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+from flask import Flask, render_template, request, redirect, Response, send_file
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+import multiprocessing
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+import face_recognition
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+#from numba import jit
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+import numpy as np
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+import os
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+#from flask_cors import CORS
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+app = Flask(__name__)
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+#CORS(app)
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+lst = []
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+Gallery="Gallery"
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+People='People'
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+
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+@app.route('/', methods=['GET'])
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+def resume():
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+ #return render_template('index.html')
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+ return 'Attendence app running'
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+
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+def createEncodings(image):
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+ print("Encoding")
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+ """
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+ Create face encodings for a given image and also return face locations in the given image.
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+ Parameters
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+ -------
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+ image : cv2 mat
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+ Image you want to detect faces from.
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+ Returns
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+ -------
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+ known_encodings : list of np array
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+ List of face encodings in a given image
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+ face_locations : list of tuples
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+ list of tuples for face locations in a given image
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+ """
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+ # Find face locations for all faces in an image
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+ face_locations = face_recognition.face_locations(image)
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+ # Create encodings for all faces in an image
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+ known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
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+ return known_encodings, face_locations
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+#@app.route('/registered', methods=["POST","GET"])
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+def registered(url_list):
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+ input=url_list
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+
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+ from pathlib import Path
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+ Path(People).mkdir(exist_ok=True)
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+ Path(People+"/" + input["FileName"]).mkdir(exist_ok=True)
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+
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+ a = input
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+ # print(a)
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+ x = a['FileData']
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+ # print(x)
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+ y = a['FileName']
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+ #z = a['FileType']
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+ z='jpg'
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+ # CreatedBy=a['CreatedBy']
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+
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+ name = y+ '.'+ z
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+ print(name)
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+ # print(y)
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+ # image = y.split("/")
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+ # filename=image[-1]
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+
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+ # print(x)
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+ try:
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+ img_data = x.encode()
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+ except AttributeError:
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+ return "Successfully saved encoding........."
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+
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+ import base64
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+
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+ with open(People+"/" + input["FileName"] + "/" + name, "wb") as fh:
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+ fh.write(base64.decodebytes(img_data))
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+
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+ img = People+"/" + y + "/" + name
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+ saveLocation = People+"/" + y + "/" + y + ".pickle"
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+
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+ ############ detecting no of faceses #######################
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+
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+
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+ # import cv2
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+ # import numpy as np
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+ # import dlib
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+
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+
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+ # # Connects to your computer's default camera
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+ # cap = cv2.imread(img)
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+
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+ # # Detect the coordinates
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+ # detector = dlib.get_frontal_face_detector()
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+
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+ # number_of_faces=[]
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+ # # Capture frames continuously
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+ # # while True:
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+
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+ # # Capture frame-by-frame
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+ # # ret, frame = cap
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+ # frame = cap
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+
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+ # # RGB to grayscale
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+ # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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+ # faces = detector(gray)
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+
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+ # # Iterator to count faces
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+ # i = 0
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+ # for face in faces:
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+
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+ # # Get the coordinates of faces
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+ # x, y = face.left(), face.top()
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+ # x1, y1 = face.right(), face.bottom()
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+ # cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
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+ # # Increment iterator for each face in faces
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+ # i = i+1
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+
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+ # # Display the box and faces
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+ # cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
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+ # cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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+
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+ # # if len(i)>1:
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+ # # print(i)
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+ # number_of_faces.append(i)
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+
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+
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+ # if (len(number_of_faces))>1:
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+ # print("Group Photo")
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+ # return "Group Photo"
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+ # elif (len(number_of_faces))==1:
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+ # print("Single Photo")
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+ # pass
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+
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+
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+
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+
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+
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+ def saveEncodings(encs, names, fname='encodings.pickle'):
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+ """
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+ Save encodings in a pickle file to be used in future.
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+
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+ Parameters
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+ ----------
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+ encs : List of np arrays
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+ List of face encodings.
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+ names : List of strings
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+ List of names for each face encoding.
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+ fname : String, optional
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+ Name/Location for pickle file. The default is "encodings.pickle".
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+
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+ Returns
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+ -------
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+ None.
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+
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+ """
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+
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+ data = []
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+ d = [{"name": nm, "encoding": enc} for (nm, enc) in zip(names, encs)]
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+ data.extend(d)
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+
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+ encodingsFile = fname
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+
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+ # dump the facial encodings data to disk
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+ print("[INFO] serializing encodings...")
