Face Recognition By Transfer Learning V2

Face recognition with Inceptionv3

(Inspired by: https://gogul09.github.io/software/flower-recognition-deep-learning)

Step 1: Organize imports

from keras import backend as K
K.set_image_dim_ordering('tf') # this is for Xception
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.applications.xception import Xception, preprocess_input 
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing import image
from keras.models import Model
import _pickle   

Using TensorFlow backend.

from sklearn.preprocessing import LabelEncoder
import numpy as np  
import glob
import cv2
import h5py
import os
import json
import datetime
import time 
import sys 
import argparse 
from PIL import Image
from matplotlib import pyplot as plt
%matplotlib inline

[OPTIONAL] Handle the GPU partition

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=gpu_config))

Step 2: Load configuration written in .json

!more config.json

{
        "model"                 : "inceptionv3",
        "weights"               : "imagenet",
        "include_top"           : false,

        "train_path"            : "C:/Users/ECE/workspace/SimpleFaceRecognitionDemo/front_database/train",
        "features_path"         : "output/inceptionv3/features.h5",
        "labels_path"           : "output/inceptionv3/labels.h5",
        "results"                   : "output/inceptionv3/results.txt",
        "classifier_path"       : "output/inceptionv3/classifier.cpickle",

        "test_size"             : 0.10,
        "seed"                      : 1983,
        "num_classes"           : 10
}

with open('config.json') as f:    
	config = json.load(f)

# config variables
model_name		= config["model"]
weights 		= config["weights"]
include_top 	= config["include_top"]
train_path 		= config["train_path"]
features_path	= config["features_path"]
labels_path 	= config["labels_path"]
test_size		= config["test_size"]
results			= config["results"]

Step 3: Create output directory

!mkdir "output\inceptionv3"

Step 4: Create base model

if model_name == "vgg16":
	base_model = VGG16(weights=weights)
	model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
	image_size = (224, 224)
elif model_name == "vgg19":
	base_model = VGG19(weights=weights)
	model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
	image_size = (224, 224)
elif model_name == "resnet50":
	base_model = ResNet50(weights=weights)
	model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
	image_size = (224, 224)
elif model_name == "inceptionv3":
	base_model = InceptionV3(weights=weights)
	model = Model(input=base_model.input, output=base_model.get_layer('mixed7').output)
	image_size = (299, 299)
elif model_name == "xception":
	base_model = Xception(weights=weights)
	model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
	image_size = (299, 299)
else:
	base_model = None

C:\Program Files\Anaconda3\envs\tensorflow-gpu\lib\site-packages\ipykernel_launcher.py:15: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor("in..., outputs=Tensor("mi...)`
  from ipykernel import kernelapp as app

Step 5: Acquire the labels from the train path

train_labels = os.listdir(train_path)
le = LabelEncoder()
le.fit([tl for tl in train_labels])

LabelEncoder()

Step 6: Acquire the features

features = []
labels   = []

i = 0
for label in train_labels:
	cur_path = train_path + "/" + label
	for image_path in glob.glob(cur_path + "/*.pgm"):
		img = image.load_img(image_path, target_size=image_size)
		x = image.img_to_array(img)
		x = np.expand_dims(x, axis=0)
		x = preprocess_input(x)
		feature = model.predict(x)
		flat = feature.flatten()
		features.append(flat)
		labels.append(label)
		print("[INFO] processed - %s" % i)
		i += 1
	print("[INFO] completed label - %s" % label)

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[INFO] completed label - person_9_venu

Step 7: Encode the labels using LabelEncoder

targetNames = np.unique(labels)
le = LabelEncoder()
le_labels = le.fit_transform(labels)

print ("[STATUS] training labels: \n %s" % le_labels)
print ("[STATUS] training labels shape: %s" % le_labels.shape)

[STATUS] training labels: 
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9]
[STATUS] training labels shape: 500

Step 8: Save features and labels

h5f_data = h5py.File(features_path, 'w')
h5f_data.create_dataset('dataset_1', data=np.array(features))

h5f_label = h5py.File(labels_path, 'w')
h5f_label.create_dataset('dataset_1', data=np.array(le_labels))

h5f_data.close()
h5f_label.close()

# Checking the output
!ls C:\Users\ECE\workspace\SimpleFaceRecognitionDemo\output\inceptionv3

(tensorflow-gpu) C:\Users\ECE\workspace\SimpleFaceRecognitionDemo>dir C:\Users\ECE\workspace\SimpleFaceRecognitionDemo\output\inceptionv3  
 Volume in drive C is Windows
 Volume Serial Number is 4E4C-DF71

 Directory of C:\Users\ECE\workspace\SimpleFaceRecognitionDemo\output\inceptionv3

03/10/2017  02:19 PM    <DIR>          .
03/10/2017  02:19 PM    <DIR>          ..
03/10/2017  02:19 PM       443,906,048 features.h5
03/10/2017  02:19 PM             6,048 labels.h5
               2 File(s)    443,912,096 bytes
               2 Dir(s)  33,764,777,984 bytes free

