Experiments/plot2.R

In duncantl/Rtesseract: Interface to the tesseract OCR system

setMethod("plot", "TesseractBaseAPI",
          function(x, y, level = "word", ...) {
              plot.OCR(x, level = level, ...)
          })


plot.OCR =
function(api, level = "word",
         ri = GetIterator(api),
         filename = GetInputName(api),
         img = readImage(filename),
         bbox = BoundingBoxes(ri, level),
         border = "red",
         outer.border = "green",
         cropToBoxes = FALSE, margin = .05,
         ...)
{
    if(!is.matrix(bbox))
       m = do.call(rbind, bbox)
    else
       m = bbox

if(cropToBoxes) {
    # The intent here is to crop the image so that we don't show it all but limit
    # it to a range that only includes the bounding boxes we observe.

    if(length(margin) == 1)
       margin = c(1 - margin, 1 + margin)
browser()
      # put the y's going from bottom to top, unlike their originals which are top to bottom.


    orig = dim(img)
    m[,2] = orig[1] - m[,2]
    m[,4] = orig[1] - m[,4]            

    mx = c(min(m[,1]), max(m[,3]))*margin
    my = c(min(m[,2]), max(m[,4]))*margin
    

    
    img = img[ sort(orig[1] - seq(as.integer(my[1]), as.integer(my[2]))), seq(as.integer(mx[1]), as.integer(mx[2])), ]

    
    r = nrow(img)
    c = ncol(img)

    plot(0, type = "n", xlab = "", ylab = "", xlim = mx, ylim = my, ..., xaxs = "i", yaxs = "i")

    
    if(!is.null(img) && !is.na(img))
       rasterImage(img, mx[1], my[1], mx[2], my[2])



     # Draw the bounding boxes for the detected elements.
    r = orig[1] # - my[2]
#    rect(m[,1], r - m[,2], m[,3], r - m[,4], border = border)
#    rect(m[,1], r - m[,2], m[,3], r - m[,4], border = border)
    rect(m[,1],  m[,2], m[,3],  m[,4], border = border)
#    rect(m[,1], m[,2], m[,3], m[,4], border = border)        
     # And now the outer containing rectangle enclosing all the bounding boxes
#XXX wrong.    
    rect(min(m[,1]),  min(m[,2]), max(m[,3]),  max(m[,4]), border = outer.border)    
    
} else {  # show whole image
    r = nrow(img)
    c = ncol(img)
    
    plot(0, type = "n", xlab = "", ylab = "", xlim = c(0, c), ylim = c(0, r), ...)
    
    if(!is.null(img) && !is.na(img))
       rasterImage(img, 0, 0, c, r)
     # Draw the bounding boxes for the detected elements.
    rect(m[,1], r - m[,2], m[,3], r - m[,4], border = border)
     # And now the outer containing rectangle enclosing all the bounding boxes
    rect(min(m[,1]), r - min(m[,2]), max(m[,3]), r - max(m[,4]), border = outer.border)
}

    NULL
}


plot.ConfusionMatrix =
function(x, y, col = rgb(seq(1, 0, length = max(x)), 1, 1), xlab = "Actual", ylab = "Predicted", ...)
{
  image(x, col = col, axes = FALSE, ..., xlab = xlab, ylab = ylab)
  box()
  u = par()$usr
  d = (u[2] - u[1])/nrow(x)
  axis(1, seq(u[1] + d/2, by = d, length = nrow(x)), rownames(x))

  d = (u[4] - u[3])/ncol(x)
  axis(2, seq(u[3] + d/2, by = d, length = ncol(x)), colnames(x))  
}

plotSubImage = # plot.BoundingBox =
function(box, img, ...)
{
  pos = box
  k = img[ pos[2]:pos[4],  pos[1]:pos[3], ]
  plot(0, type = "n", xlim = c(0, ncol(k)), ylim = c(0, nrow(k)), ...)
  rasterImage(k, 0, 0, ncol(k), nrow(k))
}