Upload files to 'Events/src'
This commit is contained in:
+492
-492
@@ -1,493 +1,493 @@
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import requests
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import time
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import multiprocessing
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from PIL import Image
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from functools import partial
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import queue
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import pickle
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import time
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import numpy as np
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import face_recognition
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import os
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from flask import Flask, render_template, request, redirect, send_file
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# import shutil
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import cv2
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import datetime
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from flask import request
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# Gallery = "D:/share/biz/mt/Copy_Gallery/" + str(seconds).replace("]", "").replace("[", "").replace("'", "")
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# People = 'D:/share/biz/mt/People/' + str(seconds).replace("]", "").replace("[", "").replace("'", "") + "/"
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app = Flask(__name__)
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@app.route('/', methods=["GET", "POST"])
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def home():
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return "EVENT APP RUNNING.............."
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def download(eventid):
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print("process started with event id = "+str(eventid))
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Gallery = "/home/ubuntu/AI/Events/Gallery/" + eventid+ "/"
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People = "/home/ubuntu/AI/Events/guestimage/"+ eventid + "/"
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def saveEncodings(encs, names, fname="encodings.pickle"):
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"""
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Save encodings in a pickle file to be used in future.
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Parameters
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----------
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encs : List of np arrays
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List of face encodings.
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names : List of strings
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List of names for each face encoding.
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fname : String, optional
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Name/Location for pickle file. The default is "encodings.pickle".
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Returns
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-------
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None.
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"""
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data = []
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d = [{"name": nm, "encoding": enc} for (nm, enc) in zip(names, encs)]
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data.extend(d)
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encodingsFile = fname
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# dump the facial encodings data to disk
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print("[INFO] serializing encodings...")
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f = open(encodingsFile, "wb")
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f.write(pickle.dumps(data))
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f.close()
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# Function to read encodings
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def readEncodingsPickle(fname):
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"""
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Read Pickle file.
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Parameters
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----------
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fname : String
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Name of pickle file.(Full location)
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Returns
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-------
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encodings : list of np arrays
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list of all saved encodings
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names : List of Strings
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List of all saved names
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"""
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data = pickle.loads(open(fname, "rb").read())
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data = np.array(data)
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encodings = [d["encoding"] for d in data]
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names = [d["name"] for d in data]
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return encodings, names
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# Function to create encodings and get face locations
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def createEncodings(image):
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print("encoding..")
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#print('Detecting_face...........')
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"""
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Create face encodings for a given image and also return face locations in the given image.
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Parameters
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----------
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image : cv2 mat
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Image you want to detect faces from.
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Returns
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-------
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known_encodings : list of np array
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List of face encodings in a given image
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face_locations : list of tuples
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list of tuples for face locations in a given image
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"""
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# Find face locations for all faces in an image
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face_locations = face_recognition.face_locations(image)
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# Create encodings for all faces in an image
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known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
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return known_encodings, face_locations
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# Function to compare encodings
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def compareFaceEncodings(unknown_encoding, known_encodings, known_names):
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"""
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Compares face encodings to check if 2 faces are same or not.
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Parameters
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----------
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unknown_encoding : np array
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Face encoding of unknown people.
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known_encodings : np array
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Face encodings of known people.
