Tag Archives: cv2.warpAffine()

Affine Transformation

What is an Affine Transformation?

An affine transformation is any transformation that preserves collinearity, parallelism as well as the ratio of distances between the points (e.g. midpoint of a line remains the midpoint after transformation). It doesn’t necessarily preserve distances and angles.

Thus all the geometric transformations we discussed so far such as translation, rotation, scaling, etc are all affine transformations as all the above properties are preserved in these transformations. To understand in simple terms, one can think of the affine transformation as a composition of rotation, translation, scaling, and shear.

In general, the affine transformation can be expressed in the form of a linear transformation followed by a vector addition as shown below

Since the transformation matrix (M) is defined by 6 (2×3 matrix as shown above) constants, thus to find this matrix we first select 3 points in the input image and map these 3 points to the desired locations in the unknown output image according to the use-case as shown below (This way we will have 6 equations and 6 unknowns and that can be easily solved).

For instance, if you want to take the mirror image, you can define the 3 points as (you may choose any 3).

Once the transformation matrix is calculated, then we apply the affine transformation to the entire input image to get the final transformed image. Let’s see how to do this using OpenCV-Python.

OpenCV

OpenCV provides a function cv2.getAffineTransform() that takes as input the three pairs of corresponding points and outputs the transformation matrix. The basic syntax is shown below.

transform_mat = cv2.getAffineTransform(src, dst)

# src: coordinates in the source image
# dst: coordinates in the output image

transform_mat = cv2.getAffineTransform(src, dst)

# src: coordinates in the source image

# dst: coordinates in the output image

Once the transformation matrix (M) is calculated, pass it to the cv2.warpAffine() function that applies an affine transformation to an image. The syntax of this function is given below.

dst = cv.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]] )
 
# src: input image
# M: Transformation matrix
# dsize: size of the output image
# flags: interpolation method to be used

dst = cv.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]] )

# src: input image

# M: Transformation matrix

# dsize: size of the output image

# flags: interpolation method to be used

Now, let’s take the above example of a mirror image and see how to apply affine transformation using OpenCV-Python. Below are the steps.

Read the image
Define the 3 pairs of corresponding points (See image above)
Calculate the transformation matrix using cv2.getAffineTransform()
Apply the affine transformation using cv2.warpAffine()

import cv2
import numpy as np

# Read the image
img = cv2.imread('D:/downloads/opencv_logo.PNG')
rows, cols = img.shape[:2]

# Define the 3 pairs of corresponding points 
input_pts = np.float32([[0,0], [cols-1,0], [0,rows-1]])
output_pts = np.float32([[cols-1,0], [0,0], [cols-1,rows-1]])

# Calculate the transformation matrix using cv2.getAffineTransform()
M= cv2.getAffineTransform(input_pts , output_pts)

# Apply the affine transformation using cv2.warpAffine()
dst = cv2.warpAffine(img, M, (cols,rows))

# Display the image
out = cv2.hconcat([img, dst])
cv2.imshow('Output', out)
cv2.waitKey(0)

import cv2

import numpy as np

# Read the image

img = cv2.imread('D:/downloads/opencv_logo.PNG')

rows, cols = img.shape[:2]

# Define the 3 pairs of corresponding points

input_pts = np.float32([[0,0], [cols-1,0], [0,rows-1]])

output_pts = np.float32([[cols-1,0], [0,0], [cols-1,rows-1]])

# Calculate the transformation matrix using cv2.getAffineTransform()

M= cv2.getAffineTransform(input_pts , output_pts)

# Apply the affine transformation using cv2.warpAffine()

dst = cv2.warpAffine(img, M, (cols,rows))

# Display the image

out = cv2.hconcat([img, dst])

cv2.imshow('Output', out)

cv2.waitKey(0)

Below is the output image. Here, left image represents the original image while the right one is the transformed mirror image.

Now define the 3 different point pairs and see how the transformation looks like. That’s all for this blog. In the next blog, we will discuss Perspective transformation. Hope you enjoy reading.

If you have any doubts/suggestions please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

How to write rotated text using OpenCV-Python?

