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<img src="man/figures/hexsticker_platypus.png" align="right" alt="" width="130" />platypus
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R package for object detection and image segmentation
With platypus it is easy create advanced computer vision models like
YOLOv3 and U-Net in a few lines of code.
How to install?
You can install the latest version of platypus with remotes:
remotes::install_github("maju116/platypus")
(master branch contains the stable version. Use develop branch for
latest features)
To install previous versions you can run:
remotes::install_github("maju116/platypus", ref = "0.1.0")
In order to install platypus you need to install keras and
tensorflow packages and Tensorflow version >= 2.0.0
(Tensorflow 1.x will not be supported!)
YOLOv3 bounding box prediction with pre-trained COCO weights:
To create YOLOv3 architecture use:
library(tidyverse)
library(platypus)
library(abind)
test_yolo <- yolo3(
net_h = 416, # Input image height. Must be divisible by 32
net_w = 416, # Input image width. Must be divisible by 32
grayscale = FALSE, # Should images be loaded as grayscale or RGB
n_class = 80, # Number of object classes (80 for COCO dataset)
anchors = coco_anchors # Anchor boxes
)
test_yolo
#> Model
#> Model: "yolo3"
#> ________________________________________________________________________________
#> Layer (type) Output Shape Param # Connected to
#> ================================================================================
#> input_img (InputLayer) [(None, 416, 416, 0
#> ________________________________________________________________________________
#> darknet53 (Model) multiple 40620640 input_img[0][0]
#> ________________________________________________________________________________
#> yolo3_conv1 (Model) (None, 13, 13, 51 11024384 darknet53[1][2]
#> ________________________________________________________________________________
#> yolo3_conv2 (Model) (None, 26, 26, 25 2957312 yolo3_conv1[1][0]
#> darknet53[1][1]
#> ________________________________________________________________________________
#> yolo3_conv3 (Model) (None, 52, 52, 12 741376 yolo3_conv2[1][0]
#> darknet53[1][0]
#> ________________________________________________________________________________
#> grid1 (Model) (None, 13, 13, 3, 4984063 yolo3_conv1[1][0]
#> ________________________________________________________________________________
#> grid2 (Model) (None, 26, 26, 3, 1312511 yolo3_conv2[1][0]
#> ________________________________________________________________________________
#> grid3 (Model) (None, 52, 52, 3, 361471 yolo3_conv3[1][0]
#> ================================================================================
#> Total params: 62,001,757
#> Trainable params: 61,949,149
#> Non-trainable params: 52,608
#> ________________________________________________________________________________
You can now load YOLOv3 Darknet weights trained on COCO dataset. Download pre-trained weights from here and run:
test_yolo %>% load_darknet_weights("development/yolov3.weights")
Calculate predictions for new images:
test_img_paths <- list.files(system.file("extdata", "images", package = "platypus"), full.names = TRUE, pattern = "coco")
test_imgs <- test_img_paths %>%
map(~ {
image_load(., target_size = c(416, 416), grayscale = FALSE) %>%
image_to_array() %>%
`/`(255)
}) %>%
abind(along = 4) %>%
aperm(c(4, 1:3))
test_preds <- test_yolo %>% predict(test_imgs)
str(test_preds)
#> List of 3
#> $ : num [1:2, 1:13, 1:13, 1:3, 1:85] 0.294 0.478 0.371 1.459 0.421 ...
#> $ : num [1:2, 1:26, 1:26, 1:3, 1:85] -0.214 1.093 -0.092 2.034 -0.286 ...
#> $ : num [1:2, 1:52, 1:52, 1:3, 1:85] 0.242 -0.751 0.638 -2.419 -0.282 ...