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+ print("[INFO] Encodings Created sucessfully")
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+ f = open(encodingsFile, "wb")
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+ f.write(pickle.dumps(data))
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+ f.close()
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+
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+ # Function to create encodings and get face locations
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+
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+
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+ def processKnownPeopleImages(img=img, saveLocation=saveLocation):
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+ """
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+ Process images of known people and create face encodings to compare in future.
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+ Eaach image should have just 1 face in it.
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+ Parameters
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+ ----------
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+ path : STRING, optional
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+ Path for known people dataset. The default is "C:/inetpub/vhosts/port82/wwwroot/_files/People".
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+ It should be noted that each image in this dataset should contain only 1 face.
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+ saveLocation : STRING, optional
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+ Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
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+ Returns
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+ -------
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+ None.
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+ """
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+ known_encodings = []
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+ known_names = []
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+ # for img in os.listdir(path):
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+
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+ imgPath = img
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+ # Read image
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+ image = cv2.imread(imgPath)
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+ name = img.rsplit('.')[0]
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+ # Resize
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+ try:
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+ print(image.shape)
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+ except AttributeError:
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+ return "Successfully saved encoding........."
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+ image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
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+ # Get locations and encodings
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+ encs, locs = createEncodings(image)
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+ try:
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+ known_encodings.append(encs[0])
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+ except IndexError:
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+ os.remove(saveLocation)
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+ print('------------------------------------- save location --------------------------------')
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+ print(saveLocation)
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+ return "hello world!"
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+
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+
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+ # known_encodings.append(encs[0])
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+ known_names.append(name)
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+ for loc in locs:
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+ top, right, bottom, left = loc
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+ # Show Image
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+ # cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
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+ # cv2.imshow("Image", image)
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+ # cv2.waitKey(1)
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+ # cv2.destroyAllWindows()
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+ saveEncodings(known_encodings, known_names, saveLocation)
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+
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+ import cv2
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+ #import face_recognition
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+ import pickle
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+
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+ processKnownPeopleImages(img, saveLocation)
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+ return 'Successfully saved encoding.........'
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+
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+
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+# ******************************** COMPARUISION *********************************************************
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+#@app.route('/submit', methods=["POST","GET"])
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+def submit(url_list):
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+
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+ from datetime import datetime
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+ import pytz
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+ tz_NY = pytz.timezone('Asia/Kolkata')
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+ datetime_NY = datetime.now(tz_NY)
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+ India_Date = (datetime_NY.strftime("%Y-%m-%d"))
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+ India_Date = str(India_Date)
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+ # India_Time = (datetime_NY.strftime("%I:%M:%S %p"))
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+ # India_Time = str(India_Time)
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+ input=url_list
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+ import pickle
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+ import cv2
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+
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+ from pathlib import Path
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+ Path(Gallery).mkdir(exist_ok=True)
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+ Path(Gallery+"/"+ India_Date).mkdir(exist_ok=True)
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+ Path(Gallery+"/"+ India_Date +'/'+ input["FileName"]).mkdir(exist_ok=True)
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+
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+ a = input
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+ # print(a)
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+ x = a['FileData']
