Illustration of vision transforms
This example illustrates the various transforms available in the mindspore.dataset.vision module.
Preparation
[1]:
from download import download
import matplotlib.pyplot as plt
from PIL import Image
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
# Download opensource datasets
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/flamingos.jpg"
download(url, './flamingos.jpg', replace=True)
orig_img = Image.open('flamingos.jpg')
# Env set for randomness and prepare plot function
ds.config.set_seed(66)
def plot(imgs, first_origin=True, **kwargs):
num_rows = 1
num_cols = len(imgs) + first_origin
_, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
if first_origin:
imgs = [orig_img] + imgs
for idx, img in enumerate(imgs):
ax = axs[0, idx]
ax.imshow(img, **kwargs)
ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
if first_origin:
axs[0, 0].set(title='Original image')
axs[0, 0].title.set_size(8)
plt.tight_layout()
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/flamingos.jpg (45 kB)
file_sizes: 100%|███████████████████████████| 45.8k/45.8k [00:00<00:00, 639kB/s]
Successfully downloaded file to ./flamingos.jpg
Geometric Transforms
Geometric image transformation refers to the process of altering the geometric properties of an image, such as its shape, size, orientation, or position. It involves applying mathematical operations to the image pixels or coordinates to achieve the desired transformation.
Pad
The mindspore.dataset.vision.Pad transform pads the borders of image with some pixels.
[2]:
padded_imgs = [vision.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
plot(padded_imgs)
Resize
The mindspore.dataset.vision.Resize transform resizes an image to a given size.
[3]:
resized_imgs = [vision.Resize(size=size)(orig_img) for size in (30, 50, 100)]
plot(resized_imgs)
CenterCrop
The mindspore.dataset.vision.CenterCrop transform crop the image at the center with given size.
[4]:
center_crops = [vision.CenterCrop(size=size)(orig_img) for size in (30, 50, 100)]
plot(center_crops)
FiveCrop
The mindspore.dataset.vision.FiveCrop transform crops the given image into one central crop and four corners.
[5]:
(top_left, top_right, bottom_left, bottom_right, center) = vision.FiveCrop(size=(100, 100))(orig_img)
plot([top_left, top_right, bottom_left, bottom_right, center], True)
RandomPerspective
The mindspore.dataset.vision.RandomPerspective transform performs random perspective transform on an image.
[6]:
perspective_transformer = vision.RandomPerspective(distortion_scale=0.6, prob=1.0)
perspective_imgs = [perspective_transformer(orig_img) for _ in range(4)]
plot(perspective_imgs)
RandomRotation
The mindspore.dataset.vision.RandomRotation transform rotates an image with random angle.
[7]:
rotater = vision.RandomRotation(degrees=(0, 180))
rotated_imgs = [rotater(orig_img) for _ in range(4)]
plot(rotated_imgs)
RandomAffine
The mindspore.dataset.vision.RandomAffine transform performs random affine transform on an image.
[8]:
affine_transformer = vision.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), scale=(0.5, 0.75))
affine_imgs = [affine_transformer(orig_img) for _ in range(4)]
plot(affine_imgs)
RandomCrop
The mindspore.dataset.vision.RandomCrop transform crops an image at a random location.
[9]:
cropper = vision.RandomCrop(size=(128, 128))
crops = [cropper(orig_img) for _ in range(4)]
plot(crops)
RandomResizedCrop
The mindspore.dataset.vision.RandomResizedCrop transform crops an image at a random location, and then resizes the crop to a given size.
[10]:
resize_cropper = vision.RandomResizedCrop(size=(32, 32))
resized_crops = [resize_cropper(orig_img) for _ in range(4)]
plot(resized_crops)
Photometric Transforms
Photometric image transformation refers to the process of modifying the photometric properties of an image, such as its brightness, contrast, color, or tone. These transformations are applied to change the visual appearance of an image while preserving its geometric structure.
Grayscale
The mindspore.dataset.vision.Grayscale transform converts an image to grayscale.
[11]:
gray_img = vision.Grayscale()(orig_img)
plot([gray_img], cmap='gray')
RandomColorAdjust
The mindspore.dataset.vision.RandomColorAdjust transform randomly changes the brightness, contrast, saturation and hue of the input image.
[12]:
jitter = vision.RandomColorAdjust(brightness=.5, hue=.3)
jitted_imgs = [jitter(orig_img) for _ in range(4)]
plot(jitted_imgs)
GaussianBlur
The mindspore.dataset.vision.GaussianBlur transform performs gaussian blur transform on an image.
[13]:
blurrer = vision.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5))
blurred_imgs = [blurrer(orig_img) for _ in range(4)]
plot(blurred_imgs)
RandomInvert
The mindspore.dataset.vision.RandomInvert transform randomly inverts the colors of the given image.
[14]:
inverter = vision.RandomInvert()
invertered_imgs = [inverter(orig_img) for _ in range(4)]
plot(invertered_imgs)
RandomPosterize
The mindspore.dataset.vision.RandomPosterize transform randomly reduces the bit depth of the color channels of image to create a high contrast and vivid color image.
[15]:
posterizer = vision.RandomPosterize(bits=2)
posterized_imgs = [posterizer(orig_img) for _ in range(4)]
plot(posterized_imgs)
RandomSolarize
The mindspore.dataset.vision.RandomSolarize transform randomly solarizes the image by inverting pixel values within specified threshold.
