(images, number_of_times_to_upsample=1, batch_size=128)
return list(map(convert_cnn_detections_to_css, raw_detections_batched))
Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
    If you are using a GPU, this can give you much faster results since the GPU
    can process batches of images at once. If you aren't using a GPU, you don't need this function.

    :param images: A list of images (each as a numpy array)
    :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
    :param batch_size: How many images to include in each GPU processing batch.
    :return: A list of tuples of found face locations in css (top, right, bottom, left) order
Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
    If you are using a GPU, this can give you much faster results since the GPU
    can process batches of images at once. If you aren't using a GPU, you don't need this function.
Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector If you are using a GPU this can give you much faster results since the GPU can process batches of images at once . If you aren t using a GPU you don t need this function .
def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128):
    """
    Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
    If you are using a GPU, this can give you much faster results since the GPU
    can process batches of images at once. If you aren't using a GPU, you don't need this function.

    :param images: A list of images (each as a numpy array)
    :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
    :param batch_size: How many images to include in each GPU processing batch.
    :return: A list of tuples of found face locations in css (top, right, bottom, left) order
    """
    def convert_cnn_detections_to_css(detections):
        return [_trim_css_to_bounds(_rect_to_css(face.rect), images[0].shape) for face in detections]

    raw_detections_batched = _raw_face_locations_batched(images, number_of_times_to_upsample, batch_size)

    return list(map(convert_cnn_detections_to_css, raw_detections_batched))
def batch_face_locations ( images , number_of_times_to_upsample = 1 , batch_size = 128 ) : def convert_cnn_detections_to_css ( detections ) : return [ _trim_css_to_bounds ( _rect_to_css ( face . rect ) , images [ 0 ] . shape ) for face in detections ] raw_detections_batched = _raw_face_locations_batched ( images , number_of_times_to_upsample , batch_size ) return list ( map ( convert_cnn_detections_to_css , raw_detections_batched ) )