(image, min_face_size=20.0,
                 thresholds=[0.6, 0.7, 0.8],
                 nms_thresholds=[0.7, 0.7, 0.7])
return bounding_boxes, landmarks
Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.

    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.
Arguments : image : an instance of PIL . Image . min_face_size : a float number . thresholds : a list of length 3 . nms_thresholds : a list of length 3 .
def detect_faces(image, min_face_size=20.0,
                 thresholds=[0.6, 0.7, 0.8],
                 nms_thresholds=[0.7, 0.7, 0.7]):
    """
    Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.

    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
    """

    # LOAD MODELS
    pnet = PNet()
    rnet = RNet()
    onet = ONet()
    onet.eval()

    # BUILD AN IMAGE PYRAMID
    width, height = image.size
    min_length = min(height, width)

    min_detection_size = 12
    factor = 0.707  # sqrt(0.5)

    # scales for scaling the image
    scales = []

    # scales the image so that
    # minimum size that we can detect equals to
    # minimum face size that we want to detect
    m = min_detection_size/min_face_size
    min_length *= m

    factor_count = 0
    while min_length > min_detection_size:
        scales.append(m*factor**factor_count)
        min_length *= factor
        factor_count += 1

    # STAGE 1

    # it will be returned
    bounding_boxes = []

    # run P-Net on different scales
    for s in scales:
        boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
        bounding_boxes.append(boxes)

    # collect boxes (and offsets, and scores) from different scales
    bounding_boxes = [i for i in bounding_boxes if i is not None]
    bounding_boxes = np.vstack(bounding_boxes)

    keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
    bounding_boxes = bounding_boxes[keep]

    # use offsets predicted by pnet to transform bounding boxes
    bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
    # shape [n_boxes, 5]

    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 2

    img_boxes = get_image_boxes(bounding_boxes, image, size=24)
    #img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True)
    with torch.no_grad():
        img_boxes = torch.FloatTensor(img_boxes)
    output = rnet(img_boxes)
    offsets = output[0].data.numpy()  # shape [n_boxes, 4]
    probs = output[1].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[1])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]

    keep = nms(bounding_boxes, nms_thresholds[1])
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 3

    img_boxes = get_image_boxes(bounding_boxes, image, size=48)
    if len(img_boxes) == 0: 
        return [], []
    #img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True)
    with torch.no_grad():
        img_boxes = torch.FloatTensor(img_boxes)
    output = onet(img_boxes)
    landmarks = output[0].data.numpy()  # shape [n_boxes, 10]
    offsets = output[1].data.numpy()  # shape [n_boxes, 4]
    probs = output[2].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[2])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]
    landmarks = landmarks[keep]

    # compute landmark points
    width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
    height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
    xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
    landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1)*landmarks[:, 0:5]
    landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1)*landmarks[:, 5:10]

    bounding_boxes = calibrate_box(bounding_boxes, offsets)
    keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
    bounding_boxes = bounding_boxes[keep]
    landmarks = landmarks[keep]

