image.py
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00001 import sys
00002 
00003 from .registry import converts_from_numpy, converts_to_numpy
00004 from sensor_msgs.msg import Image
00005 
00006 import numpy as np
00007 from numpy.lib.stride_tricks import as_strided
00008 
00009 name_to_dtypes = {
00010         "rgb8":    (np.uint8,  3),
00011         "rgba8":   (np.uint8,  4),
00012         "rgb16":   (np.uint16, 3),
00013         "rgba16":  (np.uint16, 4),
00014         "bgr8":    (np.uint8,  3),
00015         "bgra8":   (np.uint8,  4),
00016         "bgr16":   (np.uint16, 3),
00017         "bgra16":  (np.uint16, 4),
00018         "mono8":   (np.uint8,  1),
00019         "mono16":  (np.uint16, 1),
00020         
00021     # for bayer image (based on cv_bridge.cpp)
00022         "bayer_rggb8":  (np.uint8,  1),
00023         "bayer_bggr8":  (np.uint8,  1),
00024         "bayer_gbrg8":  (np.uint8,  1),
00025         "bayer_grbg8":  (np.uint8,  1),
00026         "bayer_rggb16": (np.uint16, 1),
00027         "bayer_bggr16": (np.uint16, 1),
00028         "bayer_gbrg16": (np.uint16, 1),
00029         "bayer_grbg16": (np.uint16, 1),
00030 
00031     # OpenCV CvMat types
00032         "8UC1":    (np.uint8,   1),
00033         "8UC2":    (np.uint8,   2),
00034         "8UC3":    (np.uint8,   3),
00035         "8UC4":    (np.uint8,   4),
00036         "8SC1":    (np.int8,    1),
00037         "8SC2":    (np.int8,    2),
00038         "8SC3":    (np.int8,    3),
00039         "8SC4":    (np.int8,    4),
00040         "16UC1":   (np.int16,   1),
00041         "16UC2":   (np.int16,   2),
00042         "16UC3":   (np.int16,   3),
00043         "16UC4":   (np.int16,   4),
00044         "16SC1":   (np.uint16,  1),
00045         "16SC2":   (np.uint16,  2),
00046         "16SC3":   (np.uint16,  3),
00047         "16SC4":   (np.uint16,  4),
00048         "32SC1":   (np.int32,   1),
00049         "32SC2":   (np.int32,   2),
00050         "32SC3":   (np.int32,   3),
00051         "32SC4":   (np.int32,   4),
00052         "32FC1":   (np.float32, 1),
00053         "32FC2":   (np.float32, 2),
00054         "32FC3":   (np.float32, 3),
00055         "32FC4":   (np.float32, 4),
00056         "64FC1":   (np.float64, 1),
00057         "64FC2":   (np.float64, 2),
00058         "64FC3":   (np.float64, 3),
00059         "64FC4":   (np.float64, 4)
00060 }
00061 
00062 @converts_to_numpy(Image)
00063 def image_to_numpy(msg):
00064         if not msg.encoding in name_to_dtypes:
00065                 raise TypeError('Unrecognized encoding {}'.format(msg.encoding))
00066         
00067         dtype_class, channels = name_to_dtypes[msg.encoding]
00068         dtype = np.dtype(dtype_class)
00069         dtype = dtype.newbyteorder('>' if msg.is_bigendian else '<')
00070         shape = (msg.height, msg.width, channels)
00071 
00072         data = np.fromstring(msg.data, dtype=dtype).reshape(shape)
00073         data.strides = (
00074                 msg.step,
00075                 dtype.itemsize * channels,
00076                 dtype.itemsize
00077         )
00078 
00079         if channels == 1:
00080                 data = data[...,0]
00081         return data
00082 
00083 
00084 @converts_from_numpy(Image)
00085 def numpy_to_image(arr, encoding):
00086         if not encoding in name_to_dtypes:
00087                 raise TypeError('Unrecognized encoding {}'.format(encoding))
00088 
00089         im = Image(encoding=encoding)
00090 
00091         # extract width, height, and channels
00092         dtype_class, exp_channels = name_to_dtypes[encoding]
00093         dtype = np.dtype(dtype_class)
00094         if len(arr.shape) == 2:
00095                 im.height, im.width, channels = arr.shape + (1,)
00096         elif len(arr.shape) == 3:
00097                 im.height, im.width, channels = arr.shape
00098         else:
00099                 raise TypeError("Array must be two or three dimensional")
00100 
00101         # check type and channels
00102         if exp_channels != channels:
00103                 raise TypeError("Array has {} channels, {} requires {}".format(
00104                         channels, encoding, exp_channels
00105                 ))
00106         if dtype_class != arr.dtype.type:
00107                 raise TypeError("Array is {}, {} requires {}".format(
00108                         arr.dtype.type, encoding, dtype_class
00109                 ))
00110 
00111         # make the array contiguous in memory, as mostly required by the format
00112         contig = np.ascontiguousarray(arr)
00113         im.data = contig.tostring()
00114         im.step = contig.strides[0]
00115         im.is_bigendian = (
00116                 arr.dtype.byteorder == '>' or 
00117                 arr.dtype.byteorder == '=' and sys.byteorder == 'big'
00118         )
00119 
00120         return im


ros_numpy
Author(s): Eric Wieser
autogenerated on Tue Mar 28 2017 04:16:38