added facedb tutorial

examples/face_db/README.md

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+This example demonstrates usage of FaceDB to query an embedding from a DB.  Please go through code to understand FaceDB usage.
+
+> This example only works in Linux.  FaceDB is currently not available for Windows.  
+
+> Free version of Face DB only supports 50 faces.
+
+> Email us at contact@baseapp.com for further queries.
+
+![face_db](https://github.com/baseapp/DeepSight-Face-Recognition-SDK/blob/master/examples/face_db/static/gen/2018-07-31_144814.jpg)
+
+
+### Running
+
+* To run this example, install the following dependencies
+
+```sh
+pip install requests 
+pip install opencv-python
+pip install scipy flask
+```
+* Next, start `Deepsight Face SDK` and let it run.
+* Next, start `facedb --serve` and let it run
+* Start the flask app using `python app.py`
+* The application will say `fifa db initialized`
+* Open a browser and point to `localhost:5101`
+* Use the gui to upload a photo
+* The application will return closest matching fifa celebrity
+
+### Files
+
+* `facedb` - This is the free version of faceDB binary.  It supports upto 50 faces.
+* `app.py` - This is the flask app.
+* `dsface.py` - A simple python wrapper to generate embeddings using Deepsight Face
+* `facedb.py` - A simple python wrapper to `facedb` binary
+*  `save_to_facedb.py` - This demonstrates how to save embeddings in `facedb`.  It reads values from `fifadb.json` and stores them in `facedb`.  It then dumps the database so that it can be loaded later.

examples/face_db/app.py

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+"""
+
+ * Author:    Azmath Moosa
+ * Created:   31st July 2018
+ * Description: Flask app to demonstrate use of FaceDB.  
+                This app can be accessed at localhost:5101
+                It takes an image and finds the closest matching
+                FIFA celebrity.
+                It uses Deepsight SDK for face detection and retrieves embeddings.
+                It then uses FaceDB to query the closest embeddings and retrieves celeb name.
+                It then shows the celeb's photo next to the input photo.
+                Please launch and run facedb and dsFace manually
+ * Dependencies: python requests, json, flask, opencv-python, numpy
+ 
+"""
+
+from flask import Flask, render_template, request, make_response, Markup, send_from_directory
+import io
+import numpy as np
+import cv2
+import utils
+import base64
+import datetime
+from facedb import FaceDBHandler
+from dsface import DSFaceHandler
+
+app = Flask(__name__)
+
+faceDB = FaceDBHandler(filename="fifadb")
+dsFace = DSFaceHandler(face_rec=True,face_lmk=True)
+
+
+@app.route("/")
+def index():
+   return render_template("input.html")
+
+@app.route("/result", methods=['POST'])
+def result():
+    photo = request.files['face']
+    in_memory_file = io.BytesIO()
+    photo.save(in_memory_file)
+    data = np.fromstring(in_memory_file.getvalue(), dtype=np.uint8)
+    color_image_flag = 1
+    img = cv2.imdecode(data, color_image_flag)    
+
+    vec, crop = dsFace.get_embedding(img)
+
+    search = faceDB.search_database(vec)
+
+    print(search)
+
+    celebname = search["meta"]
+
+    output_img = utils.read_transparent_png("static/pics/%s.png"%celebname)
+    outimg = utils.build_montages([crop, output_img], (180,180),(2,1))    
+    fname = "static/gen/"+datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S') + ".jpg"
+    cv2.imwrite(fname, outimg[0])
+
+    return render_template("result.html", name=celebname, imgpath = fname)
+
+@app.route('/<path:path>')
+def send_file(path):
+    return send_from_directory('.', path)
+
+
+if __name__ == "__main__":
+    if faceDB.load_database():
+        faceDB.reindex()
+        print("fifa db initialized")
+        app.run(host= '0.0.0.0', port=5101)
+    else:
+        print("Failed to load and init fifa database; is fifadb.db and fifadb.index files present?\
+        Use save_to_facedb.py to generate them")

