I'm using face_recognition package for face recognition
Input image file is base64 encoded,
I'm trying to decode the data and
face_recognition.face_encodings(decodedBase64Data)
And i have face encoded data list to compare.
The problem is i need to convert the base64 data to image that i can encode using face_encodings.
I tried with
decodedData = base64.b64decode(data)
encodeFace = np.frombuffer(decodedData, np.uint8)
And pass the encodedFace to
face_recognition.face_encodings(decodedBase64Data)
I get error Unsupported image type, must be 8bit gray or RGB image.
How to convert base64 into image compatible to face_encodings?
Edit :
Code attached for reference
import base64
import numpy as np
import json
import face_recognition as fr
with open('Face_Encoding_Data.json') as f:
EncodeJsonData = json.load(f)
personName = list(EncodeJsonData.keys())
encodedImgList = list(EncodeJsonData.values())
"""
EncodeJsonData = {"name1" : [encoded data 1], "name2" : [encoded data 2]}
128 byte
"""
base64Data = """ base64 encoded image with face """
encodeFace = np.frombuffer(base64.b64decode(base64Data), np.uint8)
matches = fr.compare_faces(encodedImgList, encodeFace, tolerance=0.5)
faceDist = fr.face_distance(encodedImgList, encodeFace)
matchIndex = np.argmin(faceDist)
name = "unknown"
if matches[matchIndex]:
name = personName[matchIndex]
print(name)
Please share the code for better understanding of problem or you can use below code as a refrence
import cv2
import os
import numpy as np
from PIL import Image
import time
cap = cv2.VideoCapture(1)
count=1
path='dataset2'
img=[]
imagepath = [os.path.join(path,f)for f in os.listdir(path)]
c=len(imagepath)
#for i in imagepath: i access the each images from my folder of images
while count<=c:
image = face_recognition.load_image_file("dataset2/vrushang."+str(count)+".jpg")
#now i will make list of the encoding parts to compare it runtime detected face
img.append(face_recognition.face_encodings(image)[0])
time.sleep(1)
count=count+1
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
img2 = []
img2 = img[0]
print"this is img2"
print img
while True:
ret, frame = cap.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
if process_this_frame:
face_locations = face_recognition.face_locations(small_frame)
face_encodings = face_recognition.face_encodings(small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
match = face_recognition.compare_faces(img, face_encoding)
print match
if match[0]==True:
name = "vrushang"
elif match[1]==True:
name = "hitu"
elif match[3]==True:
name = "sardar patel"
elif match[2]==True:
name = "yaksh"
else:
name = "unknown"
face_names.append(name)
process_this_frame = not process_this_frame
for (top, right, bottom, left), name in zip(face_locations, face_names):
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
cv2.imshow('Video', frame)
if cv2.waitKey(1)==27:
break```
Try this code to load encoded image for face_recognition package :
import urllib.request as ur
import face_recognition as fr
image = 'Input image file is base64 encoded'
decoded= ur.urlopen(image)
image_loaded = fr.load_image_file(decoded)
=> for test :
face_locations = fr.face_locations(image_loaded)
print("I found {} face(s) in this photograph.".format(len(face_locations)))
Related
I am trying to use a raspberry pi cam but it is not being detected. I have tried using a different cam to see if it was broken and it isn't. I had got it working beforehand but for some reason, it's now not working.
I'm using a pi 4 B
I get This error
Traceback (most recent call last): File "/home/pi/Desktop/FaceDec_Robot/Robot-python/main.py", line 39, in <module>
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) cv2.error: OpenCV(4.5.1) ../modules/imgproc/src/resize.cpp:4051: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
meaning the array is empty but dont know why.
code:
import cv2
import os
import face_recognition
import numpy as np
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture("/dev/video0", cv2.CAP_V4L)
#loop over all the images in the folder and put them in a list
known_face_encodings = []
known_face_names = []
for file in os.listdir("Tested/known/"):
if file.endswith(".jpg"):
#load the image
known_image = face_recognition.load_image_file("Tested/known/" + file)
#get the face encoding
known_face_encoding = face_recognition.face_encodings(known_image)[0]
#add the encoding to the list
known_face_encodings.append(known_face_encoding)
#add the name to the list
known_face_names.append(file[:-4])
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
#loop over the frames
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
# cv2.imshow('Video', frame)
#save the image
cv2.imwrite("Tested/test.jpg", frame)
I have also tried setting the video_capture to 0 but that still does not work
How can I integrate pickled face_data.dat to python face-recognition default example which shows real-time webcam view?
