我认为最好是全局保存坐标，而不是使用一堆高标度/低标度。它们让我头疼，可能会因为舍入而失去精确性。在

也就是说，每次检测到某个对象时，首先将其转换为全局(原始图像)坐标。我在这里写了一个小的演示，模仿你的检测和{}行为。在

初始检测：

放大，另一个检测：

放大，另一个检测：

放大，另一个检测：

放大到原始比例，检测箱位于正确位置

代码：import cv2

import matplotlib.pyplot as plt

IMG_SIZE = 224

im = cv2.cvtColor(cv2.imread('lena.jpg'), cv2.COLOR_BGR2GRAY)

im = cv2.resize(im, (IMG_SIZE, IMG_SIZE))

# Your detector results

detected_region = [

[(10, 20) , (80, 100)],

[(50, 0) , (220, 190)],

[(100, 143) , (180, 200)],

[(110, 45) , (180, 150)]

]

# Global states

x_scale = 1.0

y_scale = 1.0

x_shift = 0

y_shift = 0

x1, y1 = 0, 0

x2, y2 = IMG_SIZE-1, IMG_SIZE-1

for region in detected_region:

# Detection

x_scale = IMG_SIZE / (x2-x1)

y_scale = IMG_SIZE / (y2-y1)

x_shift = x1

y_shift = y1

cur_im = cv2.resize(im[y1:y2, x1:x2], (IMG_SIZE, IMG_SIZE))

# Assuming the detector return these results

cv2.rectangle(cur_im, region[0], region[1], (255))

plt.imshow(cur_im)

plt.show()

# Zooming in, using part of your code

context_pixels = 16

x1 = max(region[0][0] - context_pixels, 0) / x_scale + x_shift

y1 = max(region[0][1] - context_pixels, 0) / y_scale + y_shift

x2 = min(region[1][0] + context_pixels, IMG_SIZE) / x_scale + x_shift

y2 = min(region[1][1] + context_pixels, IMG_SIZE) / y_scale + y_shift

x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)

# Assuming the detector confirm its choice here

print('Confirmed detection: ', x1, y1, x2, y2)

# This time no padding

x1 = detected_region[-1][0][0] / x_scale + x_shift

y1 = detected_region[-1][0][1] / y_scale + y_shift

x2 = detected_region[-1][1][0] / x_scale + x_shift

y2 = detected_region[-1][1][1] / y_scale + y_shift

x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)

cv2.rectangle(im, (x1, y1), (x2, y2), (255, 0, 0))

plt.imshow(im)

plt.show()

这也可以防止在调整大小的图像上调整大小，这可能会产生更多的伪影并恶化探测器的性能。在