Approach

Instead of directly writing the rotated text, we can first write the text at the desired location and then rotate it. Below are the steps summarized to do this.

Create a zeros image of the desired shape
Draw the text using cv2.putText()
Rotate at the desired angle using cv2.warpAffine(). To know more, refer to this blog.

Below is the code for doing this using OpenCV-Python

import cv2
import numpy as np

# Create a zeros image
img = np.zeros((400,400), dtype=np.uint8)

# Specify the text location and rotation angle
text_location = (100,200)
angle = 30

# Draw the text using cv2.putText()
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, 'TheAILearner', text_location, font, 1, 255, 2)

# Rotate the image using cv2.warpAffine()
M = cv2.getRotationMatrix2D(text_location, angle, 1)
out = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))

# Display the results
cv2.imshow('img',out)
cv2.waitKey(0)

import cv2

import numpy as np

# Create a zeros image

img = np.zeros((400,400), dtype=np.uint8)

# Specify the text location and rotation angle

text_location = (100,200)

angle = 30

# Draw the text using cv2.putText()

font = cv2.FONT_HERSHEY_SIMPLEX

cv2.putText(img, 'TheAILearner', text_location, font, 1, 255, 2)

# Rotate the image using cv2.warpAffine()

M = cv2.getRotationMatrix2D(text_location, angle, 1)

out = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))

# Display the results

cv2.imshow('img',out)

cv2.waitKey(0)

Below is the output image for the 30-degree counterclockwise rotation. Here, the left image represents the original image while the right one is the rotated image.

Hope you enjoy reading.

If you have any doubts/suggestions please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Understanding Geometric Transformation: Translation using OpenCV-Python

Numpy

First, let’s create the transformation matrix (M). This can be easily done using numpy as shown below. Here, the image is translated by (100, 50)

M = np.float32([[1,0,100],[0,1,50]])

1	M = np.float32([[1,0,100],[0,1,50]])

Next, let’s convert the image coordinates to the form [x,y,1]. This can be done as

# get the coordinates in the form of (0,0),(0,1)...
# the shape is (2, rows*cols)
orig_coord = np.indices((cols, rows)).reshape(2,-1)
# stack the rows of 1 to form [x,y,1]
orig_coord_f = np.vstack((orig_coord, np.ones(rows*cols)))

# get the coordinates in the form of (0,0),(0,1)...

# the shape is (2, rows*cols)

orig_coord = np.indices((cols, rows)).reshape(2,-1)

# stack the rows of 1 to form [x,y,1]

orig_coord_f = np.vstack((orig_coord, np.ones(rows*cols)))

Now apply the transformation by multiplying the transformation matrix with coordinates.

transform_coord = np.dot(M, orig_coord_f)
# Change into int type
transform_coord = transform_coord.astype(np.int)

transform_coord = np.dot(M, orig_coord_f)

# Change into int type

transform_coord = transform_coord.astype(np.int)

Keep only the coordinates that fall within the image boundary.

indices = np.all((transform_coord[1]<rows, transform_coord[0]<cols, transform_coord[1]>=0, transform_coord[0]>=0), axis=0)

1	indices = np.all((transform_coord[1]<rows, transform_coord[0]<cols, transform_coord[1]>=0, transform_coord[0]>=0), axis=0)

Now, create a zeros image similar to the original image and project all the points onto the new image.

img1 = np.zeros_like(img)
img1[transform_coord[1][indices], transform_coord[0][indices]] = img[orig_coord[1][indices], orig_coord[0][indices]]

1 2	img1 = np.zeros_like(img) img1[transform_coord[1][indices], transform_coord[0][indices]] = img[orig_coord[1][indices], orig_coord[0][indices]]

Display the final image.

out = cv2.hconcat([img,img1])
cv2.imshow('a1',out)
cv2.waitKey(0)

out = cv2.hconcat([img,img1])

cv2.imshow('a1',out)

cv2.waitKey(0)

