标签：ide center opp ima options entry compile policy practice
Welcome to the first assignment of week 4! Here you will build a face recognition system. Many of the ideas presented here are from FaceNet. In lecture, we also talked about DeepFace.
Face recognition problems commonly fall into two categories:
FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.
In this assignment, you will:
In this exercise, we will be using a pre-trained model which represents ConvNet activations using a "channels first" convention, as opposed to the "channels last" convention used in lecture and previous programming assignments. In other words, a batch of images will be of shape \((m, n_C, n_H, n_W)\) instead of \((m, n_H, n_W, n_C)\). Both of these conventions have a reasonable amount of traction among open-source implementations; there isn‘t a uniform standard yet within the deep learning community.
Let‘s load the required packages.
from keras.models import Sequential
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.merge import Concatenate
from keras.layers.core import Lambda, Flatten, Dense
from keras.initializers import glorot_uniform
from keras.engine.topology import Layer
from keras import backend as K
K.set_image_data_format('channels_first')
import cv2
import os
import numpy as np
from numpy import genfromtxt
import pandas as pd
import tensorflow as tf
from fr_utils import *
from inception_blocks_v2 import *
%matplotlib inline
%load_ext autoreload
%autoreload 2
np.set_printoptions(threshold=np.nan)Using TensorFlow backend.In Face Verification, you‘re given two images and you have to tell if they are of the same person. The simplest way to do this is to compare the two images pixel-by-pixel. If the distance between the raw images are less than a chosen threshold, it may be the same person!
Of course, this algorithm performs really poorly, since the pixel values change dramatically due to variations in lighting, orientation of the person‘s face, even minor changes in head position, and so on.
You‘ll see that rather than using the raw image, you can learn an encoding \(f(img)\) so that element-wise comparisons of this encoding gives more accurate judgements as to whether two pictures are of the same person.
The FaceNet model takes a lot of data and a long time to train. So following common practice in applied deep learning settings, let‘s just load weights that someone else has already trained. The network architecture follows the Inception model from Szegedy et al.. We have provided an inception network implementation. You can look in the file inception_blocks.py to see how it is implemented (do so by going to "File->Open..." at the top of the Jupyter notebook).
The key things you need to know are:
Run the cell below to create the model for face images.
FRmodel = faceRecoModel(input_shape=(3, 96, 96))print("Total Params:", FRmodel.count_params())Total Params: 3743280** Expected Output **
By using a 128-neuron fully connected layer as its last layer, the model ensures that the output is an encoding vector of size 128. You then use the encodings the compare two face images as follows:
So, an encoding is a good one if:
The triplet loss function formalizes this, and tries to "push" the encodings of two images of the same person (Anchor and Positive) closer together, while "pulling" the encodings of two images of different persons (Anchor, Negative) further apart.
For an image \(x\), we denote its encoding \(f(x)\), where \(f\) is the function computed by the neural network.
Training will use triplets of images \((A, P, N)\):
These triplets are picked from our training dataset. We will write \((A^{(i)}, P^{(i)}, N^{(i)})\) to denote the \(i\)-th training example.
You‘d like to make sure that an image \(A^{(i)}\) of an individual is closer to the Positive \(P^{(i)}\) than to the Negative image \(N^{(i)}\)) by at least a margin \(\alpha\):
\[\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2 + \alpha < \mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2\]
You would thus like to minimize the following "triplet cost":
\[\mathcal{J} = \sum^{m}_{i=1} \large[ \small \underbrace{\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2}_\text{(1)} - \underbrace{\mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2}_\text{(2)} + \alpha \large ] \small_+?\tag{3}\]
Here, we are using the notation "\([z]_+\)" to denote \(max(z,0)\).
Notes:
Most implementations also normalize the encoding vectors to have norm equal one (i.e., \(\mid \mid f(img)\mid \mid_2\)=1); you won‘t have to worry about that here.
Exercise: Implement the triplet loss as defined by formula (3). Here are the 4 steps:
Useful functions: tf.reduce_sum(), tf.square(), tf.subtract(), tf.add(), tf.maximum().
For steps 1 and 2, you will need to sum over the entries of \(\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2\) and \(\mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2\) while for step 4 you will need to sum over the training examples.
