Non-Distorted Image Resizing with OpenCV
When we resize an image, we usually want to maintain its aspect ratio and avoid any distortion. In this article, we will discuss how to achieve non-distorted image resizing using OpenCV.
Resizing an Image with OpenCV
The OpenCV library provides a function called
resize() which can be used to resize an image. However, if we simply use this function, it may result in a distorted image. To avoid distortion, we need to use the correct interpolation method.
Interpolation is the method used to estimate the color of a pixel after resizing an image. OpenCV provides various interpolation methods, including:
cv2.INTER_NEAREST: Nearest neighbor interpolation, which simply takes the value of the nearest pixel.
cv2.INTER_LINEAR: Bilinear interpolation, which takes a weighted average of the four nearest pixels.
cv2.INTER_CUBIC: Bicubic interpolation, which takes a weighted average of the 16 nearest pixels.
cv2.INTER_LANCZOS4: Lanczos interpolation, which takes a weighted average of the 8 nearest pixels.
To achieve non-distorted resizing, we need to use the correct interpolation method. In most cases,
cv2.INTER_LINEAR is a good choice, as it provides a good balance between speed and quality. However, if we need higher quality, we can use
cv2.INTER_LANCZOS4, although these methods are slower.
Here is an example code snippet that demonstrates how to achieve non-distorted resizing using OpenCV:
import cv2 # Load an image img = cv2.imread("image.jpg") # Set the new size new_size = (640, 480) # Resize the image using bilinear interpolation resized_img = cv2.resize(img, new_size, interpolation=cv2.INTER_LINEAR)
In this example, we first load an image using
cv2.imread(). We then set the new size using a tuple, and finally, we resize the image using
cv2.INTER_LINEAR as the interpolation method.
In conclusion, non-distorted image resizing can be achieved using OpenCV by selecting the correct interpolation method. By using
cv2.INTER_LANCZOS4, we can resize images while maintaining their aspect ratio and avoiding distortion.