[Python] Utilisation d'OpenCV avec Python (transformation d'image)
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
Si vous souhaitez effectuer une rotation autour du centre de l'image, utilisez getRotationMatrix2D et warpAffine. Cependant, il est plus facile d'utiliser la rotation de scipy décrite plus loin.
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()
Si l'image est rectangulaire
scipy la rotation est facile à faire avec scipy
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])
Je ne sais pas quel flipCode est vertical ou horizontal
flipCode = 0 ... vertical flipCode = 1 ... horizontal
Vous pouvez utiliser fliplr et flipud de numpy décrit plus loin. lr signifie gauche, droite, ud signifie haut, bas.
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) Retournement horizontal numpy.flipud(m) Retournement vertical
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()
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