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import gradio as gr
import requests
import datadog_api_client
from PIL import Image
def face_crop(image, face_rect):
x1 = face_rect.get('x1')
y1 = face_rect.get('y1')
x2 = face_rect.get('x2')
y2 = face_rect.get('y2')
width = x2 - x1 + 1
height = y2 - y2 + 1
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if x2 >= image.width:
x2 = image.width - 1
if y2 >= image.height:
y2 = image.height - 1
face_image = image.crop((x1, y1, x2, y2))
face_image_ratio = face_image.width / float(face_image.height)
resized_w = int(face_image_ratio * 150)
resized_h = 150
face_image = face_image.resize((int(resized_w), int(resized_h)))
return face_image
def compare_face(image1, image2):
try:
img_bytes1 = io.BytesIO()
image1.save(img_bytes1, format="JPEG")
img_bytes1.seek(0)
except:
return ["Failed to open image1", {"resultCode": "Failed to open image1"}]
try:
img_bytes2 = io.BytesIO()
image2.save(img_bytes2, format="JPEG")
img_bytes2.seek(0)
except:
return ["Failed to open image2", {"resultCode": "Failed to open image2"}]
url = "http://127.0.0.1:8080/compare_face"
files = {'image1': img_bytes1, 'image2': img_bytes2}
result = requests.post(url=url, files=files)
if result.ok:
json_result = result.json()
if json_result.get("resultCode") != "Ok":
return [json_result.get("resultCode"), json_result]
html = ""
faces1 = json_result.get("faces1", {})
faces2 = json_result.get("faces2", {})
results = json_result.get("results", {})
for result in results:
score = result.get('score')
face1_idx = result.get('face1')
face2_idx = result.get('face2')
face_image1 = face_crop(image1, faces1[face1_idx])
face_value1 = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image1, format="PNG"))
face_image2 = face_crop(image2, faces2[face2_idx])
face_value2 = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image2, format="PNG"))
match_icon = '<svg fill="red" width="19" height="32" viewBox="0 0 19 32"><path d="M0 13.92V10.2H19V13.92H0ZM0 21.64V17.92H19V21.64H0Z"></path><path d="M14.08 0H18.08L5.08 32H1.08L14.08 0Z"></path></svg>'
if score > 0.7:
match_icon = '<svg fill="green" width="19" height="32" viewBox="0 0 19 32"><path d="M0 13.9202V10.2002H19V13.9202H0ZM0 21.6402V17.9202H19V21.6402H0Z"></path></svg>'
item_value = ('<div style="align-items: center; gap: 10px; display: flex; flex-direction: column;">'
'<div style="display: flex; align-items: center; gap: 20px;">'
'{face_value1}'
'{match_icon}'
'{face_value2}'
'</div>'
'<div style="text-align: center; margin-top: 10px;">'
'Score: {score}'
'</div>'
'</div>'
).format(face_value1=face_value1, face_value2=face_value2, match_icon=match_icon, score=f"{score:.2f}")
html += item_value
html += '<hr style="border: 1px solid #C0C0C0; margin: 10px 0;"/>'
return html
else:
return result.text
with gr.Blocks(css=".gradio-container {background-color: #F4E5E0}") as demo:
with gr.Row():
with gr.Column(scale=7):
with gr.Row():
with gr.Column():
image_input1 = gr.Image(type='pil')
gr.Examples(['face_examples/1.jpg', 'face_examples/3.jpg', 'face_examples/7.jpg', 'face_examples/9.jpg'],
inputs=image_input1)
with gr.Column():
image_input2 = gr.Image(type='pil')
gr.Examples(['face_examples/2.jpg', 'face_examples/4.jpg', 'face_examples/8.jpg', 'face_examples/10.jpg'],
inputs=image_input2)
face_recog_button = gr.Button("Face Recognition")
with gr.Column(scale=3):
recog_html_output = gr.HTML()
face_recog_button.click(compare_face, inputs=[image_input1, image_input2], outputs=recog_html_output)
demo.launch(server_name="0.0.0.0", server_port=7860) |