def PIL2Ipl(input):
"""Converts a PIL image to the OpenCV/IPL CvMat data format.
Supported input image formats are:
RGB
L
F
"""
if not (isinstance(input, PIL.Image.Image)):
raise TypeError, 'Must be called with PIL.Image.Image!'
# mode dictionary:
# (pil_mode : (ipl_depth, ipl_channels)
mode_list = {
"RGB": (cv.IPL_DEPTH_8U, 3),
"L": (cv.IPL_DEPTH_8U, 1),
"F": (cv.IPL_DEPTH_32F, 1)
}
if not mode_list.has_key(input.mode):
raise ValueError, 'unknown or unsupported input mode'
result = cv.cvCreateImage(
cv.cvSize(input.size[0], input.size[1]), # size
mode_list[input.mode][0], # depth
mode_list[input.mode][1] # channels
)
# set imageData
result.imageData = input.tostring()
return result
def PIL2Ipl(input):
"""Converts a PIL image to the OpenCV/IPL CvMat data format.
Supported input image formats are:
RGB
L
F
"""
if not (isinstance(input, PIL.Image.Image)):
raise TypeError, 'Must be called with PIL.Image.Image!'
# mode dictionary:
# (pil_mode : (ipl_depth, ipl_channels)
mode_list = {
"RGB" : (cv.IPL_DEPTH_8U, 3),
"L" : (cv.IPL_DEPTH_8U, 1),
"F" : (cv.IPL_DEPTH_32F, 1)
}
if not mode_list.has_key(input.mode):
raise ValueError, 'unknown or unsupported input mode'
result = cv.cvCreateImage(
cv.cvSize(input.size[0], input.size[1]), # size
mode_list[input.mode][0], # depth
mode_list[input.mode][1] # channels
)
# set imageData
result.imageData = input.tostring()
return result
def ellipse(self, dx, dy, width, height, color = (0, 255, 0), border = -1, line = 8):
"""Draw a ellipse on image"""
rotation = 0
start_angle = 0
stop_angle = 360
shift = 0
cv.Ellipse(self.frame,
cv.cvPoint (dx, dy),
cv.cvSize(width, height),
rotation, start_angle, stop_angle, color, border, line, shift)
def NumPy2Ipl(input):
"""Converts a numpy array to the OpenCV/IPL CvMat data format.
Supported input array layouts:
2 dimensions of numpy.uint8
3 dimensions of numpy.uint8
2 dimensions of numpy.float32
2 dimensions of numpy.float64
"""
if not isinstance(input, numpy.ndarray):
raise TypeError, 'Must be called with numpy.ndarray!'
# Check the number of dimensions of the input array
ndim = input.ndim
if not ndim in (2, 3):
raise ValueError, 'Only 2D-arrays and 3D-arrays are supported!'
# Get the number of channels
if ndim == 2:
channels = 1
else:
channels = input.shape[2]
# Get the image depth
if input.dtype == numpy.uint8:
depth = cv.IPL_DEPTH_8U
elif input.dtype == numpy.float32:
depth = cv.IPL_DEPTH_32F
elif input.dtype == numpy.float64:
depth = cv.IPL_DEPTH_64F
# supported modes list: [(channels, dtype), ...]
modes_list = [(1, numpy.uint8), (3, numpy.uint8), (1, numpy.float32),
(1, numpy.float64)]
# Check if the input array layout is supported
if not (channels, input.dtype) in modes_list:
raise ValueError, 'Unknown or unsupported input mode'
result = cv.cvCreateImage(
cv.cvSize(input.shape[1], input.shape[0]), # size
depth, # depth
channels # channels
)
# set imageData
result.imageData = input.tostring()
return result
def NumPy2Ipl(input):
"""Converts a numpy array to the OpenCV/IPL CvMat data format.
Supported input array layouts:
2 dimensions of numpy.uint8
3 dimensions of numpy.uint8
2 dimensions of numpy.float32
2 dimensions of numpy.float64
"""
if not isinstance(input, numpy.ndarray):
raise TypeError, 'Must be called with numpy.ndarray!'
# Check the number of dimensions of the input array
ndim = input.ndim
if not ndim in (2, 3):
raise ValueError, 'Only 2D-arrays and 3D-arrays are supported!'
