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import aiohttp
import gradio as gr
import numba
import requests
import base64
from PIL import Image
import io
import json
from numba import jit
import matplotlib.pyplot as plt
examples = ["examples/0002_01_00_01_55.jpg",
"examples/0-spoof.jpg",
"examples/0.jpg",
"examples/3.jpg",
"examples/6-mask.jpg",
"examples/AGL752VM_id147_s0_150.png",
"examples/FT720P_G780_REDMI4X_id0_s0_105.png",
"examples/7.jpg"]
async def spoof_trigger(b64):
url = "https://spoofapi1.azurewebsites.net/api/spoofvisualize"
payload = {"img": b64}
headers = {
'x-functions-key': 'wGw3zXXPlLCez-VrcSs9RTahE4gLC674pf7Fp6Au2kUHAzFuNnZZMw==',
'Content-Type': 'text/plain'
}
async with aiohttp.ClientSession() as session:
async with session.post(url, json=payload, headers=headers) as response:
response_text = await response.text()
return response_text
# @jit
async def predict_image(img):
# Convert NumPy array to PIL Image
img = Image.fromarray(img.astype('uint8'))
# Create a BytesIO object
buffer = io.BytesIO()
# Save the PIL Image to the BytesIO object
img.save(buffer, format='JPEG')
# Get the base64 representation
img_base64 = base64.b64encode(buffer.getvalue()).decode()
print(len(img_base64))
# # img_base64 to plot
# img = Image.open(io.BytesIO(base64.b64decode(img_base64)))
# # img save
# img.save("img.jpg")
res = await spoof_trigger(img_base64)
# print(json.loads(res))
spoof_res = json.loads(res)['spoof_res']
annotated_image = json.loads(res)['annotated_image']
conf_score = float( json.loads(spoof_res)['confidence_score'])
# img_base64 to plot
img = Image.open(io.BytesIO(base64.b64decode(annotated_image)))
# img save
img.save("cache/img.jpg")
confidences = {'Real': 1-conf_score, 'Fake': conf_score}
return (confidences,annotated_image)
with gr.Blocks(title="Spoof-Demo", css="#custom_header {min-height: 3rem; text-align: center} #custom_title {min-height: 3rem; text-align: center}") as demo :
gr.Markdown("## Face Antispoof-Demo", elem_id="custom_title")
gr.Markdown("Gradio Demo for Face Antispoofing", elem_id="custom_header")
gr.Markdown("👨💻 Only fot research preview Intended")
with gr.Row():
with gr.Column():
with gr.Box():
gr.Markdown("### Input")
image = gr.Image(label="Input Image")
image.style(height=240)
btn = gr.Button(text="Submit")
btn.style(full_width=True)
with gr.Column():
with gr.Box():
gr.Markdown("### Output")
output_image = gr.Image(label="Output Image")
output_image.style(height=240)
label_probs = gr.outputs.Label()
btn.click(predict_image, image , outputs=[label_probs,output_image ],api_name="Face Antispoofing")
gr.Examples(
examples=examples,
inputs=image,
outputs = output_image,
fn=predict_image,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch(debug=True) |