5 Best Ways to Rotate Image Without Cutting Off Sides Using OpenCV Python

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💡 Problem Formulation:

Rotating an image is a common task in image processing. However, a straightforward rotation often leads to the cutting off of the image’s corners, losing vital picture information. For instance, when a square image is rotated 45 degrees, the corners extend beyond the original image’s square bounds, and a naive rotation would clip these parts. The desired output is a correctly rotated image that retains all original content, with the newly created corners filled in a visually consistent way.

Method 1: Expand the borders before rotation

By expanding the image’s borders sufficiently before rotation, we can prevent the corners from being cut off. OpenCV provides functions to calculate the new image size and to pad the image, preventing loss of data during the rotation.

Here’s an example:

import cv2
import numpy as np

def rotate_image_without_cropping(img, angle):
    h, w = img.shape[:2]
    center = (w // 2, h // 2)
    # Calculate the size of the new image
    abs_cos, abs_sin = abs(np.cos(np.radians(angle))), abs(np.sin(np.radians(angle)))
    bound_w = int(h * abs_sin + w * abs_cos)
    bound_h = int(h * abs_cos + w * abs_sin)

    # Adjust the rotation matrix to the center and apply the padding
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotation_matrix[0, 2] += bound_w / 2 - center[0]
    rotation_matrix[1, 2] += bound_h / 2 - center[1]

    rotated_img = cv2.warpAffine(img, rotation_matrix, (bound_w, bound_h))
    return rotated_img

# Read the image
img = cv2.imread('example.png')
rotated_img = rotate_image_without_cropping(img, 45)
cv2.imwrite('rotated_example.png', rotated_img)

The output is a rotated image file saved as ‘rotated_example.png’ which shows the entire image rotated by 45 degrees with added borders to avoid clipping.

This code snippet reads an image, calculates the new border sizes to avoid clipping during rotation, adjusts the rotation matrix accordingly, and applies the rotation using OpenCV’s warpAffine function. The result is a rotated image with all parts intact.

Method 2: Scale the image down before rotation

Scaling the image down ensures that when it is rotated, none of the sides will extend beyond the original image’s boundaries. This method relies on resizing the original image to a smaller size, rotating it, and then resizing the result back to the original dimensions.

Here’s an example:

import cv2
import numpy as np

def scale_and_rotate_image(img, angle, scale):
    h, w = img.shape[:2]
    center = (w // 2, h // 2)
    # Calculate the scale to prevent cropping
    new_size = (int(w*scale), int(h*scale))
    resized_img = cv2.resize(img, new_size, interpolation=cv2.INTER_AREA)
    # Rotate the scaled image
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated_img = cv2.warpAffine(resized_img, rotation_matrix, new_size)
    # Resize to original dimensions
    final_img = cv2.resize(rotated_img, (w, h), interpolation=cv2.INTER_AREA)
    return final_img

# Read the image
img = cv2.imread('example.png')
scaled_rotated_img = scale_and_rotate_image(img, 45, 0.5)
cv2.imwrite('scaled_rotated_example.png', scaled_rotated_img)

The output is a rotated image file saved as ‘scaled_rotated_example.png’ which shows the downscaled image rotated and then upscaled back to its original size with no clipping.

The code snippet scales the image down, rotates it, and then resizes it back up. This method avoids clipping, but the final image might be less sharp due to the downscaling and upscaling processes.

Method 3: Rotate the image within the image canvas

This method involves rotating the image such that none of the sides extend past the original canvas, fitting the rotation entirely within the original dimensions. It effectively uses only the central part of the image that won’t extend past the original borders when rotated.

