Gender Detection Python
In recent years, data science has become an essential tool for gaining insights and making informed decisions across various industries. One area where data science has shown significant potential is gender detection. By leveraging machine learning algorithms and Python programming, data scientists can develop accurate models to predict gender based on various features. This comprehensive guide aims to provide an overview of gender detection using Python for data science projects.
Gender Detection Using Python: Understanding
Gender detection involves determining the gender of an individual based on certain characteristics or attributes. These attributes can include facial features, voice patterns, names, or even social media profiles. Data scientists can employ machine learning techniques to train models that learn ways and relationships from labeled data, enabling them to predict gender accurately.
Gender detection refers to the automated process of determining the gender of an individual based on visual cues, primarily facial features. Python offers a wide range of libraries and tools that simplify the implementation of gender detection algorithms, making it an ideal language for such tasks.
Gender Detection Using Python: Key Steps
Data Collection and Preprocessing
The first step in any gender detection project is collecting and preprocessing the data. A robust dataset with diverse samples is crucial for training an accurate gender detection model. Additionally, data preprocessing techniques like normalization, resizing, and noise removal can significantly enhance the performance of the model.
To develop a gender detection model, a robust dataset is required. The dataset should consist of labeled samples where the gender is known. Various sources can provide such data, including publicly available datasets, social media profiles, or specialized data collection methods. Once the data is gathered, it needs to be cleaned and preprocessed to ensure consistency and remove any noise or biases.
Feature Extraction
Feature extraction involves selecting relevant characteristics from the collected data that can help differentiate between genders. For gender detection, features such as facial landmarks, voice frequency, or textual attributes can be considered. Python offers several libraries, such as OpenCV and librosa, that facilitate feature extraction from images, audio files, and text, respectively.
Feature extraction plays a vital role in gender detection. Python libraries such as OpenCV and dlib provide powerful tools for extracting facial features like eyes, nose, and mouth. These features serve as valuable input for machine learning algorithms.
Building a Gender Detection Model
Python provides an extensive range of libraries and frameworks, such as sci-kit-learn, TensorFlow, and Keras, which simplify the development of machine learning models. These libraries offer pre-implemented algorithms like decision trees, support vector machines (SVM), or deep neural networks that can be trained on the extracted features. The choice of the model depends on the nature and complexity of the data.
Once the data is collected and preprocessed, the next step is training a gender detection model. Python offers various machine learning frameworks, such as sci-kit-learn and TensorFlow, that facilitate the development and training of gender classification models. Algorithms like Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN) are commonly employed for this task.
Training and Evaluation
Once the model is built, it needs to be trained using the labeled dataset. The data is divided into training and testing sets, allowing the model to learn patterns from the training data and evaluate its performance on unseen data. Various evaluation metrics, such as accuracy, precision, recall, and F1 score, can be utilized to measure the model’s effectiveness. Iterative refinement of the model can be performed by adjusting hyperparameters and employing techniques like cross-validation.
After training the model, it is essential to evaluate its performance. Metrics like accuracy, precision, and recall provide valuable insights into the model’s effectiveness. Fine-tuning the model based on evaluation results can help enhance its accuracy and robustness.
Model Evaluation and Fine-tuning
Once the model is built, it needs to be trained using the labeled dataset. The data is divided into training and testing sets, allowing the model to learn patterns from the training data and evaluate its performance on unseen data. Various evaluation metrics, such as accuracy, precision, recall, and F1 score, can be utilized to measure the model’s effectiveness. Iterative refinement of the model can be performed by adjusting hyperparameters and employing techniques like cross-validation.
After training the model, it is essential to evaluate its performance. Metrics like accuracy, precision, and recall provide valuable insights into the model’s effectiveness. Fine-tuning the model based on evaluation results can help enhance its accuracy and robustness.
Deployment and Integration
After achieving satisfactory performance, the gender detection model can be deployed and integrated into different applications or systems. Python provides web frameworks like Flask and Django that allow developers to create APIs or web services for real-time gender detection. The model can also be incorporated into existing software solutions, enabling gender-based analytics and insights.
