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SadhulaSaiKumar
2023-04-12 11:01:16 +00:00
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from flask import Flask, render_template, request, redirect, Response, send_file
import multiprocessing
import face_recognition
#from numba import jit
import numpy as np
import os
#from flask_cors import CORS
app = Flask(__name__)
#CORS(app)
lst = []
@app.route('/', methods=['GET'])
def resume():
#return render_template('index.html')
return 'Attendence app running'
def createEncodings(image):
print("Encoding")
"""
Create face encodings for a given image and also return face locations in the given image.
Parameters
-------
image : cv2 mat
Image you want to detect faces from.
Returns
-------
known_encodings : list of np array
List of face encodings in a given image
face_locations : list of tuples
list of tuples for face locations in a given image
"""
# Find face locations for all faces in an image
face_locations = face_recognition.face_locations(image)
# Create encodings for all faces in an image
known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
return known_encodings, face_locations
#@app.route('/registered', methods=["POST","GET"])
def registered(url_list):
input=url_list
from pathlib import Path
Path("ppeople").mkdir(exist_ok=True)
Path("ppeople/" + input["FileName"]).mkdir(exist_ok=True)
a = input
# print(a)
x = a['FileData']
# print(x)
y = a['FileName']
#z = a['FileType']
z='jpg'
# CreatedBy=a['CreatedBy']
name = y+ '.'+ z
print(name)
# print(y)
# image = y.split("/")
# filename=image[-1]
# print(x)
img_data = x.encode()
import base64
with open("ppeople/" + input["FileName"] + "/" + name, "wb") as fh:
fh.write(base64.decodebytes(img_data))
img = "ppeople/" + y + "/" + name
saveLocation = "ppeople/" + y + "/" + y + ".pickle"
############ detecting no of faceses #######################
# import cv2
# import numpy as np
# import dlib
# # Connects to your computer's default camera
# cap = cv2.imread(img)
# # Detect the coordinates
# detector = dlib.get_frontal_face_detector()
# number_of_faces=[]
# # Capture frames continuously
# # while True:
# # Capture frame-by-frame
# # ret, frame = cap
# frame = cap
# # RGB to grayscale
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# faces = detector(gray)
# # Iterator to count faces
# i = 0
# for face in faces:
# # Get the coordinates of faces
# x, y = face.left(), face.top()
# x1, y1 = face.right(), face.bottom()
# cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
# # Increment iterator for each face in faces
# i = i+1
# # Display the box and faces
# cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# # if len(i)>1:
# # print(i)
# number_of_faces.append(i)
# if (len(number_of_faces))>1:
# print("Group Photo")
# return "Group Photo"
# elif (len(number_of_faces))==1:
# print("Single Photo")
# pass
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...")
print("[INFO] Encodings Created sucessfully")
f = open(encodingsFile, "wb")
f.write(pickle.dumps(data))
f.close()
# Function to create encodings and get face locations
def processKnownPeopleImages(img=img, saveLocation=saveLocation):
"""
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 = img
# Read image
image = cv2.imread(imgPath)
name = img.rsplit('.')[0]
# Resize
try:
print(image.shape)
except AttributeError:
return "Successfully saved encoding........."
image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
# Get locations and encodings
encs, locs = createEncodings(image)
try:
known_encodings.append(encs[0])
except IndexError:
os.remove(saveLocation)
print('------------------------------------- save location --------------------------------')
print(saveLocation)
return "hello world!"
# known_encodings.append(encs[0])
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)
import cv2
#import face_recognition
import pickle
processKnownPeopleImages(img, saveLocation)
return 'Successfully saved encoding.........'
