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Commit
•
c2b0164
1
Parent(s):
81efcf0
Upload 9 files
Browse files- Dockerfile +19 -0
- app.py +217 -0
- gradio/demo.py +114 -0
- gradio/examples/1.jpg +0 -0
- gradio/examples/2.jpg +0 -0
- gradio/examples/3.jpg +0 -0
- gradio/examples/4.jpg +0 -0
- requirements.txt +6 -0
- run.sh +4 -0
Dockerfile
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FROM ubuntu:20.04
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RUN ln -snf /usr/share/zoneinfo/$CONTAINER_TIMEZONE /etc/localtime && echo $CONTAINER_TIMEZONE > /etc/timezone
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RUN apt-get update -y
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RUN apt-get install -y python3 python3-pip python3-opencv
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RUN apt-get install -y libcurl4-openssl-dev libssl-dev
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RUN mkdir -p /home/FaceOnLive_v6
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RUN mkdir -p /home/FaceOnLive_v6/facewrapper
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WORKDIR /home/FaceOnLive_v6
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COPY ./facewrapper ./facewrapper
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COPY ./facewrapper/libs/libimutils.so /usr/lib
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COPY ./gradio ./gradio
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COPY ./openvino /usr/lib
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COPY ./app.py ./app.py
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COPY ./run.sh .
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COPY ./requirements.txt ./requirements.txt
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RUN pip3 install -r requirements.txt
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RUN chmod a+x run.sh
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CMD ["./run.sh"]
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EXPOSE 8000
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app.py
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import sys
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sys.path.append('.')
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from flask import Flask, request, jsonify
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from time import gmtime, strftime
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import os
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import base64
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import json
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import cv2
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import numpy as np
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from facewrapper.facewrapper import ttv_version
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from facewrapper.facewrapper import ttv_get_hwid
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from facewrapper.facewrapper import ttv_init
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from facewrapper.facewrapper import ttv_init_offline
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from facewrapper.facewrapper import ttv_extract_feature
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from facewrapper.facewrapper import ttv_compare_feature
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app = Flask(__name__)
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app.config['SITE'] = "http://0.0.0.0:8000/"
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app.config['DEBUG'] = False
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licenseKey = os.environ.get("LICENSE_KEY")
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licensePath = "license.txt"
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modelFolder = os.path.abspath(os.path.dirname(__file__)) + '/facewrapper/dict'
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version = ttv_version()
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print("version: ", version.decode('utf-8'))
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ret = ttv_init(modelFolder.encode('utf-8'), licenseKey.encode('utf-8'))
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if ret != 0:
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print(f"online init failed: {ret}");
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hwid = ttv_get_hwid()
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print("hwid: ", hwid.decode('utf-8'))
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ret = ttv_init_offline(modelFolder.encode('utf-8'), licensePath.encode('utf-8'))
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if ret != 0:
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print(f"offline init failed: {ret}")
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exit(-1)
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else:
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print(f"offline init ok")
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else:
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print(f"online init ok")
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@app.route('/api/compare_face', methods=['POST'])
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def compare_face():
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file1 = request.files['image1']
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image1 = cv2.imdecode(np.fromstring(file1.read(), np.uint8), cv2.IMREAD_COLOR)
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if image1 is None:
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result = "image1: is null!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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file2 = request.files['image2']
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image2 = cv2.imdecode(np.fromstring(file2.read(), np.uint8), cv2.IMREAD_COLOR)
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if image2 is None:
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result = "image2: is null!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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faceRect1 = np.zeros([4], dtype=np.int32)
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feature1 = np.zeros([2048], dtype=np.uint8)
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featureSize1 = np.zeros([1], dtype=np.int32)
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ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
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if ret <= 0:
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if ret == -1:
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result = "license error!"
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elif ret == -2:
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result = "init error!"