import face_recognition
import cv2
import numpy as np
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
obama_face_encoding,
biden_face_encoding
]
known_face_names = [
"Barack Obama",
"Joe Biden"
]
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face enqcodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
I've pickled the images and tried modifying the program as follows but It's showing the wrong names.
import face_recognition
import cv2
import numpy as np
import pickle
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load face encodings
with open('dataset_faces.dat', 'rb') as f:
all_face_encodings = pickle.load(f)
# Grab the list of names and the list of encodings
face_names = list(all_face_encodings.keys())
face_encodings = np.array(list(all_face_encodings.values()))
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face enqcodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
# face_distances = face_recognition.face_distance(face_encodings, face_encoding)
# best_match_index = np.argmin(face_distances)
# if matches[best_match_index]:
# name = face_names[best_match_index]
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
I've checked pickled data using this script. It is showing the correct output to the relevant image.
import face_recognition
import pickle
import numpy as np
# Load face encodings
with open('dataset_faces.dat', 'rb') as f:
all_face_encodings = pickle.load(f)
# Grab the list of names and the list of encodings
face_names = list(all_face_encodings.keys())
face_encodings = np.array(list(all_face_encodings.values()))
# Try comparing an unknown image
unknown_image = face_recognition.load_image_file("obama.jpg")
unknown_face = face_recognition.face_encodings(unknown_image)
result = face_recognition.compare_faces(face_encodings, unknown_face)
# Print the result as a list of names with True/False
names_with_result = list(zip(face_names, result))
print(names_with_result)
i have a code for face recognition by open cv python
import face_recognition as fr
import os
import cv2
import face_recognition
import numpy as np
from time import sleep
def get_encoded_faces():
"""
looks through the faces folder and encodes all
the faces
:return: dict of (name, image encoded)
"""
encoded = {}
for dirpath, dnames, fnames in os.walk("./faces"):
for f in fnames:
if f.endswith(".jpg") or f.endswith(".png"):
face = fr.load_image_file("faces/" + f)
encoding = fr.face_encodings(face)[0]
encoded[f.split(".")[0]] = encoding
return encoded
def unknown_image_encoded(img):
"""
encode a face given the file name
"""
face = fr.load_image_file("faces/" + img)
encoding = fr.face_encodings(face)[0]
return encoding
def classify_face(im):
"""
will find all of the faces in a given image and label
them if it knows what they are
:param im: str of file path
:return: list of face names
"""
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
img = cv2.imread(im, 1)
#img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
#img = img[:,:,::-1]
face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
# Display the resulting image
while True:
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names
print(classify_face("test-image");
it is taking image that we save to test but i want it to take image from camera then recognize it So any one here can please tell how i can change test image so it will take test image from camera not what we save in data base to test ...........
If you want to check single frame from camera then simply use
cap = cv2.VideoCapture(0)
status, img = cap.read()
instead of
img = cv2.imread(im, 1)
If you want to check faces in stream then you need to put all in loop
def classify_face(im):
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
cap = cv2.VideoCapture(0)
while True:
status, img = cap.read()
face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
cv2.imshow('Video', img)
print(face_names)
if cv2.waitKey(1) & 0xFF == ord('q'):
return
EDIT: Full code (with small changes) for work with video stream. It works for me. But if previous function did works for you then this version may not help you - function classify_face is almost the same as in previous example.