The full code can be found below

import numpy as np
import cv2

# Read an image
img = cv2.imread('D:/downloads/opencv_logo.PNG')
rows,cols,_ = img.shape

# Create the transformation matrix
M = np.float32([[1,0,100],[0,1,50]])
# get the coordinates in the form of (0,0),(0,1)...
# the shape is (2, rows*cols)
orig_coord = np.indices((cols, rows)).reshape(2,-1)
# stack the rows of 1 to form [x,y,1]
orig_coord_f = np.vstack((orig_coord, np.ones(rows*cols)))
transform_coord = np.dot(M, orig_coord_f)
# Change into int type
transform_coord = transform_coord.astype(np.int)
# Keep only the coordinates that fall within the image boundary.
indices = np.all((transform_coord[1]<rows, transform_coord[0]<cols, transform_coord[1]>=0, transform_coord[0]>=0), axis=0)
# Create a zeros image and project the points
img1 = np.zeros_like(img)
img1[transform_coord[1][indices], transform_coord[0][indices]] = img[orig_coord[1][indices], orig_coord[0][indices]]
# Display the image
out = cv2.hconcat([img,img1])
cv2.imshow('a2',out)
cv2.waitKey(0)

import numpy as np

import cv2

# Read an image

img = cv2.imread('D:/downloads/opencv_logo.PNG')

rows,cols,_ = img.shape

# Create the transformation matrix

M = np.float32([[1,0,100],[0,1,50]])

# get the coordinates in the form of (0,0),(0,1)...

# the shape is (2, rows*cols)

orig_coord = np.indices((cols, rows)).reshape(2,-1)

# stack the rows of 1 to form [x,y,1]

orig_coord_f = np.vstack((orig_coord, np.ones(rows*cols)))

transform_coord = np.dot(M, orig_coord_f)

# Change into int type

transform_coord = transform_coord.astype(np.int)

# Keep only the coordinates that fall within the image boundary.

indices = np.all((transform_coord[1]<rows, transform_coord[0]<cols, transform_coord[1]>=0, transform_coord[0]>=0), axis=0)

# Create a zeros image and project the points

img1 = np.zeros_like(img)

img1[transform_coord[1][indices], transform_coord[0][indices]] = img[orig_coord[1][indices], orig_coord[0][indices]]

# Display the image

out = cv2.hconcat([img,img1])

cv2.imshow('a2',out)

cv2.waitKey(0)

Below is the output. Here, left image represents the original image while the right one is the translated image.

OpenCV-Python

Now, let’s discuss how to translate images using OpenCV-Python.

OpenCV provides a function cv2.warpAffine() that applies an affine transformation to an image. You just need to provide the transformation matrix (M). The basic syntax for the function is given below.


dst = cv.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]	)

# src: input image
# M: Transformation matrix
# dsize: size of the output image
# flags: interpolation method to be used

dst = cv.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]] )

# src: input image

# M: Transformation matrix

# dsize: size of the output image

# flags: interpolation method to be used

Below is a sample code where the image is translated by (100, 50).

import numpy as np
import cv2

# Read an image
img = cv2.imread('D:/downloads/opencv_logo.PNG')
rows,cols,_ = img.shape

# Create the transformation matrix
M = np.float32([[1,0,100],[0,1,50]])

# Pass it to warpAffine function
dst = cv2.warpAffine(img,M,(cols,rows))

# Display the concatenated image
out = cv2.hconcat([img, dst])
cv2.imshow('img',out)
cv2.waitKey(0)

import numpy as np

import cv2

# Read an image

img = cv2.imread('D:/downloads/opencv_logo.PNG')

rows,cols,_ = img.shape

# Create the transformation matrix

M = np.float32([[1,0,100],[0,1,50]])

# Pass it to warpAffine function

dst = cv2.warpAffine(img,M,(cols,rows))

# Display the concatenated image

out = cv2.hconcat([img, dst])

cv2.imshow('img',out)

cv2.waitKey(0)

Below is the output. Here, left image represents the original image while the right one is the translated image.

Compare the outputs of both implementations. That’s all for image translation. In the next blog, we will discuss another geometric transformation known as rotation in detail. Hope you enjoy reading.

If you have any doubts/suggestions please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.