Morphological Transform¶
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import random
import cv2
from matplotlib import pyplot as plt
import albumentations as A
import random import cv2 from matplotlib import pyplot as plt import albumentations as A
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def visualize(image):
plt.figure(figsize=(10, 5))
plt.axis('off')
plt.imshow(image)
def visualize(image): plt.figure(figsize=(10, 5)) plt.axis('off') plt.imshow(image)
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def load_rgb(image_path):
image = cv2.imread(image_path)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def load_rgb(image_path): image = cv2.imread(image_path) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
Load the image from the disk
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img_path = "../images/scan.jpeg"
img = load_rgb(img_path)
img_path = "../images/scan.jpeg" img = load_rgb(img_path)
Visualize the original image¶
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visualize(img)
visualize(img)
Dilation¶
Dilation expands the white (foreground) regions in a binary or grayscale image.
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transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation='dilation')], p=1)
transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation='dilation')], p=1)
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transformed = transform(image=img)
visualize(transformed["image"])
transformed = transform(image=img) visualize(transformed["image"])
Erosion¶
Erosion shrinks the white (foreground) regions in a binary or grayscale image.
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transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation='erosion')], p=1)
transform = A.Compose([A.Morphological(p=1, scale=(2, 3), operation='erosion')], p=1)
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transformed = transform(image=img)
visualize(transformed["image"])
transformed = transform(image=img) visualize(transformed["image"])