Delete 'Attendence/attendence.py'

Attendence/attendence.py

@@ -1,561 +0,0 @@
-from flask import Flask, render_template, request, redirect, Response, send_file
-import multiprocessing
-import face_recognition
-#from numba import jit
-import numpy as np
-import os
-#from flask_cors import CORS
-app = Flask(__name__)
-#CORS(app)
-lst = []
-Gallery="Gallery"
-People='People'
-
-@app.route('/', methods=['GET'])
-def resume():
-    #return render_template('index.html')
-    return 'Attendence app running'
-
-def createEncodings(image):
-    print("Encoding")
-    """
-    Create face encodings for a given image and also return face locations in the given image.
-    Parameters
-    -------
-    image : cv2 mat
-        Image you want to detect faces from.
-    Returns
-    -------
-    known_encodings : list of np array
-        List of face encodings in a given image
-    face_locations : list of tuples
-        list of tuples for face locations in a given image
-    """
-    # Find face locations for all faces in an image
-    face_locations = face_recognition.face_locations(image)
-    # Create encodings for all faces in an image
-    known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
-    return known_encodings, face_locations
-#@app.route('/registered', methods=["POST","GET"])
-def registered(url_list):
-    input=url_list
-
-    from pathlib import Path
-    Path(People).mkdir(exist_ok=True)
-    Path(People+"/" + input["FileName"]).mkdir(exist_ok=True)
-
-    a = input
-    # print(a)
-    x = a['FileData']
-    # print(x)
-    y = a['FileName']
-    #z = a['FileType']
-    z='jpg'
-    # CreatedBy=a['CreatedBy']
-
-    name = y+ '.'+ z
-    print(name)
-    # print(y)
-    # image = y.split("/")
-    # filename=image[-1]
-
-    # print(x)
-    try:
-        img_data = x.encode()
-    except AttributeError:
-        return "Successfully saved encoding........."
-
-    import base64
-
-    with open(People+"/" + input["FileName"] + "/" + name, "wb") as fh:
-        fh.write(base64.decodebytes(img_data))
-
-    img = People+"/" + y + "/" + name
-    saveLocation = People+"/" + y + "/" + y + ".pickle"
-
-    ############ detecting no of faceses #######################
-
-
-    # import cv2
-    # import numpy as np
-    # import dlib
-
-
-    # # Connects to your computer's default camera
-    # cap = cv2.imread(img)
-
-    # # Detect the coordinates
-    # detector = dlib.get_frontal_face_detector()
-
-    # number_of_faces=[]
-    # # Capture frames continuously
-    # # while True:
-
-    # # Capture frame-by-frame
-    # # ret, frame = cap
-    # frame = cap
-
-    # # RGB to grayscale
-    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    # faces = detector(gray)
-
-    # # Iterator to count faces
-    # i = 0
-    # for face in faces:
-
-    #     # Get the coordinates of faces
-    #     x, y = face.left(), face.top()
-    #     x1, y1 = face.right(), face.bottom()
-    #     cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
-    # # Increment iterator for each face in faces
-    #     i = i+1
-
-    # # Display the box and faces
-    #     cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
-    #                 cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-        
-    #     # if len(i)>1:
-    #     #     print(i)
-    #     number_of_faces.append(i)
-
-
-    # if (len(number_of_faces))>1:
-    #     print("Group Photo")
-    #     return "Group Photo"
-    # elif (len(number_of_faces))==1:
-    #     print("Single Photo")
-    #     pass
-
-
-
-
-
-    def saveEncodings(encs, names, fname='encodings.pickle'):
-        """
-        Save encodings in a pickle file to be used in future.
-
-        Parameters
-        ----------
-        encs : List of np arrays
-            List of face encodings.
-        names : List of strings
-            List of names for each face encoding.
-        fname : String, optional
-            Name/Location for pickle file. The default is "encodings.pickle".
-
-        Returns
-        -------
-        None.
-
-        """
-
-        data = []
-        d = [{"name": nm, "encoding": enc} for (nm, enc) in zip(names, encs)]
-        data.extend(d)
-
-        encodingsFile = fname
-
-        # dump the facial encodings data to disk
-        print("[INFO] serializing encodings...")
-        print("[INFO] Encodings Created sucessfully")
-        f = open(encodingsFile, "wb")
-        f.write(pickle.dumps(data))
-        f.close()
-
-    # Function to create encodings and get face locations
-
-
-    def processKnownPeopleImages(img=img, saveLocation=saveLocation):
-        """
-        Process images of known people and create face encodings to compare in future.
-        Eaach image should have just 1 face in it.
-        Parameters
-        ----------
-        path : STRING, optional
-            Path for known people dataset. The default is "C:/inetpub/vhosts/port82/wwwroot/_files/People".
-            It should be noted that each image in this dataset should contain only 1 face.
-        saveLocation : STRING, optional
-            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
-        Returns
-        -------
-        None.