Gender Detection in Real-time
In computer vision, real-time gender detection algorithms utilize image processing techniques to extract facial features from live video streams or camera feeds. These algorithms can identify specific facial landmarks and analyze their distribution, shape, or texture to determine the gender of individuals in real time. The advancements in deep learning and convolutional neural networks (CNNs) have significantly improved the accuracy and efficiency of gender detection in real-time scenarios.
Also try: Face Recognition in Python – Data Analysis Projects
Real-time gender detection is a practical application of Python-based gender detection models. By utilizing libraries like OpenCV, we can process video streams or webcam input to identify and classify the genders of individuals in real time.
The Relevance of Gender Detection in Data Science Projects
Gender Detection Using Python: Demographic Analysis
Gender detection algorithms can contribute to a demographic analysis by providing insights into the gender distribution within a given dataset. This information can be useful in fields like market research, social sciences, and public policy.
Gender Detection Using Python: Facial Recognition Systems
Gender detection is a fundamental component of facial recognition systems. By accurately identifying the gender of individuals, these systems can further enhance their capabilities, enabling applications in areas such as surveillance, security, and user experience customization.
Gender Detection Using Python: Marketing and Advertising
Understanding the gender demographics of target audiences can significantly impact marketing and advertising strategies. Python-based gender detection models can assist in analyzing consumer behavior, enabling businesses to tailor their campaigns more effectively.
Gender Detection Using Python: Human-Computer Interaction
Integrating gender detection into human-computer interaction systems can lead to personalized user experiences. By adapting interfaces based on gender, Python-powered applications can provide tailored recommendations, content, and services.
Gender Detection Using Python: Code
You can find all code and datasets on GitHub.
1: Importing Libraries
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import f1_score
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.utils import np_utils
from keras.optimizers import SGD
from IPython.core.display import display, HTML
from PIL import Image
from io import BytesIO
import base64
plt.style.use('ggplot')
%matplotlib inline
2: Data Exploration
# set variables main_folder = '../input/celeba-dataset/' images_folder = main_folder + 'img_align_celeba/img_align_celeba/' EXAMPLE_PIC = images_folder + '000506.jpg' TRAINING_SAMPLES = 10000 VALIDATION_SAMPLES = 2000 TEST_SAMPLES = 2000 IMG_WIDTH = 178 IMG_HEIGHT = 218 BATCH_SIZE = 16 NUM_EPOCHS = 20
3: Load the attributes of every picture
# import the data set that include the attribute for each picture
df_attr = pd.read_csv(main_folder + 'list_attr_celeba.csv')
df_attr.set_index('image_id', inplace=True)
df_attr.replace(to_replace=-1, value=0, inplace=True) #replace -1 by 0
df_attr.shape
4: List of the available attribute in the CelebA dataset
# List of available attributes
for i, j in enumerate(df_attr.columns):
print(i, j)
# plot picture and attributes
img = load_img(EXAMPLE_PIC)
plt.grid(False)
plt.imshow(img)
df_attr.loc[EXAMPLE_PIC.split('/')[-1]][['Smiling','Male','Young']] #some attributes
5: Distribution of the Attribute
# Female or Male?
plt.title('Female or Male')
sns.countplot(y='Male', data=df_attr, color="c")
plt.show()
6: Split Dataset into Training, Validation, and Test
# Recomended partition df_partition = pd.read_csv(main_folder + 'list_eval_partition.csv') df_partition.head()
# display counter by partition # 0 -> TRAINING # 1 -> VALIDATION # 2 -> TEST df_partition['partition'].value_counts().sort_index()
# join the partition with the attributes
df_partition.set_index('image_id', inplace=True)
df_par_attr = df_partition.join(df_attr['Male'], how='inner')
df_par_attr.head()
7: Generate Partitions (Train, Validation, Test)
def load_reshape_img(fname):
img = load_img(fname)
x = img_to_array(img)/255.
