Question

我正在用Python实现对象本地化。我遇到的一个问题是，当我在执行操作时调整可观察区域的大小时，我不知道如何同时更改基本事实框。因此，发生这种情况：

地面真相框未调整大小以准确地适合平面。因此，我无法正确定位。我当前用于格式化下一个状态的函数如下：

def next_state(init_input, b, b_prime, g, a):
    """ 
    Returns the observable region of the next state.

    Formats the next state's observable region, defined
    by b_prime, to be of dimension (224, 224, 3). Adding 16
    additional pixels of context around the original bounding box.
    The ground truth box must be reformatted according to the
    new observable region.

    :param init_input:
        The initial input volume of the current episode.

    :param b:
        The current state's bounding box.

    :param b_prime:
        The subsequent state's bounding box.

    :param g:
        The ground truth box of the target object.

    :param a:
        The action taken by the agent at the current step.
    """

    # Determine the pixel coordinates of the observable region for the following state
    context_pixels = 16
    x1 = max(b_prime[0] - context_pixels, 0)
    y1 = max(b_prime[1] - context_pixels, 0)
    x2 = min(b_prime[2] + context_pixels, IMG_SIZE)
    y2 = min(b_prime[3] + context_pixels, IMG_SIZE)

    # Determine observable region
    observable_region = cv2.resize(init_input[y1:y2, x1:x2], (224, 224))

    # Difference between crop region and image dimensions
    x1_diff = x1
    y1_diff = y1
    x2_diff = IMG_SIZE - x2
    y2_diff = IMG_SIZE - y2

    # Resize ground truth box
    g[0] = int(g[0] - 0.5 * x1_diff)  # x1
    g[1] = int(g[1] - 0.5 * y1_diff)  # y1
    g[2] = int(g[2] + 0.5 * x2_diff)  # x2
    g[3] = int(g[3] + 0.5 * y2_diff)  # y2

    return observable_region, g

我似乎无法正确获得尺寸的变化。我遵循了this的帖子，以便最初调整边框的大小。但是，这种解决方案在这种情况下似乎不起作用。

边界框/地面真理框的格式为：b = [x1, y1, x2, y2]

init_input的尺寸为(224, 224, 3)。 IMG_SIZE = 224和context_pixels = 16

这是另一个示例：

似乎地面真相框的大小正确，但是位置不正确。

更新

我已经更新了上面的代码部分。比例因子似乎是解决问题的错误方法。通过仅增加/减去要放大的像素数，我已经接近了很多。我相信现在与插值有关，因此如果有人可以帮助实现完美，那将是巨大的帮助。

新示例：

更新2

提供了solution。

Answer 1

我的问题在this帖子中被一个名为@lenik的用户解决。

在将比例因子应用于地面真值框g的像素坐标之前，必须首先减去零偏移，以使x1, y1成为0, 0。这样可以使缩放正常工作。

因此，转换后任意随机点(x,y)的坐标可以计算为：

x_new = (x - x1) * IMG_SIZE / (x2 - x1)
y_new = (y - y1) * IMG_SIZE / (y2 - y1)

在代码中以及与我的问题有关的解决方案如下：

def next_state(init_input, b_prime, g):
    """
    Returns the observable region of the next state.

    Formats the next state's observable region, defined
    by b_prime, to be of dimension (224, 224, 3). Adding 16
    additional pixels of context around the original bounding box.
    The ground truth box must be reformatted according to the
    new observable region.

    :param init_input:
        The initial input volume of the current episode.

    :param b_prime:
        The subsequent state's bounding box.

    :param g:
        The ground truth box of the target object.
    """

    # Determine the pixel coordinates of the observable region for the following state
    context_pixels = 16
    x1 = max(b_prime[0] - context_pixels, 0)
    y1 = max(b_prime[1] - context_pixels, 0)
    x2 = min(b_prime[2] + context_pixels, IMG_SIZE)
    y2 = min(b_prime[3] + context_pixels, IMG_SIZE)

    # Determine observable region
    observable_region = cv2.resize(init_input[y1:y2, x1:x2], (224, 224), interpolation=cv2.INTER_AREA)

    # Resize ground truth box 
    g[0] = int((g[0] - x1) * IMG_SIZE / (x2 - x1))  # x1
    g[1] = int((g[1] - y1) * IMG_SIZE / (y2 - y1))  # y1
    g[2] = int((g[2] - x1) * IMG_SIZE / (x2 - x1))  # x2
    g[3] = int((g[3] - y1) * IMG_SIZE / (y2 - y1))  # y2

    return observable_region, g

根据图片调整边框的大小

更新

更新2

1 个答案: