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DeepFace | Face analysis mainly based | Computer Vision library
kandi X-RAY | DeepFace Summary
DeepFace is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow applications. DeepFace has no bugs, it has no vulnerabilities and it has medium support. However DeepFace build file is not available. You can download it from GitHub.
a deep face analysis implement, mainly based on -Caffe. At this time, face analysis tasks like detection, alignment and recognition have been done. -中文Readme. Each task is divide by different folder. ###baseline Face detection using sliding windows style, it first train a face/noface two class classification network, and then tranform to a full convolution network, detection is based on the heatmap when input a large full size image. face detection result example：. ##Face key points detection. ###try1_1 face 5 key points detection using DeepID architecture. face 5 key points detection result example：. ##Face Verification ###deepid Face verification based on DeepID network architecture. face verification ROC result：. We collect the face datasets usually used by recently years' paper, and divide by different task. Also we simply describe each of the dataset.(In Chinese). For more implement details, please reference my blog. It is welcome for everyone to make suggestions and improvement.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse
Support
- DeepFace has a medium active ecosystem.
- It has 1465 star(s) with 638 fork(s). There are 135 watchers for this library.
- It had no major release in the last 12 months.
- There are 15 open issues and 22 have been closed. On average issues are closed in 34 days. There are no pull requests.
- It has a neutral sentiment in the developer community.
- The latest version of DeepFace is current.
DeepFace Support
Best in #Computer Vision
Average in #Computer Vision
DeepFace Support
Best in #Computer Vision
Average in #Computer Vision
Quality
- DeepFace has no bugs reported.
DeepFace Quality
Best in #Computer Vision
Average in #Computer Vision
DeepFace Quality
Best in #Computer Vision
Average in #Computer Vision
Security
- DeepFace has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
DeepFace Security
Best in #Computer Vision
Average in #Computer Vision
DeepFace Security
Best in #Computer Vision
Average in #Computer Vision
License
- DeepFace does not have a standard license declared.
- Check the repository for any license declaration and review the terms closely.
- Without a license, all rights are reserved, and you cannot use the library in your applications.
DeepFace License
Best in #Computer Vision
Average in #Computer Vision
DeepFace License
Best in #Computer Vision
Average in #Computer Vision
Reuse
- DeepFace releases are not available. You will need to build from source code and install.
- DeepFace has no build file. You will be need to create the build yourself to build the component from source.
DeepFace Reuse
Best in #Computer Vision
Average in #Computer Vision
DeepFace Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA
kandi has reviewed DeepFace and discovered the below as its top functions. This is intended to give you an instant insight into DeepFace implemented functionality, and help decide if they suit your requirements.
- Calculates the intersection of the specified boxes .
- Calculate the face
- Evaluates the classifier .
- Convert a Hdf5 file into images
- Align image to target image .
- Generate file list .
- Generate table .
- Get multiple attributes .
- Get one attribute .
- Get face draw point
DeepFace Key Features
1983的专栏
DeepFace Examples and Code Snippets
OpenCv: change position of putText()
CopyDownload
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame,
result['dominant_emotion'],
(50, 50),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['gender'],
(50, 80),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['dominant_race'],
(50, 110),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
font_scale = 1
font_thickness = 2
x = 50
y = 50
for text in result.values():
cv2.putText(frame,
text,
(x, y),
font, font_scale,
(220, 220, 220),
font_thickness,
cv2.LINE_4)
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
y += (height + 10) # +10 margin
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame,
result['dominant_emotion'],
(50, 50),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['gender'],
(50, 80),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['dominant_race'],
(50, 110),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
font_scale = 1
font_thickness = 2
x = 50
y = 50
for text in result.values():
cv2.putText(frame,
text,
(x, y),
font, font_scale,
(220, 220, 220),
font_thickness,
cv2.LINE_4)
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
y += (height + 10) # +10 margin
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame,
result['dominant_emotion'],
(50, 50),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['gender'],
(50, 80),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.putText(frame,
result['dominant_race'],
(50, 110),
font, 1,
(220, 220, 220),
2,
cv2.LINE_4)
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
import cv2
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
result = {'dominant_emotion': 'hello', "gender": 'world', "dominant_race": 'of python'}
font = cv2.FONT_HERSHEY_DUPLEX
font_scale = 1
font_thickness = 2
x = 50
y = 50
for text in result.values():
cv2.putText(frame,
text,
(x, y),
font, font_scale,
(220, 220, 220),
font_thickness,
cv2.LINE_4)
(width, height), baseline = cv2.getTextSize(text, font, font_scale, font_thickness)
y += (height + 10) # +10 margin
cv2.imshow('Facial rec.', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Can DeepFace verify() accept an image array or PIL Image object?
