根據圖像調整邊界框的大小

Question

我在Python中實現對象本地化。 我遇到的一個問題是，當我在采取行動時調整可觀察區域的大小時，我不知道如何同時更改地面實況框。 因此，發生這種情況：

地面實況框不會調整大小以准確適應飛機。 因此，我無法正確本地化。 我當前格式化下一個狀態的函數如下：

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

我似乎無法正確地改變尺寸。 我按照這篇文章來初步調整邊界框的大小。 然而，在這種情況下，該解決方案似乎並不起作用。

邊界框/地面實況框的格式為： b = [x1, y1, x2, y2]

init_input具有維度(224, 224, 3) init_input (224, 224, 3) 。 IMG_SIZE = 224 ， context_pixels = 16

這是一個額外的例子：

似乎地面實況盒的大小是正確的，但位置是關閉的。

更新

我已經更新了上面的代碼部分。 比例因子似乎是解決問題的錯誤方法。 通過添加/減去要放大的像素數量，我已經接近了很多。 我相信現在與插值有關，所以如果有人可以幫忙解決這個問題，那將是一個很大的幫助。

新例子：

更新2

提供了一種解決方案 。

Answer 1

我的問題在這篇文章中被名為@lenik的用戶解決了。

在將比例因子應用於地面實況框g的像素坐標之前，必須首先減去零偏移，使x1, y1變為0, 0 。 這允許縮放工作正常。

因此，變換后任意隨機點(x,y)的坐標可以計算為：

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

在代碼中以及與我的問題相關的解決方案如下：

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