python - 如何重新缩放图像坐标的信息以在相似图像的缩放版本上工作

Question

我使用yolov3来检测416x416大小的帧中的对象。我使用边界框信息在该416x416图像上绘制了框。

但是由于图片太小，我无法正确看到它，所以我使用的框架具有暗淡的1920x1080。我想缩放边界框信息和x，y坐标，以使其缩放到高暗淡的图片，但是我无法正确缩放它。

显然，信息远未解决。

注意!在传递帧之前，我使用此方法将帧的大小从1920,1080调整为416,416

def letterbox_resize(img, size=(resized_image_size,resized_image_size), padColor=0):

    h, w = img.shape[:2]
    sh, sw = size

    # interpolation method
    if h > sh or w > sw: # shrinking image
        interp = cv2.INTER_AREA
    else: # stretching image
        interp = cv2.INTER_CUBIC

    # aspect ratio of image
    aspect = w/h  # if on Python 2, you might need to cast as a float: float(w)/h

    # compute scaling and pad sizing
    if aspect > 1: # horizontal image
        new_w = sw
        new_h = np.round(new_w/aspect).astype(int)
        pad_vert = (sh-new_h)/2
        pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)
        pad_left, pad_right = 0, 0
    elif aspect < 1: # vertical image
        new_h = sh
        new_w = np.round(new_h*aspect).astype(int)
        pad_horz = (sw-new_w)/2
        pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int)
        pad_top, pad_bot = 0, 0
    else: # square image
        new_h, new_w = sh, sw
        pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0

    # set pad color
    if len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided
        padColor = [padColor]*3

    # scale and pad
    scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)
    scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=padColor)

    return scaled_img

如果有人帮助我编写脚本，可以重新调整yolo预测的x，y，w，h信息，以便我可以在图像上正确绘制准确的方框。

Answer 1

重新缩放过程未考虑顶部的零填充区域。在乘以缩放比例之前，请除去顶部的零垫，您应该能够得到正确的结果。

这是所有3种情况的示例代码，其中边界框是与YOLO结果相对应的点。

def boundBox_restore(boundingbox, ori_size=(ori_image_width,ori_image_height), resized_size=(resized_image_size,resized_image_size)):

    h, w = ori_size
    sh, sw = resized_size

    scale_ratio =  w / sw

    ox,oy,ow,oh = boundingbox

    # aspect ratio of image
    aspect = w/h  # if on Python 2, you might need to cast as a float: float(w)/h

    # compute scaling and pad sizing
    if aspect > 1: # horizontal image
        new_w = sw
        new_h = np.round(new_w/aspect).astype(int)
        pad_vert = (sh-new_h)/2
        pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)
        pad_left, pad_right = 0, 0
    elif aspect < 1: # vertical image
        new_h = sh
        new_w = np.round(new_h*aspect).astype(int)
        pad_horz = (sw-new_w)/2
        pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int)
        pad_top, pad_bot = 0, 0
    else: # square image
        new_h, new_w = sh, sw
        pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0


    # remove pad
    ox = ox - pad_left
    oy = oy - pad_top

    # rescale
    ox = ox * scale_ratio
    oy = oy * scale_ratio
    ow = ow * scale_ratio
    oh = oh * scale_ratio


    return (ox,oy,oh,ow)