def getMipMaps(mesh):
    
    mipmaps = {}
    for effect in mesh.effects:
        for prop in effect.supported:
            propval = getattr(effect, prop)
            if isinstance(propval, collada.material.Map):
                image_name = propval.sampler.surface.image.path
                image_data = propval.sampler.surface.image.data

                try:
                    im = Image.open(StringIO(image_data))
                    im.load()
                except __HOLE__:
                    from panda3d.core import Texture
                    from panda3d.core import StringStream
                    from panda3d.core import PNMImage
                    
                    #PIL failed, so lets try DDS reader with panda3d
                    t = Texture(image_name)
                    success = t.readDds(StringStream(image_data))
                    if success == 0:
                        raise FilterException("Failed to read image file %s" % image_name)
        
                    #convert DDS to PNG
                    outdata = t.getRamImageAs('RGBA').getData()
                    try:
                        im = Image.fromstring('RGBA', (t.getXSize(), t.getYSize()), outdata)
                        im.load()
                    except IOError:
                        raise FilterException("Failed to read image file %s" % image_name)
                    
                #Keep JPG in same format since JPG->PNG is pretty bad
                if im.format == 'JPEG':
                    output_format = 'JPEG'
                    output_extension = 'jpg'
                    output_options = {'quality': 95, 'optimize':True}
                else:
                    output_format = 'PNG'
                    output_extension = 'png'
                    output_options = {'optimize':True}
                    
                #store a copy to the original image so we can resize from it directly each time
                orig_im = im
                
                width, height = im.size
                
                #round down to power of 2
                width = int(math.pow(2, int(math.log(width, 2))))
                height = int(math.pow(2, int(math.log(height, 2))))

                pil_images = []

                while True:
                    im = orig_im.resize((width, height), Image.ANTIALIAS)
                    pil_images.insert(0, im)
                    if width == 1 and height == 1:
                        break
                    width = max(width / 2, 1)
                    height = max(height / 2, 1)

                tar_buf = StringIO()
                tar = tarfile.TarFile(fileobj=tar_buf, mode='w')
              
                cur_offset = 0
                byte_ranges = []
                for i, pil_img in enumerate(pil_images):
                    buf = StringIO()
                    pil_img.save(buf, output_format, **output_options)
                    file_len = buf.tell()
                    cur_name = '%dx%d.%s' % (pil_img.size[0], pil_img.size[1], output_extension)
                    tar_info = tarfile.TarInfo(name=cur_name)
                    tar_info.size=file_len
                    buf.seek(0)
                    tar.addfile(tarinfo=tar_info, fileobj=buf)
                    
                    #tar files have a 512 byte header
                    cur_offset += 512
                    file_start = cur_offset
                    
                    byte_ranges.append({'offset':file_start,
                                        'length':file_len,
                                        'width':pil_img.size[0],
                                        'height':pil_img.size[1]})
                    
                    #file lengths are rounded up to nearest 512 multiple
                    file_len = 512 * ((file_len + 512 - 1) / 512)
                    cur_offset += file_len
                
                tar.close()
                
                mipmaps[propval.sampler.surface.image.path] = (tar_buf.getvalue(), byte_ranges)
    return mipmaps
dataset/ETHPy150Open pycollada/meshtool/meshtool/filters/optimize_filters/save_mipmaps.py/getMipMaps