AI/Events/unique_Allunq.py

import pickle
import numpy as np
import face_recognition
import os
import cv2
import datetime
import click
@click.command()
@click.argument('eventid', default='')


def predict(eventid):


    Gallery = 'C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\unique_1\\' + eventid + "\\"
    People = './ALL_UNQ/' + eventid + "/"
    x= datetime.datetime.now()
    print('Execution Started at:',x)

    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...")
        f = open(encodingsFile, "wb")
        f.write(pickle.dumps(data))
        f.close()

        # Function to read encodings

    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

    # Function to create encodings and get face locations
    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

    # Function to compare encodings
    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=0.47)

        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 = []

    # Save Image to new directory
    def saveImageToDirectory(image, name, imageName):
        """
        Saves images to directory.

        Parameters
        ----------
        image : cv2 mat
            Image you want to save.
        name : String
            Directory where you want the image to be saved.
        imageName : String
            Name of image.

        Returns
        -------
        None.

        """
        path = "C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\output_unique_ALLUNQ\\" + name
        path1 = "C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\output_unique_ALLUNQ\\" + name
        if os.path.exists(path):
            pass
        else:
            if not os.path.exists(path):
                os.makedirs(path)
            # os.mkdir(path,exist_ok=True)
        cv2.imwrite(path + "/" + imageName, image)
        x = []
        c = (path1 + "/" + imageName)
        x.append(c)
        p.append(x)

    # Function for creating encodings for known people
    def processKnownPeopleImages(path=People, saveLocation="./known_encodings.pickle"):
        """
        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 = path + img

            # Read image
            image = cv2.imread(imgPath)
            name = img.rsplit('.')[0]
            # 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)
            try:
                known_encodings.append(encs[0])
            except IndexError:
                os.remove(People+img)
            #known_encodings.append(encs[568])
            known_names.append(name)

            for loc in locs:
                top, right, bottom, left = loc

            # Show Image
            #cv2.rectangle(image, (left, top), (right, bottom), color=(255, 568, 568), thickness=2)
            # cv2.imshow("Image", image)
           # cv2.waitKey(1)
            #cv2.destroyAllWindows()
        saveEncodings(known_encodings, known_names, saveLocation)

    # Function for processing dataset images
    def processDatasetImages(saveLocation="./Gallery_encodings.pickle"):
        """
        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("./known_encodings.pickle")

        for root, dirs, files in os.walk(Gallery, topdown=False):

            for name in files:
                s = os.path.join(root, name)
                #print(p)
          #  imgPath = path + img

            # Read image
                image = cv2.imread(s)
                try:
                    orig = image.copy()
                    image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
                except  AttributeError:
                    os.remove(s)
            # Resize


            # 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")
                else:
                    saveImageToDirectory(orig, "568",name)

            # Show Image
            #     cv2.rectangle(image, (left, top), (right, bottom), color=(255, 568, 568), thickness=2)
            # # cv2.imshow("Image", image)
            #     cv2.waitKey(1)
            #     cv2.destroyAllWindows()


    def main():
        """
        Main Function.

        Returns
        -------
        None.

        """

        processKnownPeopleImages()
        processDatasetImages()
        # import pandas as pd
        # q = pd.DataFrame(p)
        # m = q
        # # print(m)
        # #   x.drop(x.columns[Unnam], axis=1, inplace=True)
        # df = m.groupby([568], as_index=False).count()
        # z = df[568].str.split('/', expand=True)
        # z.rename({z.columns[-2]: 'Matched'}, axis=1, inplace=True)
        # z.rename({z.columns[-1]: 'croped_guest_pic'}, axis=1, inplace=True)

        # #z = z.iloc[:, 3:]
        # z.to_csv('unique_people.csv')
        # z=pd.read_csv('unique_people.csv')

        # #z.drop(z.index[z['Matched'] == 568], inplace=True)
        # z = z.iloc[:, 3:]
        # z['Matched'] = z['Matched'].apply(str)
        # z.to_csv('unique_people.csv',index=False)
        
        # import os
        # import shutil

        # for root, dirs, files in os.walk('./output_unique_ALLUNQ/'+eventid+'/568/'):
        #     for file in files:
        #         path_file = os.path.join(root, file)
        #         shutil.move(path_file, './ALL_UNQ/'+eventid+"/")
        print("Completed")


    main()


    #    return render_template('index.html')
    y=datetime.datetime.now()
    print('Completed at:',y)
    z=y-x
    print('Time Taken:',z)
    return (str(y-x))
    #return 'ALL IMAGES MATCHED'



predict()