import gradio as gr import requests import datadog_api_client from PIL import Image def check_liveness(frame): url = "http://127.0.0.1:8080/check_liveness" file = {'file': open(frame, 'rb')} r = requests.post(url=url, files=file) result = r.json().get('face_state').get('result') html = None faces = None if r.json().get('face_state').get('is_not_front') is not None: liveness_score = r.json().get('face_state').get('liveness_score') eye_closed = r.json().get('face_state').get('eye_closed') is_boundary_face = r.json().get('face_state').get('is_boundary_face') is_not_front = r.json().get('face_state').get('is_not_front') is_occluded = r.json().get('face_state').get('is_occluded') is_small = r.json().get('face_state').get('is_small') luminance = r.json().get('face_state').get('luminance') mouth_opened = r.json().get('face_state').get('mouth_opened') quality = r.json().get('face_state').get('quality') html = ("" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "
Face StateValue
Result{result}
Liveness Score{liveness_score}
Quality{quality}
Luminance{luminance}
Is Small{is_small}
Is Boundary{is_boundary_face}
Is Not Front{is_not_front}
Face Occluded{is_occluded}
Eye Closed{eye_closed}
Mouth Opened{mouth_opened}
".format(liveness_score=liveness_score, quality=quality, luminance=luminance, is_small=is_small, is_boundary_face=is_boundary_face, is_not_front=is_not_front, is_occluded=is_occluded, eye_closed=eye_closed, mouth_opened=mouth_opened, result=result)) else: html = ("" "" "" "" "" "" "" "" "" "
Face StateValue
Result{result}
".format(result=result)) try: image = Image.open(frame) for face in r.json().get('faces'): 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 >= 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))) if faces is None: faces = face_image else: new_image = Image.new('RGB',(faces.width + face_image.width + 10, 150), (80,80,80)) new_image.paste(faces,(0,0)) new_image.paste(face_image,(faces.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.
Besides that, we can provide several AI models and development services in machine learning. ## Simple Installation & Simple API ``` sudo docker pull kbyai/face-liveness-detection:latest sudo docker run -e LICENSE="xxxxx" -p 8080:8080 -p 9000:9000 kbyai/face-liveness-detection:latest ``` ## KYC Verification Demo https://github.com/kby-ai/KYC-Verification """ ) with gr.TabItem("Face Liveness Detection"): with gr.Row(): with gr.Column(): live_image_input = gr.Image(type='filepath') gr.Examples(['live_examples/1.jpg', 'live_examples/2.jpg', 'live_examples/3.jpg', 'live_examples/4.jpg'], inputs=live_image_input) check_liveness_button = gr.Button("Check Liveness") with gr.Column(): liveness_face_output = gr.Image(type="pil").style(height=150) livness_result_output = gr.HTML() check_liveness_button.click(check_liveness, inputs=live_image_input, outputs=[liveness_face_output, livness_result_output]) demo.launch(server_name="0.0.0.0", server_port=9000)