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Dockerfile

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+FROM ubuntu:20.04
+RUN ln -snf /usr/share/zoneinfo/$CONTAINER_TIMEZONE /etc/localtime && echo $CONTAINER_TIMEZONE > /etc/timezone
+RUN apt-get update -y
+RUN apt-get install -y python3 python3-pip python3-opencv
+RUN apt-get install -y libcurl4-openssl-dev libssl-dev
+RUN mkdir -p /home/FaceOnLive_v6
+RUN mkdir -p /home/FaceOnLive_v6/facewrapper
+WORKDIR /home/FaceOnLive_v6
+COPY ./facewrapper ./facewrapper
+COPY ./facewrapper/libs/libimutils.so /usr/lib
+COPY ./gradio ./gradio
+COPY ./openvino /usr/lib
+COPY ./app.py ./app.py
+COPY ./run.sh .
+COPY ./requirements.txt ./requirements.txt
+RUN pip3 install -r requirements.txt
+RUN chmod a+x run.sh
+CMD ["./run.sh"]
+EXPOSE 8000

app.py

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+import sys
+sys.path.append('.')
+
+from flask import Flask, request, jsonify
+from time import gmtime, strftime
+import os
+import base64
+import json
+import cv2
+import numpy as np
+
+from facewrapper.facewrapper import ttv_version
+from facewrapper.facewrapper import ttv_get_hwid
+from facewrapper.facewrapper import ttv_init
+from facewrapper.facewrapper import ttv_init_offline
+from facewrapper.facewrapper import ttv_extract_feature
+from facewrapper.facewrapper import ttv_compare_feature
+
+app = Flask(__name__) 
+
+app.config['SITE'] = "http://0.0.0.0:8000/"
+app.config['DEBUG'] = False
+
+licenseKey = os.environ.get("LICENSE_KEY")
+licensePath = "license.txt"
+modelFolder = os.path.abspath(os.path.dirname(__file__)) + '/facewrapper/dict'
+
+version = ttv_version()
+print("version: ", version.decode('utf-8'))
+
+ret = ttv_init(modelFolder.encode('utf-8'), licenseKey.encode('utf-8'))
+if ret != 0:
+    print(f"online init failed: {ret}");
+
+    hwid = ttv_get_hwid()
+    print("hwid: ", hwid.decode('utf-8'))
+
+    ret = ttv_init_offline(modelFolder.encode('utf-8'), licensePath.encode('utf-8'))
+    if ret != 0:
+        print(f"offline init failed: {ret}")
+        exit(-1)
+    else:
+        print(f"offline init ok")
+
+else:
+    print(f"online init ok")
+
+@app.route('/api/compare_face', methods=['POST'])
+def compare_face():
+    file1 = request.files['image1']
+    image1 = cv2.imdecode(np.fromstring(file1.read(), np.uint8), cv2.IMREAD_COLOR)
+    if image1 is None:
+        result = "image1: is null!"
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    file2 = request.files['image2']
+    image2 = cv2.imdecode(np.fromstring(file2.read(), np.uint8), cv2.IMREAD_COLOR)
+    if image2 is None:
+        result = "image2: is null!"
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+    
+    faceRect1 = np.zeros([4], dtype=np.int32)
+    feature1 = np.zeros([2048], dtype=np.uint8)
+    featureSize1 = np.zeros([1], dtype=np.int32)
+
+    ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
+    if ret <= 0:
+        if ret == -1:
+            result = "license error!"
+        elif ret == -2:
+            result = "init error!"
+        elif ret == 0:
+            result = "image1: no face detected!"
+
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    faceRect2 = np.zeros([4], dtype=np.int32)
+    feature2 = np.zeros([2048], dtype=np.uint8)
+    featureSize2 = np.zeros([1], dtype=np.int32)
+
+    ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
+    if ret <= 0:
+        if ret == -1:
+            result = "license error!"
+        elif ret == -2:
+            result = "init error!"
+        elif ret == 0:
+            result = "image2: no face detected!"
+
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    similarity = ttv_compare_feature(feature1, feature2)
+    if similarity > 0.7:
+        result = "same"
+    else:
+        result = "different"
+  
+    status = "ok"
+    response = jsonify(
+    {
+        "status": status, 
+        "data": {
+            "result": result, 
+            "similarity": float(similarity), 
+            "face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])}, 
+            "face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])}, 
+            }
+    })
+
+    response.status_code = 200
+    response.headers["Content-Type"] = "application/json; charset=utf-8"
+    return response
+
+
+@app.route('/api/compare_face_base64', methods=['POST'])
+def coompare_face_base64():
+    content = request.get_json()
+    imageBase641 = content['image1']
+    image1 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase641), dtype=np.uint8), cv2.IMREAD_COLOR)
+
+    if image1 is None:
+        result = "image1: is null!"
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    imageBase642 = content['image2']
+    image2 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase642), dtype=np.uint8), cv2.IMREAD_COLOR)
+
+    if image2 is None:
+        result = "image2: is null!"
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    faceRect1 = np.zeros([4], dtype=np.int32)
+    feature1 = np.zeros([2048], dtype=np.uint8)
+    featureSize1 = np.zeros([1], dtype=np.int32)
+
+    ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
+    if ret <= 0:
+        if ret == -1:
+            result = "license error!"
+        elif ret == -2:
+            result = "init error!"
+        elif ret == 0:
+            result = "image1: no face detected!"
+
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    faceRect2 = np.zeros([4], dtype=np.int32)
+    feature2 = np.zeros([2048], dtype=np.uint8)
+    featureSize2 = np.zeros([1], dtype=np.int32)
+
+    ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
+    if ret <= 0:
+        if ret == -1:
+            result = "license error!"
+        elif ret == -2:
+            result = "init error!"
+        elif ret == 0:
+            result = "image2: no face detected!"
+
+        status = "ok"
+        response = jsonify({"status": status, "data": {"result": result}})
+        response.status_code = 200
+        response.headers["Content-Type"] = "application/json; charset=utf-8"
+        return response
+
+    similarity = ttv_compare_feature(feature1, feature2)
+    if similarity > 0.7:
+        result = "same"
+    else:
+        result = "different"
+  
+    status = "ok"
+    response = jsonify(
+    {
+        "status": status, 
+        "data": {
+            "result": result, 
+            "similarity": float(similarity), 
+            "face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])}, 
+            "face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])}, 
+            }
+    })
+    response.status_code = 200
+    response.headers["Content-Type"] = "application/json; charset=utf-8"
+    return response
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 8000))
+    app.run(host='0.0.0.0', port=port)

