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Update demo.py
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import os
import gradio as gr
import requests
import json
import io
from gradio.components import Image
from PIL import Image as PILImage, ImageDraw, ImageFont # This import may be needed if you're processing images
from PIL import Image
from PIL import Image
import io
import base64
def face_crop(image, face_rect):
x = face_rect.get('x')
y = face_rect.get('y')
width = face_rect.get('width')
height = face_rect.get('height')
if x < 0:
x = 0
if y < 0:
y = 0
if x + width >= image.width:
width = image.width - x
if y + height >= image.height:
height = image.height - y
face_image = image.crop((x, y, x + width - 1, y + height - 1))
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 pil_image_to_base64(image, format="PNG"):
"""
Converts a PIL.Image object to a Base64-encoded string.
:param image: PIL.Image object
:param format: Format to save the image, e.g., "PNG", "JPEG"
:return: Base64-encoded string
"""
# Save the image to a BytesIO buffer
buffer = io.BytesIO()
image.save(buffer, format=format)
buffer.seek(0) # Rewind the buffer
# Convert the buffer's contents to a Base64 string
base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8')
return base64_string
def compare_face(image1, image2, verifyThreshold):
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://127.0.0.1:8000/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 > verifyThreshold:
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, json_result]
else:
return [result.text, {"resultCode": result.text}]
def detect_face(image):
try:
img_bytes = io.BytesIO()
image.save(img_bytes, format="JPEG")
img_bytes.seek(0)
except:
return ["Failed to open image", {"resultCode": "Failed to open image"}]
url = "http://127.0.0.1:8000/detect_face"
files = {'image': img_bytes}
result = requests.post(url=url, files=files)
if result.ok:
json_result = result.json()
html = ""
resultCode = json_result.get("resultCode")
if resultCode == "Ok":
faces = json_result.get("result", {})
for face in faces:
face_rect = face.get("rect", {})
angles = face.get("angles", {})
age_gender = face.get("age_gender", {})
emotion = face.get("emotion", {})
attribute = face.get("attribute", {})
face_image = face_crop(image, face_rect)
face_value = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image, format="PNG"))
item_value = ('<div style="display: flex; justify-content: center; align-items: flex-start; margin: 10px;">'
'<div style="display: flex; align-items: flex-start; gap: 40px; ">'
'{face_value}'
'<div style="display: flex; gap: 20px; border-left: 1px solid #C0C0C0; padding-left: 20px;">'
'<div>'
'<p><b>Age</b></p>'
'<p><b>Gender</b></p>'
'<p><b>Mask</b></p>'
'<p><b>Left Eye</b></p>'
'<p><b>Right Eye</b></p>'
'<p><b>Yaw</b></p>'
'<p><b>Roll</b></p>'
'<p><b>Pitch</b></p>'
'</div>'
'<div>'
'<p>{age}</p>'
'<p>{gender}</p>'
'<p>{masked}</p>'
'<p>{left_eye}</p>'
'<p>{right_eye}</p>'
'<p>{yaw}</p>'
'<p>{roll}</p>'
'<p>{pitch}</p>'
'</div>'
'</div>'
'<div style="display: flex; gap: 20px; border-left: 1px solid #C0C0C0; padding-left: 20px;">'
'<div>'
'<p><b>Neutral</b></p>'
'<p><b>Happy</b></p>'
'<p><b>Angry</b></p>'
'<p><b>Surprised</b></p>'
'<p><b>Disgusted</b></p>'
'<p><b>Sad</b></p>'
'<p><b>Scared</b></p>'
'</div>'
'<div>'
'<p>{neutral}</p>'
'<p>{happy}</p>'
'<p>{angry}</p>'
'<p>{surprised}</p>'
'<p>{disgusted}</p>'
'<p>{sad}</p>'
'<p>{scared}</p>'
'</div>'
'</div>'
'</div></div>').format(face_value=face_value,
age=age_gender.get('age'),
gender="Female" if age_gender.get('gender') == 0 else "Male",
neutral=f"{emotion.get('neutral'):.2f}",
happy=f"{emotion.get('happy'):.2f}",
angry=f"{emotion.get('angry'):.2f}",
surprised=f"{emotion.get('surprised'):.2f}",
disgusted=f"{emotion.get('disgusted'):.2f}",
sad=f"{emotion.get('sad'):.2f}",
scared=f"{emotion.get('scared'):.2f}",
masked="Yes" if attribute.get('masked') == 1 else "No",
left_eye="Open" if attribute.get('left_eye_opened') == 1 else "Close",
right_eye="Open" if attribute.get('right_eye_opened') == 1 else "Close",
yaw=f"{angles.get('yaw'):.2f}",
roll=f"{angles.get('roll'):.2f}",
pitch=f"{angles.get('pitch'):.2f}",
)
html += item_value
html += '<hr style="border: 1px solid #C0C0C0; margin: 10px 0;"/>'
else:
html = "No face!"