Here’s an example:

import cv2

def rotate_within_canvas(img, angle):
    h, w = img.shape[:2]
    center = (w // 2, h // 2)
    # Calculate the maximum rectangle within the image
    side = min(h, w)
    # Define the rotation matrix
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    # Apply the rotation
    rotated_img = cv2.warpAffine(img, rotation_matrix, (w, h))
    # Crop to the maximum square within the rotated image
    center_rot = (rotated_img.shape[1] // 2, rotated_img.shape[0] // 2)
    final_img = rotated_img[center_rot[1]-side//2:center_rot[1]+side//2, center_rot[0]-side//2:center_rot[0]+side//2]
    return final_img

# Read the image
img = cv2.imread('example.png')
rotated_within_img = rotate_within_canvas(img, 45)
cv2.imwrite('rotated_within_example.png', rotated_within_img)

The output is an image file saved as ‘rotated_within_example.png’ which shows a square rotated within the confines of the original image dimensions.

This code snippet rotates the image, and then crops it to the largest square that fits within the rotated image. The resulting image does not suffer from clipping, but it discards the outer regions of the original image.

Method 4: Use transparent padding

If maintaining the original size is important and clipping is not an option, we can add a transparent border around the image such that when rotated, the corners will fall within this transparent area instead of being clipped.

Here’s an example:

import cv2
import numpy as np

def rotate_with_transparent_padding(img, angle):
    h, w = img.shape[:2]
    # Create a larger image with alpha channel
    # Make the new image larger to fit the original image's corners upon rotation
    diag = int(np.sqrt(h**2 + w**2))
    new_img = np.zeros((diag, diag, 4), dtype=np.uint8)
    # Find the coordinates to place the old image in this new canvas
    x_offset = (diag - w) // 2
    y_offset = (diag - h) // 2
    new_img[y_offset:y_offset+h, x_offset:x_offset+w, :3] = img
    new_img[y_offset:y_offset+h, x_offset:x_offset+w, 3] = 255  # Full opacity
    # Apply the rotation
    center = (diag // 2, diag // 2)
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated_img = cv2.warpAffine(new_img, rotation_matrix, (diag, diag), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
    return rotated_img

# Read the image
img = cv2.imread('example.png', cv2.IMREAD_UNCHANGED)
rotated_transparent_img = rotate_with_transparent_padding(img, 45)
cv2.imwrite('rotated_transparent_example.png', rotated_transparent_img)

The output is an image file with alpha channel saved as ‘rotated_transparent_example.png’, showing the original image rotated within a transparent square canvas.

The code snippet creates an image with alpha transparency larger than the original to accommodate the rotated corners. It then places the original image in the new canvas, applies the rotation, and saves the result with transparency preserved.

Bonus One-Liner Method 5: OpenCV’s getRotationMatrix2D with Expand Option

OpenCV 4.2.0 introduced the flag cv2.ROTATE_90_CLOCKWISE which allows for seamless 90 degrees rotations without cropping. This can be employed as one-liner which, however, is limited to 90-degree increments and does not allow for arbitrary angles.

Here’s an example:

import cv2

# Read the image
img = cv2.imread('example.png')
# Rotate the image by 90 degrees clockwise
rotated_img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)

# Save the result
cv2.imwrite('rotated_90_example.png', rotated_img)

The output is an image file saved as ‘rotated_90_example.png’, showing the image rotated by 90 degrees without any clipping.

This one-liner uses OpenCV’s rotate function with the ROTATE_90_CLOCKWISE flag to quickly rotate an image by 90 degrees, effortlessly fitting the rotated image within the original dimensions.

Summary/Discussion

  • Method 1: Expand the borders before rotation. Strengths: Retains full image data. Weaknesses: Results in a larger image which might not be suitable in all contexts.
  • Method 2: Scale the image down before rotation. Strengths: Maintains original image size. Weaknesses: Risks loss of detail through scaling operations.
  • Method 3: Rotate the image within the image canvas. Strengths: Preserves image dimensions without added space. Weaknesses: Part of the image data is lost through cropping.
  • Method 4: Use transparent padding. Strengths: Prevents clipping and can work with original size if alpha transparency is supported. Weaknesses: Might not be suitable for all image formats, particularly those that don’t support transparency.
  • Bonus Method 5: OpenCV’s getRotationMatrix2D with Expand Option. Strengths: Quick and easy for 90-degree rotations. Weaknesses: Not capable of arbitrary angle rotations and limited by the OpenCV’s rotate function capabilities.