-        """
-        known_encodings = []
-        known_names = []
-        # for img in os.listdir(path):
-
-        imgPath = img
-        # Read image
-        image = cv2.imread(imgPath)
-        name = img.rsplit('.')[0]
-        # Resize
-        try:
-            print(image.shape)
-        except AttributeError:
-            return "Successfully saved encoding........."
-        image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
-        # Get locations and encodings
-        encs, locs = createEncodings(image)
-        try:
-            known_encodings.append(encs[0])
-        except IndexError:
-            os.remove(saveLocation)
-            print('------------------------------------- save location --------------------------------')
-            print(saveLocation)
-            return "hello world!"
-        
-            
-        # known_encodings.append(encs[0])
-        known_names.append(name)
-        for loc in locs:
-            top, right, bottom, left = loc
-        # Show Image
-        # cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
-        # cv2.imshow("Image", image)
-        # cv2.waitKey(1)
-        # cv2.destroyAllWindows()
-        saveEncodings(known_encodings, known_names, saveLocation)
-
-    import cv2
-    #import face_recognition
-    import pickle
-
-    processKnownPeopleImages(img, saveLocation)
-    return 'Successfully saved encoding.........'
-
-
-# ********************************   COMPARUISION *********************************************************
-#@app.route('/submit', methods=["POST","GET"])
-def submit(url_list):
-
-    from datetime import datetime
-    import pytz
-    tz_NY = pytz.timezone('Asia/Kolkata')
-    datetime_NY = datetime.now(tz_NY)
-    India_Date = (datetime_NY.strftime("%Y-%m-%d"))
-    India_Date = str(India_Date)
-    # India_Time = (datetime_NY.strftime("%I:%M:%S %p"))
-    # India_Time = str(India_Time)
-    input=url_list
-    import pickle
-    import cv2
-
-    from pathlib import Path
-    Path(Gallery).mkdir(exist_ok=True)
-    Path(Gallery+"/"+ India_Date).mkdir(exist_ok=True)
-    Path(Gallery+"/"+ India_Date +'/'+ input["FileName"]).mkdir(exist_ok=True)
-
-    a = input
-    # print(a)
-    x = a['FileData']
-    # print(x)
-    y = a['FileName']
-   # z = a['FileType']
-    z='jpg'
-    # CreatedBy=a['CreatedBy']
-
-    name = y + '.' + z
-    # print(name)
-    # print(y)
-    # image = y.split("/")
-    # filename=image[-1]
-
-    # print(x)
-    img_data = x.encode()
-
-    import base64
-    
-
-    with open(Gallery+"/"+India_Date+'/' + input["FileName"] + "/" + name, "wb") as fh:
-        fh.write(base64.decodebytes(img_data))
-
-    path = Gallery+"/" +India_Date+'/'+ y + "/" + name
-    pickle_location = People+"/" + y + "/" + y + ".pickle"
-    import pathlib
-    file = pathlib.Path(pickle_location)
-    if file.exists ():
-        pass
-    else:
-        print ("pickle File not exist")
-        print(name)
-        return "Face not found in profile (please change your profile)"
-
-    check_faces=People+"/" + y + "/" + y + ".jpg"
-    print(check_faces)
-
-
-    ############ detecting no of faceses #######################
-
-
-    import cv2
-    import numpy as np
-    import dlib
-
-
-    # Connects to your computer's default camera
-    cap = cv2.imread(check_faces)
-
-    # Detect the coordinates
-    detector = dlib.get_frontal_face_detector()
-
-    number_of_faces=[]
-    # Capture frames continuously
-    # while True:
-
-    # Capture frame-by-frame
-    # ret, frame = cap
-    frame = cap
-
-    # RGB to grayscale
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = detector(gray)
-
-    # Iterator to count faces
-    i = 0
-    for face in faces:
-
-        # Get the coordinates of faces
-        x, y = face.left(), face.top()
-        x1, y1 = face.right(), face.bottom()
-        cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
-    # Increment iterator for each face in faces
-        i = i+1
-
-    # Display the box and faces
-        cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-        
-        # if len(i)>1:
-        #     print(i)
-        number_of_faces.append(i)
-
-
-    if (len(number_of_faces))>1:
-        print("Group Photo")
-        return "Group Photo"
-    elif (len(number_of_faces))==1:
-        print("Single Photo")
-        pass
-
-
-
-
-    def readEncodingsPickle(fname):
-        """
-        Read Pickle file.
-
-        Parameters
-        ----------
-        fname : String
-            Name of pickle file.(Full location)
-
-        Returns
-        -------
-        encodings : list of np arrays
-            list of all saved encodings
-        names : List of Strings
-            List of all saved names
-
-        """
-
-
-        data = pickle.loads(open(fname, "rb").read())
-
-
-        data = np.array(data)
-        encodings = [d["encoding"] for d in data]
-        names = [d["name"] for d in data]
-        return encodings, names
-
-
-    def compareFaceEncodings(unknown_encoding, known_encodings, known_names):
-        """
-        Compares face encodings to check if 2 faces are same or not.