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+ # print(x)
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+ y = a['FileName']
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+ # z = a['FileType']
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+ z='jpg'
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+ # CreatedBy=a['CreatedBy']
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+
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+ name = y + '.' + z
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+ # print(name)
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+ # print(y)
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+ # image = y.split("/")
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+ # filename=image[-1]
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+
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+ # print(x)
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+ img_data = x.encode()
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+
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+ import base64
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+
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+
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+ with open(Gallery+"/"+India_Date+'/' + input["FileName"] + "/" + name, "wb") as fh:
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+ fh.write(base64.decodebytes(img_data))
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+
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+ path = Gallery+"/" +India_Date+'/'+ y + "/" + name
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+ pickle_location = People+"/" + y + "/" + y + ".pickle"
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+ import pathlib
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+ file = pathlib.Path(pickle_location)
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+ if file.exists ():
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+ pass
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+ else:
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+ print ("pickle File not exist")
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+ print(name)
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+ return "Face not found in profile (please change your profile)"
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+
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+ check_faces=People+"/" + y + "/" + y + ".jpg"
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+ print(check_faces)
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+
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+
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+ ############ detecting no of faceses #######################
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+
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+
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+ import cv2
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+ import numpy as np
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+ import dlib
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+
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+
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+ # Connects to your computer's default camera
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+ cap = cv2.imread(check_faces)
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+
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+ # Detect the coordinates
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+ detector = dlib.get_frontal_face_detector()
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+
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+ number_of_faces=[]
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+ # Capture frames continuously
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+ # while True:
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+
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+ # Capture frame-by-frame
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+ # ret, frame = cap
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+ frame = cap
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+
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+ # RGB to grayscale
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+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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+ faces = detector(gray)
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+
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+ # Iterator to count faces
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+ i = 0
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+ for face in faces:
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+
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+ # Get the coordinates of faces
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+ x, y = face.left(), face.top()
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+ x1, y1 = face.right(), face.bottom()
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+ cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
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+ # Increment iterator for each face in faces
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+ i = i+1
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+
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+ # Display the box and faces
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+ cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
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+ cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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327
|
+
|
|
|
328
|
+ # if len(i)>1:
|
|
|
329
|
+ # print(i)
|
|
|
330
|
+ number_of_faces.append(i)
|
|
|
331
|
+
|
|
|
332
|
+
|
|
|
333
|
+ if (len(number_of_faces))>1:
|
|
|
334
|
+ print("Group Photo")
|
|
|
335
|
+ return "Group Photo"
|
|
|
336
|
+ elif (len(number_of_faces))==1:
|
|
|
337
|
+ print("Single Photo")
|
|
|
338
|
+ pass
|
|
|
339
|
+
|
|
|
340
|
+
|
|
|
341
|
+
|
|
|
342
|
+
|
|
|
343
|
+ def readEncodingsPickle(fname):
|
|
|
344
|
+ """
|
|
|
345
|
+ Read Pickle file.
|
|
|
346
|
+
|
|
|
347
|
+ Parameters
|
|
|
348
|
+ ----------
|
|
|
349
|
+ fname : String
|
|
|
350
|
+ Name of pickle file.(Full location)
|
|
|
351
|
+
|
|
|
352
|
+ Returns
|
|
|
353
|
+ -------
|
|
|
354
|
+ encodings : list of np arrays
|
|
|
355
|
+ list of all saved encodings
|
|
|
356
|
+ names : List of Strings
|
|
|
357
|
+ List of all saved names
|
|
|
358
|
+
|
|
|
359
|
+ """
|
|
|
360
|
+
|
|
|
361
|
+
|
|
|
362
|
+ data = pickle.loads(open(fname, "rb").read())
|
|
|
363
|
+
|
|
|
364
|
+
|
|
|
365
|
+ data = np.array(data)
|
|
|
366
|
+ encodings = [d["encoding"] for d in data]
|
|
|
367
|
+ names = [d["name"] for d in data]
|
|
|
368
|
+ return encodings, names
|
|
|
369
|
+
|
|
|
370
|
+
|
|
|
371
|
+ def compareFaceEncodings(unknown_encoding, known_encodings, known_names):
|
|
|
372
|
+ """
|
|
|
373
|
+ Compares face encodings to check if 2 faces are same or not.
|
|
|
374
|
+
|
|
|
375
|
+ Parameters
|
|
|
376
|
+ ----------
|
|
|
377
|
+ unknown_encoding : np array
|
|
|
378
|
+ Face encoding of unknown people.
|
|
|
379
|
+ known_encodings : np array
|
|
|
380
|
+ Face encodings of known people.
