AI/Events/src/multi_pcs.py

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 "Done"

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