# Get the number of channels
if ndim == 2:
channels = 1
else:
channels = input.shape[2]
# Get the image depth
if input.dtype == numpy.uint8:
depth = cv.IPL_DEPTH_8U
elif input.dtype == numpy.float32:
depth = cv.IPL_DEPTH_32F
elif input.dtype == numpy.float64:
depth = cv.IPL_DEPTH_64F
# supported modes list: [(channels, dtype), ...]
modes_list = [(1, numpy.uint8), (3, numpy.uint8), (1, numpy.float32), (1, numpy.float64)]
# Check if the input array layout is supported
if not (channels, input.dtype) in modes_list:
raise ValueError, 'Unknown or unsupported input mode'
result = cv.cvCreateImage(
cv.cvSize(input.shape[1], input.shape[0]), # size
depth, # depth
channels # channels
)
# set imageData
result.imageData = input.tostring()
return result
def Ipl2NumPy(input):
"""Converts an OpenCV image to a numpy array.
Supported input image formats are
IPL_DEPTH_8U x 1 channel
IPL_DEPTH_8U x 3 channels
IPL_DEPTH_32F x 1 channel
IPL_DEPTH_32F x 2 channels
IPL_DEPTH_32S x 1 channel
IPL_DEPTH_64F x 1 channel
IPL_DEPTH_64F x 2 channels
"""
import ipdb
ipdb.set_trace()
if not isinstance(input, cv.CvMat):
raise TypeError, 'must be called with a cv.CvMat!'
# data type dictionary:
# (channels, depth) : numpy dtype
ipl2dtype = {
(1, cv.IPL_DEPTH_8U): numpy.uint8,
(3, cv.IPL_DEPTH_8U): numpy.uint8,
(1, cv.IPL_DEPTH_32F): numpy.float32,
(2, cv.IPL_DEPTH_32F): numpy.float32,
(1, cv.IPL_DEPTH_32S): numpy.int32,
(1, cv.IPL_DEPTH_64F): numpy.float64,
(2, cv.IPL_DEPTH_64F): numpy.float64
}
key = (input.nChannels, input.depth)
if not ipl2dtype.has_key(key):
raise ValueError, 'unknown or unsupported input mode'
# Get the numpy array and reshape it correctly
# ATTENTION: flipped dimensions width/height on 2007-11-15
if input.nChannels == 1:
array_1d = numpy.fromstring(input.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width))
elif input.nChannels == 2:
array_1d = numpy.fromstring(input.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width, 2))
elif input.nChannels == 3:
# Change the order of channels from BGR to RGB
rgb = cv.cvCreateImage(cv.cvSize(input.width, input.height),
input.depth, 3)
cv.cvCvtColor(input, rgb, cv.CV_BGR2RGB)
array_1d = numpy.fromstring(rgb.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width, 3))
def Ipl2NumPy(input):
"""Converts an OpenCV image to a numpy array.
Supported input image formats are
IPL_DEPTH_8U x 1 channel
IPL_DEPTH_8U x 3 channels
IPL_DEPTH_32F x 1 channel
IPL_DEPTH_32F x 2 channels
IPL_DEPTH_32S x 1 channel
IPL_DEPTH_64F x 1 channel
IPL_DEPTH_64F x 2 channels
"""
import ipdb
ipdb.set_trace()
if not isinstance(input, cv.CvMat):
raise TypeError, "must be called with a cv.CvMat!"
# data type dictionary:
# (channels, depth) : numpy dtype
ipl2dtype = {
(1, cv.IPL_DEPTH_8U): numpy.uint8,
(3, cv.IPL_DEPTH_8U): numpy.uint8,
(1, cv.IPL_DEPTH_32F): numpy.float32,
(2, cv.IPL_DEPTH_32F): numpy.float32,
(1, cv.IPL_DEPTH_32S): numpy.int32,
(1, cv.IPL_DEPTH_64F): numpy.float64,
(2, cv.IPL_DEPTH_64F): numpy.float64,
}
key = (input.nChannels, input.depth)
if not ipl2dtype.has_key(key):
raise ValueError, "unknown or unsupported input mode"
# Get the numpy array and reshape it correctly
# ATTENTION: flipped dimensions width/height on 2007-11-15
if input.nChannels == 1:
array_1d = numpy.fromstring(input.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width))
elif input.nChannels == 2:
array_1d = numpy.fromstring(input.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width, 2))
elif input.nChannels == 3:
# Change the order of channels from BGR to RGB
rgb = cv.cvCreateImage(cv.cvSize(input.width, input.height), input.depth, 3)
cv.cvCvtColor(input, rgb, cv.CV_BGR2RGB)
array_1d = numpy.fromstring(rgb.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width, 3))
def test05_Ipl2NumPy(self):
"""Test the adaptors.Ipl2NumPy function."""