Transform raw predictions into bounding boxes:
test_boxes <- get_boxes(
preds = test_preds, # Raw predictions form YOLOv3 model
anchors = coco_anchors, # Anchor boxes
labels = coco_labels, # Class labels
obj_threshold = 0.6, # Object threshold
nms = TRUE, # Should non-max suppression be applied
nms_threshold = 0.6, # Non-max suppression threshold
correct_hw = FALSE # Should height and width of bounding boxes be corrected to image height and width
)
test_boxes
#> [[1]]
#> # A tibble: 8 x 7
#> xmin ymin xmax ymax p_obj label_id label
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
#> 1 0.207 0.718 0.236 0.865 0.951 1 person
#> 2 0.812 0.758 0.846 0.868 0.959 1 person
#> 3 0.349 0.702 0.492 0.884 1.00 3 car
#> 4 0.484 0.543 0.498 0.558 0.837 3 car
#> 5 0.502 0.543 0.515 0.556 0.821 3 car
#> 6 0.439 0.604 0.469 0.643 0.842 3 car
#> 7 0.541 0.554 0.667 0.809 0.999 6 bus
#> 8 0.534 0.570 0.675 0.819 0.954 7 train
#>
#> [[2]]
#> # A tibble: 3 x 7
#> xmin ymin xmax ymax p_obj label_id label
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
#> 1 0.0236 0.0705 0.454 0.909 1.00 23 zebra
#> 2 0.290 0.206 0.729 0.901 0.997 23 zebra
#> 3 0.486 0.407 0.848 0.928 1.00 23 zebra
Plot / save images:
plot_boxes(
images_paths = test_img_paths, # Images paths
boxes = test_boxes, # Bounding boxes
correct_hw = TRUE, # Should height and width of bounding boxes be corrected to image height and width
labels = coco_labels # Class labels
)
YOLOv3 Object detection with custom dataset:
Download images and annotations: BCCD dataset
Generate custom anchor boxes:
library(tidyverse)
library(platypus)
library(abind)
BCCD_path <- "development/BCCD/"
annot_path <- file.path(BCCD_path, "Annotations/")
blood_labels <- c("Platelets", "RBC", "WBC")
n_class <- length(blood_labels)
net_h <- 416 # Must be divisible by 32
net_w <- 416 # Must be divisible by 32
anchors_per_grid <- 3
blood_anchors <- generate_anchors(
anchors_per_grid = anchors_per_grid, # Number of anchors (per one grid) to generate
annot_path = annot_path, # Annotations directory
labels = blood_labels, # Class labels
n_iter = 10, # Number of k-means++ iterations
annot_format = "pascal_voc", # Annotations format
seed = 55, # Random seed
centroid_fun = mean # Centroid function
)
#> label n
#> 1 Platelets 361
#> 2 RBC 4153
#> 3 WBC 372
blood_anchors
#> [[1]]
#> [[1]][[1]]
#> [1] 0.3552235 0.4417515
#>
#> [[1]][[2]]
#> [1] 0.2911290 0.3292675
#>
#> [[1]][[3]]
#> [1] 0.1971296 0.2346442
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [1] 0.1757463 0.1592062
#>
#> [[2]][[2]]
#> [1] 0.1652637 0.2065506
#>
#> [[2]][[3]]
#> [1] 0.1630269 0.2439239
#>
#>
#> [[3]]
#> [[3]][[1]]
#> [1] 0.1391842 0.1769376
#>
#> [[3]][[2]]
#> [1] 0.1245985 0.2258089
#>
#> [[3]][[3]]
#> [1] 0.06237392 0.08062560
Build YOLOv3 model and compile it with correct loss and metric:
blood_yolo <- yolo3(
net_h = net_h, # Input image height
net_w = net_w, # Input image width
grayscale = FALSE, # Should images be loaded as grayscale or RGB
n_class = n_class, # Number of object classes (80 for COCO dataset)
anchors = blood_anchors # Anchor boxes
)
blood_yolo %>% load_darknet_weights("development/yolov3.weights") # Optional
blood_yolo %>% compile(
optimizer = optimizer_adam(lr = 1e-5),
loss = yolo3_loss(blood_anchors, n_class = n_class),
metrics = yolo3_metrics(blood_anchors, n_class = n_class)
)
Create data generators:
train_blood_yolo_generator <- yolo3_generator(
annot_path = file.path(BCCD_path, "train", "Annotations/"),
images_path = file.path(BCCD_path, "train", "JPEGImages/"),
net_h = net_h,
net_w = net_w,
batch_size = 16,
shuffle = FALSE,
labels = blood_labels
)
#> 291 images with corresponding annotations detected!
#> Set 'steps_per_epoch' to: 19
valid_blood_yolo_generator <- yolo3_generator(
annot_path = file.path(BCCD_path, "valid", "Annotations/"),
images_path = file.path(BCCD_path, "valid", "JPEGImages/"),
net_h = net_h,
net_w = net_w,
batch_size = 16,
shuffle = FALSE,
labels = blood_labels
)
#> 69 images with corresponding annotations detected!