CopyDownload
results = DeepFace.verify(np.array(PILIMAGE), ...)
picture= "extracted_face_picture/single_face_picture.jpg"
picture= Image.open(picture)
.
.
df.verify(picture, np.array(frame), "Facenet")
df.verify(np.array(picture),np.array(frame), "Facenet")
picture= "extracted_face_picture/single_face_picture.jpg"
picture= Image.open(picture)
.
.
df.verify(picture, np.array(frame), "Facenet")
df.verify(np.array(picture),np.array(frame), "Facenet")
Tensor Tensor("flatten/Reshape:0", shape=(?, 2622), dtype=float32) is not an element of this graph
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from tensorflow.python.keras.backend import set_session
sess = tf.Session()
#This is a global session and graph
graph = tf.get_default_graph()
set_session(sess)
#now where you are calling the model
global sess
global graph
with graph.as_default():
set_session(sess)
input_descriptor = [model.predict(face), img]
Cannot set headers after they are sent to client
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if (!fs.existsSync(dir)){
fs.mkdirSync(dir);
success = false;
message = 'Cannot detect the person.Please add name in the textbox provided below to save the person.';
res.status(200).json({message: message,success:success});
res.end();
var facialScript = new PythonShell('face_detect.py',options)
facialScript.on('message',(response)=>{
console.log(response);
res.status(200).send(response);
//res.end();
})
if (!fs.existsSync(dir)){
fs.mkdirSync(dir);
success = false;
message = 'Cannot detect the person.Please add name in the textbox provided below to save the person.';
res.status(200).json({message: message,success:success});
res.end();
var facialScript = new PythonShell('face_detect.py',options)
facialScript.on('message',(response)=>{
console.log(response);
res.status(200).send(response);
//res.end();
})
let result="";
facialScript.on('message',(response)=>{
result=result+response;//assuming that response is string you can change this as per your convenience .
})
facialScript.on("end",()=>{
res.send(result);
})
My OpenCV Live Webcam Demo Doesn't Show Accurate Emotions
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Predictions = torch.argmax(Pred)
Predictions = torch.argmax(Pred).item()
Predictions = torch.argmax(Pred)
Predictions = torch.argmax(Pred).item()
from numba import cuda, numpy_support and ImportError: cannot import name 'numpy_support' from 'numba'
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conda create -n rapids-0.17 -c rapidsai -c nvidia -c conda-forge \
-c defaults rapids-blazing=0.17 python=3.7 cudatoolkit=10.2
Memory leakage issue in python list
CopyDownload
for i in range(len(idendities) - 1):
for j in range(i + 1, len(idendities)):
for cross_sample in itertools.product(samples_list[i], samples_list[j]):
# do something ...
import csv
for i in range(0, len(idendities) - 1):
for j in range(i + 1, len(idendities)):
for cross_sample in itertools.product(samples_list[i], samples_list[j]):
with open('results.csv', 'a+') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([cross_sample[0], cross_sample[1]])
for i in range(len(idendities) - 1):
for j in range(i + 1, len(idendities)):
for cross_sample in itertools.product(samples_list[i], samples_list[j]):