a = adaptors.Ipl2NumPy(self.ipl_image)
a_1d = numpy.reshape(a, (a.size, ))
# For 3-channel IPL images the order of channels will be BGR
# but NumPy array order of channels will be RGB so a conversion
# is needed before we can compare both images
if self.ipl_image.nChannels == 3:
rgb = cv.cvCreateImage(cv.cvSize(self.ipl_image.width, self.ipl_image.height), self.ipl_image.depth, 3)
cv.cvCvtColor(self.ipl_image, rgb, cv.CV_BGR2RGB)
self.assert_(a_1d.tostring() == rgb.imageData,
'The returned image has not been properly constructed.')
else:
self.assert_(a_1d.tostring() == self.ipl_image.imageData,
'The returned image has not been properly constructed.')
def ellipse(self,
dx,
dy,
width,
height,
color=(0, 255, 0),
border=-1,
line=8):
"""Draw a ellipse on image"""
rotation = 0
start_angle = 0
stop_angle = 360
shift = 0
cv.Ellipse(self.frame, cv.cvPoint(dx, dy), cv.cvSize(width,
height), rotation,
start_angle, stop_angle, color, border, line, shift)
def Ipl2NumPy(input):
"""Converts an OpenCV/IPL image to a numpy array.
Supported input image formats are
IPL_DEPTH_8U x 1 channel
IPL_DEPTH_8U x 3 channels
IPL_DEPTH_32F x 1 channel
IPL_DEPTH_64F x 1 channel
"""
if not isinstance(input, cv.CvMat):
raise TypeError, 'must be called with a cv.CvMat!'
# assert that the channels are interleaved
if input.dataOrder != 0:
raise ValueError, 'dataOrder must be 0 (interleaved)!'
# data type dictionary:
# (channels, depth) : numpy dtype
ipl2dtype = {
(1, cv.IPL_DEPTH_8U) : numpy.uint8,
(3, cv.IPL_DEPTH_8U) : numpy.uint8,
(1, cv.IPL_DEPTH_32F) : numpy.float32,
(1, cv.IPL_DEPTH_64F) : numpy.float64
}
key = (input.nChannels, input.depth)
if not ipl2dtype.has_key(key):
raise ValueError, 'unknown or unsupported input mode'
# Get the numpy array and reshape it correctly
if input.nChannels == 1:
array_1d = numpy.fromstring(input.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width))
elif input.nChannels == 3:
# Change the order of channels from BGR to RGB
rgb = cv.cvCreateImage(cv.cvSize(input.width, input.height), input.depth, 3)
cv.cvCvtColor(input, rgb, cv.CV_BGR2RGB)
array_1d = numpy.fromstring(rgb.imageData, dtype=ipl2dtype[key])
return numpy.reshape(array_1d, (input.height, input.width, 3))
def logPolar(im,center=None,radius=None,M=None,size=(64,128)):
'''
Produce a log polar transform of the image. See OpenCV for details.
The scale space is calculated based on radius or M. If both are given
M takes priority.
'''
#M=1.0
w,h = im.size
if radius == None:
radius = 0.5*min(w,h)
if M == None:
#rho=M*log(sqrt(x2+y2))
#size[0] = M*log(r)
M = size[0]/np.log(radius)
if center == None:
center = pv.Point(0.5*w,0.5*h)
src = im.asOpenCV()
dst = cv.cvCreateImage( cv.cvSize(size[0],size[1]), 8, 3 );
cv.cvLogPolar( src, dst, center.asOpenCV(), M, cv.CV_INTER_LINEAR+cv.CV_WARP_FILL_OUTLIERS )
return pv.Image(dst)
def initialize_video(self):
webcam_frame = cv.QueryFrame( self.capture )
if not webcam_frame:
print "Frame acquisition failed."
return False
self.webcam_pixbuf = gtk.gdk.pixbuf_new_from_data(
webcam_frame.imageData,
gtk.gdk.COLORSPACE_RGB,
False,
8,
webcam_frame.width,
webcam_frame.height,
webcam_frame.widthStep)
self.video_image.set_from_pixbuf(self.webcam_pixbuf)
self.display_frame = cv.cvCreateImage( cv.cvSize(webcam_frame.width, webcam_frame.height), cv.IPL_DEPTH_8U, 3)
return True