R/estimate_roi.r

Defines functions estimate_roi

Documented in estimate_roi

#' Estimates a region of interest (ROI) using the
#' approximate horizon location in an image.
#'
#' This method works auto-magically but might
#' fail in some instances. As such there is a
#' plotting and output function to visualize the
#' actual ROI for quality control
#'
#' @param img: RGB image to process (filename or 3-layer RGB stack or brick)
#' @param padding: % of the image width / height to pad
#' @param plot: plot resulting image with all available information
#' @keywords region of interest selection
#' @export
#' @examples
#' # no examples yet

estimate_roi = function(img,
                        padding = 0.1,
                        plot = FALSE){
  
  # verify data formats if not transform
  # to the correct data format
  if (class(img) == "character"){
    img = raster::brick(img)
  }

  # calculate Gcc if image has 3 layers
  #if (nlayers(img)==3){
  #  img = img[[2]] / sum(img)
  #}

  # if possible use the bcc index
  # to find the horizon, distant pixels
  # or sky in general are always more blue
  # than those closer to the camera or containing
  # non vegetative or soil components
  if (nlayers(img) == 3){
    img = img[[3]] / sum(img)
  }
  
  # calculate some basic image statistics to be used
  # in further processing
  img_mid = ncol(img)/2
  img_width = ncol(img)
  img_height = nrow(img)

  # padding by 10% (values in pixels)
  padding_y = round(nrow(img) * padding)
  padding_x = round(ncol(img) * padding)

  # estimate the horizon locations
  horizon_locations = estimate_horizon(img)
  
  # I will split the image in two halfs to estimate the left and right
  # median locations / height of the horizon in the image as to compensate
  # for a slanted horizon (rather common in the images)
  left_horizon_location = median(horizon_locations[1:img_mid],na.rm = TRUE)
  right_horizon_location = median(horizon_locations[img_mid:img_width],na.rm = TRUE)

  # in case of missing values on one half of the image, subsitute
  if ( is.na(left_horizon_location) ){
    left_horizon_location = right_horizon_location
  }
  
  if ( is.na(right_horizon_location) ){
    right_horizon_location = left_horizon_location
  }
  
  if ( is.na(left_horizon_location) & is.na(right_horizon_location) ){
    left_horizon_location = round(img_height / 3)
    right_horizon_location = round(img_height / 3)
  }
  
  # I'm using the padding values and various coordinates to set the final
  # polygon coordinates
  left_x = padding_x
  right_x = ncol(img) - padding_x

  # format the coordinates in x-coordinate and y-coordinate vectors
  # go counter clockwise from the bottom of the image
  x = c(left_x,
        right_x,
        right_x,
        left_x)
  y = c(padding_y,
        padding_y,
        right_horizon_location - padding_y,
        left_horizon_location - padding_y)

  # now convert the raw coordinates into a SpatialPolygons
  # object for easier subsetting or conversion to a raster image
  roi = sp::SpatialPolygons(
    list(sp::Polygons(
      list(sp::Polygon(
        matrix(c(x,y), ncol=2, byrow=FALSE)
        )), "bb")))

  # provide the option of plotting all data for feedback
  # and debugging, and post-processing QA/QC
  if (plot == TRUE){
    if (class(img) == "character" | raster::nlayers(img) == 3){
      plotRGB(img)
    } else {
      plot(img)
    }
    lines(1:ncol(img),
          horizon_locations,
          lwd = 2,
          col='red')
    lines(roi,
          lwd = 2,
          lty = 2,
          col = 'yellow')
  }

  # return data as a SpatialPolygon object
  # can be converted to matrix or vector if needed
  return(list("roi" = roi,
              "horizon" = horizon_locations))
}
khufkens/cropmonitor documentation built on Sept. 2, 2017, 4:19 p.m.