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elif ret == 0:
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result = "image1: no face detected!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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faceRect2 = np.zeros([4], dtype=np.int32)
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feature2 = np.zeros([2048], dtype=np.uint8)
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featureSize2 = np.zeros([1], dtype=np.int32)
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ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
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if ret <= 0:
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if ret == -1:
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result = "license error!"
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elif ret == -2:
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result = "init error!"
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elif ret == 0:
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result = "image2: no face detected!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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similarity = ttv_compare_feature(feature1, feature2)
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if similarity > 0.7:
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result = "same"
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else:
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result = "different"
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status = "ok"
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response = jsonify(
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{
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"status": status,
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"data": {
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"result": result,
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"similarity": float(similarity),
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"face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])},
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"face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])},
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}
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})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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@app.route('/api/compare_face_base64', methods=['POST'])
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def coompare_face_base64():
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content = request.get_json()
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imageBase641 = content['image1']
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image1 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase641), dtype=np.uint8), cv2.IMREAD_COLOR)
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if image1 is None:
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result = "image1: is null!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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imageBase642 = content['image2']
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image2 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase642), dtype=np.uint8), cv2.IMREAD_COLOR)
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if image2 is None:
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result = "image2: is null!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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faceRect1 = np.zeros([4], dtype=np.int32)
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feature1 = np.zeros([2048], dtype=np.uint8)
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featureSize1 = np.zeros([1], dtype=np.int32)
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ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
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if ret <= 0:
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if ret == -1:
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result = "license error!"
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elif ret == -2:
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result = "init error!"
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elif ret == 0:
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result = "image1: no face detected!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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faceRect2 = np.zeros([4], dtype=np.int32)
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feature2 = np.zeros([2048], dtype=np.uint8)
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featureSize2 = np.zeros([1], dtype=np.int32)
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ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
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if ret <= 0:
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if ret == -1:
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result = "license error!"
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elif ret == -2:
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result = "init error!"
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elif ret == 0:
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result = "image2: no face detected!"
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status = "ok"
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response = jsonify({"status": status, "data": {"result": result}})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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similarity = ttv_compare_feature(feature1, feature2)
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if similarity > 0.7:
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result = "same"
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else:
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result = "different"
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status = "ok"
