AI/Events/Allunq_People.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 predict123(eventid):

    original_working_directory = os.getcwd()
    new_networked_directory = r'\\192.168.88.99\\Bizgaze\\port6003\\wwwroot\\_files\\'
    # change to the networked directory
    os.chdir(new_networked_directory)



    People = "./ALL_UNQ/" + eventid + "/"
    Gallery = './guestimage/'+ 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 = []
    def f_CSVwrite():
        import pandas as pd
        q = pd.DataFrame(p)
        #print(q)
        m = q
        # print(m)
        #   x.drop(x.columns[Unnam], axis=1, inplace=True)
        df = m.groupby([0], as_index=False).count()
        z = df[0].str.split('/', expand=True)


        z.to_csv('all_people.csv',index=False)
        import pandas as pd
        df2 = pd.read_csv('./all_people.csv')
        df2.rename({df2.columns[-1]: 'test'}, axis=1, inplace=True)
        df2.rename({df2.columns[-2]: 'Matched'}, axis=1, inplace=True)
        df2 = df2[['Matched', 'test']]


        import pandas as pd
        import os
        c = []
        for root, dirs, files in os.walk(Gallery,
                                         topdown=False):
            for name in files:
                # print(name)
                L = os.path.join(root, name)
                c.append(L)
        df = pd.DataFrame(c)

        df1 = df[0].str.split("/", expand=True)
        #df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
        # print('this is df1')
        # print(df1)
        df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
        merge = pd.merge(df2, df1, on='test', how='left')
        merge.rename({merge.columns[-1]: 'EventName'}, axis=1, inplace=True)
        # merge.to_csv('merge.csv')
        mergesplit = merge.loc[:, 'test'].str.split(".", expand=True)
        mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
        mergesplit = mergesplit.loc[:, 'ImageName']

        #merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
        #merge['EventName'] = merge['abc']
        merge['Imagepath'] = "\\_files\\1\\Gallery\\" + merge['EventName'] + '\\' + + merge['test']


        frames = [merge, mergesplit]

        r = pd.concat(frames, axis=1, join='inner')


        df2 = r.dropna(subset=['Matched'])


        #df2['Matched'] = df2['Matched'].astype(str)
        #df2['Matched'] = df2['Matched'].astype(int)
        column_list = ['Matched', 'Imagepath', 'ImageName', 'EventName']
        df2[column_list].to_csv('all_people_fina123.csv', index=False)
        df2[column_list].to_json('all_people_final123.json', orient="records")


        # import requests
        # import json

        # with open('all_people_final123.json', 'r') as json_file:
        #     json_load = json.load(json_file)
        #     #url = "https://test.bizgaze.app:8443/apis/v4/bizgaze/integrations/testevents/save"

        # payload = json.dumps(json_load).replace("]", "").replace("[", "")
        # print(payload)
        # headers = {
        #     'Authorization': 'stat 7f1007799b1f42999544d0338251bb19',
        #     'Content-Type': 'application/json'
        #     }
        # response = requests.request("POST", url, headers=headers, data=payload)
        # print("##############################################################")
        # print(response.text)

        # p.clear()

    # 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.

        """
        # from pathlib import Path
        # a=
        # Path('Allunq_People/' + eventid).mkdir(exist_ok=True)
        path = original_working_directory+"Allunq_People/" +eventid+'/'+ name
        path1 = original_working_directory+"Allunq_People/" +eventid+'/'+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)
        f_CSVwrite()

    # Function for creating encodings for known people
    def processKnownPeopleImages(path=People, saveLocation="./Zero_gallery_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
            print(imgPath)
            import pathlib
            
            file = pathlib.Path(str(path+"Thumbs.db"))
            if file.exists ():
                os.remove(path+"Thumbs.db")
            else:
                pass
            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("./Zero_gallery_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)
                orig = image.copy()
                # print(imgPath)
                # import pathlib
            
                # file = pathlib.Path(str(path+"Thumbs.db"))
                # if file.exists ():
                #     os.remove(path+"Thumbs.db")
                # else:
                #     pass
            # 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")

                else:
                    saveImageToDirectory(orig, "0",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)
        # #print(q)
        # m = q
        # # print(m)
        # #   x.drop(x.columns[Unnam], axis=1, inplace=True)
        # df = m.groupby([0], as_index=False).count()
        # z = df[0].str.split('/', expand=True)


        # z.to_csv('all_people.csv',index=False)
        # import pandas as pd
        # df2 = pd.read_csv('./all_people.csv')
        # df2.rename({df2.columns[-1]: 'test'}, axis=1, inplace=True)
        # df2.rename({df2.columns[-2]: 'Matched'}, axis=1, inplace=True)
        # df2 = df2[['Matched', 'test']]


        # import pandas as pd
        # import os
        # c = []
        # for root, dirs, files in os.walk(Gallery,
        #                                  topdown=False):
        #     for name in files:
        #         # print(name)
        #         L = os.path.join(root, name)
        #         c.append(L)
        # df = pd.DataFrame(c)

        # df1 = df[0].str.split("/", expand=True)
        # #df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
        # # print('this is df1')
        # # print(df1)
        # df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
        # merge = pd.merge(df2, df1, on='test', how='left')
        # merge.rename({merge.columns[-1]: 'EventName'}, axis=1, inplace=True)
        # # merge.to_csv('merge.csv')
        # mergesplit = merge.loc[:, 'test'].str.split(".", expand=True)
        # mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
        # mergesplit = mergesplit.loc[:, 'ImageName']

        # #merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
        # #merge['EventName'] = merge['abc']
        # merge['Imagepath'] = "\\_files\\1\\Gallery\\" + merge['EventName'] + '\\' + + merge['test']


        # frames = [merge, mergesplit]

        # r = pd.concat(frames, axis=1, join='inner')


        # df2 = r.dropna(subset=['Matched'])


        # #df2['Matched'] = df2['Matched'].astype(str)
        # #df2['Matched'] = df2['Matched'].astype(int)
        # column_list = ['Matched', 'Imagepath', 'ImageName', 'EventName']
        # df2[column_list].to_csv('all_people_fina123.csv', index=False)
        # df2[column_list].to_json('all_people_final123.json', orient="records")

        # print("Completed")

    if __name__ == "__main__":
        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'



predict123()