    return bounding_boxes, landmarks
def detect_faces ( image , min_face_size = 20.0 , thresholds = [ 0.6 , 0.7 , 0.8 ] , nms_thresholds = [ 0.7 , 0.7 , 0.7 ] ) : # LOAD MODELS pnet = PNet ( ) rnet = RNet ( ) onet = ONet ( ) onet . eval ( ) # BUILD AN IMAGE PYRAMID width , height = image . size min_length = min ( height , width ) min_detection_size = 12 factor = 0.707 # sqrt(0.5) # scales for scaling the image scales = [ ] # scales the image so that # minimum size that we can detect equals to # minimum face size that we want to detect m = min_detection_size / min_face_size min_length *= m factor_count = 0 while min_length > min_detection_size : scales . append ( m * factor ** factor_count ) min_length *= factor factor_count += 1 # STAGE 1 # it will be returned bounding_boxes = [ ] # run P-Net on different scales for s in scales : boxes = run_first_stage ( image , pnet , scale = s , threshold = thresholds [ 0 ] ) bounding_boxes . append ( boxes ) # collect boxes (and offsets, and scores) from different scales bounding_boxes = [ i for i in bounding_boxes if i is not None ] bounding_boxes = np . vstack ( bounding_boxes ) keep = nms ( bounding_boxes [ : , 0 : 5 ] , nms_thresholds [ 0 ] ) bounding_boxes = bounding_boxes [ keep ] # use offsets predicted by pnet to transform bounding boxes bounding_boxes = calibrate_box ( bounding_boxes [ : , 0 : 5 ] , bounding_boxes [ : , 5 : ] ) # shape [n_boxes, 5] bounding_boxes = convert_to_square ( bounding_boxes ) bounding_boxes [ : , 0 : 4 ] = np . round ( bounding_boxes [ : , 0 : 4 ] ) # STAGE 2 img_boxes = get_image_boxes ( bounding_boxes , image , size = 24 ) #img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True) with torch . no_grad ( ) : img_boxes = torch . FloatTensor ( img_boxes ) output = rnet ( img_boxes ) offsets = output [ 0 ] . data . numpy ( ) # shape [n_boxes, 4] probs = output [ 1 ] . data . numpy ( ) # shape [n_boxes, 2] keep = np . where ( probs [ : , 1 ] > thresholds [ 1 ] ) [ 0 ] bounding_boxes = bounding_boxes [ keep ] bounding_boxes [ : , 4 ] = probs [ keep , 1 ] . reshape ( ( - 1 , ) ) offsets = offsets [ keep ] keep = nms ( bounding_boxes , nms_thresholds [ 1 ] ) bounding_boxes = bounding_boxes [ keep ] bounding_boxes = calibrate_box ( bounding_boxes , offsets [ keep ] ) bounding_boxes = convert_to_square ( bounding_boxes ) bounding_boxes [ : , 0 : 4 ] = np . round ( bounding_boxes [ : , 0 : 4 ] ) # STAGE 3 img_boxes = get_image_boxes ( bounding_boxes , image , size = 48 ) if len ( img_boxes ) == 0 : return [ ] , [ ] #img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True) with torch . no_grad ( ) : img_boxes = torch . FloatTensor ( img_boxes ) output = onet ( img_boxes ) landmarks = output [ 0 ] . data . numpy ( ) # shape [n_boxes, 10] offsets = output [ 1 ] . data . numpy ( ) # shape [n_boxes, 4] probs = output [ 2 ] . data . numpy ( ) # shape [n_boxes, 2] keep = np . where ( probs [ : , 1 ] > thresholds [ 2 ] ) [ 0 ] bounding_boxes = bounding_boxes [ keep ] bounding_boxes [ : , 4 ] = probs [ keep , 1 ] . reshape ( ( - 1 , ) ) offsets = offsets [ keep ] landmarks = landmarks [ keep ] # compute landmark points width = bounding_boxes [ : , 2 ] - bounding_boxes [ : , 0 ] + 1.0 height = bounding_boxes [ : , 3 ] - bounding_boxes [ : , 1 ] + 1.0 xmin , ymin = bounding_boxes [ : , 0 ] , bounding_boxes [ : , 1 ] landmarks [ : , 0 : 5 ] = np . expand_dims ( xmin , 1 ) + np . expand_dims ( width , 1 ) * landmarks [ : , 0 : 5 ] landmarks [ : , 5 : 10 ] = np . expand_dims ( ymin , 1 ) + np . expand_dims ( height , 1 ) * landmarks [ : , 5 : 10 ] bounding_boxes = calibrate_box ( bounding_boxes , offsets ) keep = nms ( bounding_boxes , nms_thresholds [ 2 ] , mode = 'min' ) bounding_boxes = bounding_boxes [ keep ] landmarks = landmarks [ keep ] return bounding_boxes , landmarks