|
|
|
381
|
+ known_names : list of strings
|
|
|
382
|
+ Names of known people
|
|
|
383
|
+
|
|
|
384
|
+ Returns
|
|
|
385
|
+ -------
|
|
|
386
|
+ acceptBool : Bool
|
|
|
387
|
+ face matched or not
|
|
|
388
|
+ duplicateName : String
|
|
|
389
|
+ Name of matched face
|
|
|
390
|
+ distance : Float
|
|
|
391
|
+ Distance between 2 faces
|
|
|
392
|
+
|
|
|
393
|
+ """
|
|
|
394
|
+ duplicateName = ""
|
|
|
395
|
+ distance = 0.0
|
|
|
396
|
+ matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.54)
|
|
|
397
|
+ face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
|
|
|
398
|
+
|
|
|
399
|
+ best_match_index = np.argmin(face_distances)
|
|
|
400
|
+
|
|
|
401
|
+ distance = face_distances[best_match_index]
|
|
|
402
|
+ if matches[best_match_index]:
|
|
|
403
|
+ acceptBool = True
|
|
|
404
|
+ duplicateName = known_names[best_match_index]
|
|
|
405
|
+ else:
|
|
|
406
|
+ acceptBool = False
|
|
|
407
|
+ duplicateName = ""
|
|
|
408
|
+ return acceptBool, duplicateName, distance
|
|
|
409
|
+
|
|
|
410
|
+ #p = []
|
|
|
411
|
+
|
|
|
412
|
+
|
|
|
413
|
+ def processDatasetImages(path=path, pickle_location=pickle_location):
|
|
|
414
|
+ """
|
|
|
415
|
+ Process image in dataset from where you want to separate images.
|
|
|
416
|
+ It separates the images into directories of known people, groups and any unknown people images.
|
|
|
417
|
+ Parameters
|
|
|
418
|
+ ----------
|
|
|
419
|
+ path : STRING, optional
|
|
|
420
|
+ Path for known people dataset. The default is "D:/port1004/port1004/wwwroot/_files/People".
|
|
|
421
|
+ It should be noted that each image in this dataset should contain only 1 face.
|
|
|
422
|
+ saveLocation : STRING, optional
|
|
|
423
|
+ Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
|
|
|
424
|
+
|
|
|
425
|
+ Returns
|
|
|
426
|
+ -------
|
|
|
427
|
+ None.
|
|
|
428
|
+
|
|
|
429
|
+ """
|
|
|
430
|
+ # Read pickle file for known people to compare faces from
|
|
|
431
|
+
|
|
|
432
|
+ people_encodings, names = readEncodingsPickle(pickle_location)
|
|
|
433
|
+ # print(p)
|
|
|
434
|
+ # imgPath = path + img
|
|
|
435
|
+
|
|
|
436
|
+ # Read image
|
|
|
437
|
+ # path=r"C:\Users\katku\Pictures\final\100011460000611.jpg"
|
|
|
438
|
+ image = cv2.imread(path)
|
|
|
439
|
+ #orig = image.copy()
|
|
|
440
|
+
|
|
|
441
|
+ # Resize
|
|
|
442
|
+ image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
|
|
|
443
|
+
|
|
|
444
|
+ # Get locations and encodings
|
|
|
445
|
+ encs, locs = createEncodings(image)
|
|
|
446
|
+
|
|
|
447
|
+ # Save image to a group image folder if more than one face is in image
|
|
|
448
|
+ # if len(locs) > 1:
|
|
|
449
|
+ # saveImageToDirectory(orig, "Group", img)
|
|
|
450
|
+
|
|
|
451
|
+ # Processing image for each face
|
|
|
452
|
+ i = 0
|
|
|
453
|
+ knownFlag = 0
|
|
|
454
|
+ for loc in locs:
|
|
|
455
|
+ top, right, bottom, left = loc
|
|
|
456
|
+ unknown_encoding = encs[i]
|
|
|
457
|
+ i += 1
|
|
|
458
|
+ acceptBool, duplicateName, distance = compareFaceEncodings(unknown_encoding, people_encodings, names)
|
|
|
459
|
+ if acceptBool:
|
|
|
460
|
+ # saveImageToDirectory(orig, duplicateName,name)
|
|
|
461
|
+ knownFlag = 1
|
|
|
462
|
+ if knownFlag == 1:
|
|
|
