Detección de género usando OpenCV en Python

Aprenda a realizar la detección de género en rostros detectados en imágenes usando la biblioteca OpenCV en Python.

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La predicción automática de género a partir de imágenes faciales ha llamado mucho la atención recientemente, debido a su amplia aplicación en varios problemas de análisis facial. Sin embargo, debido a las grandes variaciones de las imágenes faciales (como la variación en la iluminación, la escala y la oclusión), los modelos existentes todavía están por detrás del nivel de precisión deseado que es necesario para explotar estos modelos en aplicaciones del mundo real.

El objetivo de este tutorial es desarrollar una utilidad liviana basada en la línea de comandos, a través de módulos basados en Python, para detectar automáticamente rostros en una imagen estática y predecir el género de las personas detectadas utilizando un modelo de detección de género basado en el aprendizaje profundo.

Prerrequisitos

Entran en juego los siguientes componentes:

OpenCV : es una biblioteca de código abierto para visión por computadora, aprendizaje automático y procesamiento de imágenes. OpenCV admite una amplia variedad de lenguajes de programación como Python, C ++, Java y se utiliza para todo tipo de análisis de imágenes y videos, como detección y reconocimiento facial, edición de fotos, reconocimiento óptico de caracteres y mucho más. El uso de OpenCV tiene muchos beneficios entre los que se encuentran:
- OpenCV es una biblioteca de código abierto y es gratuita.
- OpenCV es rápido ya que está escrito en C / C ++.
- OpenCV es compatible con la mayoría de los sistemas operativos, como Windows, Linux y macOS.
filetype: es un paquete de Python pequeño y sin dependencias para inferir tipos de archivos y MIME.

Para el propósito de este artículo, usaremos modelos Caffe entrenados previamente, uno para la detección de rostros tomado del tutorial de detección de rostros y otro modelo para la detección de la edad. A continuación se muestra la lista de archivos necesarios para incluir en nuestro directorio de proyectos:

gender_net.caffemodel: Es el modelo de ponderaciones previamente entrenado para la detección de género. Puedes descargarlo aquí .
deploy_gender.prototxt: es la arquitectura del modelo para el modelo de detección de género (un archivo de texto sin formato con una estructura similar a JSON que contiene todas las definiciones de la capa de la red neuronal). Consíguelo aquí .
res10_300x300_ssd_iter_140000_fp16.caffemodel: Los pesos del modelo previamente entrenados para la detección de rostros, descargue aquí .
deploy.prototxt.txt: Esta es la arquitectura del modelo para el modelo de detección de rostros, descárguelo aquí .

Después de descargar los 4 archivos necesarios, colóquelos en la carpeta de pesos:

Pesos y arquitecturas de modelos descargados

Para comenzar, instalemos OpenCV y NumPy:

$ pip install opencv-python numpy

Abra un nuevo archivo de Python y sígalo. Primero, importemos los módulos necesarios e inicialicemos las variables necesarias:

# Import Libraries
import cv2
import numpy as np

# The gender model architecture
# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ
GENDER_MODEL = 'weights/deploy_gender.prototxt'
# The gender model pre-trained weights
# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP
GENDER_PROTO = 'weights/gender_net.caffemodel'
# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean
# substraction to eliminate the effect of illunination changes
MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
# Represent the gender classes
GENDER_LIST = ['Male', 'Female']
# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
FACE_PROTO = "weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"

A continuación, carguemos nuestros modelos:

# load face Caffe model
face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)
# Load gender prediction model
gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)

Al igual que el tutorial de detección de edad , antes de comenzar a detectar el género, necesitamos una forma de detectar rostros, la siguiente función se toma principalmente del tutorial de detección de rostros :

def get_faces(frame, confidence_threshold=0.5):
    # convert the frame into a blob to be ready for NN input
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0))
    # set the image as input to the NN
    face_net.setInput(blob)
    # perform inference and get predictions
    output = np.squeeze(face_net.forward())
    # initialize the result list
    faces = []
    # Loop over the faces detected
    for i in range(output.shape[0]):
        confidence = output[i, 2]
        if confidence > confidence_threshold:
            box = output[i, 3:7] * \
                np.array([frame.shape[1], frame.shape[0],
                         frame.shape[1], frame.shape[0]])
            # convert to integers
            start_x, start_y, end_x, end_y = box.astype(np.int)
            # widen the box a little
            start_x, start_y, end_x, end_y = start_x - \
                10, start_y - 10, end_x + 10, end_y + 10
            start_x = 0 if start_x < 0 else start_x
            start_y = 0 if start_y < 0 else start_y
            end_x = 0 if end_x < 0 else end_x
            end_y = 0 if end_y < 0 else end_y
            # append to our list
            faces.append((start_x, start_y, end_x, end_y))
    return faces

