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showcasepytorch classificationpytorch semantic segmentationexample bboxesexample keypointsmigrating from torchvision to albumentationsexampleexample bboxes2example chromatic aberrationexample d4example documentsexample domain adaptationexample gridshuffleexample hfhubexample kaggle saltexample mosaicexample multi targetexample OverlayElementsexample textimageexample weather transformsexample xymaskingreplayserialization
Lib ComparisonFAQAPI Reference
showcasepytorch classificationpytorch semantic segmentationexample bboxesexample keypointsmigrating from torchvision to albumentationsexampleexample bboxes2example chromatic aberrationexample d4example documentsexample domain adaptationexample gridshuffleexample hfhubexample kaggle saltexample mosaicexample multi targetexample OverlayElementsexample textimageexample weather transformsexample xymaskingreplayserialization
Morphological Transform 🔗
import albumentations as A
import cv2
from matplotlib import pyplot as plt
def visualize(image):
plt.figure(figsize=(10, 5))
plt.axis("off")
plt.imshow(image)
Load the image from the disk
img_path = "../images/scan.jpeg"
img = cv2.imread(img_path, cv2.IMREAD_COLOR_RGB)
Visualize the original image 🔗
visualize(img)

Dilation 🔗
Dilation expands the white (foreground) regions in a binary or grayscale image.
transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation="dilation")], p=1, seed=137, strict=True)
transformed = transform(image=img)
visualize(transformed["image"])

Erosion 🔗
Erosion shrinks the white (foreground) regions in a binary or grayscale image.
transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation="erosion")], p=1, seed=137, strict=True)
transformed = transform(image=img)
visualize(transformed["image"])