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response = jsonify(
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{
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"status": status,
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"data": {
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"result": result,
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"similarity": float(similarity),
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"face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])},
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"face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])},
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}
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})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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if __name__ == '__main__':
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port = int(os.environ.get("PORT", 8000))
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app.run(host='0.0.0.0', port=port)
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gradio/demo.py
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import gradio as gr
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import requests
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import json
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from PIL import Image
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def compare_face(frame1, frame2):
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url = "http://127.0.0.1:8000/api/compare_face"
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files = {'image1': open(frame1, 'rb'), 'image2': open(frame2, 'rb')}
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r = requests.post(url=url, files=files)
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faces = None
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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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data = r.json().get('data')
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if data.get('face1') is not None:
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face = data.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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32 |
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if y2 >= image1.height:
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y2 = image1.height - 1
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34 |
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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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39 |
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|
40 |
+
face1 = face1.resize((int(resized_w), int(resized_h)))
|
41 |
+
|
42 |
+
if data.get('face2') is not None:
|
43 |
+
face = data.get('face2')
|
44 |
+
x1 = face.get('x1')
|
45 |
+
y1 = face.get('y1')
|
46 |
+
x2 = face.get('x2')
|
47 |
+
y2 = face.get('y2')
|
48 |
+
|
49 |
+
if x1 < 0:
|
50 |
+
x1 = 0
|
51 |
+
if y1 < 0:
|
52 |
+
y1 = 0
|
53 |
+
if x2 >= image2.width:
|
54 |
+
x2 = image2.width - 1
|
55 |
+
if y2 >= image2.height:
|
56 |
+
y2 = image2.height - 1
|
57 |
+
|
58 |
+
face2 = image2.crop((x1, y1, x2, y2))
|
59 |
+
face_image_ratio = face2.width / float(face2.height)
|
60 |
+
resized_w = int(face_image_ratio * 150)
|
61 |
+
resized_h = 150
|
62 |
+
|
63 |
+
face2 = face2.resize((int(resized_w), int(resized_h)))
|
64 |
+
|
65 |
+
if face1 is not None and face2 is not None:
|
66 |
+
new_image = Image.new('RGB',(face1.width + face2.width + 10, 150), (80,80,80))
|
67 |
+
|
68 |
+
new_image.paste(face1,(0,0))
|
69 |
+
new_image.paste(face2,(face1.width + 10, 0))
|
70 |
+
faces = new_image.copy()
|
71 |
+
elif face1 is not None and face2 is None:
|
72 |
+
new_image = Image.new('RGB',(face1.width + face1.width + 10, 150), (80,80,80))
|
73 |
+
|
74 |
+
new_image.paste(face1,(0,0))
|
75 |
+
faces = new_image.copy()
|
76 |
+
elif face1 is None and face2 is not None:
|
77 |
+
new_image = Image.new('RGB',(face2.width + face2.width + 10, 150), (80,80,80))
|
78 |
+
|
79 |
+
new_image.paste(face2,(face2.width + 10, 0))
|
80 |
+
faces = new_image.copy()
|
81 |
+
except:
|
82 |
+
pass
|
83 |
+
|
84 |
+
return [r.json(), faces]
|
85 |
+
|
86 |
+
with gr.Blocks() as demo:
|
87 |
+
gr.Markdown(
|
88 |
+
"""
|
89 |
+
# Face Recognition
|
90 |
+
Get your own Face Recognition Server by duplicating this space.<br/>
|
91 |
+
Or run on your own machine using docker.<br/>
|
92 |
+
```docker run -it -p 7860:7860 --platform=linux/amd64 \
|
93 |
+
-e LICENSE_KEY="YOUR_VALUE_HERE" \
|
94 |
+
registry.hf.space/faceonlive-face-recognition-sdk:latest ```<br/><br/>
|
95 |
+
Contact us at contact@faceonlive.com for issues and support.<br/>
|
96 |
+
"""
|
97 |
+
)
|
98 |
+
with gr.Row():
|
99 |
+
with gr.Column():
|
100 |
+
compare_face_input1 = gr.Image(type='filepath', height=480)
|
101 |
+
gr.Examples(['gradio/examples/1.jpg', 'gradio/examples/2.jpg'],
|
102 |
+
inputs=compare_face_input1)
|
103 |
+
compare_face_button = gr.Button("Compare Face")
|
104 |
+
with gr.Column():
|
105 |
+
compare_face_input2 = gr.Image(type='filepath', height=480)
|
106 |
+
gr.Examples(['gradio/examples/3.jpg', 'gradio/examples/4.jpg'],
|
107 |
+
inputs=compare_face_input2)
|
108 |
+
with gr.Column():
|
109 |
+
compare_face_output = gr.Image(type="pil", height=150)
|
110 |
+
compare_result_output = gr.JSON(label='Result')
|
111 |
+
|
112 |
+
compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[compare_result_output, compare_face_output])
|
113 |
+
|
114 |
+
demo.launch(server_name="0.0.0.0", server_port=7860)
|
gradio/examples/1.jpg
ADDED
![]() |
gradio/examples/2.jpg
ADDED
![]() |
gradio/examples/3.jpg
ADDED
![]() |
gradio/examples/4.jpg
ADDED
![]() |
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
flask
|
2 |
+
flask-cors
|
3 |
+
gradio
|
4 |
+
opencv-python
|
5 |
+
numpy==1.20.3
|
6 |
+
pillow
|
run.sh
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/bin/bash
|
2 |
+
|
3 |
+
exec python3 app.py &
|
4 |
+
exec python3 gradio/demo.py
|