2-poissonCNN.R

# Set image size
width <- 50
height <- 50

extract_feature <- function(dir_path, width, height, labelsExist = T) {
      img_size <- width * height
      
      ## List images in path
      images_names <- list.files(dir_path)
      
      if(labelsExist){
            ## Select only cats or dogs images
            catdog <- str_extract(images_names, "^(cat|dog)")
            # Set cat == 0 and dog == 1
            key <- c("cat" = 0, "dog" = 1)
            y <- key[catdog]
      }
      
      print(paste("Start processing", length(images_names), "images"))
      ## This function will resize an image, turn it into greyscale
      feature_list <- pblapply(images_names, function(imgname) {
            ## Read image
            img <- readImage(file.path(dir_path, imgname))
            ## Resize image
            img_resized <- resize(img, w = width, h = height)
            ## Set to grayscale (normalized to max)
            grayimg <- channel(img_resized, "gray")
            ## Get the image as a matrix
            img_matrix <- grayimg@.Data
            ## Coerce to a vector (row-wise)
            img_vector <- as.vector(t(img_matrix))
            return(img_vector)
      })
      ## bind the list of vector into matrix
      feature_matrix <- do.call(rbind, feature_list)
      feature_matrix <- as.data.frame(feature_matrix)
      ## Set names
      names(feature_matrix) <- paste0("pixel", c(1:img_size))
      
      if(labelsExist){
            return(list(X = feature_matrix, y = y))
      }else{
            return(feature_matrix)
      }
}