463
|
+ print("Match Found")
|
|
|
464
|
+
|
|
|
465
|
+
|
|
|
466
|
+ #print(path)
|
|
|
467
|
+ with_extension = path.split("/")[-1]
|
|
|
468
|
+ without_extension = with_extension.split(".")[0]
|
|
|
469
|
+
|
|
|
470
|
+ # output_s = {"FileID": without_extension,
|
|
|
471
|
+ # "Date": India_Date,
|
|
|
472
|
+ # "Time": India_Time}
|
|
|
473
|
+ # output_json = json.dumps(output_s)
|
|
|
474
|
+ output_json='Matched successfully'
|
|
|
475
|
+ print(loc)
|
|
|
476
|
+ lst.append(output_json)
|
|
|
477
|
+
|
|
|
478
|
+ print(output_json)
|
|
|
479
|
+ # exit()
|
|
|
480
|
+
|
|
|
481
|
+ else:
|
|
|
482
|
+ print('Not Matched')
|
|
|
483
|
+ pass
|
|
|
484
|
+ # saveImageToDirectory(orig, "0",name)
|
|
|
485
|
+
|
|
|
486
|
+
|
|
|
487
|
+ import numpy as np
|
|
|
488
|
+ import json
|
|
|
489
|
+
|
|
|
490
|
+ processDatasetImages(path, pickle_location)
|
|
|
491
|
+ return lst[0]
|
|
|
492
|
+ #return 'matched successfully'
|
|
|
493
|
+
|
|
|
494
|
+
|
|
|
495
|
+@app.route('/detect', methods=["POST"])
|
|
|
496
|
+def detect():
|
|
|
497
|
+ if __name__ == "__main__":
|
|
|
498
|
+
|
|
|
499
|
+ url_list=[]
|
|
|
500
|
+ Dataset= request.get_json()
|
|
|
501
|
+ # id = "100013660000125"
|
|
|
502
|
+ url_list.append(Dataset)
|
|
|
503
|
+ # multiprocessing
|
|
|
504
|
+ pool_size = multiprocessing.cpu_count() * 2
|
|
|
505
|
+ with multiprocessing.Pool(pool_size) as pool:
|
|
|
506
|
+ try:
|
|
|
507
|
+ results = pool.map(submit, url_list)
|
|
|
508
|
+ except FileNotFoundError:
|
|
|
509
|
+ return 'plese get registered with your PhotoID'
|
|
|
510
|
+ except IndexError:
|
|
|
511
|
+ #return 'unable to recognize face'
|
|
|
512
|
+ return 'failed'
|
|
|
513
|
+
|
|
|
514
|
+ pool.close()
|
|
|
515
|
+
|
|
|
516
|
+ return results[0]
|
|
|
517
|
+
|
|
|
518
|
+
|
|
|
519
|
+
|
|
|
520
|
+@app.route('/register', methods=["POST"])
|
|
|
521
|
+def register():
|
|
|
522
|
+ print("hello start..........")
|
|
|
523
|
+ if __name__ == "__main__":
|
|
|
524
|
+
|
|
|
525
|
+ url_list=[]
|
|
|
526
|
+ Dataset= request.get_json()
|
|
|
527
|
+ # id = "100013660000125"
|
|
|
528
|
+ url_list.append(Dataset)
|
|
|
529
|
+ UserLocation=Dataset["FilePath"]
|
|
|
530
|
+ print(UserLocation)
|
|
|
531
|
+
|
|
|
532
|
+ # if "cO2" in UserLocation or UserLocation is None:
|
|
|
533
|
+ # pass
|
|
|
534
|
+ # else:
|
|
|
535
|
+ # return "Please update the URL in the integration"
|
|
|
536
|
+
|
|
|
537
|
+
|
|
|
538
|
+ # multiprocessing
|
|
|
539
|
+ pool_size = multiprocessing.cpu_count() * 2
|
|
|
540
|
+ with multiprocessing.Pool(pool_size) as pool:
|
|
|
541
|
+ try:
|
|
|
542
|
+ results = pool.map(registered, url_list)
|
|
|
543
|
+ except IndexError:
|
|
|
544
|
+ pass
|
|
|
545
|
+ print('face not found')
|
|
|
546
|
+ except FileNotFoundError:
|
|
|
547
|
+ pass
|
|
|
548
|
+
|
|
|
549
|
+
|
|
|
550
|
+ #os.remove(img)
|
|
|
551
|
+ # return 'unable to recognize face'
|
|
|
552
|
+
|
|
|
553
|
+ pool.close()
|
|
|
554
|
+ #return results[0]
|
|
|
555
|
+ return 'Successfully saved encoding.........'
|
|
|
556
|
+
|
|
|
557
|
+
|
|
|
558
|
+
|
|
|
559
|
+
|
|
|
560
|
+if __name__ == "__main__":
|
|
|
561
|
+ app.run(host='0.0.0.0',port =5007,debug=False)
|