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known_names : list of strings
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Names of known people
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Returns
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-------
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acceptBool : Bool
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face matched or not
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duplicateName : String
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Name of matched face
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distance : Float
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Distance between 2 faces
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"""
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duplicateName = ""
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distance = 0.0
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matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.47)
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face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
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best_match_index = np.argmin(face_distances)
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distance = face_distances[best_match_index]
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if matches[best_match_index]:
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acceptBool = True
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duplicateName = known_names[best_match_index]
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else:
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acceptBool = False
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duplicateName = ""
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return acceptBool, duplicateName, distance
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p = []
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def f_CSVwrite():
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import pandas as pd
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q = pd.DataFrame(p)
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#print(q)
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m = q
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# print(m)
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# x.drop(x.columns[Unnam], axis=1, inplace=True)
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df = m.groupby([0], as_index=False).count()
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z = df[0].str.split('/', expand=True)
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z.to_csv('zzzzzzzzzzzzz.csv',index=False)
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import pandas as pd
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df2 = pd.read_csv('zzzzzzzzzzzzz.csv')
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df2.rename({df2.columns[-1]: 'test'}, axis=1, inplace=True)
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df2.rename({df2.columns[-2]: 'Matched'}, axis=1, inplace=True)
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df2 = df2[['Matched', 'test']]
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import pandas as pd
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import os
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c = []
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for root, dirs, files in os.walk(Gallery,
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topdown=False):
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for name in files:
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# print(name)
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L = os.path.join(root, name)
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c.append(L)
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df = pd.DataFrame(c)
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df1 = df[0].str.split("/", expand=True)
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#df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
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# print('this is df1')
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# print(df1)
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df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
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merge = pd.merge(df2, df1, on='test', how='left')
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merge.rename({merge.columns[-1]: 'EventName'}, axis=1, inplace=True)
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# merge.to_csv('merge.csv')
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mergesplit = merge.loc[:, 'test'].str.split(".", expand=True)
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mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
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mergesplit = mergesplit.loc[:, 'ImageName']
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#merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
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#merge['EventName'] = merge['abc']
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merge['Imagepath'] = "\\_files\\1\\Gallery\\" + merge['EventName'] + '\\' + + merge['test']
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frames = [merge, mergesplit]
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r = pd.concat(frames, axis=1, join='inner')
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df2 = r.dropna(subset=['Matched'])
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#df2['Matched'] = df2['Matched'].astype(str)
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#df2['Matched'] = df2['Matched'].astype(int)
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column_list = ['Matched', 'Imagepath', 'ImageName', 'EventName']
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df2[column_list].to_csv('events.csv', index=False)
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df2[column_list].to_json('events.json', orient="records")
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# import requests
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# import json
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# with open('events.json', 'r') as json_file:
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# json_load = json.load(json_file)
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# url = "https://eventxstreamnew.bizgaze.com:5443/apis/v4/bizgaze/integrations/events/createpredictedimage"
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# #url = "https://eventxstreamnew.bizgaze.com:5443/apis/v4/bizgaze/integrations/json/eventwisepredicts"
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# payload = json.dumps(json_load).replace("]", "").replace("[", "")
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# print(payload)
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# headers = {
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# 'Authorization': 'stat bcc78ad858354e759249c1770957fede',
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# 'Content-Type': 'application/json'
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# }
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# response = requests.request("POST", url, headers=headers, data=payload)
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# print("Ongoing process with event id = "+str(eventid))
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# print("##############################################################")
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# print(response.text)
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p.clear()
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# Save Image to new directory
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def saveImageToDirectory(image, name, imageName):
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"""
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Saves images to directory.
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Parameters
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----------
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image : cv2 mat
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Image you want to save.
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name : String
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Directory where you want the image to be saved.
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imageName : String
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Name of image.
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Returns
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-------
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None.
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"""
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path = "./output/" + name
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path1 = "./output/" + name
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if os.path.exists(path):
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pass
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else:
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os.mkdir(path)
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cv2.imwrite(path + "/" + imageName, image)
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x = []
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c = (path1 + "/" + imageName)
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x.append(c)
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p.append(x)
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f_CSVwrite()
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# Function for creating encodings for known people
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def processKnownPeopleImages(path=People, saveLocation="./known_encodings.pickle"):
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print(People)
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"""
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Process images of known people and create face encodings to compare in future.
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Eaach image should have just 1 face in it.
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Parameters
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----------
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path : STRING, optional
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Path for known people dataset. The default is "C:/inetpub/vhosts/port82/wwwroot/_files/People".
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It should be noted that each image in this dataset should contain only 1 face.
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saveLocation : STRING, optional
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Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
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Returns
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-------
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None.
