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] def compare_face(frame1, frame2): url = "http://127.0.0.1:8081/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 = ("" "" "" "" "" "" "" "" "" "" "" "" "" "
Compare ResultValue
Result{compare_result}
Similarity{compare_similarity}
".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] def idcard_recognition(frame): url = "http://127.0.0.1:8082/idcard_recognition" files = {'file': open(frame, 'rb')} r = requests.post(url=url, files=files) html = None images = None mrz = None status = r.json().get('Status') table_value = "" if r.json().get('MRZ') is not None: mrz = r.json().get('MRZ') for key, value in r.json().items(): if key == 'Status' or key == 'Images' or key == 'MRZ' or key == 'Position': continue mrz_value = '' if mrz is not None and mrz.get(key) is not None: mrz_value = mrz[key] del mrz[key] row_value = ("" "{key}" "{value}" "{mrz_value}" "".format(key=key, value=value, mrz_value=mrz_value)) table_value = table_value + row_value if mrz is not None: for key, value in mrz.items(): if key == 'MRZ': value = value.replace('<', '<') value = value.replace(',', '

') row_value = ("" "{key}" "{value}" "{mrz_value}" "".format(key=key, value='', mrz_value=value)) table_value = table_value + row_value html = ("" "" "" "" "" "" "" "" "" "" "" "{table_value}" "
FieldValueMRZ
Status{status}
".format(status=status, table_value=table_value)) table_value = "" for key, value in r.json().items(): if key == 'Images': for image_key, image_value in value.items(): row_value = ("" "{key}" "" "".format(key=image_key, base64_image=image_value)) table_value = table_value + row_value images = ("" "" "" "" "" "{table_value}" "
FieldImage
".format(table_value=table_value)) return [html, images] with gr.Blocks() as demo: gr.Markdown( """ # KBY-AI Technology We offer SDKs for face recognition, liveness detection, and ID card recognition. """ ) 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]) 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]) with gr.TabItem("ID Card Recognition"): with gr.Row(): with gr.Column(scale=3): id_image_input = gr.Image(type='filepath') gr.Examples(['idcard_examples/1.jpg', 'idcard_examples/2.jpg', 'idcard_examples/3.jpg'], inputs=id_image_input) id_recognition_button = gr.Button("ID Card Recognition") with gr.Column(scale=5): id_result_output = gr.HTML() with gr.Column(scale=2): image_result_output = gr.HTML() id_recognition_button.click(idcard_recognition, inputs=id_image_input, outputs=[id_result_output, image_result_output]) demo.launch(server_name="0.0.0.0", server_port=9000)