Ejemplo n.º 1
0
    def initTexture(self):
        data = np.load(self.texture_file)
        data = rescale_data_to_image(data)
        w, h = data.shape

        # generate a texture id, make it current
        self.texture = gl.glGenTextures(1)
        gl.glBindTexture(gl.GL_TEXTURE_2D, self.texture)

        # texture mode and parameters controlling wrapping and scaling
        gl.glTexEnvf(gl.GL_TEXTURE_ENV, gl.GL_TEXTURE_ENV_MODE, gl.GL_MODULATE)
        gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_REPEAT)
        gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_REPEAT)
        gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
        gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)

        # map the image data to the texture. note that if the input
        # type is GL_FLOAT, the values must be in the range [0..1]
        gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGB, w, h, 0,
                        gl.GL_LUMINANCE, gl.GL_UNSIGNED_BYTE, data)
Ejemplo n.º 2
0

__author__ = 'mavinm'
__date__ = '3/21/14'

if __name__ == "__main__":
    filenames = {
        "tmp/bc_data_gaussian_sigma=0.001.bin": "report/bc_gaussian-0.001.png",
        "tmp/bc_data_gaussian_sigma=0.002.bin": "report/bc_gaussian-0.002.png",
        "tmp/bc_data_gaussian_sigma=0.003.bin": "report/bc_gaussian-0.003.png",
        "tmp/bc_data_gaussian_sigma=0.004.bin": "report/bc_gaussian-0.004.png",

        "tmp/gc_data_gaussian_sigma=0.001.bin": "report/gc_gaussian-0.001.png",
        "tmp/gc_data_gaussian_sigma=0.002.bin": "report/gc_gaussian-0.002.png",
        "tmp/gc_data_gaussian_sigma=0.003.bin": "report/gc_gaussian-0.003.png",
        "tmp/gc_data_gaussian_sigma=0.004.bin": "report/gc_gaussian-0.004.png",

        "tmp/mea_lea_data_gaussian_sigma=0.001.bin": "report/mea_lea_gaussian-0.001.png",
        "tmp/mea_lea_data_gaussian_sigma=0.002.bin": "report/mea_lea_gaussian-0.002.png",
        "tmp/mea_lea_data_gaussian_sigma=0.003.bin": "report/mea_lea_gaussian-0.003.png",
        "tmp/mea_lea_data_gaussian_sigma=0.004.bin": "report/mea_lea_gaussian-0.004.png",
    }

    for fn, out in filenames.iteritems():
        data = np.load(fn)
        sigma = fn.split("=")[1].rstrip(".bin")
        print sigma
        rescaled = rescale_data_to_image(data)
        im = Image.fromarray(rescaled)
        im.save(out)