AI/Events/src/myproject.py

import pickle
import numpy as np
import face_recognition
import os
from flask import Flask, render_template, request, redirect, send_file
#import shutil
import cv2

app = Flask(__name__)
app.config["IMAGE_UPLOADS"] = "C:/Users/Bizgaze/PycharmProjects/face_recogniction/People"
datasetPath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/1/Gallery/"
peoplePath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/People/"
@app.route('/', methods=['GET'])
def home():
    return render_template('index.html')


@app.route('/Display', methods=['GET', "POST"])
def Display():
    return render_template('Display.html')


@app.route("/upload", methods=["GET", "POST"])
def upload():
    if request.method == "POST":

        if request.files:

            image = request.files["image"]
            try:
                image.save(os.path.join(
                    app.config["IMAGE_UPLOADS"], image.filename))
            except IsADirectoryError:
                return render_template('index.html')
            # image.save(os.path.join(
            #     app.config["IMAGE_UPLOADS"], image.filename))

            print("Image saved")

            return redirect(request.url)

    return 'ok'


@app.route('/predict', methods=["GET", "POST"])
def predict():
    print('starting')
    def saveEncodings(encs, names, fname="encodings.pickle"):
        print('encoding')
        """
        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):
        """
        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 = "./output/" + name
        path1 = "./output/" + name
        if os.path.exists(path):
            pass
        else:
            os.mkdir(path)
        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=peoplePath, 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:
                pass
            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)

    # 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(datasetPath, 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()

            # 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, 0, 0), thickness=2)
            # # cv2.imshow("Image", image)
            #     cv2.waitKey(1)
            #     cv2.destroyAllWindows()


    def main():
        """
        Main Function.

        Returns
        -------
        None.

        """

        processKnownPeopleImages()
        processDatasetImages()
        # shutil.make_archive('./Images', 'zip','./output')
        # p='./Images.zip'
        # return send_file(p,as_attachment=True)


        # import pandas as pd
        # q = pd.DataFrame(p)
        # x = q
        # # x.drop(x.columns[0], axis=1, inplace=True)
        # df = x.groupby([0], as_index=False).count()
        # z = df[0].str.split('/', expand=True)


        # for i, group in z.groupby([2]):
        #     group.drop(group.columns[2], axis=1, inplace=True)
            #group.to_csv(f'./output1/{i}.csv', index=False, sep='/', header=False)


##############################csv creation code ##############################
        import pandas as pd
        q = pd.DataFrame(p)
        m = q
        #print(m)
        #   x.drop(x.columns[Unnam], axis=1, inplace=True)
        df = m.groupby([0], as_index=False).count()
        first_column_name = df.columns[0]

        # Rename the first column
        df.rename(columns={first_column_name: 'col'}, inplace=True)
        #print(df)
        z = df['col'].str.split('/', expand=True)

        z['ImagePath'] = z[3]

        result = z.drop([0,1,3], axis=1)
        result.rename({result.columns[-1]: 'test'}, axis=1, inplace=True)
        # print(result)
        result.to_csv('results1.csv')
        import pandas as pd
        import os
        c = []
        for root, dirs, files in os.walk(datasetPath, topdown=False):
            for name in files:
                # print(name)
                L = os.path.join(root, name)
                c.append(L)
        df = pd.DataFrame(c)
        #print('seconfdf')
      
        first_column_name = df.columns[0]

        # Rename the first column
        df.rename(columns={first_column_name: 'col'}, inplace=True)
        print(df)
        df1 = df['col'].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(df1, result, on='test', how='left')
        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')
        r=r.iloc[:,3:]
        #print(r)
        r.to_csv('path.csv', index=False)
        #r.to_json(r'./matched.json', orient="records")
        column_list = ['Matched','Imagepath', 'ImageName', 'EventName']
        r[column_list].to_json('matched.json', orient="records")

#############################################################################################



       #  merge.rename({merge.columns[-3]: 'ImagePath'}, axis=1, inplace=True)
       #
       #  # print(merge)
       #  merge1 = merge.iloc[:, -2]
       #  merge12= merge.iloc[:, -3]
       #
       #  # merge1.rename({merge1.columns[-1]: 'abc'}, axis=1, inplace=True)
       #  merge2 = merge.iloc[:, -1].str.split(".", expand=True)
       #  merge2.rename({merge2.columns[-1]: 'drop'}, axis=1, inplace=True)
       #  #merge2.rename({merge2.columns[-2]: 'ImageName'}, axis=1, inplace=True)
       #  print('this is merge1')
       #  print(merge1)
       #  print('this is merge2')
       #  print(merge2)
       #  mergefinal = pd.concat([merge1, merge2], axis=1, join='inner')
       #  # print(mergefinal)
       #  # print('-----------------')
       #
       #  mergefinal.drop(columns=mergefinal.columns[-1], axis=1, inplace=True)
       #  # print(mergefinal)
       #  # print('--------------------------------------------------------------------------------')
       # # mergefinal.rename({mergefinal.columns[-1]: 'ImageName'}, axis=1, inplace=True)
       #  # print('this is filename')
       #  # print(mergefinal)
       #  #mergefinal.rename({mergefinal.columns[-2]: 'EventName'}, axis=1, inplace=True)
       #  # print('this is foldername')
       #  # print(mergefinal)
       #
       #  frames = [mergefinal, merge12]
       #
       #  r = pd.concat(frames, axis=1, join='inner')
       #
       #
       #  r.to_csv('Imagepath1.csv', index=False)
       #  r.to_json('Imagepath1.json', orient="records")
       #  import shutil
        # import os
        # # import shutil module
        # import shutil
        #
        # # import os module
        # import os
        # #################### move code#############
        # # base path
        # base_path = 'C:\\Users\\Bizgaze\\PycharmProjects\\face_recogniction\\move'
        # import os
        # dir_list = []
        # rootdir = 'C:\\Users\\Bizgaze\\PycharmProjects\\face_recogniction\\Dataset'
        # for file in os.listdir(rootdir):
        #     d = os.path.join(rootdir, file)
        #     if os.path.isdir(d):
        #         dir_list.append(d)
        #
        # # list of directories we want to move.
        # # dir_list = ['test2', 'test4', 'test5', 'does_not_exist']
        #
        # # path to destination directory
        # # dest = os.path.join(base_path, 'dest')
        #
        # print("Before moving directories:")
        # print(os.listdir(base_path))
        #
        # # traverse each directory in dir_list
        # for dir_ in dir_list:
        #
        #     # create path to the directory in the
        #     # dir_list.
        #     source = os.path.join(base_path, dir_)
        #
        #     # check if it is an existing directory
        #     if os.path.isdir(source):
        #         # move to destination path
        #         shutil.move(source, base_path)
        #
        # print("After moving directories:")
        # print(os.listdir(base_path))

        print("Completed")

    if __name__ == "__main__":
        main()

    #    return render_template('index.html')
    p = './matched.json'
    return send_file(p,as_attachment=True)

   # return 'ALL IMAGES MATCHED'


@app.route('/json')
def json():
    p = './matched.json'
    return send_file(p,as_attachment=True)


if __name__ == "__main__":
    app.run(host="0.0.0.0",port=8081)