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Added augmentations #749

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65331e8
Added augmentations
Om-Doiphode Aug 14, 2024
75a7ec1
Added augmentations
Om-Doiphode Aug 14, 2024
5a326e8
Fixed formatting issues
Om-Doiphode Aug 14, 2024
047f29e
Fixed doc issue
Om-Doiphode Aug 14, 2024
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141 changes: 141 additions & 0 deletions deepforest/augmentations.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,141 @@
import albumentations as A
from deepforest import utilities, get_data
import cv2
from albumentations.pytorch import ToTensorV2


def get_augmentations(augment=True):
try:
config_path = get_data("deepforest_config.yml")
except Exception as e:
raise ValueError("No config file provided and deepforest_config.yml "
"not found either in local directory or in installed "
"package location. {}".format(e))

augmentations = []
format_bbox = 'pascal_voc'
label_fields = ['category_ids']
try:
config = utilities.read_config(config_path)
augment_config = config["train" if augment else "validation"]["augmentations"]
except KeyError as e:
raise ValueError("Missing key in the configuration file: {}".format(e))

if not augment_config:
default_augmentations = config["train" if augment else "validation"][
"default_augmentations"]
for augment in default_augmentations:
augment_config_type = augment.get('type')
params = augment.get('params', {})

if augment_config_type.lower() == 'horizontalflip':
augmentations.append(A.HorizontalFlip(**params))

elif augment_config_type.lower() == 'verticalflip':
augmentations.append(A.VerticalFlip(**params))

elif augment_config_type.lower() == 'randomrotate90':
augmentations.append(A.RandomRotate90(**params))

elif augment_config_type.lower() == 'randomcrop':
augmentations.append(A.RandomCrop(**params))

elif augment_config_type.lower() == 'randombrightnesscontrast':
augmentations.append(A.RandomBrightnessContrast(**params))

elif augment_config_type.lower() == 'normalize':
augmentations.append(A.Normalize(**params))

elif augment_config_type.lower() == 'totensorv2':
augmentations.append(ToTensorV2())

elif augment_config_type.lower() == 'bbox':
format_bbox = params['format']
label_fields = params['label_fields']

transform = A.Compose(augmentations,
bbox_params=A.BboxParams(format=format_bbox,
label_fields=label_fields))

return transform

for augment in augment_config:
augment_config_type = augment.get('type')
params = augment.get('params', {})
if augment_config_type.lower() == "randomsizedbboxsafecrop":
try:
augmentations.append(A.RandomSizedBBoxSafeCrop(**params))
except KeyError as e:
raise ValueError(
"Missing parameter for RandomSizedBBoxSafeCrop: {}".format(e))

elif augment_config_type.lower() == "padifneeded":
min_height = params.get('min_height')
pad_height_divisor = params.get('pad_height_divisor', 'None')

try:
min_width = params['min_width']
position = params.get('position', 'top_left')
border_mode = params.get('border_mode', cv2.BORDER_CONSTANT)
value = params.get('value', 0)
mask_value = params.get('mask_value', None)
always_apply = params.get('always_apply', False)
p = params.get('p', 1.0)
except KeyError as e:
raise ValueError("Missing parameter for PadIfNeeded: {}".format(e))

if min_height == 'None' and pad_height_divisor != 'None':
augmentations.append(
A.PadIfNeeded(min_height=None,
min_width=min_width,
pad_height_divisor=pad_height_divisor,
position=position,
border_mode=border_mode,
value=value,
mask_value=mask_value,
always_apply=always_apply,
p=p))
elif min_height != "None" and pad_height_divisor == "None":
augmentations.append(
A.PadIfNeeded(min_height=min_height,
min_width=min_width,
position=position,
border_mode=border_mode,
value=value,
mask_value=mask_value,
always_apply=always_apply,
p=p))

elif augment_config_type.lower() == "totensorv2":
augmentations.append(ToTensorV2())

elif augment_config_type.lower() == 'horizontalflip':
augmentations.append(A.HorizontalFlip(**params))

elif augment_config_type.lower() == 'verticalflip':
augmentations.append(A.VerticalFlip(**params))

elif augment_config_type.lower() == 'randomrotate90':
augmentations.append(A.RandomRotate90(**params))

elif augment_config_type.lower() == 'randomcrop':
augmentations.append(A.RandomCrop(**params))

elif augment_config_type.lower() == 'randombrightnesscontrast':
augmentations.append(A.RandomBrightnessContrast(**params))

elif augment_config_type.lower() == 'normalize':
augmentations.append(A.Normalize(**params))

elif augment_config_type.lower() == 'totensorv2':
augmentations.append(ToTensorV2())

elif augment_config_type.lower() == 'bbox':
format_bbox = params['format']
label_fields = params['label_fields']

transform = A.Compose(augmentations,
bbox_params=A.BboxParams(format=format_bbox,
label_fields=label_fields))

return transform
58 changes: 58 additions & 0 deletions deepforest/data/deepforest_config.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,57 @@ retinanet:
train:
csv_file:
root_dir:
augmentations:
- type: RandomSizedBBoxSafeCrop
params:
height: 300
width: 300
erosion_rate: 0.0
interpolation: 1
always_apply: False
- type: PadIfNeeded
params:
always_apply: False
p: 1.0
min_height: 1024
min_width: 1024
pad_height_divisor: None
pad_width_divisor: None
position: "center" # Store as string and convert in code
border_mode: 0 # cv2.BORDER_CONSTANT as integer
value: 0
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]

default_augmentations:
- type: HorizontalFlip
params:
p: 0.5
- type: VerticalFlip
params:
p: 0.5
- type: RandomBrightnessContrast
params:
brightness_limit: 0.2
contrast_limit: 0.2
p: 0.5
- type: RandomRotate90
params:
p: 0.5
- type: Normalize
params:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
max_pixel_value: 255.0
p: 1.0
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]

# Optimizer initial learning rate
lr: 0.001
Expand Down Expand Up @@ -55,6 +106,13 @@ validation:
# callback args
csv_file:
root_dir:
augmentations:
default_augmentations:
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]
# Intersection over union evaluation
iou_threshold: 0.4
val_accuracy_interval: 20
60 changes: 59 additions & 1 deletion deepforest_config.yml
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -20,11 +20,62 @@ retinanet:
train:
csv_file:
root_dir:
augmentations:
- type: RandomSizedBBoxSafeCrop
params:
height: 300
width: 300
erosion_rate: 0.0
interpolation: 1
always_apply: False
- type: PadIfNeeded
params:
always_apply: False
p: 1.0
min_height: 1024
min_width: 1024
pad_height_divisor: None
pad_width_divisor: None
position: "center" # Store as string and convert in code
border_mode: 0 # cv2.BORDER_CONSTANT as integer
value: 0
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]

default_augmentations:
- type: HorizontalFlip
params:
p: 0.5
- type: VerticalFlip
params:
p: 0.5
- type: RandomBrightnessContrast
params:
brightness_limit: 0.2
contrast_limit: 0.2
p: 0.5
- type: RandomRotate90
params:
p: 0.5
- type: Normalize
params:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
max_pixel_value: 255.0
p: 1.0
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]

# Optimizer initial learning rate
lr: 0.001
scheduler:
type:
type:
params:
# Common parameters
T_max: 10
Expand Down Expand Up @@ -55,6 +106,13 @@ validation:
# callback args
csv_file:
root_dir:
augmentations:
default_augmentations:
- type: ToTensorV2
- type: BBox
params:
format: pascal_voc
label_fields: [category_ids]
# Intersection over union evaluation
iou_threshold: 0.4
val_accuracy_interval: 20
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