def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor( im, im, cv.CV_BGR2RGB )
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
np_im = np.frombuffer(im.imageData, dtype=numpy_type,
count=im.height*im.width*nchannels*(im.depth/8))
return np.reshape(np_im, array_size)
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor(im, im, cv.CV_BGR2RGB)
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
np_im = np.frombuffer(im.imageData,
dtype=numpy_type,
count=im.height * im.width * nchannels *
(im.depth / 8))
return np.reshape(np_im, array_size)
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor( im, im, cv.CV_BGR2RGB )
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
#Removed multiplication of size by (im.depth/8) as numpy takes
#into account of that in numpy_type
np_im = np.frombuffer(im.imageData, dtype=numpy_type,
count=im.height*im.width*nchannels)
return np.reshape(np_im, array_size)
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor(im, im, cv.CV_BGR2RGB)
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
#Removed multiplication of size by (im.depth/8) as numpy takes
#into account of that in numpy_type
np_im = np.frombuffer(im.imageData,
dtype=numpy_type,
count=im.height * im.width * nchannels)
return np.reshape(np_im, array_size)
def np2cv(im):
print 'WARNING: np2cv is not reliable or well tested (it is a bit flakey...)'
#raise AssertionError('np2cv does not work :-(')
if len(im.shape) == 3:
shp = im.shape
channels = shp[2]
height = shp[0]
width = shp[1]
#height, width, channels = im.shape
elif len(im.shape) == 2:
height, width = im.shape
channels = 1
else:
raise AssertionError(
"unrecognized shape for the input image. should be 3 or 2, but was %d."
% len(im.shape))
key = str(im.dtype)
cv_type = np2cv_type_dict[key]
print 'attempt to create opencv image with (key, width, height, channels) =', (
key, width, height, channels)
cv_im = cv.cvCreateImage(cv.cvSize(width, height), cv_type, channels)
#cv_im.imageData = im.tostring()
if True:
if len(im.shape) == 3:
for y in xrange(height):
for x in xrange(width):
pix = [float(v) for v in im[y, x]]
scalar = cv.cvScalar(*pix)
#print scalar
cv_im[y, x] = scalar
else:
for y in xrange(height):
for x in xrange(width):
pix = float(im[y, x])
cv_im[y, x] = cv.cvScalar(pix, pix, pix)
#print 'im[y,x], cv_im[y,x] =', im[y,x], cv_im[y,x]
print 'resulted in an image openCV image with the following properties:'
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(cv_im)]
print '(numpy_type, nchannels, cvmat.width, cvmat.height) =', (
numpy_type, nchannels, cv_im.width, cv_im.height)
return cv_im
def np2cv(im):
print 'WARNING: np2cv is not reliable or well tested (it is a bit flakey...)'
#raise AssertionError('np2cv does not work :-(')
if len(im.shape) == 3:
shp = im.shape
channels = shp[2]
height = shp[0]
width = shp[1]
#height, width, channels = im.shape
elif len(im.shape) == 2:
height, width = im.shape
channels = 1
else:
raise AssertionError("unrecognized shape for the input image. should be 3 or 2, but was %d." % len(im.shape))
key = str(im.dtype)
cv_type = np2cv_type_dict[key]
print 'attempt to create opencv image with (key, width, height, channels) =', (key, width, height, channels)
cv_im = cv.cvCreateImage(cv.cvSize(width, height), cv_type, channels)
#cv_im.imageData = im.tostring()
if True:
if len(im.shape) == 3:
for y in xrange(height):
for x in xrange(width):
pix = [float(v) for v in im[y,x]]
scalar = cv.cvScalar(*pix)
#print scalar
cv_im[y,x] = scalar
else:
for y in xrange(height):
for x in xrange(width):
pix = float(im[y,x])
cv_im[y,x] = cv.cvScalar(pix, pix, pix)
#print 'im[y,x], cv_im[y,x] =', im[y,x], cv_im[y,x]
print 'resulted in an image openCV image with the following properties:'
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(cv_im)]
print '(numpy_type, nchannels, cvmat.width, cvmat.height) =', (numpy_type, nchannels, cv_im.width, cv_im.height)
return cv_im
