VisumotR: Visualizing Motility on Images

# # ##
# # read.csv('../201910003_2B7_10min_LP30_1_5e8701666bcfb_hrm_Spots.txt', sep='\t') %>% as_tibble() %>%
# # rename(track=TRACK_ID, X= POSITION_X, Y=POSITION_Y, Z=POSITION_Z, time=POSITION_T, int=MAX_INTENSITY) %>%
# #   select(track, time, X, Y, Z, int) %>% mutate(X=X/0.160, Y=Y/0.160, Z=Z/0.3) -> df_3D
# # df_3D$track <- as.factor(df_3D$track)
# # 
# # 
# # images <-  list.files(pattern='.tif')
# #  visumot_frame(df_3D%>% filter(time==0) %>% mutate(X=round(X), Y=round(Y)), image='../calibrate.tiff', frame=1, dimensions=3, image_depth=8, projection='max', par.map='track')
# # # image <-  image_read(images)
# # 
# # 
# # 
# # 
# # pos <- df_3D %>% filter(time==0) %>% mutate(X=round(X), Y=round(Y)) %>% select(X,Y) %>% as.list()
# # 
# # array <- array(NA,dim=c(512,512,1))
# # 
# # for (i in c(1:length(pos$Y))){
# # array[pos$Y[i], pos$X[i],1] <- 1
# # }
# # 
# # 
# # image_read(array) %>% image_write('calibrate.tiff', format='tif')
# 
# 
df_calibrate <- tibble(track=as.factor(seq(1,510,10)), time=rep(1,51) ,X=seq(1,510,10), Y=seq(1,510,10))
#
 pos <- df_calibrate %>% select(X,Y) %>% as.list()
#
array <- array(1,dim=c(2240,2240,1))
#
for (i in c(1:length(pos$Y))){
  array[pos$Y[i], pos$X[i],1] <- 1
  }
#
#
image_read(array) %>% image_write('white.png', format='png')
#
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