examples/face_db/dsface.py

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+"""
+
+ * Author:    Azmath Moosa
+ * Created:   30th July 2018
+ * Description: Python wrapper class for dsFace.  
+                Please launch and run the dsFace separately
+ * Dependencies: python requests, opencv-python, numpy
+ 
+"""
+
+import time
+import json
+import cv2
+import requests
+import numpy as np
+
+face_api = "http://127.0.0.1:5000/inferImage?returnFaceId=true&detector=mmod&returnFaceLandmarks=true"
+
+
+
+class DSFaceHandler():
+
+    def __init__(self, api="http://127.0.0.1:5000/inferImage", face_rec=False, detector="mmod", face_lmk=False):
+        print("Please make sure dsFace is running in the background (./dsFace )")
+        self.face_api = api
+        self.detector = detector
+        self.faceRecognition = face_rec
+        self.faceLmk = face_lmk
+
+
+    def endpt(self):
+        detector = "detector="+self.detector+"&"
+        faceRecognition = "returnFaceId=true&" if self.faceRecognition else ""
+        faceLmk = "returnFaceLandmarks=true" if self.faceLmk else ""
+
+        return self.face_api + "?" + detector + faceRecognition + faceLmk
+
+
+    def get_embedding(self, img):
+        img = cv2.resize(img,(250,250))
+
+        r, imgbuf = cv2.imencode(".bmp", img)    
+        image = {'pic':bytearray(imgbuf)}
+
+        r = requests.post(self.endpt(), files=image)
+        try:
+            result = r.json()
+        except Exception as e:
+            print(e, r.text)
+
+        if len(result) > 1:
+            faces = result[:-1]
+            diag = result[-1]['diagnostics']        
+
+            for face in faces:
+                rect, embedding = [face[i] for i in ['faceRectangle','faceEmbeddings']]
+                x,y,w,h, confidence = [rect[i] for i in ['left', 'top', 'width', 'height', 'confidence']]
+
+                if confidence < 0.5:
+                    continue    
+
+                crop = img[y:y+h, x:x+w]
+
+                return np.asarray(embedding), crop
+        else:
+                return np.zeros([128]), img
+
+

examples/face_db/facedb.py

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+"""
+
+ * Author:    Azmath Moosa
+ * Created:   30th July 2018
+ * Description: Python wrapper class for facedb.  
+                Please launch and run the facedb app separately
+ * Dependencies: python requests, json
+ 
+"""
+import json
+import requests
+
+class FaceDBHandler():
+
+    def __init__(self, api="http://localhost:5100/", filename="facedb"):
+        print("Please make sure facedb is running in the background( ./facedb --serve )")
+        self.facedb_api = api
+        self.db_path = filename
+
+    def save_embedding(self, vec, meta):
+        endpt = self.facedb_api + "save_embedding"
+        body = {"embedding": vec, "meta":meta}    
+        res = requests.post(endpt,json=body).json()
+        return res
+
+    def reindex(self):
+        endpt = self.facedb_api + "reindex"
+        res = requests.post(endpt, json={}).json()
+        return res["message"] == "ok"
+
+    def dump_database(self):
+        endpt = self.facedb_api + "dump_database"        
+        res = requests.post(endpt, json={"filename":self.db_path}).json()
+        return res      
+
+    def load_database(self):
+        endpt = self.facedb_api + "load_database"
+        res = requests.post(endpt, json={"filename":self.db_path}).json()
+        return res["message"] == "ok"
+
+    def clear_database(self):
+        endpt = self.facedb_api + "clear_database"
+        res = requests.post(endpt, json={}).json()
+        return res
+
+    def print_database(self):
+        endpt = self.facedb_api + "print_database"
+        res = requests.post(endpt, json={}).json()
+        return res
+
+    def search_database(self, vec):
+        endpt = self.facedb_api + "search_embedding"
+        res = requests.post(endpt, json={"embedding": vec.tolist()})
+        try:
+            print(res.text)
+            return res.json()
+        except:
+            return {"index":0, "distance":0.0}
+
+

examples/face_db/save_to_facedb.py

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+"""
+
+ * Author:    Azmath Moosa
+ * Created:   31 July 2018
+ * Description: Loads FIFA player embeddings and names from fifadb.json and loads them into faceDB.  It then 
+                dumps database so that it can be used later.
+ * Dependencies: Deepsight Face, FaceDB, python packages:- requests, argparse, opencv-python
+ 
+"""
+
+import json
+import time
+import requests
+
+facedb_api = "http://localhost:5100/"
+json_file = "fifadb.json"
+facedb_dump_filename = "fifadb"
+
+def load_db():
+    data = json.loads(open(json_file).read())
+    names = []
+    embeddings = []
+    for d in data:
+        names.append(d['name'])
+        embeddings.append(d['embedding'])
+
+    return names, embeddings
+
+def save_embeddings(names, embeddings):
+    endpt = facedb_api + "save_embedding"
+    t = time.time()
+    count = 0    
+
+    body = {"embeddings": embeddings, "metas": names}    
+    res = requests.post(endpt,json=body)
+    count = res.json()
+
+    print(count, "items added in ", time.time() - t, "seconds")
+
+def dump_database():
+    endpt = facedb_api + "dump_database"
+    t = time.time()
+    res = requests.post(endpt, json={'filename':facedb_dump_filename})
+    print("dump_database", res.json()," ", time.time() - t, "seconds")
+
+if __name__ == "__main__":
+    names, embeddings = load_db()
+    print("items read " , len(names), len(embeddings))
+    save_embeddings(names[:50], embeddings[:50])  #limited by 50 in free version of faceDB
+    dump_database()    
+    print("done")