# GRADED FUNCTION: triplet_loss
def triplet_loss(y_true, y_pred, alpha = 0.2):
"""
Implementation of the triplet loss as defined by formula (3)
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor images, of shape (None, 128)
positive -- the encodings for the positive images, of shape (None, 128)
negative -- the encodings for the negative images, of shape (None, 128)
Returns:
loss -- real number, value of the loss
"""
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
### START CODE HERE ### (≈ 4 lines)
# Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
pos_dist = tf.reduce_sum(tf.square(tf.subtract(y_pred[0],y_pred[1])))
# Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
neg_dist = tf.reduce_sum(tf.square(tf.subtract(y_pred[0],y_pred[2])))
# Step 3: subtract the two previous distances and add alpha.
basic_loss = tf.add(tf.subtract(pos_dist,neg_dist),alpha)
# Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
loss = tf.reduce_sum(tf.maximum(basic_loss,0.0))
### END CODE HERE ###
return losswith tf.Session() as test:
tf.set_random_seed(1)
y_true = (None, None, None)
y_pred = (tf.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),
tf.random_normal([3, 128], mean=1, stddev=1, seed = 1),
tf.random_normal([3, 128], mean=3, stddev=4, seed = 1))
loss = triplet_loss(y_true, y_pred)
print("loss = " + str(loss.eval()))loss = 350.026Expected Output:
| **loss** | 528.143 |
FaceNet is trained by minimizing the triplet loss. But since training requires a lot of data and a lot of computation, we won‘t train it from scratch here. Instead, we load a previously trained model. Load a model using the following cell; this might take a couple of minutes to run.
FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
load_weights_from_FaceNet(FRmodel)Here‘re some examples of distances between the encodings between three individuals:
Let‘s now use this model to perform face verification and face recognition!
Back to the Happy House! Residents are living blissfully since you implemented happiness recognition for the house in an earlier assignment.
However, several issues keep coming up: The Happy House became so happy that every happy person in the neighborhood is coming to hang out in your living room. It is getting really crowded, which is having a negative impact on the residents of the house. All these random happy people are also eating all your food.
So, you decide to change the door entry policy, and not just let random happy people enter anymore, even if they are happy! Instead, you‘d like to build a Face verification system so as to only let people from a specified list come in. To get admitted, each person has to swipe an ID card (identification card) to identify themselves at the door. The face recognition system then checks that they are who they claim to be.
Let‘s build a database containing one encoding vector for each person allowed to enter the happy house. To generate the encoding we use img_to_encoding(image_path, model) which basically runs the forward propagation of the model on the specified image.
Run the following code to build the database (represented as a python dictionary). This database maps each person‘s name to a 128-dimensional encoding of their face.
database = {}
database["danielle"] = img_to_encoding("images/danielle.png", FRmodel)
database["younes"] = img_to_encoding("images/younes.jpg", FRmodel)
database["tian"] = img_to_encoding("images/tian.jpg", FRmodel)
database["andrew"] = img_to_encoding("images/andrew.jpg", FRmodel)
database["kian"] = img_to_encoding("images/kian.jpg", FRmodel)
database["dan"] = img_to_encoding("images/dan.jpg", FRmodel)
database["sebastiano"] = img_to_encoding("images/sebastiano.jpg", FRmodel)
database["bertrand"] = img_to_encoding("images/bertrand.jpg", FRmodel)
database["kevin"] = img_to_encoding("images/kevin.jpg", FRmodel)
database["felix"] = img_to_encoding("images/felix.jpg", FRmodel)
database["benoit"] = img_to_encoding("images/benoit.jpg", FRmodel)
database["arnaud"] = img_to_encoding("images/arnaud.jpg", FRmodel)
#database["arnaud"] = img_to_encoding("images/camera_0.jpg", FRmodel)Now, when someone shows up at your front door and swipes their ID card (thus giving you their name), you can look up their encoding in the database, and use it to check if the person standing at the front door matches the name on the ID.
Exercise: Implement the verify() function which checks if the front-door camera picture (image_path) is actually the person called "identity". You will have to go through the following steps:
As presented above, you should use the L2 distance (np.linalg.norm). (Note: In this implementation, compare the L2 distance, not the square of the L2 distance, to the threshold 0.7.)
# GRADED FUNCTION: verify
def verify(image_path, identity, database, model):
"""
Function that verifies if the person on the "image_path" image is "identity".
Arguments:
image_path -- path to an image
identity -- string, name of the person you'd like to verify the identity. Has to be a resident of the Happy house.
database -- python dictionary mapping names of allowed people's names (strings) to their encodings (vectors).
model -- your Inception model instance in Keras
Returns:
dist -- distance between the image_path and the image of "identity" in the database.
door_open -- True, if the door should open. False otherwise.