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"""
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known_encodings = []
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known_names = []
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for img in os.listdir(path):
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imgPath = path + img
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# Read image
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image = cv2.imread(imgPath)
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name = img.rsplit('.')[0]
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# Resize
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image = cv2.resize(image, (0, 0), fx=0.6, fy=0.6, interpolation=cv2.INTER_LINEAR)
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# Get locations and encodings
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encs, locs = createEncodings(image)
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try:
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known_encodings.append(encs[0])
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except IndexError:
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os.remove(People+img)
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known_names.append(name)
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for loc in locs:
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top, right, bottom, left = loc
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# Show Image
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#cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
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# cv2.imshow("Image", image)
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# cv2.waitKey(1)
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#cv2.destroyAllWindows()
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saveEncodings(known_encodings, known_names, saveLocation)
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# Function for processing dataset images
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def processDatasetImages(saveLocation="./Gallery_encodings.pickle"):
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"""
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Process image in dataset from where you want to separate images.
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It separates the images into directories of known people, groups and any unknown people images.
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Parameters
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----------
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path : STRING, optional
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Path for known people dataset. The default is "D:/port1004/port1004/wwwroot/_files/People".
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It should be noted that each image in this dataset should contain only 1 face.
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saveLocation : STRING, optional
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Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
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Returns
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-------
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None.
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"""
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# Read pickle file for known people to compare faces from
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people_encodings, names = readEncodingsPickle("./known_encodings.pickle")
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for root, dirs, files in os.walk(Gallery, topdown=False):
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for name in files:
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s = os.path.join(root, name)
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#print(p)
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# imgPath = path + img
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# Read image
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image = cv2.imread(s)
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orig = image.copy()
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# Resize
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image = cv2.resize(image, (0, 0), fx=0.6, fy=0.6, interpolation=cv2.INTER_LINEAR)
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# Get locations and encodings
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encs, locs = createEncodings(image)
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# Save image to a group image folder if more than one face is in image
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# if len(locs) > 1:
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# saveImageToDirectory(orig, "Group", img)
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# Processing image for each face
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i = 0
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knownFlag = 0
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for loc in locs:
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top, right, bottom, left = loc
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unknown_encoding = encs[i]
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i += 1
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acceptBool, duplicateName, distance = compareFaceEncodings(unknown_encoding, people_encodings, names)
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if acceptBool:
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saveImageToDirectory(orig, duplicateName,name)
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knownFlag = 1
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if knownFlag == 1:
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print("Match Found")
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else:
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saveImageToDirectory(orig, "0",name)
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# Show Image
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# cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
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# # cv2.imshow("Image", image)
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# cv2.waitKey(1)
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# cv2.destroyAllWindows()
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def main():
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"""
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Main Function.
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Returns
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-------
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None.
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"""
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processKnownPeopleImages()
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processDatasetImages()
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# import pandas as pd
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# q = pd.DataFrame(p)
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# df1 = q
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# print(df1)
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# # df1.to_csv('m.csv')
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# import pandas as pd
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# import os
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# c = []
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# for root, dirs, files in os.walk(Gallery, topdown=False):
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# for name in files:
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# L = os.path.join(root, name)
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# c.append(L)
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# df2 = pd.DataFrame(c)
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# # df.to_csv('oswalk.csv')
