How to use the dlib.rectangles function in dlib

face_regressor = dlib.shape_predictor(dlib_landmark_model)
    if args.dlib_bbox:
        face_detector = dlib.get_frontal_face_detector()

    # 3. forward
    tri = sio.loadmat('visualize/tri.mat')['tri']
    transform = transforms.Compose([ToTensorGjz(), NormalizeGjz(mean=127.5, std=128)])
    for img_fp in args.files:
        img_ori = cv2.imread(img_fp)
        if args.dlib_bbox:
            rects = face_detector(img_ori, 1)
        else:
            rects = []

        if len(rects) == 0:
            rects = dlib.rectangles()
            rect_fp = img_fp + '.bbox'
            lines = open(rect_fp).read().strip().split('\n')[1:]
            for l in lines:
                l, r, t, b = [int(_) for _ in l.split(' ')[1:]]
                rect = dlib.rectangle(l, r, t, b)
                rects.append(rect)

        pts_res = []
        Ps = []  # Camera matrix collection
        poses = []  # pose collection, [todo: validate it]
        vertices_lst = []  # store multiple face vertices
        ind = 0
        suffix = get_suffix(img_fp)
        for rect in rects:
            # whether use dlib landmark to crop image, if not, use only face bbox to calc roi bbox for cropping
            if args.dlib_landmark:

def _array_to_dlib_rectangles(rects_array):
    """
    Function to convert array of rectangles (in format [[x1,y1,w1,h1],[x2,y2,w2,h2],...]) to dlib regtangles objects.
    Usually input array is a results of OpenCV face detection and output dlib regtangles are used for landmark detection.

    :param rects_array: array with results of OpenCV face detection
    :return: dlib rectangles object
    """
    rects_dlib = dlib.rectangles()
    for (left, top, right, bottom) in rects_array:
        rects_dlib.append(dlib.rectangle(
            int(left),
            int(top),
            int(right),
            int(bottom)))
    return rects_dlib

df = pd.DataFrame(columns=['FID', 'PID', 'face_id', 'filename', 'left', 'top', 'right', 'bottom', 'confidence'])




print("Number of faces detected: {}".format(len(dets)))
counter = 0
for faces, prefix in zip(dets, f_prefix):
    # build dataframe of face detections and corresponding metadata
    for i, d in enumerate(faces):
        f_name = prefix + str(i)
        df.loc[counter] = [fids[counter], pids[counter], i,  f_name, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence]
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(
            i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))
        rects = dlib.rectangles()
        rects.extend([d.rect for d in dets])

    counter += 1

print(counter, "faces detected")
# write dataframe to CSV
df.to_csv("dnn_face_detections.csv")

print("DLIB's CNN FACE DETECTOR: DONE")