"""
### START CODE HERE ###
# Step 1: Compute the encoding for the image. Use img_to_encoding() see example above. (≈ 1 line)
encoding = img_to_encoding(image_path, model)
# Step 2: Compute distance with identity's image (≈ 1 line)
dist = np.linalg.norm(encoding-database[identity])
# Step 3: Open the door if dist < 0.7, else don't open (≈ 3 lines)
if dist<0.7:
print("It's " + str(identity) + ", welcome home!")
door_open = True
else:
print("It's not " + str(identity) + ", please go away")
door_open = False
### END CODE HERE ###
return dist, door_openYounes is trying to enter the Happy House and the camera takes a picture of him ("images/camera_0.jpg"). Let‘s run your verification algorithm on this picture:
verify("images/camera_0.jpg", "younes", database, FRmodel)It's younes, welcome home!
(0.65939283, True)Expected Output:
| **It‘s younes, welcome home!** | (0.65939283, True) |
Benoit, who broke the aquarium last weekend, has been banned from the house and removed from the database. He stole Kian‘s ID card and came back to the house to try to present himself as Kian. The front-door camera took a picture of Benoit ("images/camera_2.jpg). Let‘s run the verification algorithm to check if benoit can enter.
verify("images/camera_2.jpg", "kian", database, FRmodel)It's not kian, please go away
(0.86224014, False)Expected Output:
| **It‘s not kian, please go away** | (0.86224014, False) |
Your face verification system is mostly working well. But since Kian got his ID card stolen, when he came back to the house that evening he couldn‘t get in!
To reduce such shenanigans, you‘d like to change your face verification system to a face recognition system. This way, no one has to carry an ID card anymore. An authorized person can just walk up to the house, and the front door will unlock for them!
You‘ll implement a face recognition system that takes as input an image, and figures out if it is one of the authorized persons (and if so, who). Unlike the previous face verification system, we will no longer get a person‘s name as another input.
Exercise: Implement who_is_it(). You will have to go through the following steps:
min_dist variable to a large enough number (100). It will help you keep track of what is the closest encoding to the input‘s encoding.for (name, db_enc) in database.items().
# GRADED FUNCTION: who_is_it
def who_is_it(image_path, database, model):
"""
Implements face recognition for the happy house by finding who is the person on the image_path image.
Arguments:
image_path -- path to an image
database -- database containing image encodings along with the name of the person on the image
model -- your Inception model instance in Keras
Returns:
min_dist -- the minimum distance between image_path encoding and the encodings from the database
identity -- string, the name prediction for the person on image_path
"""
### START CODE HERE ###
## Step 1: Compute the target "encoding" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)
encoding = img_to_encoding(image_path, model)
## Step 2: Find the closest encoding ##
# Initialize "min_dist" to a large value, say 100 (≈1 line)
min_dist = 100
# Loop over the database dictionary's names and encodings.
for (name, db_enc) in database.items():
# Compute L2 distance between the target "encoding" and the current "emb" from the database. (≈ 1 line)
dist = np.linalg.norm(encoding-db_enc)
# If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)
if dist<min_dist:
min_dist = dist
identity = name
### END CODE HERE ###
if min_dist > 0.7:
print("Not in the database.")
else:
print ("it's " + str(identity) + ", the distance is " + str(min_dist))
return min_dist, identityYounes is at the front-door and the camera takes a picture of him ("images/camera_0.jpg"). Let‘s see if your who_it_is() algorithm identifies Younes.
who_is_it("images/camera_0.jpg", database, FRmodel)it's younes, the distance is 0.659393
(0.65939283, 'younes')Expected Output:
| **it‘s younes, the distance is 0.659393** | (0.65939283, ‘younes‘) |
You can change "camera_0.jpg" (picture of younes) to "camera_1.jpg" (picture of bertrand) and see the result.
Your Happy House is running well. It only lets in authorized persons, and people don‘t need to carry an ID card around anymore!
You‘ve now seen how a state-of-the-art face recognition system works.
Although we won‘t implement it here, here‘re some ways to further improve the algorithm:
What you should remember:
Congrats on finishing this assignment!
Face Recognition for the Happy House
标签：ide center opp ima options entry compile policy practice
原文地址：http://www.cnblogs.com/ranjiewen/p/7904382.html
