I was digging in the Tensorflow Object Detection API in order to check out the anchor box generations for SSD architecture. In this py file where the anchor boxes are generated on the fly, I am unable to understand the usage of base_anchor_size. In the corresponding paper, there is no mention of such thing. Two questions in short:
What is the use of base_anchor_size parameter? Is it important?
How does this parameter affect the training in the cases where the original input image is square in shape and the case when it isn't square?
In SSD architecture there are scales for anchors which are fixed ahead, e.g. linear values across the range 0.2-0.9. These values are relative to the image size. For example, given 320x320 image, then smallest anchor (with 1:1 ratio) will be 64x64, and largest anchor will be 288x288. However, if you wish to insert to your model a larger image, e.g. 640x640, but without changing the anchor sizes (for example since these are images of far objects, so there's no need for large objects; not leaving the anchor sizes untouched allows you not to fine-tune the model on the new resolution), then you can simply have a base_anchor_size=0.5, meaning the anchor scales would be 0.5*[0.2-0.9] relative to the input image size.
The default value for this parameter is [1.0, 1.0], meaning not having any affect.
The entries correspond to [height, width] relative to the maximal square you can fit in the image, meaning [min(image_height,image_width),min(image_height,image_width)]. So, if for example, your input image is VGA, i.e. 640x480, then the base_anchor_size is taken to be relative to [480,480].
Related
I have a multi-dimensional, hyper-spectral image (channels, width, height = 15, 2500, 2500). I want to compress its 15 channel dimensions into 5 channels.So, the output would be (channels, width, height = 5, 2500, 2500). One simple way to do is to apply PCA. However, performance is not so good. Thus, I want to use Variational AutoEncoder(VAE).
When I saw the available solution in Tensorflow or keras library, it shows an example of clustering the whole images using Convolutional Variational AutoEncoder(CVAE).
https://www.tensorflow.org/tutorials/generative/cvae
https://keras.io/examples/generative/vae/
However, I have a single image. What is the best practice to implement CVAE? Is it by generating sample images by moving window approach?
One way of doing it would be to have a CVAE that takes as input (and output) values of all the spectral features for each of the spatial coordinates (the stacks circled in red in the picture). So, in the case of your image, you would have 2500*2500 = 6250000 input data samples, which are all vectors of length 15. And then the dimension of the middle layer would be a vector of length 5. And, instead of 2D convolutions that are normally used along the spatial domain of images, in this case it would make sense to use 1D convolution over the spectral domain (since the values of neighbouring wavelengths are also correlated). But I think using only fully-connected layers would also make sense.
As a disclaimer, I haven’t seen CVAEs used in this way before, but like this, you would also get many data samples, which is needed in order for the learning generalise well.
Another option would be indeed what you suggested -- to just generate the samples (patches) using a moving window (maybe with a stride that is the half size of the patch). Even though you wouldn't necessarily get enough data samples for the CVAE to generalise really well on all HSI images, I guess it doesn't matter (if it overfits), since you want to use it on that same image.
I have the task to simulate a camera with a full well capacity of 10.000 Photons per sensor element
in numpy. My first Idea was to do it like that:
camera = np.random.normal(0.0,1/10000,np.shape(img))
Imgwithnoise= img+camera
but it hardly shows an effect.
Has someone an idea how to do it?
From what I interpret from your question, if each physical pixel of the sensor has a 10,000 photon limit, this points to the brightest a digital pixel can be on your image. Similarly, 0 incident photons make the darkest pixels of the image.
You have to create a map from the physical sensor to the digital image. For the sake of simplicity, let's say we work with a grayscale image.
Your first task is to fix the colour bit-depth of the image. That is to say, is your image an 8-bit colour image? (Which usually is the case) If so, the brightest pixel has a brightness value = 255 (= 28 - 1, for 8 bits.) The darkest pixel is always chosen to have a value 0.
So you'd have to map from the range 0 --> 10,000 (sensor) to 0 --> 255 (image). The most natural idea would be to do a linear map (i.e. every pixel of the image is obtained by the same multiplicative factor from every pixel of the sensor), but to correctly interpret (according to the human eye) the brightness produced by n incident photons, often different transfer functions are used.