-
-        Parameters
-        ----------
-        unknown_encoding : np array
-            Face encoding of unknown people.
-        known_encodings : np array
-            Face encodings of known people.
-        known_names : list of strings
-            Names of known people
-
-        Returns
-        -------
-        acceptBool : Bool
-            face matched or not
-        duplicateName : String
-            Name of matched face
-        distance : Float
-            Distance between 2 faces
-
-        """
-        duplicateName = ""
-        distance = 0.0
-        matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.54)
-        face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
-
-        best_match_index = np.argmin(face_distances)
-
-        distance = face_distances[best_match_index]
-        if matches[best_match_index]:
-            acceptBool = True
-            duplicateName = known_names[best_match_index]
-        else:
-            acceptBool = False
-            duplicateName = ""
-        return acceptBool, duplicateName, distance
-
-        #p = []
-
-
-    def processDatasetImages(path=path, pickle_location=pickle_location):
-        """
-        Process image in dataset from where you want to separate images.
-        It separates the images into directories of known people, groups and any unknown people images.
-        Parameters
-        ----------
-        path : STRING, optional
-            Path for known people dataset. The default is "D:/port1004/port1004/wwwroot/_files/People".
-            It should be noted that each image in this dataset should contain only 1 face.
-        saveLocation : STRING, optional
-            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
-
-        Returns
-        -------
-        None.
-
-        """
-        # Read pickle file for known people to compare faces from
-
-        people_encodings, names = readEncodingsPickle(pickle_location)
-        # print(p)
-        #  imgPath = path + img
-
-        # Read image
-        # path=r"C:\Users\katku\Pictures\final\100011460000611.jpg"
-        image = cv2.imread(path)
-        #orig = image.copy()
-
-        # Resize
-        image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
-
-        # Get locations and encodings
-        encs, locs = createEncodings(image)
-
-        # Save image to a group image folder if more than one face is in image
-        # if len(locs) > 1:
-        #     saveImageToDirectory(orig, "Group", img)
-
-        # Processing image for each face
-        i = 0
-        knownFlag = 0
-        for loc in locs:
-            top, right, bottom, left = loc
-            unknown_encoding = encs[i]
-            i += 1
-            acceptBool, duplicateName, distance = compareFaceEncodings(unknown_encoding, people_encodings, names)
-            if acceptBool:
-                # saveImageToDirectory(orig, duplicateName,name)
-                knownFlag = 1
-        if knownFlag == 1:
-            print("Match Found")
-
-
-            #print(path)
-            with_extension = path.split("/")[-1]
-            without_extension = with_extension.split(".")[0]
-
-            # output_s = {"FileID": without_extension,
-            #             "Date": India_Date,
-            #             "Time": India_Time}
-            # output_json = json.dumps(output_s)
-            output_json='Matched successfully'
-            print(loc)
-            lst.append(output_json)
-
-            print(output_json)
-            # exit()
-
-        else:
-            print('Not Matched')
-            pass
-            # saveImageToDirectory(orig, "0",name)
-
-
-    import numpy as np
-    import json
-
-    processDatasetImages(path, pickle_location)
-    return lst[0]
-    #return 'matched successfully'
-
-
-@app.route('/detect', methods=["POST"])
-def detect():
-    if __name__ == "__main__":
-
-        url_list=[]
-        Dataset=  request.get_json()
-        # id = "100013660000125"
-        url_list.append(Dataset)
-        # multiprocessing
-        pool_size = multiprocessing.cpu_count() * 2
-        with multiprocessing.Pool(pool_size) as pool:
-            try:
-                results = pool.map(submit, url_list)
-            except FileNotFoundError:
-                return 'plese get registered with your PhotoID'
-            except IndexError:
-                #return 'unable to recognize face'
-                return 'failed'
-
-        pool.close()
-
-        return results[0]
-
-
-
-@app.route('/register', methods=["POST"])
-def register():
-    print("hello start..........")
-    if __name__ == "__main__":
-
-        url_list=[]
-        Dataset=  request.get_json()
-        # id = "100013660000125"
-        url_list.append(Dataset)
-        UserLocation=Dataset["FilePath"]
-        print(UserLocation)
-
-        # if "cO2" in UserLocation or UserLocation is None:
-        #     pass
-        # else:
-        #     return "Please update the URL in the integration"
-
-
-        # multiprocessing
-        pool_size = multiprocessing.cpu_count() * 2
-        with multiprocessing.Pool(pool_size) as pool:
-            try:
-                results = pool.map(registered, url_list)
-            except IndexError:
-                pass
-                print('face not found')
-            except FileNotFoundError:
-                pass
-                
-            
-                #os.remove(img)
-               # return 'unable to recognize face'
-
-        pool.close()
-        #return results[0]
-        return 'Successfully saved encoding.........'
-
-
-
-
-if __name__ == "__main__":
-    app.run(host='0.0.0.0',port =5003,debug=False)