resize_train_test_split.R

# This script is used to resize images from 64x64 to 28x28 pixels
# Clear workspace
rm(list=ls())
# Load EBImage library
require(EBImage)
# Load data
# Setting the current working directory to be the folder containing the files
setwd("E:/Datasets/TrafficLight/MXNet Training")

X <- read.csv("miniset_X.csv", header = F)
labels <- read.csv("miniset_y.csv", header = F)


# Dataframe of resized images
rs_df <- data.frame()
rs_df_track <- data.frame() # a tracking variable to track the serial of image
img_serial <- 1:200 # give every picture a serial number

# Main loop: for each image, resize and set it to greyscale
for(i in 1:nrow(X))
{
  # Try-catch
  result <- tryCatch({
    # Image (as 1d vector)
    img <- as.numeric(X[i,])
    # Reshape as a 64x64 image (EBImage object)
    img <- Image(img, dim=c(64, 64), colormode = "Grayscale")
    # Resize image to 28x28 pixels
    img_resized <- resize(img, w = 28, h = 28)
    # Get image matrix (there should be another function to do this faster and more neatly!)
    img_matrix <- img_resized@.Data
    # Coerce to a vector
    img_vector <- as.vector(t(img_matrix))
    # Add label
    label <- labels[i,]
    vec <- c(label, img_vector)
    track_label <- c(label,img_serial[i]) # creating a tracking variable for the image number
    # Stack in rs_df using rbind
    rs_df <- rbind(rs_df, vec)
    rs_df_track <- rbind(rs_df_track, track_label)
    # Print status
    print(paste("Done",i,sep = " "))},
    # Error function (just prints the error). Btw you should get no errors!
    error = function(e){print(e)})
}


# Set names. The first columns are the labels, the other columns are the pixels.
names(rs_df) <- c("label", paste("pixel", c(1:784))) # this name row could mess up with the training later, so delete it!
#names(rs_df_track) <- c("label","serial")

# Train-test split
#-------------------------------------------------------------------------------
# Simple train-test split. No crossvalidation is done in this tutorial.

# Set seed for reproducibility purposes
set.seed(100)

# Shuffled df
shuffled <- rs_df[sample(1:200),]

set.seed(100)

shuffled_label <- rs_df_track[sample(1:200),]

# Train-test split
train_28 <- shuffled[1:160, ]
train_28_track <- shuffled_label[1:160, ]
test_28 <- shuffled[161:200, ]
test_28_track <- shuffled_label[161:200, ]

# Save train-test datasets
write.csv(train_28, "E:/Datasets/TrafficLight/MXNet Training/train_28.csv", row.names = FALSE)
write.csv(test_28, "E:/Datasets/TrafficLight/MXNet Training/test_28.csv", row.names = FALSE)
# Save tracking variables
write.csv(train_28_track, "E:/Datasets/TrafficLight/MXNet Training/train_28_track.csv", row.names = FALSE)
write.csv(test_28_track, "E:/Datasets/TrafficLight/MXNet Training/test_28_track.csv", row.names = FALSE)

# Done!
print("Done!")