotation_axis = """
async (axis) => {
const poseMakerEl = document.querySelector("pose-maker");
poseMakerEl.changeRotationAxis(axis);
}
"""
js_pose_template = """
async (pose) => {
const poseMakerEl = document.querySelector("pose-maker");
poseMakerEl.setPose(pose);
}
"""
def get_canny_filter(image):
if not isinstance(image, np.ndarray):
image = np.array(image)
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
return canny_image
def generate_images(canvas,output_type):
base64_img = canvas
image_data = base64.b64decode(base64_img.split(',')[1])
input_img = Image.open(BytesIO(image_data)).convert(
'RGB').resize((512, 512))
input_img = input_img.filter(ImageFilter.GaussianBlur(radius=2))
input_img = get_canny_filter(input_img) if output_type == "Canny" else input_img
#input_img = ImageOps.invert(input_img)
input_img.save(f"{uid}-pose.png")
out = os.path.abspath(f"{uid}-pose.png")
out_url = f'https://omnibus-model-mover.hf.space/file={out}'
return f"{uid}-pose.png",f"{uid}-pose.png",out_url
def placeholder_fn(axis):
pass
with gr.Blocks() as b:
with gr.Row():
canvas = gr.HTML(canvas_html, elem_id="canvas_html", visible=True)
with gr.Column():
rotation_axis = gr.Radio(choices=["x", "y", "z"], value="x", label="Joint rotation axis")
pose_template = gr.Radio(choices=["regular", "ballet", "handstand", "split", "kick", "chilling"], value="regular", label="Pose template")
output_type = gr.Radio(choices=["Depth", "Canny"], value="Depth", label="Image Output")
run_button = gr.Button("Generate")
out_text = gr.Textbox(label="Image URL")
out_file=gr.File()
out_im = gr.Image(type='filepath',show_share_button=True)
rotation_axis.change(fn=placeholder_fn,
inputs=[rotation_axis],
outputs=[],
queue=False,
_js=js_change_rotation_axis)
pose_template.change(fn=placeholder_fn,
inputs=[pose_template],
outputs=[],
queue=False,
_js=js_pose_template)
run_button.click(fn=generate_images,
inputs=[canvas,output_type],
outputs=[out_im,out_file,out_text],
_js=get_js_image)
b.load(None,None,None,_js=load_js)
b.launch() |