Context: I have a video sequence of a camera moving around in a room with Apriltags (sort of QRcodes) on the walls.
I want to recognize those Apriltags using a Neural Network.
Problem is that I only have Apriltags images in my training set.
What would you suggest to recognize if an image belongs to the Apriltag class or not ?
For example if the image is a chessboard how can my NN reject this image ?
Related
I'm working on signature verification and there were a bunch of things I wanted to do using Keras/ OpenCV/ PIL but couldn't find relevant information. I have loaded the dataset folder using Keras.preprocessing.image_dataset_from_directory and now need to:
Crop the signature from the image stored in the dataset. There may be rectangular borders (or a side of the border) and the border pixels aren't the same in all images.
Resize the image and also take care of augmentation in the signature.
Example Images:
Since I'm working in Keras, I thought of working with its functions but couldn't find any. How can I auto crop/ extract a signature in the dataset I've loaded? About image augmentation, should I do this in this image preprocessing stage, or implement this in CNN model I am using? I am new to image processing and Keras.
Also, because of loading entire training folder as a dataset, the labels are "Genuine" and "Forged". However, there are multiple genuine and forged signatures of a person, and there are multiple people. How do I divide the data?
Organize your directories as follows
main_dir
-train_dir
``person1_fake_dir
```person1 fake image
```person1 fake image
---etc
``person1_real_dir
---person1 real image
---person1 real image
--- etc
--person2_fake_dir
--- person2 fake image
--- person2 fake image
--- etc
--person2_real_dir
---person2 real image
---person2 real image
---etc
.
.
.
--personN_fake_dir
---personN fake image
---personN fake image
---etc
--personN_real_dir
---personN real image
---personN real image
--- etc
-test_dir
same structure as train_dir but put test images here
-valid_dir
same structure as train_dir but put validation images here
If you have N persons then you will have 2 X N classes
You can then use tf.keras.preprocessing.image.ImageDataGenerator().flow_from_directory()
to input your data. Documentation is here. You don't have to worry about cropping the images just set the image size in flow to something like (256,256).
Code below show the rest of the code you need
data_gen=tf.keras.preprocessing.image.ImageDataGenerator(resize=1/255)
train_gen=data_gen.flow_from_directory(train_dir, target_size=(224,224), color-mode='grayscale')
valid_gen=data_gen.flow_from_directory(valid_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
test_gen=data_gen.flow_from_directory(test_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.CategoricalCrossentropy(), metrics='accuracy')
history=model.fit(train_gen, epochs=20, verbose=1)
accuracy=model.evaluate (test_gen)[1]*100
print ('Model accuracy is ', accuracy)
Note your model will not be able to tell fake from real in the general case. It should work for persons 1 through N. You could try putting all the fake images in one class directory and all the real images in another class directory and train it but I suspect it will not work well in telling real from fake for the general case.
I am implementing YOLOv3 and have trained the model on my custom class ( which is tomato). I have used the darknet model 53 weights ( https://pjreddie.com/media/files/darknet53.conv.74) to start my training as per the instructions provided by many sites on training and object detection using YOLOv3 . I thought it was not necessary to list down the steps.
One of my object images used for training is shown below ( with bounding boxes using LabelImg):
The txt file for the above image for the bounding boxes contains the following coordinates , as created using labellmg:
0 0.152807 0.696655 0.300640 0.557093
0 0.468728 0.705306 0.341862 0.539792
0 0.819652 0.695213 0.337242 0.543829
0 0.317164 0.271626 0.324449 0.501730
Now when I use the same image for testing to determine the accuracy of detection, it is unable to detect all the tomatoes and moreover the bounding boxes are way off from the objects as shown below:
I am not sure what is going on.
I have cloned the git
https://github.com/AlexeyAB/darknet and did a local make and trained the model on the custom object. Nothing fancy.
The pictures above were taken from my phone. I have trained the darknet using a combination of downloaded images and custom tomato pictures I had taken from my phone. I have 290 images for training.
Maybe your model can't generalize well. Maybe your are training too much, which can cause over-fitting or even your dataset is small.
You can try testing on a never seen data (a new tomato picture) and sees if it does well.
Double-check your config files, if something is incorrect there, like you are using a yolov4 cfg in a yolov3 model.
And I recommend that you read this article in which can help you understand better how neural networks works:
https://towardsdatascience.com/understand-neural-networks-model-generalization-7baddf1c48ca
I have a hyperspectral image having 186 bands. What is appropriate way to generate ground truths so that I can use it to make training class to train a machine learning model. The image is as below:
We need to manually create masks or assign classes to regions of interest on any 2D image serving as the ground truth data (may need to convert it into the same type as the hyperspectral image data, containing only a single band information)
I have 10 different types of images in a folder. After the prediction of Images using VGG16 Module in a folder, I got some levels for those Images. How can I match those levels to the images in my folder and how can I segregate the one type of images in one folder?
Not getting anything.
('n04536866', 'violin', 0.98542005),
('n03028079', 'church', 0.35847503),
('n02690373', 'airliner', 0.945028),
('n03642806', 'laptop', 0.52074945),
I´m getting predictions like this, now i want to match these levels with my images and filter out the one kind of images in one folder.
Please read some basics about neural networks and image classification. The result of your prediction is an n-dimensional vector, where n is the number of ground truth labels, and the components of the vector are the probability for each class. So from the example above the neural network assume, that the input image which was used for this prediction has a probability of 98,54% to show a violin.
I want to do regression with images. There are images of roads and the associated steering angle. As I want to apply data augmentation in Keras I would like to flip the input images horizontally but that would imply that the steering angle has to change its sign if the image is flipped. As far as I can see the documentation does not cover this problem. Is there a tutorial explaining how this can be achieved?
You have to write your own data-generator.
Check out the ImageLoader class (custom image generator) in my code here:
https://github.com/Golbstein/EDSR-Keras/blob/master/utilities.py