[Python] Using OpenCV with Python (Image transformation)

resize

cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]])

｜Method	interpolation
Nearest Neighbor	cv2.INTER_NEAREST
Bilinear	cv2.INTER_LINEAR
Bicubic	cv2.INTER_CUBIC

In [51]: rszNN = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_NEAREST)
    ...: rszBL = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_LINEAR)
    ...: rszBC = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_CUBIC)

import numpy as np
import cv2
import matplotlib.pyplot as plt

I = cv2.imread('./data/SIDBA/Lenna.bmp')

rszNN = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_NEAREST)
rszBL = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_LINEAR)
rszBC = cv2.resize(I, (I.shape[1]*2, I.shape[0]*2), interpolation=cv2.INTER_CUBIC)

sz  = np.array([I.shape[0],I.shape[1]])
csz = np.array([32,32])
tlpos = (sz - csz)//2
brpos = tlpos + csz

croppedNN = rszNN[tlpos[0]:brpos[0],tlpos[1]:brpos[1],:]
croppedBL = rszBL[tlpos[0]:brpos[0],tlpos[1]:brpos[1],:]
croppedBC = rszBC[tlpos[0]:brpos[0],tlpos[1]:brpos[1],:]

fig, axes = plt.subplots(ncols=3)
axes[0].imshow(croppedNN)
axes[0].set_title('nearest')
axes[0].set(adjustable='box-forced',aspect='equal')
axes[1].imshow(croppedBL)
axes[1].set_title('bilinear')
axes[1].set(adjustable='box-forced',aspect='equal')
axes[2].imshow(croppedBC)
axes[2].set_title('bicubic')
axes[2].set(adjustable='box-forced',aspect='equal')
fig.show()

rotate

If you want to rotate the center of the image to the origin, use getRotationMatrix2D and warpAffine. However, it is easier to use rotate of scipy described later.

cv2.getRotationMatrix2D(center, angle, scale)

cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]])


import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage

I = cv2.imread('./data/SIDBA/Lenna.bmp')

rIntr = 15
rs = 0
re = 360

Ir = []

for r in range(rs, re+1, rIntr):
    center = (I.shape[1]*0.5,I.shape[0]*0.5)
    rotMat = cv2.getRotationMatrix2D(center, r, 1.0)    
    Irot = cv2.warpAffine(I, rotMat, (I.shape[1],I.shape[0]), flags=cv2.INTER_LINEAR)
    Ir.append(Irot)

cols = 4
rows = int(np.ceil(len(Ir) / float(cols)))

fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(3*cols,3*rows))

for idx, I in enumerate(Ir):
    r = idx // cols
    c = idx % cols
    
    title = 'rotate=%d' % (rIntr*idx)
    
    axes[r,c].imshow(cv2.cvtColor(I, cv2.COLOR_BGR2RGB))
    axes[r,c].set_title(title)
    axes[r,c].set(adjustable='box-forced',aspect='equal')
    axes[r,c].get_xaxis().set_visible(False)
    axes[r,c].get_yaxis().set_visible(False)    

for i in range(idx+1, rows*cols):
    r = i // cols
    c = i % cols
    fig.delaxes(axes[r,c])

fig.show()

If the image is rectangular

scipy rotate is easy to do with scipy

scipy.ndimage.interpolation.rotate(input, angle, axes=(1, 0), reshape=True, output=None, order=3, mode='constant', cval=0.0, prefilter=True)

import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage

I = cv2.imread('./data/SIDBA/Lenna.bmp')

rIntr = 15
rs = 0
re = 360

Ir = []

for r in range(rs, re+1, rIntr):
    Irot = ndimage.rotate(I, r, reshape=False)
    Ir.append(Irot)

cols = 4
rows = int(np.ceil(len(Ir) / float(cols)))

fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(3*cols,3*rows))

for idx, I in enumerate(Ir):
    r = idx // cols
    c = idx % cols
    
    title = 'rotate=%d' % (rIntr*idx)
    
    axes[r,c].imshow(cv2.cvtColor(I, cv2.COLOR_BGR2RGB))
    axes[r,c].set_title(title)
    axes[r,c].set(adjustable='box-forced',aspect='equal')
    axes[r,c].get_xaxis().set_visible(False)
    axes[r,c].get_yaxis().set_visible(False)    

for i in range(idx+1, rows*cols):
    r = i // cols
    c = i % cols
    fig.delaxes(axes[r,c])

fig.show()

flip

cv2.flip(src, flipCode[, dst])

I don't know which flipCode is vertical or horizontal

flipCode = 0 ... vertical flipCode = 1 ... horizontal

You may use numpy's fliplr and flipud described later. lr means left, right, ud means up, down.


import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage

I = cv2.imread('./data/SIDBA/Lenna.bmp')
Iv = cv2.flip(I, 0)
Ih = cv2.flip(I, 1)


fig, axes = plt.subplots(ncols=3, figsize=(15,10))

axes[0].imshow(cv2.cvtColor(I, cv2.COLOR_BGR2RGB))
axes[0].set_title('original')
axes[0].set(adjustable='box-forced',aspect='equal')
axes[0].get_xaxis().set_visible(False)
axes[0].get_yaxis().set_visible(False)

axes[1].imshow(cv2.cvtColor(Iv, cv2.COLOR_BGR2RGB))
axes[1].set_title('flip vertical')
axes[1].set(adjustable='box-forced',aspect='equal')
axes[1].get_xaxis().set_visible(False)
axes[1].get_yaxis().set_visible(False)

axes[2].imshow(cv2.cvtColor(Ih, cv2.COLOR_BGR2RGB))
axes[2].set_title('flip horizontal')
axes[2].set(adjustable='box-forced',aspect='equal')
axes[2].get_xaxis().set_visible(False)
axes[2].get_yaxis().set_visible(False)

fig.show()

numpy

numpy.fliplr(m) Horizontal flip numpy.flipud(m) Vertical flip


import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage

I = cv2.imread('./data/SIDBA/Lenna.bmp')
Iv = np.flipud(I)
Ih = np.fliplr(I)


fig, axes = plt.subplots(ncols=3, figsize=(15,10))

axes[0].imshow(cv2.cvtColor(I, cv2.COLOR_BGR2RGB))
axes[0].set_title('original')
axes[0].set(adjustable='box-forced',aspect='equal')
axes[0].get_xaxis().set_visible(False)
axes[0].get_yaxis().set_visible(False)

axes[1].imshow(cv2.cvtColor(Iv, cv2.COLOR_BGR2RGB))
axes[1].set_title('flip vertical')
axes[1].set(adjustable='box-forced',aspect='equal')
axes[1].get_xaxis().set_visible(False)
axes[1].get_yaxis().set_visible(False)

axes[2].imshow(cv2.cvtColor(Ih, cv2.COLOR_BGR2RGB))
axes[2].set_title('flip horizontal')
axes[2].set(adjustable='box-forced',aspect='equal')
axes[2].get_xaxis().set_visible(False)
axes[2].get_yaxis().set_visible(False)

fig.show()