# do something ...
import csv
for i in range(0, len(idendities) - 1):
for j in range(i + 1, len(idendities)):
for cross_sample in itertools.product(samples_list[i], samples_list[j]):
with open('results.csv', 'a+') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([cross_sample[0], cross_sample[1]])
class ProductList:
def __init__(self,*data):
self.data = data
self.size = 1
for d in self.data: self.size *= len(d)
def __len__(self): return self.size
def __getitem__(self,index):
if isinstance(index,slice):
return [*map(self.__getitem__,range(len(self))[index])]
result = tuple()
for d in reversed(self.data):
index,i = divmod(index,len(d))
result = (d[i],) + result
return result
def __iter__(self):
for i in range(len(self)): yield self[i]
def __contains__(self,value):
return len(value) == len(self.data) \
and all(v in d for v,d in zip(value,self.data))
def index(self,value):
index = 0
for v,d in zip(value,self.data):
index = index*len(d)+d.index(v)
return index
p = ProductList(range(1234),range(1234,5678),range(5678,9101))
print(*p[:10],sep="\n")
(0, 1234, 5678)
(0, 1234, 5679)
(0, 1234, 5680)
(0, 1234, 5681)
(0, 1234, 5682)
(0, 1234, 5683)
(0, 1234, 5684)
(0, 1234, 5685)
(0, 1234, 5686)
(0, 1234, 5687)
len(p) # 18771376008
p[27] # (2, 6, 12)
for c in p[103350956:103350960]: print(c)
(6, 4763, 5995)
(6, 4763, 5996)
(6, 4763, 5997)
(6, 4763, 5998)
p.index((6, 4763, 5995)) # 103350956
p[103350956] # (6, 4763, 5995)
(6, 4763, 5995) in p # True
(5995, 4763, 6) in p # False
class ProductList:
def __init__(self,*data):
self.data = data
self.size = 1
for d in self.data: self.size *= len(d)
def __len__(self): return self.size
def __getitem__(self,index):
if isinstance(index,slice):
return [*map(self.__getitem__,range(len(self))[index])]
result = tuple()
for d in reversed(self.data):
index,i = divmod(index,len(d))
result = (d[i],) + result
return result
def __iter__(self):
for i in range(len(self)): yield self[i]
def __contains__(self,value):
return len(value) == len(self.data) \
and all(v in d for v,d in zip(value,self.data))
def index(self,value):
index = 0
for v,d in zip(value,self.data):
index = index*len(d)+d.index(v)
return index
p = ProductList(range(1234),range(1234,5678),range(5678,9101))
print(*p[:10],sep="\n")
(0, 1234, 5678)
(0, 1234, 5679)
(0, 1234, 5680)
(0, 1234, 5681)
(0, 1234, 5682)
(0, 1234, 5683)
(0, 1234, 5684)
(0, 1234, 5685)
(0, 1234, 5686)
(0, 1234, 5687)
len(p) # 18771376008
p[27] # (2, 6, 12)
for c in p[103350956:103350960]: print(c)
(6, 4763, 5995)
(6, 4763, 5996)
(6, 4763, 5997)
(6, 4763, 5998)
p.index((6, 4763, 5995)) # 103350956
p[103350956] # (6, 4763, 5995)
(6, 4763, 5995) in p # True
(5995, 4763, 6) in p # False
for i in range(0, len(idendities) - 1):
for j in range(i + 1, len(idendities)):
cross_product = itertools.product(samples_list[i], samples_list[j])
cross_product = list(cross_product)
for cross_sample in cross_product:
negative = []
negative.append(cross_sample[0])
negative.append(cross_sample[1])
negatives.append(negative)
print(len(negatives))
negatives = pd.DataFrame(negatives, columns=["file_x", "file_y"])
negatives["decision"] = "No"
samples_list = list(identities.values())
negatives = pd.DataFrame()
if Path("positives_negatives.csv").exists():
df = pd.read_csv("positives_negatives.csv")
else:
for combo in tqdm(itertools.combinations(identities.values(), 2), desc="Negatives"):
for cross_sample in itertools.product(combo[0], combo[1]):
negatives = negatives.append(pd.Series({"file_x": cross_sample[0], "file_y": cross_sample[1]}).T,
ignore_index=True)
negatives["decision"] = "No"
negatives = negatives.sample(positives.shape[0])