x = x.reshape((1,) + x.shape)
return x
def generate_df(partition, attr, num_samples):
'''
partition
0 -> train
1 -> validation
2 -> test
'''
df_ = df_par_attr[(df_par_attr['partition'] == partition)
& (df_par_attr[attr] == 0)].sample(int(num_samples/2))
df_ = pd.concat([df_,
df_par_attr[(df_par_attr['partition'] == partition)
& (df_par_attr[attr] == 1)].sample(int(num_samples/2))])
# for Train and Validation
if partition != 2:
x_ = np.array([load_reshape_img(images_folder + fname) for fname in df_.index])
x_ = x_.reshape(x_.shape[0], 218, 178, 3)
y_ = np_utils.to_categorical(df_[attr],2)
# for Test
else:
x_ = []
y_ = []
for index, target in df_.iterrows():
im = cv2.imread(images_folder + index)
im = cv2.resize(cv2.cvtColor(im, cv2.COLOR_BGR2RGB), (IMG_WIDTH, IMG_HEIGHT)).astype(np.float32) / 255.0
im = np.expand_dims(im, axis =0)
x_.append(im)
y_.append(target[attr])
return x_, y_
8: Pre-processing Images: Data Augmentation
Let’s start with data augmentation.
# Generate image generator for data augmentation
datagen = ImageDataGenerator(
#preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
# load one image and reshape
img = load_img(EXAMPLE_PIC)
x = img_to_array(img)/255.
x = x.reshape((1,) + x.shape)
# plot 10 augmented images of the loaded iamge
plt.figure(figsize=(20,10))
plt.suptitle('Data Augmentation', fontsize=28)
i = 0
for batch in datagen.flow(x, batch_size=1):
plt.subplot(3, 5, i+1)
plt.grid(False)
plt.imshow( batch.reshape(218, 178, 3))
if i == 9:
break
i += 1
plt.show()
9: Build Data Generators
# Train data x_train, y_train = generate_df(0, 'Male', TRAINING_SAMPLES) # Train - Data Preparation - Data Augmentation with generators train_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, rotation_range=30, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, ) train_datagen.fit(x_train) train_generator = train_datagen.flow( x_train, y_train, batch_size=BATCH_SIZE, )
# Validation Data x_valid, y_valid = generate_df(1, 'Male', VALIDATION_SAMPLES) ''' # Validation - Data Preparation - Data Augmentation with generators valid_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, ) valid_datagen.fit(x_valid) validation_generator = valid_datagen.flow( x_valid, y_valid, ) '''
10: Build the Model – Gender Recognition
# Import InceptionV3 Model
inc_model = InceptionV3(weights='../input/inceptionv3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
include_top=False,
input_shape=(IMG_HEIGHT, IMG_WIDTH, 3))
print("number of layers:", len(inc_model.layers))
#inc_model.summary()
#Adding custom Layers x = inc_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation="relu")(x) x = Dropout(0.5)(x) x = Dense(512, activation="relu")(x) predictions = Dense(2, activation="softmax")(x)
# creating the final model
model_ = Model(inputs=inc_model.input, outputs=predictions)
# Lock initial layers to do not be trained
for layer in model_.layers[:52]:
layer.trainable = False
# compile the model
model_.compile(optimizer=SGD(lr=0.0001, momentum=0.9)
, loss='categorical_crossentropy'
, metrics=['accuracy'])
11: Train Model
#https://keras.io/models/sequential/ fit generator
checkpointer = ModelCheckpoint(filepath='weights.best.inc.male.hdf5',
verbose=1, save_best_only=True)
hist = model_.fit_generator(train_generator
, validation_data = (x_valid, y_valid)
, steps_per_epoch= TRAINING_SAMPLES/BATCH_SIZE
, epochs= NUM_EPOCHS
, callbacks=[checkpointer]
, verbose=1
)
# Plot loss function value through epochs
plt.figure(figsize=(18, 4))
plt.plot(hist.history['loss'], label = 'train')
plt.plot(hist.history['val_loss'], label = 'valid')
