How to eliminate row of black pixels from OpenCV WarpAffine rotation

问题

I am rotating an 3 images 180 degrees with cv2.warpAffine() and then horizontally concatenating them with cv2.hconcat(). This is adding a 1 pixel wide column of black between the images but the width of the image from img.shape is correct. If I do not rotate them the image looks good with no black columns. All 3 images are 1920 wide x 1200 high.

How can I eliminate the black column? It is similar to - warpAffine

It is not happening with Scipy. The commented out code (ndimage.rotate()) is how I solved it with Scipy - from here here. The Scipy code is slower and I have thousands of images.

EDIT

After a minute I am now using numpy just to rotate the matrix 90 degrees twice. From numpy.rot90() This seems even faster. It is also in the commented code below. For non-90 degree angles, I'll stick with the warpAffine from opencv.

import cv2
import numpy as np
from scipy import ndimage


def rotate_image(mat, angle):     
    """   Rotates an image (angle in degrees) and expands image to avoid cropping
    """
    height, width = mat.shape[:2] # image shape has 3 dimensions
    image_center = (width/2, height/2) # getRotationMatrix2D needs coordinates in reverse order (width, height) compared to shape

    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0000)

    # rotation calculates the cos and sin, taking absolutes of those.
    abs_cos = abs(rotation_mat[0,0]) 
    abs_sin = abs(rotation_mat[0,1])

    # find the new width and height bounds
    bound_w = int(height * abs_sin + width * abs_cos)
 
    bound_h = int(height * abs_cos + width * abs_sin)
    
   
    # find the new width and height bounds
    bound_w = int(height * abs_sin + width * abs_cos)    
    bound_h = int(height * abs_cos + width * abs_sin)
    print(f'Bounds w = {bound_w} Bound H = {bound_h}')
    # subtract old image center (bringing image back to original) and adding the new image center coordinates
    rotation_mat[0, 2] += bound_w/2 - image_center[0]
    rotation_mat[1, 2] += bound_h/2 - image_center[1]
  

    # rotate image with the new bounds and translated rotation matrix
    rotated_mat = cv2.warpAffine(mat, rotation_mat, (bound_w, bound_h))
    return rotated_mat

left_img = cv2.imread(r"F:\Basler\1595525164.242553_l.tiff",0)
cent_img = cv2.imread(r"F:\Basler\1595525164.242553_c.tiff",0)
rigt_img = cv2.imread(r"F:\Basler\1595525164.242553_r.tiff",0)
print(f'Shape = {rigt_img.shape} is {len(rigt_img.shape)}')

angle = 180


left_rot = rotate_image(left_img, angle)
cent_rot = rotate_image(cent_img, angle)
rigt_rot = rotate_image(cent_img, angle)
'''
left_rot = ndimage.rotate(left_img, angle)
cent_rot = ndimage.rotate(cent_img, angle)
rigt_rot = ndimage.rotate(rigt_img, angle)

THIS SEEMS THE FASTEST
left_rot = np.rot90(left_img,2)
cent_rot = np.rot90(cent_img,2)
rigt_rot = np.rot90(rigt_img,2)
'''
#lane_img = np.concatenate((left_rot, cent_rot, rigt_rot), axis=1)
lane_img = cv2.hconcat([left_rot, cent_rot, rigt_rot])
print(f'Size = {lane_img.shape}')
cv2.imwrite(r'C:\Users\Cary\Desktop\Junk\lane1.tiff', lane_img)

回答1:

The line can be removed by adding one additional line each side of image prior to rotation using copyMakeBorder:

after_mat = cv2.copyMakeBorder(
        mat,
        top=1,
        bottom=1,
        left=1,
        right=1,
        borderType=cv2.BORDER_REFLECT
    )

# rotate image with the new bounds and translated rotation matrix
rotated_mat = cv2.warpAffine(after_mat, rotation_mat, (bound_w, bound_h))

I don't know the cause of the additional line (maybe a shift due to rotation?), but code above can suppress it, hopefully without side effects.

来源：https://stackoverflow.com/questions/63235237/how-to-eliminate-row-of-black-pixels-from-opencv-warpaffine-rotation

标签

python-3.x

OpenCV

image-processing