def read_rgbd_data(self, input_queue):
# original input size
width_original = 480
height_original = 640
# input size
width = 224
height = 224
value_rgb = tf.read_file(input_queue[0])
value_depth = tf.read_file(input_queue[1])
# Decoder
png_rgb = tf.image.decode_png(value_rgb, channels=3)
tf.image_summary('image', png_rgb)
png_depth = tf.image.decode_png(value_depth, channels=1)
# Reshape
png_rgb = tf.reshape(png_rgb, [width_original, height_original, 3])
png_depth = tf.reshape(png_depth, [width_original, height_original, 1])
# Resize
png_rgb = tf.image.resize_images(png_rgb, width, height)
png_depth = tf.image.resize_images(png_depth, width, height)
# Normalize depth
png_depth = png_depth * 255.0 / tf.reduce_max(png_depth)
image = tf.concat(2, (png_rgb, png_depth))
twist = tf.reshape(input_queue[2], [1, 1, 6])
return tf.cast(image, tf.float32), tf.cast(twist, tf.float32)
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