I am working on MNIST Digit Data and looking for a way to either stretch or squeeze the image. The data is of the shape (28,28) and I want the final image to be of the same shape but either stretched or squeezed.
Is there any way to do it using torchvision.transforms or using any other method?
I think you are looking for transforms.RandomAffine.
Stretching and squeezing the image can be done by scaling it.
Related
I am trying to build a machine learning algorithm where I need to convert pictures to their binaries. I am using Pillow library to get the data from images. Since the performance of the algorithm is not great, I need extra parameters to thoroughly train the network and one of the extra parameters might be hue.
So is there a method in Python that gives me hue value of an image?
I am not sure what you are really trying to do, as Christoph says in the comments, but you can find the mean hue of all the pixels in an image with PIL like this:
from PIL import Image, ImageStat
# Load image, convert to HSV and split the channels, retain H, discard S and V
H, _, _ = Image.open('image.png').convert('RGB').convert('HSV').split()
# Print the mean Hue across all pixels
print(ImageStat.Stat(H).mean)
Note that I converted to RGB first to avoid problems that may arise if you try this with palette images, CMYK images, images with transparency and greyscale images. See here.
I am using a Dell server with 2 Nvidia V100 GPUs, Ubuntu 16.04, Tensorflow 1.7.
I am trying to figure out about when to resize my images. In the ssd_mobilenet_v1_coco.config, it has
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
I have drawn bounding boxes on many, many images with a total of 10 classes. The images are different sizes, some large, some small. My detection of objects is performing very poorly. I tried resizing a bunch of the images down to 300x300 then redrawing the bounding boxes, but this does not help and the images are pretty low quality and scaled incorrectly.
My question is: Do I need to scale my images to 300x300 before I label them? If I don't do that, will SSD Mobilenet do the resize and my bounding boxes will not line up or are they resized appropriately along with the images? I don't want to draw bounding boxes on large images then have them not be correct when SSD Mobilenet does the resize.
Thanks in advance.
i can't really answer to your question since i don't know how are you learning your SSD MobileNet.
I assume that you want to use your own dataset and convert it into tfrecord, right?
If so, you will have to handle the resizing on your own
If you are going to use COCO dataset, you can use pycocotools in order to create desired tfrecord
Best regards,
Vaclav
I have a tensor named input with dimensions 64x21x21. It is a minibatch of 64 images, each 21x21 pixels. I'd like to crop each image down to 11x11 pixels. So the output tensor I want would have dimensions 64x11x11.
I'd like to crop each image around a different "center pixel." The center pixels are given by a 2-dimensional long tensor named center with dimensions 64x2. For image i, center[i][0] gives the row index and center[i][1] gives the column index for the pixel that should be at the center in the output. We can assume that the center pixel is always at least 5 pixels away from the border.
Is there an efficient way to do this in pytorch (on the gpu)?
UPDATE: Let me clarify that the center tensor is formed by a deep neural network. It acts as a "hard attention mechanism," to use the reinforcement learning term for it. After I "crop" an image, that subimage becomes the input to another neural network. That's why I want to do the cropping in Pytorch: because the operations before and after the cropping are in Pytorch. I'd like to avoid having to transfer anything from the GPU back to the CPU.
I raised the question over on the pytorch forums, and got an answer there from smth. The grid_sample function should totally solve the problem.
https://discuss.pytorch.org/t/cropping-a-minibatch-of-images-each-image-a-bit-differently/12247
torchvision contains transforms including RandomCrop, but it doesn't seem to fit your use case if you want the images cropped in a specific way. I would recon that PyTorch, a deep learning framework, is not the appropriate tool for cropping images.
Instead, have a look at this tutorial that uses pillow. You should be able to implement your use case with this. Also have a look at pillow-simd which does some operations faster.
I am microbiology student new to computer vision, so any help will be extremely appreciated.
This question involves microscope images that I am trying to analyze. The goal I am trying to accomplish is to count bacteria in an image but I need to pre-process the image first to enhance any bacteria that are not fluorescing very brightly. I have thought about using several different techniques like enhancing the contrast or sharpening the image but it isn't exactly what I need.
I want to reduce the noise(black spaces) to 0's on the RBG scale and enhance the green spaces. I originally was writing a for loop in OpenCV with threshold limits to change each pixel but I know that there is a better way.
Here is an example that I did in photo shop of the original image vs what I want.
Original Image and enhanced Image.
I need to learn to do this in a python environment so that I can automate this process. As I said I am new but I am familiar with python's OpenCV, mahotas, numpy etc. so I am not exactly attached to a particular package. I am also very new to these techniques so I am open to even if you just point me in the right direction.
Thanks!
You can have a look at histogram equalization. This would emphasize the green and reduce the black range. There is an OpenCV tutorial here. Afterwards you can experiment with different thresholding mechanisms that best yields the bacteria.
Use TensorFlow:
create your own dataset with images of bacteria and their positions stored in accompanying text files (the bigger the dataset the better).
Create a positive and negative set of images
update default TensorFlow example with your images
make sure you have a bunch of convolution layers.
train and test.
TensorFlow is perfect for such tasks and you don't need to worry about different intensity levels.
I initially tried histogram equalization but did not get the desired results. So I used adaptive threshold using the mean filter:
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 2)
Then I applied the median filter:
median = cv2.medianBlur(th, 5)
Finally I applied morphological closing with the ellipse kernel:
k1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilate = cv2.morphologyEx(median, cv2.MORPH_CLOSE, k1, 3)
THIS PAGE will help you modify this result however you want.
This example allows the classification of images with scikit-learn:
http://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html
However, it is important that all the images have the same size (width and height, as written in the comments).
How can I modify this code to allow classification of images with different sizes?
You will need to define your own Feature Extraction.
In example from above, every pixel is represent a feature. If your images of different sizes, most trivial (but certainly not the best) thing that you can do is pad all images to the size of largest image with, for example, white pixels.
Here an example how to add boarders to image.