ComputerVision/rMxNet.R at master · TheScientistBr/ComputerVision · GitHub

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require(mxnet)
require(imager)


im <- load.image("delermando4.jpg")

preproc.image <- function(im, mean.image) {
        # crop the image
        shape <- dim(im)
        short.edge <- min(shape[1:2])
        xx <- floor((shape[1] - short.edge) / 2)
        yy <- floor((shape[2] - short.edge) / 2)
        cropped <- crop.borders(im, xx, yy)
        # resize to 224 x 224, needed by input of the model.
        resized <- resize(cropped, 224, 224)
        # convert to array (x, y, channel)
        arr <- as.array(resized) * 255
        dim(arr) <- c(224, 224, 3)
        # subtract the mean
        normed <- arr - mean.img
        # Reshape to format needed by mxnet (width, height, channel, num)
        dim(normed) <- c(224, 224, 3, 1)
        return(normed)
}

model = mx.model.load("Inception/Inception_BN", iteration=39)
mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
normed <- preproc.image(im, mean.img)

prob <- predict(model, X=im)
dim(prob)
max.idx <- max.col(t(prob))
max.idx
synsets <- readLines("Inception/synset.txt")
print(paste0("Predicted Top-class: ", synsets  [[max.idx]]))

im <- load.image("caneca.jpg")
normed <- preproc.image(im, mean.img)

prob <- predict(model, X=normed)
dim(prob)
max.idx <- max.col(t(prob))
max.idx
synsets <- readLines("Inception/synset.txt")
print(paste0("Predicted Top-class: ", synsets  [[max.idx]]))

im <- load.image("chave.jpg")
normed <- preproc.image(im, mean.img)

prob <- predict(model, X=normed)
max.idx <- max.col(t(prob))
synsets <- readLines("Inception/synset.txt")
print(paste0("Predicted Top-class: ", synsets  [[max.idx]]))