You do not want to make heatmaps from the DeSeqDataSet object. Here is an example workflow. Note it uses the pheatmap R library

# make the DeSeqDataSet
dds <- DESeq(dds)

# use the log transform on the data set
rld <- rlog(dds, blind=F)
topVarianceGenes <- head(order(rowVars(assay(rld)), decreasing=T),100)
matrix <- assay(rld)[ topVarGenes ]
matrix <- matrix - rowMeans(matrix)

# select the 'contrast' you want
annotation_data <- as.data.frame(colData(rld)[c("ConditionA","ConditionB")])
pheatmap(matrix, annotation_col=annotation_data)

The above will plot the most varied genes across all conditions. You can substitute the 100 for whichever number you like to control how many you get. If you have alot of different conditions/contrasts and want to make some more specific heatmaps, here is a function which will plot the top 20 genes for the up and down direction (i.e. largest log2FoldChange between the two conditions up and down)

plotUpDownSigGenes <- function(results, colNums, rld, title) {

    # make the lists
    upgenes <- rownames(head(results[ order( results$log2FoldChange ), ], n=20))
    downgenes <- rownames(head(results[ order( -results$log2FoldChange ), ], n=20))

    # this gives us the rows we want
    rows <- match(upgenes, row.names(rld))
    mat <- assay(rld)[rows,colNums]
    mat <- mat - rowMeans(mat)

    # the labels are hard coded at the moment :(
    df <- as.data.frame(colData(rld)[c("labelA","labelB")])
    pheatmap(mat, fontsize=5, annotation_col=df, main=paste(title,"top 20 up genes"))

    # this gives us the rows we want
    rows <- match(downgenes, row.names(rld))
    mat <- assay(rld)[rows,colNums]
    mat <- mat - rowMeans(mat)

    df <- as.data.frame(colData(rld)[c("labelA","labelB")])
    pheatmap(mat, fontsize=5, annotation_col=df, main=paste(title,"top 20 down genes"))
}

Here is an example call for the function:

contrastDEGenes <- subset(results(dds, contrast=c("A","B")), padj < 0.05)

# this part is kind of funky
# the function needs to know which columns
# correspond to the samples (to pull from rld)
aCols <- c(1,2,3)
bCols <- c(4,5,6)

# get the log transforms again
rld <- rlog(dds, blind=F)

# call
plotUpDownSigGenes(
    contrastDEGenes,
    c(aCols, bCols),
    rld,
    "Title for the plot"
)

Hope it helps!