df = pd.concat([positives, negatives]).reset_index(drop=True)
df.to_csv("positives_negatives.csv", index=False)
for i in range(0, len(idendities) - 1):
for j in range(i + 1, len(idendities)):
cross_product = itertools.product(samples_list[i], samples_list[j])
cross_product = list(cross_product)
for cross_sample in cross_product:
negative = []
negative.append(cross_sample[0])
negative.append(cross_sample[1])
negatives.append(negative)
print(len(negatives))
negatives = pd.DataFrame(negatives, columns=["file_x", "file_y"])
negatives["decision"] = "No"
samples_list = list(identities.values())
negatives = pd.DataFrame()
if Path("positives_negatives.csv").exists():
df = pd.read_csv("positives_negatives.csv")
else:
for combo in tqdm(itertools.combinations(identities.values(), 2), desc="Negatives"):
for cross_sample in itertools.product(combo[0], combo[1]):
negatives = negatives.append(pd.Series({"file_x": cross_sample[0], "file_y": cross_sample[1]}).T,
ignore_index=True)
negatives["decision"] = "No"
negatives = negatives.sample(positives.shape[0])
df = pd.concat([positives, negatives]).reset_index(drop=True)
df.to_csv("positives_negatives.csv", index=False)
ValueError: unknown format is not supported : ROC Curve
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y_test = y_test.astype(int)
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve
y_pred_proba = predictions[::, 1]
y_test = y_test.astype(int)
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
auc = roc_auc_score(y_test, y_pred_proba)
plt.figure(figsize=(7, 3))
y_test = y_test.astype(int)
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve
y_pred_proba = predictions[::, 1]
y_test = y_test.astype(int)
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
auc = roc_auc_score(y_test, y_pred_proba)
plt.figure(figsize=(7, 3))
DeepFace for extracting vector information of an image
CopyDownload
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
!wget "http://*.jpg" -O "1.jpg"
!wget "https://*.jpg" -O "2.jpg"
import cv2
from google.colab.patches import cv2_imshow
im1 = cv2.imread("1.jpg")
#cv2.imshow("img", im1)
cv2_imshow(im1)
from deepface import DeepFace
import cv2
from google.colab.patches import cv2_imshow
#backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
backends = ['mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("1.jpg", detector_backend = backend)
print(detected_face)
print(detected_face.shape)
im = cv2.cvtColor(detected_face * 255, cv2.COLOR_BGR2RGB)
#cv2.imshow("image", im)
cv2_imshow(im)
[[[0.12156863 0.05882353 0.02352941]
[0.2901961 0.18039216 0.1254902 ]
[0.3137255 0.20392157 0.14901961]
...
[0.06666667 0.01176471 0.01176471]
[0.05882353 0.01176471 0.00784314]
[0.03921569 0.00784314 0.00392157]]
[[0.26666668 0.2 0.16470589]
[0.19215687 0.08235294 0.02745098]
[0.33333334 0.22352941 0.16862746]
...
[0.03921569 0.00392157 0.00392157]
[0.04313726 0.00784314 0.00784314]
[0.04313726 0. 0.00392157]]
[[0.11764706 0.05098039 0.01568628]
[0.21176471 0.10588235 0.05882353]
[0.44313726 0.3372549 0.27058825]
...
[0.02352941 0.00392157 0. ]
[0.02352941 0.00392157 0. ]
[0.02745098 0. 0. ]]
...
[[0.24313726 0.1882353 0.13725491]
[0.24313726 0.18431373 0.13725491]
[0.22745098 0.16470589 0.11372549]
...
[0.654902 0.69803923 0.78431374]
[0.62352943 0.67058825 0.7529412 ]
[0.38431373 0.4117647 0.45882353]]
[[0.23529412 0.18039216 0.12941177]
[0.22352941 0.16862746 0.11764706]
[0.22745098 0.16470589 0.11764706]
...
[0.6392157 0.69803923 0.78039217]
[0.6156863 0.6745098 0.75686276]
[0.36862746 0.40392157 0.4627451 ]]
[[0.21568628 0.16862746 0.10980392]
[0.2 0.15294118 0.09803922]
[0.20784314 0.14901961 0.10196079]
...
