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import polyglot
import sys
import os

# avoid using 'os.path'
working_dir_parts = __file__.split(os.sep)[:-1]
sys.path.insert(0, os.sep.join(working_dir_parts[:-1]))

from image_magix import Image

load_jpeg = polyglot.eval(string="""function(file.name) {
    jimg <- read.csv(gzfile(file.name))
    return (jimg)
}""", language="R")

print("stage 1")

working_dir_parts.append("img.csv.gz")
raw_data = load_jpeg(os.sep.join(working_dir_parts))

# the dimensions are R attributes; define function to access them
getDim = polyglot.eval(string="function(v, pos) dim(v)[[pos]]", language="R")
getDataRowMajor = polyglot.eval(string="function(v) as.vector(t(v))", language="R")

print("stage 2")

# Create object of Python class 'Image' with loaded JPEG data
image = Image(getDim(raw_data, 2), getDim(raw_data, 1), getDataRowMajor(raw_data))

# Run Sobel filter
print("applying Sobel filter")
result = image.sobel()
print("-- finished")

# Run fisheye filter
print("applying fisheye filter")
result = image.fisheye()
print("-- finished")

print("stage 3")

draw = polyglot.eval(string="""function(processedImgObj) {
    require(grDevices)
    require(grid)
    mx <- matrix(processedImgObj$`@data`/255, nrow=processedImgObj$`@height`, ncol=processedImgObj$`@width`)
    cat("rows:", nrow(mx), "\n")
    grid.newpage()
    grid.raster(mx, height=unit(nrow(mx),"points"))
    cat("DONE\n")
}""", language="R")

draw(result)

polyglot.eval(string="dev.off()", language="R")
