Upload 13 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/clf_model.sav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,67 @@
+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 tensorflow.keras.preprocessing import image
+from keras_facenet import FaceNet
+import pickle
+
+# create a facenet model
+embedding_model = FaceNet(key = '20180402-114759',
+    use_prebuilt=True,
+    cache_folder= os.path.abspath(os.path.dirname(__file__)) + '/facenet_weights').model
+
+clf_model = pickle.load(open(os.path.abspath(os.path.dirname(__file__)) + '/model/clf_mode.sav', 'rb'))
+
+target_shape = (160, 160)
+
+app = Flask(__name__) 
+
+app.config['SITE'] = "http://0.0.0.0:8000/"
+app.config['DEBUG'] = False
+
+@app.route('/api/detect_human_face', methods=['POST'])
+def detect_human_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
+
+    X = np.float32([(np.float32(image1) - 127.5) / 127.5])
+    X_ft = embedding_model.predict(X, batch_size=1)
+    anomaly_score = clf_model.decision_function(X_ft) * -1
+
+    if anomaly_score > 1:
+        result = "Not Human"
+    else:
+        result = "Human"
+  
+    status = "ok"
+    response = jsonify(
+    {
+        "status": status, 
+        "data": {
+            "result": result, 
+            "anomaly_score": float(anomaly_score)
+            }
+    })
+
+    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)

facenet_weights/20180402-114759/20180402-114759-weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b71e7045497e841c00ee568f031d1a4d30908fceadf6884aef2dec4d545202b
+size 94952520

gradio/demo.py

@@ -0,0 +1,27 @@
+import gradio as gr
+import requests
+import json
+from PIL import Image
+
+def detect_human_face(frame1):
+    url = "http://127.0.0.1:8000/api/detect_human_face"
+    files = {'image1': open(frame1, 'rb')}
+
+    r = requests.post(url=url, files=files)
+
+    return [r.json()]
+
+with gr.Blocks() as demo:
+
+    with gr.Row():
+        with gr.Column():
+            face_input = gr.Image(type='filepath', height=480)
+            gr.Examples(['gradio/examples/Human.jpg', 'gradio/examples/NotHuman.jpg'], 
+                        inputs=face_input)
+            detect_human_face_button = gr.Button("Detect Human Face")
+        with gr.Column():
+            detect_result_output = gr.JSON(label='Result')
+
+    detect_human_face_button.click(detect_human_face, inputs=[face_input], outputs=[detect_result_output])
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

model/clf_model.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70ce774d1a9c411caf3d932bab867278df8e5e31b7753755dc3211e665086aae
+size 8450201

requirements.txt

@@ -0,0 +1,8 @@
+flask
+flask-cors
+gradio
+opencv-python
+numpy==1.20.3
+pillow
+pickle
+keras-facenet

run.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+exec python3 app.py &
+exec python3 gradio/demo.py