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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 check_liveness(frame): |
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url = "http://127.0.0.1:8080/check_liveness" |
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file = {'file': open(frame, 'rb')} |
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r = requests.post(url=url, files=file) |
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result = r.json().get('face_state').get('result') |
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html = None |
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faces = None |
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if r.json().get('face_state').get('is_not_front') is not None: |
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liveness_score = r.json().get('face_state').get('liveness_score') |
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eye_closed = r.json().get('face_state').get('eye_closed') |
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is_boundary_face = r.json().get('face_state').get('is_boundary_face') |
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is_not_front = r.json().get('face_state').get('is_not_front') |
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is_occluded = r.json().get('face_state').get('is_occluded') |
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is_small = r.json().get('face_state').get('is_small') |
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luminance = r.json().get('face_state').get('luminance') |
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mouth_opened = r.json().get('face_state').get('mouth_opened') |
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quality = r.json().get('face_state').get('quality') |
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html = ("<table>" |
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"<tr>" |
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"<th>Face State</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>{result}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Liveness Score</td>" |
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"<td>{liveness_score}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Quality</td>" |
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"<td>{quality}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Luminance</td>" |
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"<td>{luminance}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Is Small</td>" |
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"<td>{is_small}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Is Boundary</td>" |
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"<td>{is_boundary_face}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Is Not Front</td>" |
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"<td>{is_not_front}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Face Occluded</td>" |
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"<td>{is_occluded}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Eye Closed</td>" |
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"<td>{eye_closed}</td>" |
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"</tr>" |
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"<tr>" |
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"<td>Mouth Opened</td>" |
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"<td>{mouth_opened}</td>" |
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"</tr>" |
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"</table>".format(liveness_score=liveness_score, quality=quality, luminance=luminance, is_small=is_small, is_boundary_face=is_boundary_face, |
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is_not_front=is_not_front, is_occluded=is_occluded, eye_closed=eye_closed, mouth_opened=mouth_opened, result=result)) |
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else: |
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html = ("<table>" |
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"<tr>" |
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"<th>Face State</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>{result}</td>" |
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"</tr>" |
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"</table>".format(result=result)) |
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try: |
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image = Image.open(frame) |
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for face in r.json().get('faces'): |
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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 >= image.width: |
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x2 = image.width - 1 |
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if y2 >= image.height: |
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y2 = image.height - 1 |
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face_image = image.crop((x1, y1, x2, y2)) |
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face_image_ratio = face_image.width / float(face_image.height) |
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resized_w = int(face_image_ratio * 150) |
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resized_h = 150 |
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face_image = face_image.resize((int(resized_w), int(resized_h))) |
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if faces is None: |
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faces = face_image |
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else: |
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new_image = Image.new('RGB',(faces.width + face_image.width + 10, 150), (80,80,80)) |
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new_image.paste(faces,(0,0)) |
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new_image.paste(face_image,(faces.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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def compare_face(frame1, frame2): |
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url = "http://127.0.0.1:8081/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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def idcard_recognition(frame): |
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url = "http://127.0.0.1:8082/idcard_recognition" |
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files = {'file': open(frame, 'rb')} |
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r = requests.post(url=url, files=files) |
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html = None |
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images = None |
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mrz = None |
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status = r.json().get('Status') |
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table_value = "" |
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if r.json().get('MRZ') is not None: |
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mrz = r.json().get('MRZ') |
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for key, value in r.json().items(): |
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if key == 'Status' or key == 'Images' or key == 'MRZ' or key == 'Position': |
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continue |
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mrz_value = '' |
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if mrz is not None and mrz.get(key) is not None: |
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mrz_value = mrz[key] |
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del mrz[key] |
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row_value = ("<tr>" |
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"<td>{key}</td>" |
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"<td>{value}</td>" |
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"<td>{mrz_value}</td>" |
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"</tr>".format(key=key, value=value, mrz_value=mrz_value)) |
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table_value = table_value + row_value |
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if mrz is not None: |
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for key, value in mrz.items(): |
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if key == 'MRZ': |
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value = value.replace('<', '<') |
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value = value.replace(',', '<p>') |
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row_value = ("<tr>" |
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"<td>{key}</td>" |
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"<td>{value}</td>" |
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"<td>{mrz_value}</td>" |
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"</tr>".format(key=key, value='', mrz_value=value)) |
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table_value = table_value + row_value |
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html = ("<table>" |
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"<tr>" |
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"<th style=""width:20%"">Field</th>" |
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"<th style=""width:40%"">Value</th>" |
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"<th style=""width:40%"">MRZ</th>" |
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"</tr>" |
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"<tr>" |
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"<td>Status</td>" |
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"<td>{status}</td>" |
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"<td></td>" |
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"</tr>" |
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"{table_value}" |
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"</table>".format(status=status, table_value=table_value)) |
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table_value = "" |
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for key, value in r.json().items(): |
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if key == 'Images': |
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for image_key, image_value in value.items(): |
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row_value = ("<tr>" |
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"<td>{key}</td>" |
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"<td><img src=""data:image/png;base64,{base64_image} width = '200' height= '100' /></td>" |
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"</tr>".format(key=image_key, base64_image=image_value)) |
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table_value = table_value + row_value |
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images = ("<table>" |
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"<tr>" |
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"<th>Field</th>" |
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"<th>Image</th>" |
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"</tr>" |
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"{table_value}" |
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"</table>".format(table_value=table_value)) |
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return [html, images] |
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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 Technology |
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We offer SDKs for face recognition, liveness detection, and ID card recognition. |
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""" |
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) |
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with gr.TabItem("Face Liveness Detection"): |
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with gr.Row(): |
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with gr.Column(): |
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live_image_input = gr.Image(type='filepath') |
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gr.Examples(['gradio/live_examples/1.jpg', 'gradio/live_examples/2.jpg', 'gradio/live_examples/3.jpg', 'gradio/live_examples/4.jpg'], |
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inputs=live_image_input) |
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check_liveness_button = gr.Button("Check Liveness") |
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with gr.Column(): |
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liveness_face_output = gr.Image(type="pil").style(height=150) |
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livness_result_output = gr.HTML() |
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check_liveness_button.click(check_liveness, inputs=live_image_input, outputs=[liveness_face_output, livness_result_output]) |
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with gr.TabItem("Face Recognition"): |
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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(['gradio/face_examples/1.jpg', 'gradio/face_examples/3.jpg', 'gradio/face_examples/5.jpg', 'gradio/face_examples/7.jpg', 'gradio/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(['gradio/face_examples/2.jpg', 'gradio/face_examples/4.jpg', 'gradio/face_examples/6.jpg', 'gradio/face_examples/8.jpg', 'gradio/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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with gr.TabItem("ID Card Recognition"): |
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with gr.Row(): |
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with gr.Column(scale=3): |
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id_image_input = gr.Image(type='filepath') |
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gr.Examples(['gradio/idcard_examples/1.jpg', 'gradio/idcard_examples/2.jpg', 'gradio/idcard_examples/3.jpg'], |
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inputs=id_image_input) |
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id_recognition_button = gr.Button("ID Card Recognition") |
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with gr.Column(scale=5): |
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id_result_output = gr.HTML() |
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with gr.Column(scale=2): |
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image_result_output = gr.HTML() |
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id_recognition_button.click(idcard_recognition, inputs=id_image_input, outputs=[id_result_output, image_result_output]) |
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demo.launch(server_name="0.0.0.0", server_port=7860) |