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import gradio as gr
import requests
import datadog_api_client
from PIL import Image
def compare_face(frame1, frame2):
url = "http://127.0.0.1:8080/compare_face"
files = {'file1': open(frame1, 'rb'), 'file2': open(frame2, 'rb')}
r = requests.post(url=url, files=files)
html = None
faces = None
compare_result = r.json().get('compare_result')
compare_similarity = r.json().get('compare_similarity')
html = ("<table>"
"<tr>"
"<th>Compare Result</th>"
"<th>Value</th>"
"</tr>"
"<tr>"
"<td>Result</td>"
"<td>{compare_result}</td>"
"</tr>"
"<tr>"
"<td>Similarity</td>"
"<td>{compare_similarity}</td>"
"</tr>"
"</table>".format(compare_result=compare_result, compare_similarity=compare_similarity))
try:
image1 = Image.open(frame1)
image2 = Image.open(frame2)
face1 = None
face2 = None
if r.json().get('face1') is not None:
face = r.json().get('face1')
x1 = face.get('x1')
y1 = face.get('y1')
x2 = face.get('x2')
y2 = face.get('y2')
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if x2 >= image1.width:
x2 = image1.width - 1
if y2 >= image1.height:
y2 = image1.height - 1
face1 = image1.crop((x1, y1, x2, y2))
face_image_ratio = face1.width / float(face1.height)
resized_w = int(face_image_ratio * 150)
resized_h = 150
face1 = face1.resize((int(resized_w), int(resized_h)))
if r.json().get('face2') is not None:
face = r.json().get('face2')
x1 = face.get('x1')
y1 = face.get('y1')
x2 = face.get('x2')
y2 = face.get('y2')
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if x2 >= image2.width:
x2 = image2.width - 1
if y2 >= image2.height:
y2 = image2.height - 1
face2 = image2.crop((x1, y1, x2, y2))
face_image_ratio = face2.width / float(face2.height)
resized_w = int(face_image_ratio * 150)
resized_h = 150
face2 = face2.resize((int(resized_w), int(resized_h)))
if face1 is not None and face2 is not None:
new_image = Image.new('RGB',(face1.width + face2.width + 10, 150), (80,80,80))
new_image.paste(face1,(0,0))
new_image.paste(face2,(face1.width + 10, 0))
faces = new_image.copy()
elif face1 is not None and face2 is None:
new_image = Image.new('RGB',(face1.width + face1.width + 10, 150), (80,80,80))
new_image.paste(face1,(0,0))
faces = new_image.copy()
elif face1 is None and face2 is not None:
new_image = Image.new('RGB',(face2.width + face2.width + 10, 150), (80,80,80))
new_image.paste(face2,(face2.width + 10, 0))
faces = new_image.copy()
except:
pass
return [faces, html]
with gr.Blocks() as demo:
gr.Markdown(
"""
# KBY-AI
We offer SDKs for Face Recognition, Face Liveness Detection(Face Anti-Spoofing), and ID Card Recognition.<br/>
Besides that, we can provide several AI models and development services in machine learning.
## Simple Installation & Simple API
```
sudo docker pull kbyai/face-recognition:latest
sudo docker run -e LICENSE="xxxxx" -p 8081:8080 -p 9001:9000 kbyai/face-recognition:latest
```
## KYC Verification Demo
https://github.com/kby-ai/KYC-Verification
"""
)
with gr.TabItem("Face Recognition"):
with gr.Row():
with gr.Column():
compare_face_input1 = gr.Image(type='filepath')
gr.Examples(['face_examples/1.jpg', 'face_examples/3.jpg', 'face_examples/5.jpg', 'face_examples/7.jpg', 'face_examples/9.jpg'],
inputs=compare_face_input1)
compare_face_button = gr.Button("Compare Face")
with gr.Column():
compare_face_input2 = gr.Image(type='filepath')
gr.Examples(['face_examples/2.jpg', 'face_examples/4.jpg', 'face_examples/6.jpg', 'face_examples/8.jpg', 'face_examples/10.jpg'],
inputs=compare_face_input2)
with gr.Column():
compare_face_output = gr.Image(type="pil").style(height=150)
compare_result_output = gr.HTML(label='Result')
compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[compare_face_output, compare_result_output])
demo.launch(server_name="0.0.0.0", server_port=7860) |