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import gradio as gr |
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import requests |
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import datadog_api_client |
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from PIL import Image |
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def compare_face(frame1, frame2): |
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url = "http://127.0.0.1:8080/compare_face" |
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files = {'file1': open(frame1, 'rb'), 'file2': open(frame2, 'rb')} |
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r = requests.post(url=url, files=files) |
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html = None |
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faces = None |
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compare_result = r.json().get('compare_result') |
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compare_similarity = r.json().get('compare_similarity') |
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html = ("<table>" |
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"<tr>" |
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"<th>Compare Result</th>" |
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"<th>Value</th>" |
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"</tr>" |
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"<tr>" |
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"<td>Result</td>" |
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"<td>{compare_result}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Similarity</td>" |
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"<td>{compare_similarity}</td>" |
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"</tr>" |
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"</table>".format(compare_result=compare_result, compare_similarity=compare_similarity)) |
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try: |
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image1 = Image.open(frame1) |
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image2 = Image.open(frame2) |
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face1 = None |
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face2 = None |
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if r.json().get('face1') is not None: |
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face = r.json().get('face1') |
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x1 = face.get('x1') |
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y1 = face.get('y1') |
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x2 = face.get('x2') |
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y2 = face.get('y2') |
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if x1 < 0: |
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x1 = 0 |
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if y1 < 0: |
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y1 = 0 |
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if x2 >= image1.width: |
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x2 = image1.width - 1 |
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if y2 >= image1.height: |
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y2 = image1.height - 1 |
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face1 = image1.crop((x1, y1, x2, y2)) |
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face_image_ratio = face1.width / float(face1.height) |
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resized_w = int(face_image_ratio * 150) |
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resized_h = 150 |
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face1 = face1.resize((int(resized_w), int(resized_h))) |
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if r.json().get('face2') is not None: |
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face = r.json().get('face2') |
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x1 = face.get('x1') |
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y1 = face.get('y1') |
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x2 = face.get('x2') |
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y2 = face.get('y2') |
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if x1 < 0: |
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x1 = 0 |
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if y1 < 0: |
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y1 = 0 |
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if x2 >= image2.width: |
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x2 = image2.width - 1 |
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if y2 >= image2.height: |
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y2 = image2.height - 1 |
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face2 = image2.crop((x1, y1, x2, y2)) |
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face_image_ratio = face2.width / float(face2.height) |
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resized_w = int(face_image_ratio * 150) |
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resized_h = 150 |
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face2 = face2.resize((int(resized_w), int(resized_h))) |
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if face1 is not None and face2 is not None: |
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new_image = Image.new('RGB',(face1.width + face2.width + 10, 150), (80,80,80)) |
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new_image.paste(face1,(0,0)) |
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new_image.paste(face2,(face1.width + 10, 0)) |
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faces = new_image.copy() |
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elif face1 is not None and face2 is None: |
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new_image = Image.new('RGB',(face1.width + face1.width + 10, 150), (80,80,80)) |
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new_image.paste(face1,(0,0)) |
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faces = new_image.copy() |
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elif face1 is None and face2 is not None: |
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new_image = Image.new('RGB',(face2.width + face2.width + 10, 150), (80,80,80)) |
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new_image.paste(face2,(face2.width + 10, 0)) |
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faces = new_image.copy() |
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except: |
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pass |
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return [faces, html] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# KBY-AI - Face Liveness Detecion |
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We offer SDKs for face recognition, liveness detection(anti-spoofing) and ID card recognition. |
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We also specialize in providing outsourcing services with a variety of technical stacks like AI(Computer Vision/Machine Learning), Mobile apps, and web apps. |
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##### KYC Verification Demo - https://github.com/kby-ai/KYC-Verification-Demo-Android |
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##### ID Capture Web Demo - https://id-document-recognition-react-alpha.vercel.app |
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##### Documentation - Help Center - https://docs.kby-ai.com |
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""" |
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) |
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with gr.TabItem("Face Recognition"): |
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gr.Markdown( |
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""" |
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##### Docker Hub - https://hub.docker.com/r/kbyai/face-recognition |
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```bash |
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sudo docker pull kbyai/face-recognition:latest |
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sudo docker run -e LICENSE="xxxxx" -p 8081:8080 -p 9001:9000 kbyai/face-recognition:latest |
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``` |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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compare_face_input1 = gr.Image(type='filepath') |
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gr.Examples(['face_examples/1.jpg', 'face_examples/3.jpg', 'face_examples/5.jpg', 'face_examples/7.jpg', 'face_examples/9.jpg'], |
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inputs=compare_face_input1) |
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compare_face_button = gr.Button("Compare Face") |
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with gr.Column(): |
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compare_face_input2 = gr.Image(type='filepath') |
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gr.Examples(['face_examples/2.jpg', 'face_examples/4.jpg', 'face_examples/6.jpg', 'face_examples/8.jpg', 'face_examples/10.jpg'], |
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inputs=compare_face_input2) |
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with gr.Column(): |
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compare_face_output = gr.Image(type="pil").style(height=150) |
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compare_result_output = gr.HTML(label='Result') |
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compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[compare_face_output, compare_result_output]) |
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gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fkby-ai%2FFaceRecognition"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fkby-ai%2FFaceRecognition&countColor=%23263759" /></a>') |
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demo.launch(server_name="0.0.0.0", server_port=7860) |