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Machine Vision

OpenCV warpPerspective: Show Full Warped Image

By Jan on 03/13/2025

Learn how to prevent image cropping and display the entire warped image when using OpenCV's warpPerspective function in this comprehensive guide.

OpenCV warpPerspective: Show Full Warped Image

Table of Contents

Introduction

When using OpenCV's warpPerspective function, it's crucial to determine the appropriate size for the output image to prevent the transformed image from being clipped. This involves calculating a destination image size that encompasses the transformed corners of the original image.

Step-by-Step Guide

To ensure the entire image is visible after applying warpPerspective in OpenCV, you need to calculate a destination image size that accommodates the transformed corners of the original image. Here's a breakdown:

  1. Establish Source and Destination Points:

    • Define the four corners of your source image and the desired corresponding points in the output image. 
      src_pts = np.float32([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]) 
dst_pts = np.float32([[x1', y1'], [x2', y2'], [x3', y3'], [x4', y4']])

  2. Find the Homography Matrix:

    • Calculate the transformation matrix using findHomography. 
      M = cv2.findHomography(src_pts, dst_pts)[0]

  3. Determine Output Image Size:

    • Project the source image corners onto the destination plane using the homography matrix. 
      transformed_corners = cv2.perspectiveTransform(np.float32([[[x, y] for x, y in src_pts]]), M)
    • Find the minimum and maximum x and y values from the transformed corners to get the bounding box of the warped image. 
      min_x = int(transformed_corners[:,:,0].min())
max_x = int(transformed_corners[:,:,0].max())
min_y = int(transformed_corners[:,:,1].min())
max_y = int(transformed_corners[:,:,1].max())
    • Calculate the output image width and height. 
      output_width = max_x - min_x
output_height = max_y - min_y

  4. Apply Perspective Transformation:

    • Use warpPerspective with the calculated output size. 
      warped_img = cv2.warpPerspective(img, M, (output_width, output_height))

By following these steps, you ensure that the output image from warpPerspective is large enough to display the entire transformed image without any clipping.

Code Example

This Python code performs a perspective transformation on an image using OpenCV. It takes an image file path as input. First, it defines source and destination points, representing the corners of the object to be transformed and their desired positions. It then calculates the homography matrix based on these points. Next, it determines the output image size by finding the bounding box of the transformed corners. Finally, it applies the perspective transformation using the calculated homography matrix and displays the warped image.

import cv2
import numpy as np

# Load the image
img = cv2.imread('your_image.jpg')

# 1. Establish Source and Destination Points
src_pts = np.float32([[50, 50], [300, 50], [50, 200], [300, 200]])  # Example source points
dst_pts = np.float32([[100, 100], [300, 50], [100, 250], [300, 200]])  # Example destination points

# 2. Find the Homography Matrix
M = cv2.findHomography(src_pts, dst_pts)[0]

# 3. Determine Output Image Size
transformed_corners = cv2.perspectiveTransform(np.float32([[[x, y] for x, y in src_pts]]), M)
min_x = int(transformed_corners[:,:,0].min())
max_x = int(transformed_corners[:,:,0].max())
min_y = int(transformed_corners[:,:,1].min())
max_y = int(transformed_corners[:,:,1].max())
output_width = max_x - min_x
output_height = max_y - min_y

# 4. Apply Perspective Transformation
warped_img = cv2.warpPerspective(img, M, (output_width, output_height))

# Display the warped image
cv2.imshow('Warped Image', warped_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Explanation:

  1. Source and Destination Points: Replace the example coordinates in src_pts and dst_pts with the actual corners of your source image and their desired positions in the output.
  2. Homography Calculation: cv2.findHomography computes the transformation matrix M based on the correspondences between source and destination points.
  3. Output Size Calculation:
    • cv2.perspectiveTransform applies the homography to the source corners to find their locations in the output image.
    • The code then finds the minimum and maximum x and y coordinates of these transformed corners, defining the bounding box of the warped image.
    • The width and height of the output image are calculated from this bounding box.
  4. Warping: cv2.warpPerspective applies the perspective transformation using the calculated homography matrix M and the determined output image size.

Remember:

  • Adjust the source and destination points according to your specific image and desired transformation.
  • This code assumes you want the entire warped image visible. If you need to crop or resize the output further, you can do so after the warping step.

Additional Notes

  • Understanding the Issue: When applying a perspective transformation, the shape and size of the image change. If you don't account for this by calculating the appropriate output image size, parts of the transformed image might end up outside the bounds of the output image, leading to clipping.

  • Importance of Accurate Corner Selection: The accuracy of your perspective transformation heavily relies on the correct selection of source and destination points. Ensure these points accurately represent the corners of the object you want to transform and their desired positions in the output.

  • Handling Negative Coordinates: After transformation, some transformed corners might have negative coordinates. This is normal and indicates that those parts of the warped image would lie outside the original image boundaries. The code snippet provided accounts for this by finding the minimum x and y values, which could be negative, to ensure the entire warped image is included.

  • Alternative to Manual Calculation: While the provided code calculates the output image size manually, OpenCV offers cv2.boundingRect function which can directly compute the bounding rectangle of a set of points. You can use this function to simplify the calculation of output_width and output_height.

  • Performance Considerations: For performance-critical applications, consider optimizing the calculation of the output image size. For instance, instead of applying the perspective transformation to all four corners, you could potentially achieve the same result by transforming only two opposite corners.

  • Beyond Image Rectification: While this explanation focuses on ensuring the visibility of the entire warped image, the principles apply to any scenario where you need to determine the appropriate output image size after applying warpPerspective. This includes tasks like image rectification, viewpoint correction, and creating panoramas.

Summary

This summary outlines the steps to calculate the correct output image size for cv2.warpPerspective to prevent image clipping:

Step Description Code Snippet
1. Define Points Establish corresponding corner points in the source and desired output images. src_pts = np.float32([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])
dst_pts = np.float32([[x1', y1'], [x2', y2'], [x3', y3'], [x4', y4']])
2. Calculate Homography Compute the transformation matrix (homography) using the defined points. M = cv2.findHomography(src_pts, dst_pts)[0]
3. Determine Output Size a) Project source image corners onto the destination plane using the homography.
b) Find the minimum and maximum x and y coordinates of the transformed corners.
c) Calculate output image width and height using these boundaries.		 transformed_corners = cv2.perspectiveTransform(np.float32([[[x, y] for x, y in src_pts]]), M)
min_x = int(transformed_corners[:,:,0].min())
...
output_width = max_x - min_x
output_height = max_y - min_y
4. Apply Transformation Perform the perspective transformation using the calculated output size. warped_img = cv2.warpPerspective(img, M, (output_width, output_height))

By calculating the output size based on the transformed corners, you guarantee that the entire warped image will be visible without any portions being cut off.

Conclusion

In conclusion, accurately displaying the entire image after applying warpPerspective in OpenCV requires careful calculation of the output image size. This involves determining the bounding box of the transformed image on the destination plane, which can be achieved by finding the minimum and maximum x and y coordinates of the projected source image corners. By using these coordinates to define the output image width and height, you can ensure that warpPerspective produces a result that encompasses the entire transformed image, preventing any clipping and ensuring a visually complete transformation.

References

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