A continuación, hacer una función de utilidad para mostrar una imagen:

def display_img(title, img):
    """Displays an image on screen and maintains the output until the user presses a key"""
    # Display Image on screen
    cv2.imshow(title, img)
    # Mantain output until user presses a key
    cv2.waitKey(0)
    # Destroy windows when user presses a key
    cv2.destroyAllWindows()

A continuación, creemos dos funciones de utilidad, una para encontrar el tamaño de fuente adecuado para escribir en la imagen y otra para cambiar el tamaño de la imagen correctamente:

def get_optimal_font_scale(text, width):
    """Determine the optimal font scale based on the hosting frame width"""
    for scale in reversed(range(0, 60, 1)):
        textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1)
        new_width = textSize[0][0]
        if (new_width <= width):
            return scale/10
    return 1

# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
    # initialize the dimensions of the image to be resized and
    # grab the image size
    dim = None
    (h, w) = image.shape[:2]
    # if both the width and height are None, then return the
    # original image
    if width is None and height is None:
        return image
    # check to see if the width is None
    if width is None:
        # calculate the ratio of the height and construct the
        # dimensions
        r = height / float(h)
        dim = (int(w * r), height)
    # otherwise, the height is None
    else:
        # calculate the ratio of the width and construct the
        # dimensions
        r = width / float(w)
        dim = (width, int(h * r))
    # resize the image
    return cv2.resize(image, dim, interpolation = inter)

Ahora que sabemos cómo detectar rostros, hagamos nuestra función principal para predecir el género de cada rostro detectado:

def predict_gender(input_path: str):
    """Predict the gender of the faces showing in the image"""
    # Read Input Image
    img = cv2.imread(input_path)
    # resize the image, uncomment if you want to resize the image
    # img = cv2.resize(img, (frame_width, frame_height))
    # Take a copy of the initial image and resize it
    frame = img.copy()
    if frame.shape[1] > frame_width:
        frame = image_resize(frame, width=frame_width)
    # predict the faces
    faces = get_faces(frame)
    # Loop over the faces detected
    # for idx, face in enumerate(faces):
    for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
        face_img = frame[start_y: end_y, start_x: end_x]
        # image --> Input image to preprocess before passing it through our dnn for classification.
        # scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling)
        # size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299)
        # mean = mean substraction values to be substracted from every channel of the image.
        # swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True.
        blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=(
            227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False)
        # Predict Gender
        gender_net.setInput(blob)
        gender_preds = gender_net.forward()
        i = gender_preds[0].argmax()
        gender = GENDER_LIST[i]
        gender_confidence_score = gender_preds[0][i]
        # Draw the box
        label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
        print(label)
        yPos = start_y - 15
        while yPos < 15:
            yPos += 15
        # get the font scale for this image size
        optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25))
        box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255)
        cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2)
        # Label processed image
        cv2.putText(frame, label, (start_x, yPos),
                    cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2)

        # Display processed image
    display_img("Gender Estimator", frame)
    # uncomment if you want to save the image
    # cv2.imwrite("output.jpg", frame)
    # Cleanup
    cv2.destroyAllWindows()

Aquí está el proceso de la predict_gender()función:

Leemos la imagen de entrada usando la cv2.imread()función.
Cambiamos el tamaño de la imagen si está por encima de la frame_widthvariable, no dude en editarla según sus necesidades.
Usamos nuestra get_faces()función previamente definida para detectar rostros en la imagen.
Repetimos cada rostro, dibujamos un rectángulo a su alrededor y lo pasamos al modelo de detección de género para realizar inferencias sobre el género.
Finalmente, imprimimos el género tanto en la consola como en la imagen. Después de eso, simplemente mostramos la imagen y la guardamos en el disco si queremos.

Muy bien, llamemos a nuestra función ahora:

if __name__ == '__main__':
    # Parsing command line arguments entered by user
    import sys
    predict_gender(sys.argv[1])

Simplemente usamos el módulo sys para obtener la ruta de la imagen desde la línea de comando. Probemos esto, estoy probando en esta imagen de archivo :

$ python predict_gender.py images\\pexels-karolina-grabowska-8526635.jpg

Aquí está la salida en la consola:

Female-97.36%
Female-98.34%

Y la imagen resultante:

Detectando género usando Python con OpenCV Aquí hay otro ejemplo:

Género detectado en la imagen usando OpenCV en Python

O esto:

Conclusión

Y ahí lo tienes, ahora tienes un código Python para detectar el género en cualquier imagen usando la biblioteca OpenCV. El modelo de género parece acertado.

Si desea utilizar su cámara web para detectar el género, consulte este código .

Consulta el código completo aquí .

Enlace: https://www.thepythoncode.com

#python #opencv

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Detección de género usando OpenCV en Python

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