#> Set 'steps_per_epoch' to: 5
Fit the model:
blood_yolo %>%
fit_generator(
generator = blood_yolo_generator,
epochs = 1000,
steps_per_epoch = 19,
validation_data = valid_blood_yolo_generator,
validation_steps = 5,
callbacks = list(callback_model_checkpoint("development/BCCD/blood_w.hdf5",
save_best_only = TRUE,
save_weights_only = TRUE)
)
)
Predict on new images:
blood_yolo <- yolo3(
net_h = net_h,
net_w = net_w,
grayscale = FALSE,
n_class = n_class,
anchors = blood_anchors
)
blood_yolo %>% load_model_weights_hdf5("development/BCCD/blood_w.hdf5")
test_blood_yolo_generator <- yolo3_generator(
annot_path = file.path(BCCD_path, "test", "Annotations/"),
images_path = file.path(BCCD_path, "test", "JPEGImages/"),
net_h = net_h,
net_w = net_w,
batch_size = 4,
shuffle = FALSE,
labels = blood_labels
)
#> 4 images with corresponding annotations detected!
#> Set 'steps_per_epoch' to: 1
test_preds <- predict_generator(blood_yolo, test_blood_yolo_generator, 1)
test_boxes <- get_boxes(test_preds, blood_anchors, blood_labels,
obj_threshold = 0.6)
plot_boxes(
images_paths = list.files(file.path(BCCD_path, "test", "JPEGImages/"), full.names = TRUE),
boxes = test_boxes,
labels = blood_labels)
See full example here
U-Net image segmentation with custom dataset:
Build U-Net model and compile it with correct loss and metric:
library(tidyverse)
library(platypus)
library(abind)
train_DCB2018_path <- "development/data-science-bowl-2018/stage1_train"
test_DCB2018_path <- "development/data-science-bowl-2018/stage1_test"
blocks <- 4 # Number of U-Net convolutional blocks
n_class <- 2 # Number of classes
net_h <- 256 # Must be in a form of 2^N
net_w <- 256 # Must be in a form of 2^N
DCB2018_u_net <- u_net(
net_h = net_h,
net_w = net_w,
grayscale = FALSE,
blocks = blocks,
n_class = n_class,
filters = 16,
dropout = 0.1,
batch_normalization = TRUE,
kernel_initializer = "he_normal"
)
DCB2018_u_net %>%
compile(
optimizer = optimizer_adam(lr = 1e-3),
loss = loss_dice(),
metrics = metric_dice_coeff()
)
Create data generator:
train_DCB2018_generator <- segmentation_generator(
path = train_DCB2018_path, # directory with images and masks
mode = "nested_dirs", # Each image with masks in separate folder
colormap = binary_colormap,
only_images = FALSE,
net_h = net_h,
net_w = net_w,
grayscale = FALSE,
scale = 1 / 255,
batch_size = 32,
shuffle = TRUE,
subdirs = c("images", "masks") # Names of subdirs with images and masks
)
#> 670 images with corresponding masks detected!
#> Set 'steps_per_epoch' to: 21
Fit the model:
history <- DCB2018_u_net %>%
fit_generator(
train_DCB2018_generator,
epochs = 20,
steps_per_epoch = 21,
callbacks = list(callback_model_checkpoint(
"development/data-science-bowl-2018/DSB2018_w.hdf5",
save_best_only = TRUE,
save_weights_only = TRUE,
monitor = "dice_coeff",
mode = "max",
verbose = 1)
)
)
Predict on new images:
DCB2018_u_net <- u_net(
net_h = net_h,
net_w = net_w,
grayscale = FALSE,
blocks = blocks,
filters = 16,
dropout = 0.1,
batch_normalization = TRUE,
kernel_initializer = "he_normal"
)
DCB2018_u_net %>% load_model_weights_hdf5("development/data-science-bowl-2018/DSB2018_w.hdf5")
test_DCB2018_generator <- segmentation_generator(
path = test_DCB2018_path,
mode = "nested_dirs",
colormap = binary_colormap,
only_images = TRUE,
net_h = net_h,
net_w = net_w,
grayscale = FALSE,
scale = 1 / 255,
batch_size = 32,
shuffle = FALSE,
subdirs = c("images", "masks")
)
#> 65 images detected!
#> Set 'steps_per_epoch' to: 3
test_preds <- predict_generator(DCB2018_u_net, test_DCB2018_generator, 3)
test_masks <- get_masks(test_preds, binary_colormap)
Plot / save images with masks:
test_imgs_paths <- create_images_masks_paths(test_DCB2018_path, "nested_dirs", FALSE, c("images", "masks"), ";")$images_paths
plot_masks(
images_paths = test_imgs_paths[1:4],
masks = test_masks[1:4],
labels = c("background", "nuclei"),
colormap = binary_colormap
)
See full example here