Lets assume i have a little dataset. I want to implement data augmentation. First i implement image segmentation (after this, image will be binary image) and then implement data augmentation. Is this a good way?
For image augmentation in segmentation and instance segmentation, you have to either no change the positions of the objects contained in the image by manipulating colors for example, or modify these positions by applying translations and rotation.
So, yes this way works, but you have to take into consideration the type of data you have and what you are looking to achieve. Data augmentation isn't a ready to-go process with good results everywhere.
In case you have a:
Semantic segmentation : Each pixel of your image has a row i and a column j which are labeled as its enclosing object. This means having your main image I and a label image L with its same size linking every pixel to its object label. In this case, your data augmentation is applied to both I and L, giving a combination of the two transformed images.
Instance segmentation : Here we generate a mask for every instance of the original image and the augmentation is applied to all of them including the original, then from these transformed masks we get our new instances.
EDIT:
Take a look at CLoDSA (Classification, Localization, Detection and Segmentation Augmentor) it may help you implement your idea.
In case your dataset is small, you should add data-augmentation during the training. It is important to change the original image & the targets (masks) in the same way !!.
For example, If an image is rotated 90 degrees, then its mask should also be rotated 90 degrees. Since you are using Keras library, You should check if the ImageDataGenerator also changes the target images (masks), along with the inputs. If it doesn't, You can implement the augmentations by yourself. This repository shows how it is done in OpenCV here:
https://github.com/kochlisGit/random-data-augmentations
Related
Problem statement
I am working on a project using YOLO model to detect tools form given picture.i.e hammer, screwdrivers, bolt etc. I have only 100 pictures as training dataset and I have labelled them using polygons. I have decided to augment the data with the below given code. I’ve got 500 new images but, the problem is that I don't want to label them again. I am looking for any way out with which label bounding boxes (polygons) adjust (preserved) with news augmented images so that I can get polygons data without doing labelling again. In short, I want to preserver the label during the image augmentation process.
Code used for Augmentation
Brightness=[0.7,0.8,1] # Different brightness Levels
Rotation=[10,-10] # Different rotation Levels
# Main link source
main_path=r"./Augmentation"
# Directoy from main path
dir=os.listdir(main_path)[1:]
# Read and iterate all images from directory
for name in dir:
image = Image.open(os.path.join(main_path,name))
# Apply rotation from image
for j in Rotation: # Different rotation Levels
rotated = image.rotate(j)
ransImageRGBA = rotated.convert("RGB")
apply_br = ImageEnhance.Brightness(ransImageRGBA)
# Apply values for brightness in rotated images
for i in Brightness: # Different rotation Levels
Lightness =apply_br.enhance(i)
# below line for output
Lightness = apply_br.enhance(i).save((os.path.join(main_path, 'augmented_Images',str(i)+str(j))+name))
print('image has been augmented successfully')
look into imaug. The augmentations from this module also augment the labels. One more thing, what you are doing right now is offline augmentation. You might want to look at online augmentation. Then every epoch the pictures are augmented in a different way and you only train on the augmented pictures. This way you don't have to have a lot of discspace.
If you are using Yolov4 with darknet, image augmentation is performed automatically.
I have a 200x200 numpy array that has a shape in it which I can see when I graph it using matplotlib's imshow() function. However, there is also a lot of noise added in that picture. I am trying to use openCV to emphasize the shape and denoise the image. But it keeps throwing error messages that I don't understand. What should I do to get started on the denoising problem. The shape is visible to me as I see it but extra noise was added using the np.random.randint() function on top of the image. I want to reduce that noise
Here are some tutorials about image denoising techniques available in opencv.
Blurring out the noise
The most basic is applying a blur to average out the random noise. This will have the negative effect that the edges in the image will not be as sharp as originally. Depending on your application, this might be fine. Depending on the amount of noise, you can chance the size of the filter k. A larger value will produce a blurrier image with less noise.
k = 5
filtered_image = cv.blur(img,(k,k))
Advanced denoising
Alternatively, you can use more advanced techniques such as Non-local Means Denoising. This applies averaging across similar patches in the image. This technique has a few more parameters to tune to your specific application which you can read about here. (There are different versions of this function for greyscale and colour images, as well as for image sequences).
luminosity_filter_strength = 10
colour_filter_strength = 10
template_window_size = 7
search_window_size = 21
filtered_image = cv.fastNlMeansDenoisingColored(img,
luminosity_filter_strength,
colour_filter_strength,
template_window_size,
search_window_size)
I solved the problem using Scikit Image. They have very accessible documentation page for new comers and the error messages are a lot easier to understand. As for my problem I had to use Scikit Image's restoration library which has a lot of denoising functions much like openCV however the examples and the easy to understand error messages really helped. Playing around with Bilateral filters and Non-local Means Denoising solved the problem for me.
I'm trying to understand how I can show image segmentation results to users.
I mean that if I have this image:
I want to show to the user this result:
These images are from this Github. I have checked their code, but I haven't found where they show their results to the users.
How can I show semantic segmentation to the user?
What is the output of a semantic segmentation network?
UNet (the one in the example) and essentially every other network that deals with Semantic Segmentation produce as output an image whose size is proportional to the input image and in which each pixel is classified as one of the possible classes specified.
For binary classification, typically the raw output is a single-channel float image with values in [0,1] that must be thresholded at 0.5 in order to obtain the "foreground" binary mask. It's also possible that the network is trained implicitly with two classes (foreground/background), in which case, read on for how to deal with for multi-class classification output.
For multi-class classification, the raw output image has N channels, one per class, with value at index [x, y, c] being the score for the pixel (think of it as the probability of pixel x,y to belong to class c, although in principle scores don't have to be probabilities). For each pixel, the selected class is the one of the channel with the highest score.
Images can then be postprocessed (e.g., flattening them and assigning to each pixel class label of the "winning" class), as it seems to be the case for the example you link (if you take a look at the implementation of labelVisualize(), they use a dict mapping class codes to colors).
I'm doing object detection for texts in image and want to use Yolo to draw a bounding box where the text is in the image.
Then, how do you do data augmentation? Also, what is the difference between augmentation (contrast adjustment, gamma conversion, smoothing, noise, inversion, scaling, etc.) in ordinary image recognition?
If you have any useful website links, would you tell me plz :)
If you mean by what should you use then, it just a regular object detection task, the common augment, like flips or crop, works fine.
For the difference, if you mean by what the output images will look like then look at this repo https://github.com/albumentations-team/albumentations
But of you mean by the model performance difference then there's probably no answer for that, you can only try several ways and see what's the best.
I'm training a deep neural network to improve the quality of images. The images contain some specific types of noise that I want to reduce/remove by means of a deep learning model. In order to do so I'm using a huge dataset of similar clear high-res images with barely any noise, add the specific types of noise to the images and train the network on regenerating the original image (a custom autoencoder network). With one of the several noise types this works very well so far. Without going to far into the details, adding that particular type of noise was easy.
Now I need to add another noise type to the images, more precisely: chroma noise like in the following image (the bottom right one): link
How do I artificially generate and add chroma noise to an image in Python? I can use the full range of image processing packages, PIL, numpy, OpenCV, torchvision...
You need to convert the image to a colorspace such as HSV or CIE Lab. You then add noise to the chromacity channels (a, b in Lab, or H, S is HSV). Finally, convert back to RGB.
This colorspace conversion step is very common and most image toolkits should have that functionality.