return [html, json_result]
else:
return [result.text, {"resultCode": result.text}]
with gr.Blocks() as demo:
gr.Markdown(
f"""
<a href="https://recognito.vision" style="display: flex; align-items: center;">
<img src="https://recognito.vision/wp-content/uploads/2024/03/Recognito-modified.png" style="width: 8%; margin-right: 15px;"/>
<div>
<p style="font-size: 32px; font-weight: bold; margin: 0;">Recognito</p>
<p style="font-size: 18px; margin: 0;">www.recognito.vision</p>
</div>
</a>
<p style="font-size: 20px; font-weight: bold;">πŸ“˜ Product Documentation</p>
<div style="display: flex; align-items: center;">
&emsp;&emsp;<a href="https://docs.recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/05/book.png" style="width: 48px; margin-right: 5px;"/></a>
</div>
<p style="font-size: 20px; font-weight: bold;">🏠 Visit Recognito</p>
<div style="display: flex; align-items: center;">
&emsp;&emsp;<a href="https://recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/recognito_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.linkedin.com/company/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/linkedin_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://huggingface.co/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/hf_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://github.com/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/github_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://hub.docker.com/u/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/docker_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.youtube.com/@recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/04/youtube_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
</div>
<p style="font-size: 20px; font-weight: bold;">🀝 Contact us for our on-premise ID Document Verification SDKs deployment</p>
<div style="display: flex; align-items: center;">
&emsp;&emsp;<a target="_blank" href="mailto:hello@recognito.vision"><img src="https://img.shields.io/badge/email-hassan@recognito.vision-blue.svg?logo=gmail " alt="www.recognito.vision"></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://wa.me/+14158003112"><img src="https://img.shields.io/badge/whatsapp-+14158003112-blue.svg?logo=whatsapp " alt="www.recognito.vision"></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://t.me/recognito_vision"><img src="https://img.shields.io/badge/telegram-@recognito__vision-blue.svg?logo=telegram " alt="www.recognito.vision"></a>
&nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://join.slack.com/t/recognito-workspace/shared_invite/zt-2d4kscqgn-"><img src="https://img.shields.io/badge/slack-recognito__workspace-blue.svg?logo=slack " alt="www.recognito.vision"></a>
</div>
<br/>
"""
)
with gr.TabItem("Face Recognition"):
with gr.Row():
with gr.Column(scale=7):
with gr.Row():
with gr.Column():
image_input1 = gr.Image(type='pil')
gr.Examples(['examples/1.webp', 'examples/2.webp', 'examples/3.webp', 'examples/4.webp'],
inputs=image_input1)
with gr.Column():
image_input2 = gr.Image(type='pil')
gr.Examples(['examples/5.webp', 'examples/6.webp', 'examples/7.webp', 'examples/8.webp'],
inputs=image_input2)
verifyThreshold = gr.Slider(minimum=0, maximum=1, value=0.67, label="Verify Threshold")
face_recog_button = gr.Button("Face Recognition")
with gr.Column(scale=3):
with gr.TabItem("Output"):
recog_html_output = gr.HTML()
with gr.TabItem("JSON"):
recog_json_output = gr.JSON()
with gr.TabItem("Face Attribute"):
with gr.Row():
with gr.Column():
image_input = gr.Image(type='pil')
gr.Examples(['examples/11.webp', 'examples/12.webp', 'examples/13.webp', 'examples/14.webp'],
inputs=image_input)
face_attr_button = gr.Button("Face Attribute")
with gr.Column():
with gr.TabItem("Output"):
detect_html_output = gr.HTML()
with gr.TabItem("JSON"):
detect_json_output = gr.JSON()
face_recog_button.click(compare_face, inputs=[image_input1, image_input2, verifyThreshold], outputs=[recog_html_output, recog_json_output])
face_attr_button.click(detect_face, inputs=[image_input], outputs=[detect_html_output, detect_json_output])
demo.launch(server_name="0.0.0.0", server_port=7860)