[0.6313726 0.6901961 0.77254903]
[0.6039216 0.6627451 0.74509805]
[0.36078432 0.39607844 0.4509804 ]]]
(224, 224, 3)
"""
Modified verify function for face embedding generation
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
"""
from keras.preprocessing import image
import warnings
warnings.filterwarnings("ignore")
import time
import os
from os import path
from pathlib import Path
import gdown
import numpy as np
import pandas as pd
from tqdm import tqdm
import json
import cv2
from keras import backend as K
import keras
import tensorflow as tf
import pickle
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
def FaceEmbeddingAndDistance(img1_path, img2_path = '', model_name ='Facenet', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
#--------------------------------
#ensemble learning disabled.
if model == None:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
elif model_name == 'DeepID':
print("Using DeepID2 model backend", distance_metric,"distance.")
model = DeepID.loadModel()
elif model_name == 'Dlib':
print("Using Dlib ResNet model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
model = DlibResNet()
else:
raise ValueError("Invalid model_name passed - ", model_name)
else: #model != None
print("Already built model is passed")
#------------------------------
#face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
if model_name == 'Dlib': #this is not a regular keras model
input_shape = (150, 150, 3)
else: #keras based models
input_shape = model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
#------------------------------
#tuned thresholds for model and metric pair
threshold = functions.findThreshold(model_name, distance_metric)
#------------------------------
#----------------------
#crop and align faces
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
#----------------------
#find embeddings
img1_representation = model.predict(img1)[0,:]
img2_representation = model.predict(img2)[0,:]
print("FACE 1 Embedding:")
print(img1_representation)
print("FACE 2 Embedding:")
print(img2_representation)
#----------------------
#find distances between embeddings
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
else:
raise ValueError("Invalid distance_metric passed - ", distance_metric)
print("DISTANCE")
print(distance)
#----------------------
#decision
if distance <= threshold:
identified = "true"
else:
identified = "false"
print("IDENTIFIED")
print(identified)
FaceEmbeddingAndDistance("1.jpg", "2.jpg", model_name='Facenet', detector_backend = 'mtcnn')
FACE 1 Embedding:
[-0.7229302 -1.766835 -1.5399052 0.59634393 1.203212 -1.693247
-0.90845925 0.5264039 2.148173 -0.9786542 -0.00369854 -1.2710322
-1.5515596 -0.4111185 -0.36896533 -0.30051672 0.35091963 0.5073533
-1.7270111 -0.5230838 0.3376239 -1.0811361 1.5242224 -0.6137103
-1.3100258 0.80050004 -0.7087368 -0.64483845 1.0830203 2.6056807
-0.76527536 -0.83047277 -0.7335422 -0.01964059 -0.86749244 2.9645889
-2.426583 -0.11157394 -2.3535717 -0.65058017 0.30864614 -0.77746457
-0.6233895 0.44898677 2.5578005 -0.583796 0.8406945 1.1105415
-1.652044 -0.6351479 0.07651432 -1.0454555 -1.8752071 0.50948805
-1.6050931 -1.1769634 -0.02965304 1.5107706 0.83292925 -0.5382068
-1.5981512 -0.6405941 0.5521577 0.22957848 0.506649 0.24680384
-0.91464925 -0.18441322 -0.6801975 -1.0448433 0.52288735 -0.79405725
0.5974493 -0.40668172 -0.00640235 -0.742475 0.1928863 0.31236258
-0.37383577 -1.5883486 -1.5336255 -0.74254227 -0.8524561 -1.4625055
-2.718953 -0.7180952 -1.2140683 -0.5232462 1.2576898 -1.1097553
2.3971314 0.8855096 -0.16556528 -0.07307663 -1.8778017 0.8690948
-0.39043528 -0.5494097 -2.2382076 0.7101087 0.15859437 0.2959841
0.8605075 -0.2040207 0.77952844 0.04542177 0.92514265 -1.988945
0.9418363 1.6509243 -0.20324889 0.2974357 0.37681833 1.095943
