Code forGender Detection using OpenCV in Python Tutorial

predict_gender.py

# Import Librariesimport cv2import numpy as np# The gender model architecture# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQGENDER_MODEL = 'weights/deploy_gender.prototxt'# The gender model pre-trained weights# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHPGENDER_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 changesMODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)# Represent the gender classesGENDER_LIST = ['Male', 'Female']# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxtFACE_PROTO = "weights/deploy.prototxt.txt"# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodelFACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"# load face Caffe modelface_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)# Load gender prediction modelgender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)# Initialize frame sizeframe_width = 1280frame_height = 720def 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 facesdef 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()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-opencvdef 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)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()if __name__ == '__main__':    # Parsing command line arguments entered by user    import sys    predict_gender(sys.argv[1])

predict_gender_live.py

# Import Librariesimport cv2import numpy as np# The gender model architecture# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQGENDER_MODEL = 'weights/deploy_gender.prototxt'# The gender model pre-trained weights# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHPGENDER_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 changesMODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)# Represent the gender classesGENDER_LIST = ['Male', 'Female']# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxtFACE_PROTO = "weights/deploy.prototxt.txt"# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodelFACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"# load face Caffe modelface_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)# Load gender prediction modelgender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)# Initialize frame sizeframe_width = 1280frame_height = 720def 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 facesdef 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-opencvdef 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)def predict_gender():    """Predict the gender of the faces showing in the image"""    # create a new cam object    cap = cv2.VideoCapture(0)    while True:        _, img = cap.read()        # 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                # frame = cv2.resize(frame, (frame_height, frame_width))        cv2.imshow("Gender Estimator", frame)        if cv2.waitKey(1) == ord("q"):            break        # uncomment if you want to save the image        # cv2.imwrite("output.jpg", frame)        # Cleanup    cv2.destroyAllWindows()if __name__ == '__main__':    predict_gender()

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