plt.legend()
plt.title('Loss Function')
plt.show()
# Plot accuracy through epochs
plt.figure(figsize=(18, 4))
plt.plot(hist.history['acc'], label = 'train')
plt.plot(hist.history['val_acc'], label = 'valid')
plt.legend()
plt.title('Accuracy')
plt.show(6ty6
#load the best model
model_.load_weights('weights.best.inc.male.hdf5')
# Test Data
x_test, y_test = generate_df(2, 'Male', TEST_SAMPLES)
# generate prediction
model_predictions = [np.argmax(model_.predict(feature)) for feature in x_test ]
# report test accuracy
test_accuracy = 100 * np.sum(np.array(model_predictions)==y_test) / len(model_predictions)
print('Model Evaluation')
print('Test accuracy: %.4f%%' % test_accuracy)
print('f1_score:', f1_score(y_test, model_predictions))
12: Let’s play with the Model
#dictionary to name the prediction
gender_target = {0: 'Female'
, 1: 'Male'}
def img_to_display(filename):
# inspired on this kernel:
# https://www.kaggle.com/stassl/displaying-inline-images-in-pandas-dataframe
# credits to stassl :)
i = Image.open(filename)
i.thumbnail((200, 200), Image.LANCZOS)
with BytesIO() as buffer:
i.save(buffer, 'jpeg')
return base64.b64encode(buffer.getvalue()).decode()
def display_result(filename, prediction, target):
'''
Display the results in HTML
'''
gender = 'Male'
gender_icon = "https://i.imgur.com/nxWan2u.png"
if prediction[1] <= 0.5:
gender_icon = "https://i.imgur.com/oAAb8rd.png"
gender = 'Female'
display_html = '''
<div style="overflow: auto; border: 2px solid #D8D8D8;
padding: 5px; width: 420px;" >
<img src="data:image/jpeg;base64,{}" style="float: left;" width="200" height="200">
<div style="padding: 10px 0px 0px 20px; overflow: auto;">
<img src="{}" style="float: left;" width="40" height="40">
<h3 style="margin-left: 50px; margin-top: 2px;">{}</h3>
<p style="margin-left: 50px; margin-top: -6px; font-size: 12px">{} prob.</p>
<p style="margin-left: 50px; margin-top: -16px; font-size: 12px">Real Target: {}</p>
<p style="margin-left: 50px; margin-top: -16px; font-size: 12px">Filename: {}</p>
</div>
</div>
'''.format(img_to_display(filename)
, gender_icon
, gender
, "{0:.2f}%".format(round(max(prediction)*100,2))
, gender_target[target]
, filename.split('/')[-1]
)
display(HTML(display_html))
def gender_prediction(filename):
'''
predict the gender
input:
filename: str of the file name
return:
array of the prob of the targets.
'''
im = cv2.imread(filename)
im = cv2.resize(cv2.cvtColor(im, cv2.COLOR_BGR2RGB), (178, 218)).astype(np.float32) / 255.0
im = np.expand_dims(im, axis =0)
# prediction
result = model_.predict(im)
prediction = np.argmax(result)
return result
#select random images of the test partition
df_to_test = df_par_attr[(df_par_attr['partition'] == 2)].sample(8)
for index, target in df_to_test.iterrows():
result = gender_prediction(images_folder + index)
#display result
display_result(images_folder + index, result[0], target['Male'])
Conclusion
Gender detection in Python offers a powerful toolset for data science projects. By leveraging the capabilities of Python libraries and machine learning algorithms, accurate gender detection can be achieved. The applications of gender detection span a wide range of fields, including demographics, facial recognition, marketing, and human-computer interaction.
As data science continues to evolve, the integration of gender detection into projects will undoubtedly provide valuable insights and contribute to the development of innovative solutions. Embrace the power of gender detection in Python to unlock the full potential of your data science endeavors.