import os
import cv2
import face_recognition as fr
import numpy as np
def get_encoded_faces(folder="./faces"):
"""
looks through the faces folder and encodes all
the faces
:return: dict of (name, image encoded)
"""
encoded = {}
for dirpath, dnames, fnames in os.walk(folder):
for f in fnames:
if f.lower().endswith(".jpg") or f.lower().endswith(".png"):
fullpath = os.path.join(dirpath, f)
face = fr.load_image_file(fullpath)
# normally face_encodings check if face is on image - and it can get empty result
height, width = face.shape[:2]
encoding = fr.face_encodings(face, known_face_locations=[(0, width, height, 0)])
if len(encoding) > 0:
encoding = encoding[0]
encoded[f.split(".")[0]] = encoding
return encoded
def classify_face(im):
"""
will find all of the faces in a given image and label
them if it knows what they are
:param im: str of file path
:return: list of face names
"""
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
cap = cv2.VideoCapture(0)
while True:
status, img = cap.read()
#print('status:', status)
face_locations = fr.face_locations(img)
unknown_face_encodings = fr.face_encodings(img, face_locations)
face_names = []
for location, face_encoding in zip(face_locations, unknown_face_encodings): # I moved `zip()` in this place
# See if the face is a match for the known face(s)
matches = fr.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = fr.face_distance(faces_encoded, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
top, right, bottom, left = location
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
print('face_names:', face_names)
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names
# --- main ---
print(classify_face("test-image"))
cv2.destroyAllWindows()
I have some code here and it is a little sloppy, is there any way that I could put the images, encodings, and names into separate files and import them into the main code for use? I have tried putting them into a separate file and then importing them, but it still shows a not defined error? Can anyone help me find out why, or how to fix it.
main code
import cv2
import numpy as np
# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name,(left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
Code I Wish To Separate
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
I just want to make it neater and easier to access.
Hi everyone I'm working on OpenCV(Python)on a face recognition program. I have two files, one which captures a new user's face and stores it by the name supplied by user. The second file recognizes the user using webcam. Now, my concern is that the user is getting recognised correctly but the name is only shown and not saved. How could I save the name of the recognised person so that it can be transfered or done some operations upon?
#__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
fn_name = "aditya"
path = os.path.join(fn_dir, fn_name)
if not os.path.isdir(path):
os.mkdir(path)
(im_width, im_height) = (112, 92)
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
# The program loops until it has 20 images of the face.
count = 0
while count < 20:
(rval, im) = webcam.read()
im = cv2.flip(im, 1, 0)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
faces = haar_cascade.detectMultiScale(mini)
faces = sorted(faces, key=lambda x: x[3])
if faces:
face_i = faces[0]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path)
if n[0]!='.' ]+[0])[-1] + 1
cv2.imwrite('%s/%s.png' % (path, pin), face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.putText(im, fn_name, (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN,
1,(0, 255, 0))
count += 1
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
Code for face recognition from the dataset
__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
# Part 1: Create fisherRecognizer
print('Training...')
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(fn_dir):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(fn_dir, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
lable = id
images.append(cv2.imread(path, 0))
lables.append(int(lable))
id += 1
(im_width, im_height) = (112, 92)
# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.createFisherFaceRecognizer()
model.train(images, lables)
# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
while True:
(rval, frame) = webcam.read()
frame=cv2.flip(frame,1,0)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
faces = haar_cascade.detectMultiScale(mini)
for i in range(len(faces)):
face_i = faces[i]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Write the name of recognized face
# [1]
cv2.putText(frame,
'%s - %.0f' % (names[prediction[0]],prediction[1]),
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
break
This is my code. where i am not using any sql-server.
I am encoding images from the folder and it will show the recognized face with the name of the image saved. if the image is saved as .. abc.jpg. then it will detect the face during live streaming and show abc.jpg
here is my code :
from PIL import Image
import face_recognition
import cv2
import os
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
known_face_encodings=[]
known_face_names = []
user_appeared = []
root = "/home/erp-next/open cv/dataset/"
for filename in os.listdir(root):
if filename.endswith('.jpg' or '.png'):
try:
print(filename)
path = os.path.join(root, filename)
filter_image = face_recognition.load_image_file(path)
filter_face_encoding = face_recognition.face_encodings(filter_image)
known_face_encodings.append(filter_face_encoding[0])
known_face_names.append(filename)
except:
print("An exception occurred : " + filename )
#print(known_face_encodings)
print(known_face_names)
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
# process_this_frame = True
def face():
while True:
process_this_frame = True
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
k = cv2.waitKey(1)
if k%256 == 27:
# ESC pressed
print("Escape hit, closing...")
break
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
print(name)
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
face()
i am also using face_recognition library to encode and detect face.
Thanks.