Sure, here's an example Python program that uses the OpenCV library to rotate an image of a record so that its label is upright:

```python
import cv2
import numpy as np

# load input image
img = cv2.imread('record.jpg')

# convert image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# apply edge detection to find the contour of the record
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# find the largest contour (assumed to be the record) and its minimum area bounding rectangle
largest_contour = max(contours, key=cv2.contourArea)
rect = cv2.minAreaRect(largest_contour)

# calculate the angle of the bounding rectangle and rotate the image by that angle
angle = rect[2]
if angle < -45:
    angle += 90
rotation_matrix = cv2.getRotationMatrix2D(rect[0], angle, 1)
rotated = cv2.warpAffine(img, rotation_matrix, (img.shape[1], img.shape[0]), flags=cv2.INTER_CUBIC)

# save the output image as a PNG file
cv2.imwrite('rotated_record.png', rotated)
```

In this program, we first load the input image of the record using the cv2.imread function. We then convert the image to grayscale and apply edge detection using the Canny algorithm to find the contour of the record. The largest contour (which is assumed to be the record) is then found using the max function, and its minimum area bounding rectangle is calculated using the cv2.minAreaRect function.

We then calculate the angle of the bounding rectangle using the rect[2] attribute, and rotate the image by that angle using the cv2.getRotationMatrix2D and cv2.warpAffine functions. Finally, we save the output image as a PNG file using the cv2.imwrite function.

Note that this program assumes that the record is the largest contour in the image, and that its label is perpendicular to the edges of the record. If these assumptions do not hold, the program may not correctly rotate the image.
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