A transfer function in a simplified version is just a mathematical function doing this map - logarithmic TFs are quite common.
Also, since it seems like you're generating noise, it is unwise and conceptually wrong to add camera itself to the image img. What you should do, is fix a noise threshold first - this can correspond to the maximum number of photons that can affect a pixel reading as the maximum noise value. Then you generate random numbers (according to some distribution, if so required) in the range 0 --> noise_threshold. Finally, you use the map created earlier to add this noise to the image array.
Hope this helps and is in tune with what you wish to do. Cheers!
I am trying to detect some very small object (~25x25 pixels) from large image (~ 2040, 1536 pixels) using faster rcnn model from object_detect_api from here https://github.com/tensorflow/models/tree/master/research/object_detection
I am very confused about the following configuration parameters(I have read the proto file and also tried modify them and test):
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
I am kind of very new to this area, if some one can explain a bit about these parameters to me it would be very appreciated.
My Question is how should I adjust above (or other) parameters to accommodate for the fact that I have very small fix-sized objects to detect in large image.
Thanks
I don't know the actual answer, but I suspect that the way Faster RCNN works in Tensorflow object detection is as follows:
this article says:
"Anchors play an important role in Faster R-CNN. An anchor is a box. In the default configuration of Faster R-CNN, there are 9 anchors at a position of an image. The following graph shows 9 anchors at the position (320, 320) of an image with size (600, 800)."
and the author gives an image showing an overlap of boxes, those are the proposed regions that contain the object based on the "CNN" part of the "RCNN" model, next comes the "R" part of the "RCNN" model which is the region proposal. To do that, there is another neural network that is trained alongside the CNN to figure out the best fit box. There are a lot of "proposals" where an object could be based on all the boxes, but we still don't know where it is.
This "region proposal" neural net's job is to find the correct region and it is trained based on the labels you provide with the coordinates of each object in the image.
Looking at this file, I noticed:
line 174: heights = scales / ratio_sqrts * base_anchor_size[0]
line 175: widths = scales * ratio_sqrts * base_anchor_size[[1]]
which seems to be the final goal of the configurations found in the config file(to generate a list of sliding windows with known widths and heights). While the base_anchor_size is created as a default of [256, 256]. In the comments the author of the code wrote:
"For example, setting scales=[.1, .2, .2]
and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
.1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
and aspect ratio 1/2. Each box is multiplied by "base_anchor_size" before
placing it over its respective center."
which gives insight into how these boxes are created, the code seems to be creating a list of boxes based on the scales =[stuff] and aspect_ratios = [stuff] parameters that will be used to slide over the image. The scale is fairly straightforward and is how much the default square box of 256 by 256 should be scaled before it is used and the aspect ratio is the thing that changes the original square box into a rectangle that is more closer to the (scaled) shape of the objects you expect to encounter.
Meaning, to optimally configure the scales and aspect ratios, you should find the "typical" sizes of the object in the image whatever it is ex(20 by 30, 5 by 10 ,etc) and figure out how much the default of 256 by 256 square box should be scaled to optimally fit that, then find the "typical" aspect ratios of your objects(according to google an aspect ratio is: the ratio of the width to the height of an image or screen.) and set those as your aspect ratio parameters.
Note: it seems that the number of elements in the scales and aspect_ratios lists in the config file should be the same but I don't know for sure.
Also I am not sure about how to find the optimal stride, but if your objects are smaller than 16 by 16 pixels the sliding window you created by setting the scales and aspect ratios to what you want might just skip your object altogether.
As I believe proposal anchors are generated only for model types of Faster RCNN. In this file you have specified what parameters may be set for anchors generation within line you mentioned from config.
I tried setting base_anchor_size, however I failed. Though this FasterRCNNTutorial tutorial mentions that:
[...] you also need to configure the anchor sizes and aspect ratios in the .config file. The base anchor size is 255,255.
The anchor ratios will multiply the x dimension and divide the y dimension, so if you have an aspect ratio of 0.5 your 255x255 anchor becomes 128x510. Each aspect ratio in the list is applied, then the results are multiplied by the scales. So the first step is to resize your images to the training/testing size, then manually check what the smallest and largest objects you expect are, and what the most extreme aspect ratios will be. Set up the config file with values that will cover these cases when the base anchor size is adjusted by the aspect ratios and multiplied by the scales.
