albumentations | Fast image augmentation library and an easy-to-use wrapper | Computer Vision library
kandi X-RAY | albumentations Summary
kandi X-RAY | albumentations Summary
Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.
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Top functions reviewed by kandi - BETA
- Transform an image using elastic transformation
- Generates a random number
- Generate a random distribution
- Return a function to process image chunks
- Return the parameters dependent on the image
- Order the points according to the x coordinate system
- Expand a transformation matrix
- Draw a normal distribution
- Adjust contrast of the contrast of the image
- Make a table of transformers
- Evaluate a cv2 image
- Return the params dependent on the image
- Get params dependent on target
- Get installed package versions
- Apply elastic transformation to a bounding box
- Handle translate argument
- Returns information about the transforms
- Get parameters dependent on target
- Load a transform from a file
- Generate parameters dependent on target
- Adjusts the size of the pad if needed
- Returns a dictionary of parameters dependent on the image
- Return parameters dependent on target image
- Check for outdated styles
- Update params
- Filter bounding boxes by visibility
albumentations Key Features
albumentations Examples and Code Snippets
Lines of Code : 185License : Non-SPDX (NOASSERTION)
Copyimport tensorflow as tf
from ImageDataAugmentor.image_data_augmentor import *
import albumentations
...
AUGMENTATIONS = albumentations.Compose([
albumentations.Transpose(p=0.5),
albumentations.Flip(p=0.5),
albumentations.OneOf([
License : No LicenseCopypip install git+https://github.com/lukemelas/pytorch-pretrained-gans
pip install hydra-core==1.1.0dev5 pytorch_lightning albumentations tqdm retry kornia
config
├── data_gen
│ ├── generated.yaml # <- for generating data with 1 laten def get_augmentation(patch_size):
return Compose([
Resize(patch_size, always_apply=True),
#CropNonEmptyMaskIfExists(patch_size, always_apply=True),
Normalize(always_apply=True),
ElasticTransform((0, 0.25)),
def set_shapes(img, label, img_shape=(128,128,3)):
img.set_shape(img_shape)
label.set_shape([1,])
return img, label
transforms.Compose([transforms.RandomChoice([transforms.Resize(256),
transforms.Resize(480)]),
transforms.RandomCrop(224)
])
traapt-get install -y libpq-dev && pip3 install numpy pandas==1.0.5 \
xlrd Pillow==8.1.0 opencv-python albumentations flask==1.1.2 \
flask-cors==3.0.10 flask-basicauth==0.2.0 flask-sqlalchemy==2.5.1 \
requests gunicorn==20- External dataset
- Heavy and meaningful augmentation
- Modified the loss function
pip install albumentations==0.4.6
docker build -t my_bonk_example .
[...]
Removing intermediate container xxxxxxxxxxxxx
---> 57778e7c9788
Step 19/31 : RUN mkdir -p /tmp/spark-events
---> Running in afd21d853bcb
Removing intermediate container xxxxxxxxxxxxx
---># Dockerfile
FROM nvidia/cuda:10.0-devel
RUN nvidia-smi
RUN set -xe \
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
WORKDIR /SingleMCommunity Discussions
Trending Discussions on albumentations
QUESTION
I have a question about mean and standard deviation in image augmentation.
Are the two parameters recommended to be filled in?
If so, how could I know the number? Do I have to iterate through the data， also each channel of image, before the train to get it?
...ANSWER
Answered 2022-Mar-14 at 22:43Yes it is strongly recommended to normalize your images in most of the cases, obviously you will face some situations that does not require normalization. The reason is to keep the values in a certain range. The output of the network, even if the network is 'big', is strongly influenced by the input data range. If you keep your input range out of control, your predictions will drastically change from one to another. Thus, the gradient would be out of control too and might make your training unefficient. I invite you to read this and that answers to have more details about the 'why' behind normalization and have a deeper understanding of the behaviours.
It is quite common to normalize images with imagenet mean & standard deviation : mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]. Of course you could also consider, if your dataset is enough realistic, in a production context, to use its own mean and std instead of imagenet's.
Finally keep in mind those values since, once your model will be trained, you will still need to normalize any new image to achieve a good accuracy with your future inferences.
QUESTION
I use Albumentations augmentations in my computer vision tasks. However, I don't fully understand when to use normalization on my images (I use min-max normalization). Do I need to use normalization before augmentation functions, but values would not be between 0-1, or do I use normalization just after augmentations, so that values are between 0-1, or I use normalization in both cases - before and after augmentations?
For example, when I use Sharpen, values are not in 0-1 range (they vary in -0.5-1.5 range). Does that affect model performance? If yes, how?
Thanks in advance.
...ANSWER
Answered 2022-Feb-11 at 10:30The basic idea is that you should have the input of your neural network around 0 and with a variance of 1. There is a mathematical reason why it helps the learning process of neural network. This is not the case for other algorithms like tree boosting.
If you train from scratch the type of normalization (min max or other) should not impact the model performance (except if, for exemple your max/min value is really extrem compare to your other data point).
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I am running the following code: [https://pastebin.com/LK8tKZtN] The error obtained is following:
File "C:\Users\Admin\PycharmProjects\BugsClassfications\main2.py", line 45, in set_shapes * label.set_shape([])
...
ANSWER
Answered 2022-Jan-12 at 07:34If you change the shape of your labels, it should work:
QUESTION
The colab notebooks are not working even I used pip install:
ANSWER
Answered 2022-Jan-03 at 15:52Good news. As of this morning, you no longer have to use the setup_underactuated, and can install with pip, e.g. via
QUESTION
I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:
...ANSWER
Answered 2021-Dec-12 at 16:12Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.
QUESTION
I followed the pytorch tutorial for object detection on the website here. I decided to add more augmentations using albumentations if it would improve my traning. However after calling the __getitem__() method in the dataset class I get this error.
ANSWER
Answered 2021-Nov-06 at 08:06Images in PyTorch are loaded via pillow library (PIL.Image.open specifically).
If you look at albumentations docs its transformations required torch.Tensor (or np.ndarray object).
In order to do it, you should place A.ToTensorV2 as a first transformation and use other documentation transforms after that.
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
I trained resnet-18 model in pytorch. And it works well in pytorch.
But, when I converts it to onnx and predicts in cv2, model predicts only 1~2 label(it should predict 0~17 labels).
this is my model export code
ANSWER
Answered 2021-Sep-23 at 18:28The problem with your code probably has to do with preprocessing the images differently: self.transform rescales the image, but when you are reading blob, you are not doing that. To verify this, you can read the same image and check if the image and blob are equal (e.g. using torch.allclose), when the actual (random) augmentations are disabled.
QUESTION
I have a situation where I need to use ImageFolder with the albumentations lib to make the augmentations in pytorch - custom dataloader is not an option.
To this end, I am stumped and I am not able to get ImageFolder to work with albumenations. I have tried something along these lines:
...ANSWER
Answered 2021-Sep-12 at 12:08You need to use ToTensorV2 transformation as the final one:
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