Python Face_Recognition with base64 编码图像

Question

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)

Answer 1

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```

Answer 2

have you solved this problem? can you give me the program code?

Answer 3

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)))