I think it's pretty straightforward. I also used this 'workaround'.
im recently using tensorflow api object detection. The default SSD-MobileNet v1 is using 300 x 300 images as input training image, but i gonna edit the image size as width and height in different values. For instance, 320 * 180. Are aspects ratio in .config represent the real ratio of the anchors width/height ratio or they are just for the square images?
You can change the "size" to any different value , the general guidance is preserve the aspect ratio of the original image while the size can be different value.
Aspect ratios represent the real ratio of anchors. You can use it for different input ratios, but you will get the best result if you use input ratio similar to square images.
How can I do a basic face alignment on a 2-dimensional image with the assumption that I have the position/coordinates of the mouth and eyes.
Is there any algorithm that I could implement to correct the face alignment on images?
Face (or image) alignment refers to aligning one image (or face in your case) with respect to another (or a reference image/face). It is also referred to as image registration. You can do that using either appearance (intensity-based registration) or key-point locations (feature-based registration). The second category stems from image motion models where one image is considered a displaced version of the other.
In your case the landmark locations (3 points for eyes and nose?) provide a good reference set for straightforward feature-based registration. Assuming you have the location of a set of points in both of the 2D images, x_1 and x_2 you can estimate a similarity transform (rotation, translation, scaling), i.e. a planar 2D transform S that maps x_1 to x_2. You can additionally add reflection to that, though for faces this will most-likely be unnecessary.
Estimation can be done by forming the normal equations and solving a linear least-squares (LS) problem for the x_1 = Sx_2 system using linear regression. For the 5 unknown parameters (2 rotation, 2 translation, 1 scaling) you will need 3 points (2.5 to be precise) for solving 5 equations. Solution to the above LS can be obtained through Direct Linear Transform (e.g. by applying SVD or a matrix pseudo-inverse). For cases of a sufficiently large number of reference points (i.e. automatically detected) a RANSAC-type method for point filtering and uncertainty removal (though this is not your case here).
After estimating S, apply image warping on the second image to get the transformed grid (pixel) coordinates of the entire image 2. The transform will change pixel locations but not their appearance. Unavoidably some of the transformed regions of image 2 will lie outside the grid of image 1, and you can decide on the values for those null locations (e.g. 0, NaN etc.).
For more details: R. Szeliski, "Image Alignment and Stitching: A Tutorial" (Section 4.3 "Geometric Registration")
In OpenCV see: Geometric Image Transformations, e.g. cv::getRotationMatrix2D cv::getAffineTransform and cv::warpAffine. Note though that you should estimate and apply a similarity transform (special case of an affine) in order to preserve angles and shapes.
For the face there is lot of variability in feature points. So it won't be possible to do a perfect fit of all feature points by just affine transforms. The only way to align all the points perfectly is to warp the image given the points. Basically you can do a triangulation of image given the points and do a affine warp of each triangle to get the warped image where all the points are aligned.
Face detection could be handled based on the just eye positions.
Herein, OpenCV, Dlib and MTCNN offers to detect faces and eyes. Besides, it is a python based framework but deepface wraps those methods and offers an out-of-the box detection and alignment function.
detectFace function applies detection and alignment in the background respectively.
#!pip install deepface
from deepface import DeepFace
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
DeepFace.detectFace("img.jpg", detector_backend = backends[0])
Besides, you can apply detection and alignment manually.
from deepface.commons import functions
img = functions.load_image("img.jpg")
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
detected_face = functions.detect_face(img = img, detector_backend = backends[3])
plt.imshow(detected_face)
aligned_face = functions.align_face(img = img, detector_backend = backends[3])
plt.imshow(aligned_face)
processed_img = functions.detect_face(img = aligned_face, detector_backend = backends[3])
plt.imshow(processed_img)
There's a section Aligning Face Images in OpenCV's Face Recognition guide:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_tutorial.html#aligning-face-images
The script aligns given images at the eyes. It's written in Python, but should be easy to translate to other languages. I know of a C# implementation by Sorin Miron:
http://code.google.com/p/stereo-face-recognition/