File size: 5,978 Bytes
c88be80
 
 
b0f434e
2912dde
 
c88be80
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ad55b16
28dcf2b
4be039c
4acc4e1
70df087
 
 
 
 
 
28dcf2b
 
 
c88be80
 
 
28dcf2b
 
 
 
 
 
 
 
 
c88be80
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fecade2
c88be80
0273261
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
import gradio as gr
import requests
import datadog_api_client
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

def compare_face(frame1, frame2):
    url = "http://127.0.0.1:8080/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 = ("<table>"
                "<tr>"
                    "<th>Compare Result</th>"
                    "<th>Value</th>"
                "</tr>"
                "<tr>"
                    "<td>Result</td>"
                    "<td>{compare_result}</td>"
                "</tr>"
                "<tr>"
                    "<td>Similarity</td>"
                    "<td>{compare_similarity}</td>"
                "</tr>"
                "</table>".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]

with gr.Blocks() as demo:
    gr.Markdown(
        """
    # KBY-AI - Face Recognition
    We offer SDKs for face recognition, liveness detection(anti-spoofing) and ID card recognition.

    <h4 style="display: flex; align-items: center;">
        ID Document Liveness Detection - Linux - <a href="https://web.kby-ai.com">https://web.kby-ai.com</a>
        <span>
            <img src="https://github.com/kby-ai/.github/assets/125717930/bcf351c5-8b7a-496e-a8f9-c236eb8ad59e" style="margin: 4px; width: 36px; height: 20px">
        </span>
    </h4>
    
    ##### KYC Verification Demo - https://github.com/kby-ai/KYC-Verification-Demo-Android
    ##### ID Capture Web Demo - https://id-document-recognition-react-alpha.vercel.app
    ##### Documentation - Help Center - https://docs.kby-ai.com
    """
    )
    with gr.TabItem("Face Recognition"):
        gr.Markdown(
            """
        ##### Docker Hub - https://hub.docker.com/r/kbyai/face-recognition
        ```bash
        sudo docker pull kbyai/face-recognition:latest
        sudo docker run -e LICENSE="xxxxx" -p 8081:8080 -p 9001:9000 kbyai/face-recognition:latest
        ```
        """
        )
        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])
    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>')

demo.launch(server_name="0.0.0.0", server_port=7860, share=True)