It is convenient to invert the colors, since we are going to integrate out values and darker pixels are closer to value zero. Moreover, the each image should be transformed to gray scale.
In Ubuntu:
for img in $(find . -iname '*.tiff'); do echo -n "Converting $img"; convert -colorspace GRAY $img $img && echo ' [Done]'; done
for img in $(find . -iname '*.tiff'); do echo -n "Inverting $img"; convert -negate $img $img && echo ' [Done]'; done
for img in $(find . -iname '*.tiff'); do echo -n "Cropping $img"; convert -crop 1645x4243+160+160 $img $img && echo ' [Done]'; done
library(solefinder) library(tidyverse) image(lena, col = grey.colors(128)) image(t(lena[512:1, ]), col = grey.colors(128)) resize_image(lena, 1/4) ## summary(c(lena)) expand.grid(x = 1:512, y = 512:1) %>% mutate(pixel = c(t(lena))) %>% ggplot(aes(x, y)) + geom_point(aes(color = pixel)) + scale_color_gradient(low = "black", high = "white") expand.grid(x = 1:512, y = 512:1) %>% mutate(pixel = c(t(lena))) %>% ggplot(aes(x, y)) + geom_raster(aes(fill = pixel)) + scale_fill_gradient(low = "black", high = "white")
if (!require(EBImage)) { source("http://bioconductor.org/biocLite.R") biocLite("EBImage") library("EBImage") } library(solefinder) microbenchmark::microbenchmark( EPImage = display(t(lena), method = "raster"), Base = image(t(lena)[, 512:1]), times = 10)
```{R, eval = FALSE}
library(tiff) library(EBImage) library(tidyverse)
img_dir <- c("/home/guillermo/Desktop/tmp/") img_dir <- c("/home/guillermo/Desktop/Shoe_data_everspry/")
img_filenames <- list.files(img_dir, pattern = ".tiff") img_adr <- paste0(img_dir, img_filenames)
boot1 <- readTIFF(img_adr[1]) boot2 <- readTIFF(img_adr[2])
display(t(boot1), method = "raster") display(t(boot2), method = "raster")
boot1 <- img_adr[1] %>% readTIFF() %>% .[-c(1:160, nrow(.) - 1:160 + 1), -c(1:160, ncol(.) - 1:160 + 1)]
boot1[boot1 < 0.3] <- 0
boot1 %>% display(method = "raster") boot1 %>% flip() %>% display(method = "raster") boot1 %>% flop() %>% display(method = "raster") boot1 %>% rotate(30) %>% display(method = "raster") boot1 %>% rotate(135) %>% display(method = "raster") boot1 %>% EBImage::transpose() %>% display(method = "raster")
save(boot1, file="boot1.rda") save(boot2, file="boot2.rda")
```r ## Crop images ## Check if EBImage package is installed, if not, it will install it. if (!require(EBImage)) { source("http://bioconductor.org/biocLite.R") biocLite("EBImage") library("EBImage") } ## Load other packages library(dplyr) library(tiff) ## Setting images directory, reading image names and writing image ## adresses. img_dir <- c("/home/guillermo/Desktop/shoes_copy/") ## Get filenames and create image addresses img_filenames <- list.files(img_dir, pattern = ".tiff") img_adr <- paste0(img_dir, img_filenames) ## Show the first image files head(img_filenames) ## Read sample shoe shoe <- readImage(img_adr[20]) display(shoe, method = "raster") ## ## Change to gray scale ## shoe %>% channel(mode = "gray") %>% display(method = "raster", all = TRUE) ## Crop ruler shoe <- shoe %>% .[-c(1:160, nrow(.) - 1:160 + 1), -c(1:160, ncol(.) - 1:160 + 1)] display(shoe, method = "raster") ## Remove some noise threshold <- 0.8 shoe[shoe > threshold] <- threshold shoe <- shoe %>% {(. - min(.))/(max(.) - min(.))} display(shoe, method = "raster") crop_and_reduce_noise <- function (img_adress) { ## Read sample shoe shoe <- readImage(img_adress) ## ## Change to gray scale ## shoe %>% channel(mode = "gray") %>% display(method = "raster", all = TRUE) ## Crop ruler shoe <- shoe %>% .[-c(1:160, nrow(.) - 1:160 + 1), -c(1:160, ncol(.) - 1:160 + 1)] ## Remove some noise threshold <- 0.8 shoe[shoe > threshold] <- threshold shoe <- shoe %>% {(. - min(.))/(max(.) - min(.))} write }
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