gradio/demo.py

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+import gradio as gr
+import requests
+import json
+from PIL import Image
+
+def compare_face(frame1, frame2):
+    url = "http://127.0.0.1:8000/api/compare_face"
+    files = {'image1': open(frame1, 'rb'), 'image2': open(frame2, 'rb')}
+
+    r = requests.post(url=url, files=files)
+    faces = None
+
+    try:
+        image1 = Image.open(frame1)
+        image2 = Image.open(frame2)
+
+        face1 = None
+        face2 = None
+        data = r.json().get('data')
+        if data.get('face1') is not None:
+            face = data.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 data.get('face2') is not None:
+            face = data.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 [r.json(), faces]
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # Face Recognition
+    Get your own Face Recognition Server by duplicating this space.<br/>
+        Or run on your own machine using docker.<br/>
+    ```docker run -it -p 7860:7860 --platform=linux/amd64 \
+	-e LICENSE_KEY="YOUR_VALUE_HERE" \
+	registry.hf.space/faceonlive-face-recognition-sdk:latest ```<br/><br/>
+    Contact us at contact@faceonlive.com for issues and support.<br/>
+    """
+    )
+    with gr.Row():
+        with gr.Column():
+            compare_face_input1 = gr.Image(type='filepath', height=480)
+            gr.Examples(['gradio/examples/1.jpg', 'gradio/examples/2.jpg'], 
+                        inputs=compare_face_input1)
+            compare_face_button = gr.Button("Compare Face")
+        with gr.Column():
+            compare_face_input2 = gr.Image(type='filepath', height=480)
+            gr.Examples(['gradio/examples/3.jpg', 'gradio/examples/4.jpg'], 
+                        inputs=compare_face_input2)
+        with gr.Column():
+            compare_face_output = gr.Image(type="pil", height=150)
+            compare_result_output = gr.JSON(label='Result')
+
+    compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[compare_result_output, compare_face_output])
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+flask
+flask-cors
+gradio
+opencv-python
+numpy==1.20.3
+pillow

run.sh

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+#!/bin/bash
+
+exec python3 app.py &
+exec python3 gradio/demo.py