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import gradio as gr | |
import numpy as np | |
import cv2 | |
from fastapi import FastAPI, Request, Response | |
from src.body import Body | |
body_estimation = Body('model/body_pose_model.pth') | |
def pil2cv(image): | |
''' PIL型 -> OpenCV型 ''' | |
new_image = np.array(image, dtype=np.uint8) | |
if new_image.ndim == 2: # モノクロ | |
pass | |
elif new_image.shape[2] == 3: # カラー | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR) | |
elif new_image.shape[2] == 4: # 透過 | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA) | |
return new_image | |
with open("static/poseEditor.js", "r") as f: | |
file_contents = f.read() | |
app = FastAPI() | |
async def some_fastapi_middleware(request: Request, call_next): | |
path = request.scope['path'] # get the request route | |
response = await call_next(request) | |
if path == "/": | |
response_body = "" | |
async for chunk in response.body_iterator: | |
response_body += chunk.decode() | |
some_javascript = f""" | |
<script type="text/javascript" defer> | |
{file_contents} | |
</script> | |
""" | |
response_body = response_body.replace("</body>", some_javascript + "</body>") | |
del response.headers["content-length"] | |
return Response( | |
content=response_body, | |
status_code=response.status_code, | |
headers=dict(response.headers), | |
media_type=response.media_type | |
) | |
return response | |
# make cndidate to json | |
def candidate_to_json_string(arr): | |
a = [f'[{x:.2f}, {y:.2f}]' for x, y, *_ in arr] | |
return '[' + ', '.join(a) + ']' | |
# make subset to json | |
def subset_to_json_string(arr): | |
arr_str = ','.join(['[' + ','.join([f'{num:.2f}' for num in row]) + ']' for row in arr]) | |
return '[' + arr_str + ']' | |
def estimate_body(source): | |
print("estimate_body") | |
if source == None: | |
return None | |
candidate, subset = body_estimation(pil2cv(source)) | |
print(candidate_to_json_string(candidate)) | |
print(subset_to_json_string(subset)) | |
return "{ \"candidate\": " + candidate_to_json_string(candidate) + ", \"subset\": " + subset_to_json_string(subset) + " }" | |
def image_changed(image): | |
if (image == None): | |
return None | |
json = estimate_body(image) | |
return json, image.width, image.height | |
html_text = f""" | |
<canvas id="canvas" width="512" height="512"></canvas> | |
<script type="text/javascript" defer>{file_contents}</script> | |
""" | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(scale=1): | |
source = gr.Image(type="pil") | |
width = gr.Slider(label="Width", mininmum=512, maximum=1024, step=64, value=512, key="Width", interactive=True) | |
height = gr.Slider(label="Height", mininmum=512, maximum=1024, step=64, value=512, key="Height", interactive=True) | |
startBtn = gr.Button(value="Start edit") | |
json = gr.JSON(label="Body") | |
with gr.Column(scale=2): | |
gr.HTML("<ul><li>ctrl + drag to scale</li><li>alt + drag to translate</li><li>shift + drag to rotate(move right first, then up or down)</li></ul>") | |
html = gr.HTML(html_text) | |
saveBtn = gr.Button(value="Save") | |
source.change( | |
fn = image_changed, | |
inputs = [source], | |
outputs = [json, width, height]) | |
startBtn.click( | |
fn = None, | |
inputs = [json, width, height], | |
outputs = [], | |
_js="(json, w, h) => { initializePose(json,w,h); return []; }") | |
saveBtn.click( | |
fn = None, | |
inputs = [], outputs = [], | |
_js="() => { savePose(); }") | |
gr.mount_gradio_app(app, demo, path="/") | |