# ******************************** COMPARUISION *********************************************************
#@app.route('/submit', methods=["POST","GET"])
def submit(url_list):
from datetime import datetime
import pytz
tz_NY = pytz.timezone('Asia/Kolkata')
datetime_NY = datetime.now(tz_NY)
India_Date = (datetime_NY.strftime("%Y-%m-%d"))
India_Date = str(India_Date)
# India_Time = (datetime_NY.strftime("%I:%M:%S %p"))
# India_Time = str(India_Time)
input=url_list
import pickle
import cv2
from pathlib import Path
Path("GGallery/"+ India_Date).mkdir(exist_ok=True)
Path("GGallery/"+ India_Date +'/'+ input["FileName"]).mkdir(exist_ok=True)
a = input
# print(a)
x = a['FileData']
# print(x)
y = a['FileName']
# z = a['FileType']
z='jpg'
# CreatedBy=a['CreatedBy']
name = y + '.' + z
# print(name)
# print(y)
# image = y.split("/")
# filename=image[-1]
# print(x)
img_data = x.encode()
import base64
with open("GGallery/"+India_Date+'/' + input["FileName"] + "/" + name, "wb") as fh:
fh.write(base64.decodebytes(img_data))
path = "GGallery/" +India_Date+'/'+ y + "/" + name
pickle_location = "ppeople/" + y + "/" + y + ".pickle"
import pathlib
file = pathlib.Path(pickle_location)
if file.exists ():
pass
else:
print ("pickle File not exist")
print(name)
return "Face not found in profile (please change your profile)"
check_faces="ppeople/" + y + "/" + y + ".jpg"
print(check_faces)
############ detecting no of faceses #######################
import cv2
import numpy as np
import dlib
# Connects to your computer's default camera
cap = cv2.imread(check_faces)
# Detect the coordinates
detector = dlib.get_frontal_face_detector()
number_of_faces=[]
# Capture frames continuously
# while True:
# Capture frame-by-frame
# ret, frame = cap
frame = cap
# RGB to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
# Iterator to count faces
i = 0
for face in faces:
# Get the coordinates of faces
x, y = face.left(), face.top()
x1, y1 = face.right(), face.bottom()
cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
# Increment iterator for each face in faces
i = i+1
# Display the box and faces
cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# if len(i)>1:
# print(i)
number_of_faces.append(i)
if (len(number_of_faces))>1:
print("Group Photo")
return "Group Photo"
elif (len(number_of_faces))==1:
print("Single Photo")
pass
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
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=0.54)
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 processDatasetImages(path=path, pickle_location=pickle_location):
"""
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(pickle_location)
# print(p)
# imgPath = path + img
# Read image
# path=r"C:\Users\katku\Pictures\final\100011460000611.jpg"
image = cv2.imread(path)
#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")
#print(path)
with_extension = path.split("/")[-1]
without_extension = with_extension.split(".")[0]
# output_s = {"FileID": without_extension,
# "Date": India_Date,
# "Time": India_Time}
# output_json = json.dumps(output_s)
output_json='Matched successfully'
print(loc)
lst.append(output_json)
print(output_json)
# exit()
else:
print('Not Matched')
pass
# saveImageToDirectory(orig, "0",name)
import numpy as np
import json
processDatasetImages(path, pickle_location)
return lst[0]
#return 'matched successfully'
@app.route('/detect', methods=["POST"])
def detect():
if __name__ == "__main__":
url_list=[]
Dataset= request.get_json()
# id = "100013660000125"
url_list.append(Dataset)
# multiprocessing
pool_size = multiprocessing.cpu_count() * 2
with multiprocessing.Pool(pool_size) as pool:
try:
results = pool.map(submit, url_list)
except FileNotFoundError:
return 'plese get registered with your PhotoID'
except IndexError:
#return 'unable to recognize face'
return 'failed'
pool.close()
return results[0]
@app.route('/register', methods=["POST"])
def register():
print("hello start..........")
if __name__ == "__main__":
url_list=[]
Dataset= request.get_json()
# id = "100013660000125"
url_list.append(Dataset)
# multiprocessing
pool_size = multiprocessing.cpu_count() * 2
with multiprocessing.Pool(pool_size) as pool:
try:
results = pool.map(registered, url_list)
except IndexError:
pass
print('face not found')
except FileNotFoundError:
pass
#os.remove(img)
# return 'unable to recognize face'
pool.close()
#return results[0]
return 'Successfully saved encoding.........'
if __name__ == "__main__":
app.run(host='0.0.0.0',port =5003,debug=False)