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https://github.com/maju116/platypus
R package for object detection and image segmentation.
https://github.com/maju116/platypus
Last synced: 3 months ago
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R package for object detection and image segmentation.
- Host: GitHub
- URL: https://github.com/maju116/platypus
- Owner: maju116
- License: mit
- Created: 2019-10-03T17:17:06.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-06T10:45:10.000Z (almost 3 years ago)
- Last Synced: 2024-05-17T14:31:52.813Z (6 months ago)
- Language: R
- Homepage:
- Size: 15 MB
- Stars: 128
- Watchers: 10
- Forks: 9
- Open Issues: 31
-
Metadata Files:
- Readme: README.Rmd
- Changelog: CHANGELOG.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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- jimsghstars - maju116/platypus - R package for object detection and image segmentation. (R)
README
---
output:
md_document:
variant: markdown_github
params:
repo_name: platypus
repo_url: https://github.com/maju116/platypus
chagelog_url: https://github.com/maju116/platypus/blob/master/CHANGELOG.md
code_of_coduct: https://github.com/maju116/platypus/blob/develop/CODE_OF_CONDUCT.md
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# `r params$repo_name`
[](https://codecov.io/gh/maju116/platypus)
**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`:
```{r, eval=FALSE}
remotes::install_github("maju116/platypus")
```
(`master` branch contains the stable version. Use `develop` branch for latest features)
To install [previous versions](`r params$changelog_url`) you can run:
```{r, eval=FALSE}
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:
```{r, message = FALSE}
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
```
You can now load [YOLOv3 Darknet](https://pjreddie.com/darknet/yolo/) weights trained on [COCO dataset](https://cocodataset.org/#home). Download pre-trained weights from [here](https://pjreddie.com/media/files/yolov3.weights) and run:
```{r}
test_yolo %>% load_darknet_weights("development/yolov3.weights")
```
Calculate predictions for new images:
```{r}
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)
```
Transform raw predictions into bounding boxes:
```{r}
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
```
Plot / save images:
```{r}
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](https://www.kaggle.com/surajiiitm/bccd-dataset?)
Generate custom anchor boxes:
```{r}
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
)
blood_anchors
```
Build `YOLOv3` model and compile it with correct loss and metric:
```{r}
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:
```{r}
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
)
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
)
```
Fit the model:
```{r, eval = FALSE}
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:
```{r, message = FALSE}
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
)
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](https://github.com/maju116/platypus/blob/master/examples/Blood%20Cell%20Detection/Blood-Cell-Detection.md)
U-Net image segmentation with custom dataset:
---------------
Build `U-Net` model and compile it with correct loss and metric:
```{r, message = FALSE, warning = FALSE}
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:
```{r}
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
)
```
Fit the model:
```{r, eval = FALSE}
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:
```{r, message = FALSE}
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")
)
test_preds <- predict_generator(DCB2018_u_net, test_DCB2018_generator, 3)
test_masks <- get_masks(test_preds, binary_colormap)
```
Plot / save images with masks:
```{r, warning = FALSE}
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](https://github.com/maju116/platypus/blob/master/examples/2018%20Data%20Science%20Bowl/2018-Data-Science-Bowl.md)