1.6308782 -1.2553837 -0.10246387 -1.4697052 -0.5832107 -0.34192032
-1.1347024 1.5154309 -0.00527111 -1.165709 -0.7296148 -0.20767921
1.2530949 -0.9487353 ]
FACE 2 Embedding:
[ 0.9399996 1.3996615 -1.2931366 0.6869738 -0.03219241 0.96111965
0.7378809 -0.24804354 -0.8128112 0.19901593 0.48911542 -0.91603553
-1.1671298 0.88576627 0.25427592 1.1395477 0.45400882 -1.4845027
-0.90582514 -1.1371222 0.47669724 1.2933927 1.4533392 -0.46943524
0.10245587 -1.4916894 -2.3223586 -0.10979578 1.7803721 1.0051152
-0.09164213 -0.64848715 -1.4191641 1.811776 0.73174113 0.2582223
-0.26430857 1.7021953 -1.0571098 -1.1215096 0.3606074 1.5136883
-0.30045512 0.26225814 -0.19101554 1.269355 1.0674374 -0.2550623
-1.0582973 1.7474637 -1.7739134 -0.67914337 -0.1877765 1.1581128
-2.281225 1.3955555 -1.2690883 -0.16299461 1.337664 -0.8831901
-0.6862674 2.0526903 -0.6325836 1.333468 -0.10851342 -0.64831966
-1.0277263 1.4572504 -0.29905424 -0.33187118 -0.54727656 1.1528811
0.12454037 -1.5835186 -0.2271783 1.3911225 1.0170195 0.5741334
-1.3088373 -0.5950714 -0.6856393 -0.910367 -2.0136826 -0.73777384
0.319223 -2.1968741 0.9673934 -0.604423 -0.08049382 -1.948634
1.88159 0.20169139 0.7295723 -1.0224706 1.2995481 -0.3402595
1.1711328 -0.64862376 0.42063504 -0.01502114 -0.7048841 1.4360497
-1.2988033 0.31773448 1.534014 0.98858756 1.3450235 -0.9417385
0.26414695 -0.01988658 0.7418235 -0.04945141 -0.44838902 1.5288658
-1.1905407 0.13961646 -0.17101136 -0.18599203 -1.9648114 0.66071814
-0.07431012 1.5870664 1.5989372 -0.21751085 0.78908855 -1.5576671
0.02266342 0.20999858]
DISTANCE
0.807837575674057
IDENTIFIED
false
from deepface import DeepFace
from deepface.commons import functions
models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
model = DeepFace.build_model(models[0])
target_size = model.layers[0].input_shape
img1_path = "img1.jpg"
img2_path = "img2.jpg"
#detect and align
img1 = functions.preprocess_face(img1_path, target_size = target_size)
img2 = functions.preprocess_face(img2_path, target_size = target_size)
#find vector embeddings
img1_embedding = model.predict(img1)
img2_embedding = model.predict(img2)
How to execute docker health check only one time?
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HEALTHCHECK CMD sh -c "if [ ! -f /tmp/health.txt ]; then touch /tmp/health.txt && python api/initRequest.py || exit 0 ; else echo \"initRequest.py already executed\"; fi"
Community Discussions
Trending Discussions on DeepFace
Trending Discussions on DeepFace
QUESTION
Bounding boxes returned without detected face image in dlib python
Asked 2022-Jan-18 at 13:43I'm trying to detect multiple faces in a picture using the deepface library with dlib as the backend detector. I'm using the DlibWrapper.py from the deeepface library and i have the following issue: In some cases, the detector returns the bounding box coordinates but doesn't return the detected face image detected face-box coordinates.
I was wondering if this bug occurs because of the negative values of some coordinates of the bounding boxes but i figured out that was not the case, as the negative values are features, not bugs. Here is the DlibWrapper from the deepface library.
ANSWER
Answered 2022-Jan-18 at 13:43Solved!There are edge cases where original rectangle is partially outside the image window. That happens with dlib. So, instead of
- detected_face = img[top:bottom, left:right],
the detected face should be
- detected_face = img[max(0, top): min(bottom, img_height), max(0, left): min(right, img_width)]
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DeepFace
You can download it from GitHub.
You can use DeepFace like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use DeepFace like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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