[16]:
solarizer = vision.RandomSolarize(threshold=(0, 192))
solarized_imgs = [solarizer(orig_img) for _ in range(4)]
plot(solarized_imgs)
RandomAdjustSharpness
The mindspore.dataset.vision.RandomAdjustSharpness transform randomly adjusts the sharpness of the given image.
[17]:
sharpness_adjuster = vision.RandomAdjustSharpness(degree=2)
sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
plot(sharpened_imgs)
RandomAutoContrast
The mindspore.dataset.vision.RandomAutoContrast transform randomly applies autocontrast to the given image.
[18]:
autocontraster = vision.RandomAutoContrast()
autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
plot(autocontrasted_imgs)
RandomEqualize
The mindspore.dataset.vision.RandomEqualize transform randomly equalizes the histogram of the given image.
[19]:
equalizer = vision.RandomEqualize()
equalized_imgs = [equalizer(orig_img) for _ in range(4)]
plot(equalized_imgs)
Augmentation Transforms
The following transforms are combinations of multiple transforms, they usually come from papers.
AutoAugment
The mindspore.dataset.vision.AutoAugment transform applies AutoAugment method based on AutoAugment: Learning Augmentation Strategies from Data.
[20]:
augmenter = vision.AutoAugment(policy=vision.AutoAugmentPolicy.IMAGENET)
imgs = [augmenter(orig_img) for _ in range(4)]
plot(imgs)
RandAugment
The mindspore.dataset.vision.RandAugment applies RandAugment method based on RandAugment: Learning Augmentation Strategies from Data.
[21]:
augmenter = vision.RandAugment()
imgs = [augmenter(orig_img) for _ in range(4)]
plot(imgs)
TrivialAugmentWide
The mindspore.dataset.vision.TrivialAugmentWide applies TrivialAugmentWide method based on TrivialAugmentWide: Tuning-free Yet State-of-the-Art Data Augmentation.
[22]:
augmenter = vision.TrivialAugmentWide()
imgs = [augmenter(orig_img) for _ in range(4)]
plot(imgs)
Randomly-applied transforms
Some transforms are randomly-applied with a probability. That is, the transformed image may be the same as the original one.
RandomHorizontalFlip
The mindspore.dataset.vision.RandomHorizontalFlip transform performs horizontal flip of an image, with a given probability.
[23]:
hflipper = vision.RandomHorizontalFlip(0.5)
transformed_imgs = [hflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)
RandomVerticalFlip
The mindspore.dataset.vision.RandomVerticalFlip transform performs vertical flip of an image, with a given probability.
[24]:
vflipper = vision.RandomVerticalFlip(0.5)
transformed_imgs = [vflipper(orig_img) for _ in range(4)]
plot(transformed_imgs)
RandomApply
The mindspore.dataset.transforms.RandomApply transform randomly applies a list of transforms, with a given probability.
[25]:
import mindspore.dataset.transforms as T
applier = T.RandomApply(transforms=[vision.RandomCrop(size=(64, 64))], prob=0.5)
transformed_imgs = [applier(orig_img) for _ in range(4)]
plot(transformed_imgs)
Process Image File In Dataset Pipeline
Use the mindspore.dataset.ImageFolderDataset to read image content into dataset pipeline and then we can do further transforms based on pipeline.
[26]:
from download import download
import os
import mindspore.dataset as ds
# Download a small imagenet as example
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/imageset.zip"
download(url, "./", kind="zip", replace=True)
# There are 5 classes in the image folder.
os.listdir("./imageset")
# Load these 5 classes into dataset pipeline
dataset = ds.ImageFolderDataset("./imageset", shuffle=False)
# check the column names inside the dataset. "image" column represents the image content and "label" column represents the corresponding label of image.
print("column names:", dataset.get_col_names())
# since the original image is not decoded, apply decode first on "image" column
dataset = dataset.map(vision.Decode(), input_columns=["image"])
# check results
print(">>>>> after decode")
for data, label in dataset:
print(data.shape, label)
# let's do some transforms on dataset
# apply resize on images
dataset = dataset.map(vision.Resize(size=(48, 48)), input_columns=["image"])
# check results
print(">>>>> after resize")
images = []
for image, label in dataset:
images.append(image.asnumpy())
print(image.shape, label)
plot(images[:5], first_origin=False)
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/imageset.zip (45 kB)
file_sizes: 100%|███████████████████████████| 45.7k/45.7k [00:00<00:00, 996kB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
column names: ['image', 'label']
>>>>> after decode
(64, 64, 3) 0
(64, 64, 3) 0
(64, 64, 3) 0
(64, 64, 3) 1
(64, 64, 3) 1
(64, 64, 3) 1
(64, 64, 3) 1
(64, 64, 3) 2
(64, 64, 3) 2
(64, 64, 3) 2
(64, 64, 3) 3
(64, 64, 3) 3
(64, 64, 3) 3
(64, 64, 3) 4
(64, 64, 3) 4
(64, 64, 3) 4
>>>>> after resize
(48, 48, 3) 0
(48, 48, 3) 0
(48, 48, 3) 0
(48, 48, 3) 1
(48, 48, 3) 1
(48, 48, 3) 1
(48, 48, 3) 1
(48, 48, 3) 2
(48, 48, 3) 2
(48, 48, 3) 2
(48, 48, 3) 3
(48, 48, 3) 3
(48, 48, 3) 3
(48, 48, 3) 4
(48, 48, 3) 4
(48, 48, 3) 4
