Augmenting Data
In most case for AI projects, there are not enough data to train a model. So that’s when Data Augmentation comes into play! You can create new data from what you already have and use that to train the model as well.
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Implementation
First import all requirements
import cv2
import albumentations as A
import numpy as np
from matplotlib import pyplot as plt
Load your image and convert the image to RGB format
image = cv2.imread("PATH/TO/brita.jpg")
# openCV uses BGR format but albumentation uses RGB format
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
I’m going to use pascal_VOC, so the parameters I have to set for the bounding box of the image will be like the code below.
You can get the coordinates by using LabelImg.(I’ll write a tutorial on that later on)
# pascal_voc(x_min,y_min,x_max,y_max)
bboxes = [[470, 126, 786, 610]]
Right down all the Augmentation methods, you want to apply to the image.
transform = A.Compose([
A.Resize(width=1920, height=1080),
A.RandomCrop(width=1280, height=720),
A.Rotate(limit=50, p=0.9), # p=0.9->90% chance of doing this process
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.2),
A.VerticalFlip(p=0.1),
A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.4),
A.OneOf([
A.Blur(blur_limit=3, p=0.5),
A.ColorJitter(p=0.5),
], p=1.0)#Oneof: Only activate either one
],
bbox_params=A.BboxParams(format="pascal_voc", min_area=2048, label_fields=[]))
# min_area: show at least 2048 pixels of the bounding box
Augment Data
images_list = [image] #List to store created images
saved_bboxes = [bboxes[0]] #list for bbox coordinates
image = np.array(image) #Turn image to a numpy array
# Iterate 16 times
for i in range(16):
augmentations = transform(
image=image, bboxes=bboxes)
augmented_img = augmentations["image"]
images_list.append(augmented_img)
saved_bboxes.append(augmentations["bboxes"][0])
Create Function to display the results
def plot_examples(images, bboxes=None):
fig = plt.figure(figsize=(15, 15))
columns = 4
rows = 5
for i in range(1, len(images)):
if bboxes is not None:
img = visualize_bbox(
images[i - 1], bboxes[i - 1], class_name="Brita")
else:
img = images[i-1]
fig.add_subplot(rows, columns, i)
plt.imshow(img)
plt.show()
Display Bounding boxes
plot_examples(images_list, saved_bboxes)
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