Note: The new files are features.h5 and labels.h5.

from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import h5py
import seaborn as sns

Step 9: Load the user configs for training/testing

with open('config.json') as f:    
	config = json.load(f)

!more config.json

{
        "model"                 : "inceptionv3",
        "weights"               : "imagenet",
        "include_top"           : false,

        "train_path"            : "C:/Users/ECE/workspace/SimpleFaceRecognitionDemo/front_database/train",
        "features_path"         : "output/inceptionv3/features.h5",
        "labels_path"           : "output/inceptionv3/labels.h5",
        "results"                   : "output/inceptionv3/results.txt",
        "classifier_path"       : "output/inceptionv3/classifier.cpickle",

        "test_size"             : 0.10,
        "seed"                      : 1983,
        "num_classes"           : 10
}

test_size 	= config["test_size"]
seed 		= config["seed"]
features_path	= config["features_path"]
labels_path 	= config["labels_path"]
results  	= config["results"]
classifier_path = config["classifier_path"]
train_path 	= config["train_path"]
num_classes	= config["num_classes"]

Step 10: Import features and labels

h5f_data = h5py.File(features_path, 'r')
h5f_label = h5py.File(labels_path, 'r')

features_string = h5f_data['dataset_1']
labels_string   = h5f_label['dataset_1']

features = np.array(features_string)
labels   = np.array(labels_string)

h5f_data.close()
h5f_label.close()

print ("[INFO] features shape: {}".format(features.shape))
print ("[INFO] labels shape: {}".format(labels.shape))

[INFO] features shape: (500, 221952)
[INFO] labels shape: (500,)

Step 11: Split the training and testing data

(trainData, testData, trainLabels, testLabels) = train_test_split(np.array(features), 
                                                                  np.array(labels),
                                                                  test_size=test_size, 
                                                                  random_state=seed)

print ("[INFO] splitted train and test data...")
print ("[INFO] train data  : {}".format(trainData.shape))
print ("[INFO] test data   : {}".format(testData.shape))
print ("[INFO] train labels: {}".format(trainLabels.shape))
print ("[INFO] test labels : {}".format(testLabels.shape))

[INFO] splitted train and test data...
[INFO] train data  : (450, 221952)
[INFO] test data   : (50, 221952)
[INFO] train labels: (450,)
[INFO] test labels : (50,)

Step 12: Use Logistic Regression Model

model = LogisticRegression(random_state=seed)

model.fit(trainData, trainLabels)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=1983, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Step 13: Evaluate the model

f = open(results, "w")
rank_1 = 0
rank_5 = 0

# loop over test data
for (label, features) in zip(testLabels, testData):
	# predict the probability of each class label and take the top-5 class labels
	predictions = model.predict_proba(np.atleast_2d(features))[0]
	predictions = np.argsort(predictions)[::-1][:5]

	# rank-1 prediction increment
	if label == predictions[0]:
		rank_1 += 1

	# rank-5 prediction increment
	if label in predictions:
		rank_5 += 1

# convert accuracies to percentages
rank_1 = (rank_1 / float(len(testLabels))) * 100
rank_5 = (rank_5 / float(len(testLabels))) * 100

# write the accuracies to file
f.write("rank-1: {:.2f}\n".format(rank_1))
f.write("rank-5: {:.2f}\n\n".format(rank_5))

# evaluate the model of test data
preds = model.predict(testData)

# write the classification report to file
f.write("{}\n".format(classification_report(testLabels, preds)))
f.close()

!more C:\Users\ECE\workspace\SimpleFaceRecognitionDemo\output\inceptionv3\results.txt

rank-1: 100.00
rank-5: 100.00

             precision    recall  f1-score   support

          0       1.00      1.00      1.00         5
          1       1.00      1.00      1.00         5
          2       1.00      1.00      1.00         4
          3       1.00      1.00      1.00         7
          4       1.00      1.00      1.00         3
          5       1.00      1.00      1.00         4
          6       1.00      1.00      1.00         6
          7       1.00      1.00      1.00         5
          8       1.00      1.00      1.00         7
          9       1.00      1.00      1.00         4

avg / total       1.00      1.00      1.00        50

Step 14: Save the model

f = open(classifier_path, "wb")
f.write(_pickle.dumps(model))
f.close()

Step 15: Plot the Confusion Matrix

labels = sorted(list(os.listdir(train_path)))

# plot the confusion matrix
cm = confusion_matrix(testLabels, preds)
fig = plt.figure(figsize=(12, 12))
plt.rcParams.update({'font.size': 24})
sns.heatmap(cm, 
            annot=True,
            cmap="Set2")
plt.show()

C:\Program Files\Anaconda3\envs\tensorflow-gpu\lib\site-packages\matplotlib\figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "

_config.yml

-mkc

Written on October 3, 2017