欢迎来到我的世界，糊涂虫！
OpenCV是一个计算机视觉库，非常适合这类任务。您可以使用pip install opencv-python安装它。简而言之，以下是所需的步骤：
1.找出黑色像素的位置。

1.找出最大的黑色像素块存在的地方，通过将它们转换为轮廓，然后计算它们各自的大小。（注意右侧背面悬挂的几个黑色像素，用绿色突出显示）

1.找到与最大轮廓最匹配的旋转矩形。

1.调整覆盖图像的大小并旋转它。在覆盖图像周围创建一个衬垫，以便在旋转它时它不会被切断。

（使用的图像是Gary Larson's "Cow Tools"，我使用了一个高大的图像来展示缩放属性）
1.将生成的叠加图像粘贴到原始图像的顶部。在计算位置时一定要考虑到垫子，否则它会走到你打算去的东南方。

瞧！

import cv2
import numpy as np

# Load images
img = cv2.imread('stack_car.jpg')
# Replace the overlay image with your own,
# for now I will use Gary Larson's "Cow Tools" because it showcases the
# scaling property, what a fantastic image though
overlay_img = cv2.imread('cow_tools.png', -1) 

# Convert image to grayscale for easier thresholding
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image
_, thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY_INV)

# Show the threshold image
cv2.imshow('Threshold Image', thresh)
cv2.waitKey(0)

# Find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Draw contours on a copy of the image for visualization
img_contours = img.copy()
cv2.drawContours(img_contours, contours, -1, (0, 255, 0), 3)
cv2.imshow('Contours', img_contours)
cv2.waitKey(0)

# Find the largest chunk of black pixels
max_contour = max(contours, key=cv2.contourArea)

# Get a rotated box around the largest contour
rotated_rect = cv2.minAreaRect(max_contour)

# draw the rotated rectangle on a copy of the image for visualization
img_rotated_rect = img.copy()
box = cv2.boxPoints(rotated_rect)
box = np.int0(box)
cv2.drawContours(img_rotated_rect, [box], 0, (0, 0, 255), 2)
cv2.imshow('Rotated Rectangle', img_rotated_rect)
cv2.waitKey(0)

# Get the rotation angle, width, and height from the rotated rectangle
center, (width, height), angle = rotated_rect

# Resize the overlay image to match the rotated rectangle
overlay_img_resized = cv2.resize(overlay_img, (int(width), int(height)))

# Add some transparent padding to the image so that it doesn't get cut off when it rotates
# (I'm not sure of the best math for pad, but overshooting is better than undershooting)
pad = int(max(width, height))
overlay_img_resized = cv2.copyMakeBorder(overlay_img_resized, pad, pad, pad, pad, cv2.BORDER_CONSTANT, value=(0, 0, 0, 0))

# Rotate the image by the angle
M = cv2.getRotationMatrix2D((overlay_img_resized.shape[1] / 2, overlay_img_resized.shape[0] / 2), -angle, 1)
overlay_img_resized = cv2.warpAffine(overlay_img_resized, M, (overlay_img_resized.shape[1], overlay_img_resized.shape[0]))

# Show the resized overlay image
cv2.imshow('Resized Overlay Image', overlay_img_resized)
cv2.waitKey(0)

# Overlay the rotated and resized image onto the original image,
# make sure to take pad into account, otherwise it will drift southeast
for i in range(overlay_img_resized.shape[0]):
    for j in range(overlay_img_resized.shape[1]):
        if overlay_img_resized[i, j][3] != 0:
            img[int(center[1]-height/2)-pad+i, int(center[0]-width/2)-pad+j] = overlay_img_resized[i, j][:3]

# Show the final image
cv2.imshow('Final Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()