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# import pandas as pd
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# # df1 = pd.read_csv('m.csv')
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# # df2 = pd.read_csv('oswalk.csv')
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# df1 = df1[0].str.split('/', expand=True)
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# df1.rename({df1.columns[-2]: 'Matched'}, axis=1, inplace=True)
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# df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
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# df2 = df2[0].str.split("\\", expand=True)
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# df2.rename({df2.columns[-1]: 'test'}, axis=1, inplace=True)
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# df2.rename({df2.columns[-2]: 'Eventname'}, axis=1, inplace=True)
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# merge = pd.merge(df2, df1, on='test', how='left')
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# mergesplit = merge.loc[:, 'test'].str.split(".", expand=True)
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# mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
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# mergesplit = mergesplit.loc[:, 'ImageName']
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# merge['Imagepath'] = "/_files/1/Gallery/" + merge['Eventname'] + '/' + merge['test']
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# frames = [merge, mergesplit]
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# r = pd.concat(frames, axis=1, join='inner')
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# first_column = r.pop('Matched')
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# r.insert(0, 'Matched', first_column)
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# second_column = r.pop('Imagepath')
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# r.insert(1, 'Imagepath', second_column)
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# third_column = r.pop('ImageName')
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# r.insert(2, 'ImageName', third_column)
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# fourth_column = r.pop('Eventname')
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# r.insert(3, 'Eventname', fourth_column)
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# r = r.iloc[:, 0:4]
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# r.sort_values(by=['Matched'], inplace=True)
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# print(r)
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# r.to_csv('path.csv', index=False)
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# r.to_json(r'matched.json', orient="records")
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print("process Ended with event id = "+str(eventid))
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main()
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@app.route('/eventwise', methods=["GET", "POST"])
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def eventwise():
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if __name__ == "__main__":
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url_list=[]
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Dataset= request.args.get('Dataset')
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# id = "100013660000125"
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url_list.append(Dataset)
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# multiprocessing
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with multiprocessing.Pool(processes=10) as pool:
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results = pool.map(download,url_list)
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pool.close()
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return "none"
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if __name__ == "__main__":
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import requests
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||||
import time
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import multiprocessing
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||||
from PIL import Image
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||||
from functools import partial
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import queue
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||||
import pickle
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||||
import time
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import numpy as np
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import face_recognition
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||||
import os
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||||
from flask import Flask, render_template, request, redirect, send_file
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||||
# import shutil
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||||
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)
|
||||
+65
-48
@@ -8,8 +8,8 @@ import cv2
|
||||
|
||||
app = Flask(__name__)
|
||||
app.config["IMAGE_UPLOADS"] = "C:/Users/Bizgaze/PycharmProjects/face_recogniction/People"
|
||||
datasetPath = "./Gallery/"
|
||||
peoplePath = "./guestimage/"
|
||||
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')
|
||||
@@ -44,7 +44,9 @@ def upload():
|
||||
|
||||
@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.
|
||||
|
||||
@@ -153,7 +155,7 @@ def predict():
|
||||
"""
|
||||
duplicateName = ""
|
||||
distance = 0.0
|
||||
matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.5)
|
||||
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]
|
||||
@@ -324,9 +326,9 @@ def predict():
|
||||
|
||||
processKnownPeopleImages()
|
||||
processDatasetImages()
|
||||
shutil.make_archive('./Images', 'zip','./output')
|
||||
p='./Images.zip'
|
||||
return send_file(p,as_attachment=True)
|
||||
# shutil.make_archive('./Images', 'zip','./output')
|
||||
# p='./Images.zip'
|
||||
# return send_file(p,as_attachment=True)
|
||||
|
||||
|
||||
# import pandas as pd
|
||||
@@ -343,52 +345,65 @@ def predict():
|
||||
|
||||
|
||||
##############################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)
|
||||
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]
|
||||
|
||||
# z['ImagePath'] = z[3]
|
||||
# Rename the first column
|
||||
df.rename(columns={first_column_name: 'col'}, inplace=True)
|
||||
#print(df)
|
||||
z = df['col'].str.split('/', expand=True)
|
||||
|
||||
# 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)
|
||||
z['ImagePath'] = z[3]
|
||||
|
||||
# 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' ]
|
||||
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]
|
||||
|
||||
# merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
|
||||
# merge['EventName'] = merge['abc']
|
||||
# merge['Imagepath']="/_files/1/Gallery/"+merge['EventName']+'/'+ + merge['test']
|
||||
# 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' ]
|
||||
|
||||
# frames = [merge, mergesplit]
|
||||
merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
|
||||
merge['EventName'] = merge['abc']
|
||||
merge['Imagepath']="/_files/1/Gallery/"+merge['EventName']+'/'+ + merge['test']
|
||||
|
||||
# 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")
|
||||
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")
|
||||
|
||||
#############################################################################################
|
||||
|
||||
@@ -477,8 +492,10 @@ def predict():
|
||||
main()
|
||||
|
||||
# return render_template('index.html')
|
||||
p = './matched.json'
|
||||
return send_file(p,as_attachment=True)
|
||||
|
||||
return 'ALL IMAGES MATCHED'
|
||||
# return 'ALL IMAGES MATCHED'
|
||||
|
||||
|
||||
@app.route('/json')
|
||||
@@ -488,6 +505,6 @@ def json():
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host="0.0.0.0",port=8081,debug=True)
|
||||
app.run(host="0.0.0.0",port=8081)
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user