Upload files to 'Events/src'

Events/src/multi_pcs.py

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+import requests
+import time
+import multiprocessing
+from PIL import Image
+from functools import partial
+import queue
+import pickle
+import time
+
+import numpy as np
+import face_recognition
+import os
+from flask import Flask, render_template, request, redirect, send_file
+# import shutil
+import cv2
+import datetime
+from flask import request
+
+# Gallery = "D:/share/biz/mt/Copy_Gallery/" + str(seconds).replace("]", "").replace("[", "").replace("'", "")
+# People = 'D:/share/biz/mt/People/' + str(seconds).replace("]", "").replace("[", "").replace("'", "") + "/"
+app = Flask(__name__)
+
+@app.route('/', methods=["GET", "POST"])
+def home():
+    return "EVENT APP RUNNING.............."
+
+
+
+def download(eventid):
+    print("process started with event id = "+str(eventid))
+
+
+    Gallery = "/home/ubuntu/AI/Events/Gallery/" + eventid+ "/" 
+    People = "/home/ubuntu/AI/Events/guestimage/"+ eventid + "/"
+
+
+
+
+
+    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..")
+        #print('Detecting_face...........')
+        """
+        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('zzzzzzzzzzzzz.csv',index=False)
+        import pandas as pd
+        df2 = pd.read_csv('zzzzzzzzzzzzz.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('events.csv', index=False)
+        df2[column_list].to_json('events.json', orient="records")
+
+
+
+
+
+        # import requests
+        # import json
+
+        # with open('events.json', 'r') as json_file:
+        #     json_load = json.load(json_file)
+        #     url = "https://eventxstreamnew.bizgaze.com:5443/apis/v4/bizgaze/integrations/events/createpredictedimage"
+        #     #url = "https://eventxstreamnew.bizgaze.com:5443/apis/v4/bizgaze/integrations/json/eventwisepredicts"
+
+        # payload = json.dumps(json_load).replace("]", "").replace("[", "")
+        # print(payload)
+        # headers = {
+        #     'Authorization': 'stat bcc78ad858354e759249c1770957fede',
+
+            
+        #     'Content-Type': 'application/json'
+        #     }
+        # response = requests.request("POST", url, headers=headers, data=payload)
+        # print("Ongoing process with event id = "+str(eventid))
+        # 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.
+
+        """
+        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)
+        f_CSVwrite()
+
+    # Function for creating encodings for known people
+    def processKnownPeopleImages(path=People, saveLocation="./known_encodings.pickle"):
+        print(People)
+        """
+        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=0.6, fy=0.6, 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_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(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()
+
+            # Resize
+                image = cv2.resize(image, (0, 0), fx=0.6, fy=0.6, 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()
+
+        # import pandas as pd
+        # q = pd.DataFrame(p)
+        # df1 = q
+        # print(df1)
+        # # df1.to_csv('m.csv')
+
+        # import pandas as pd
+        # import os
+        # c = []
+        # for root, dirs, files in os.walk(Gallery, topdown=False):
+        #     for name in files:
+        #         L = os.path.join(root, name)
+        #         c.append(L)
+        # df2 = pd.DataFrame(c)
+        # # df.to_csv('oswalk.csv')
+        # import pandas as pd
+        # # df1 = pd.read_csv('m.csv')
+        # # df2 = pd.read_csv('oswalk.csv')
+        # df1 = df1[0].str.split('/', expand=True)
+        # df1.rename({df1.columns[-2]: 'Matched'}, axis=1, inplace=True)
+        # df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
+        # df2 = df2[0].str.split("\\", expand=True)
+        # df2.rename({df2.columns[-1]: 'test'}, axis=1, inplace=True)
+        # df2.rename({df2.columns[-2]: 'Eventname'}, axis=1, inplace=True)
+        # merge = pd.merge(df2, df1, on='test', how='left')
+        # mergesplit = merge.loc[:, 'test'].str.split(".", expand=True)
+        # mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
+        # mergesplit = mergesplit.loc[:, 'ImageName']
+        # merge['Imagepath'] = "/_files/1/Gallery/" + merge['Eventname'] + '/' + merge['test']
+        # frames = [merge, mergesplit]
+        # r = pd.concat(frames, axis=1, join='inner')
+        # first_column = r.pop('Matched')
+        # r.insert(0, 'Matched', first_column)
+        # second_column = r.pop('Imagepath')
+        # r.insert(1, 'Imagepath', second_column)
+        # third_column = r.pop('ImageName')
+        # r.insert(2, 'ImageName', third_column)
+        # fourth_column = r.pop('Eventname')
+        # r.insert(3, 'Eventname', fourth_column)
+        # r = r.iloc[:, 0:4]
+        # r.sort_values(by=['Matched'], inplace=True)
+        # print(r)
+        # r.to_csv('path.csv', index=False)
+        # r.to_json(r'matched.json', orient="records")
+        print("process Ended with event id = "+str(eventid))
+
+    main()
+
+
+
+
+
+
+
+
+
+
+
+
+@app.route('/eventwise', methods=["GET", "POST"])
+def eventwise():
+    if __name__ == "__main__":
+
+        url_list=[]
+        Dataset= request.args.get('Dataset')
+        # id = "100013660000125"
+        url_list.append(Dataset)
+        # multiprocessing
+        with multiprocessing.Pool(processes=10) as pool:
+            results = pool.map(download,url_list)
+        pool.close()
+        return "none"
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0",port=8081)

Events/src/myproject.py

@@ -0,0 +1,493 @@
+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 = "./Gallery/"
+peoplePath = "./guestimage/"
+@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():
+    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):
+        """
+        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=0.5)
+        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()
+        # z = df[0].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("./Dataset", 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(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")
+
+#############################################################################################
+
+
+
+       #  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')
+
+    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,debug=True)
+
+