In the conv-nets model, I know how to visualize the filters, we can do itorch.image(model:get(1).weight)
But how could I efficiently visualize the output images after the convolution? especially those images in the second or third layer in a deep neural network?
Thanks.
Similarly to weight, you can use:
itorch.image(model:get(1).output)
To visualize the weights:
-- visualizing weights
n = nn.SpatialConvolution(1,64,16,16)
itorch.image(n.weight)
To visualize the feature maps:
-- initialize a simple conv layer
n = nn.SpatialConvolution(1,16,12,12)
-- push lena through net :)
res = n:forward(image.rgb2y(image.lena()))
-- res here is a 16x501x501 volume. We view it now as 16 separate sheets of size 1x501x501 using the :view function
res = res:view(res:size(1), 1, res:size(2), res:size(3))
itorch.image(res)
For more: https://github.com/torch/tutorials/blob/master/1_get_started.ipynb
Related
I have a row that contains the names and photos of people in Oracle, how do I make face recognition that can recognize names only by taking pictures from the camera ??
what techniques can I use?
Firstly, do not store the raw images in the blob column. You should store the vector representation of raw images. The following python code block will find the vector representation of a face image.
#!pip install deepface
from deepface.basemodels import VGGFace, Facenet
model = VGGFace.loadModel() #you can use google facenet instead of vgg
target_size = model.layers[0].input_shape
#preprocess detects facial area and aligns it
img = functions.preprocess_face(img="img.jpg", target_size=target_size)
representation = model.predict(img)[0,:]
Here, you can either pass exact image path like img.jpg or the 3D array to img argument of preprocess_face. In this way, you will store the vector representations in the blob column of oracle database.
When you have a new face image, and want to find its identity in the database find its representation again.
#preprocess detects facial area and aligns it
target_img = functions.preprocess_face(img="target.jpg", target_size=target_size)
target_representation = model.predict(target_img )[0,:]
Now, you have the vector representation of the target image and vector representations of the database images. You need to find the similarity score of target image representation and each instance of database representations.
Euclidean distance is the easiest way to compare vectors.
def findEuclideanDistance(source_representation, test_representation):
euclidean_distance = source_representation - test_representation
euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
euclidean_distance = np.sqrt(euclidean_distance)
return euclidean_distance
We will compare each data base instance to target. Suppose that representations of data base instances are stored in representations object.
distances = []
for i in range(0, len(representations)):
source_representation = representations[i]
#find the distance between target_representation and source_representation
distance = findEuclideanDistance(source_representation, target_representation )
distances.append(distance)
Distances list stores the distance of each item in the data base to target. We need to find the lowest distance.
import numpy as np
idx = np.argmax(distances)
Idx is the id of the target image in the database.
I need the size of the image as a variable.
Here is what I tried:
a = dir('C:\example\Desktop\imagefolder\*.png');
numberofImages = length(a);
%sizeofimage?
matrix = zeros(numberofImages, sizeofimage);
How can I get the size of my image?
Maybe this will help. It will give you the size of each of the images in your folder.
a = dir('C:\example\Desktop\imagefolder\*.png');
numberofImages = length(a);
for i=1:numberofImages
img = imread(a(i).name);
sizeofImage{i} = size(img)
end
You can then access the sizes of the images using this notation:
sizeofImage{1}
Which will return something like this for an image 400x400:
ans =
400 400 3
You require an array of numbers to go into zeros, and size already provides you with one. Simply horzcat that (or use square brackets) with the number of images and you're done:
matrix = zeros([numberofImages, size(image)]);
Although I strongly suggest you to stop working on your project and start taking a class in MATLAB, read a book on it or take the MathWorks own tutorial, as this is about as basic as MATLAB gets. Since MATrix LABoratory is about matrices, size is important.
I want to get only leaf from an image.
The background is a normal white paper(A4) and there is some shadow.
I apply some method (structure element,edge detection using filter) but I cannot find the general way which can apply all the image.
these are examples.
Are there better methods for this problem??
thank you
another example.
and the result I got is
By using
hsv_I = rgb2hsv(I);
Is = hsv_I(:,:,2);
Is_d = imdilate(Is,strel('diamond',4));
Is_e = imerode(Is,strel('diamond',2));
Is_de = imerode(Is_d,strel('disk',2));
Is_def = imfill(Is_de,'holes');
Is_defe = imerode(Is_def,strel('disk',5));
Then Is_defe is a mask to segment
But the method that i did is very specific. I cannot use this in general.
If you have the Image Processing Toolbox, you could do as follows:
The code below first estimates the threshold with the function graythresh, thresholds the image and fills holes with the imfill function. Suppose I is a cell containing your RGB images:
for k=1:length(I)
t=graythresh(rgb2gray(I{k}));
BW{k}=imfill(~im2bw(I{k}, t), 'holes');
subplot(length(I),1,k), imshow(BW{k});
end
I have a histology image like this:
From the image, we can observe there are two kinds of different cells.
and
Is there any way that I can separate these two types of cells into two groups?
How about using your raw image and previous code to achieve this?
% % % your old code
I=imread(file);
t1=graythresh(I);
k1=im2bw(I,t1);
k1=~k1;
se = strel('disk',1);
k0=imfill(~k1,'holes');
cc = conncomp(k0);
k0(cc.PixelIdxList{1})=0;
k1=imfill(k1,'holes');
mask=k0 | k1;
%%%%%%%%%%%%%%%%%%
This will give you:
I=rgb2hsv(I);
I=double(I);
I1=I(:,:,1); % again, the channel that can maximizing the margin between donut and full circle
Imask=(I1-0.2).*(I1-0.9)<0;
k2=mask-Imask;
k2=bwareaopen(k2,100);
This will give you:
k2=mask-Imask;
I2=zeros(size(I1,1),size(I1,2),3);
I2(:,:,1)=(k2==1)*255;
I2(:,:,3)=((I1-0.2).*(I1-0.9)<0)*255;
imshow(I2)
will finally give you (the two types are stored in two channels in the rgb image):
I would use regionprops
props=regionprops(YourBinaryImage, 'Solidity');
The objects with a high solidity will be the disks, those with a lower solidity will be the circles.
(Edit) More formally:
I=imread('yourimage.jpg');
Bw=~im2bw(I, 0.5);
BWnobord = imclearborder(Bw, 4); % clears the partial objects
Props=regionprops(BWnobord, 'All');
solidity=cell2mat({Props.Solidity});
Images={Props.Image};
Access the elements of Images where the value in solidity is higher than 0.9 and you get your disks. The circles are the other ones.
Hope it helps
I used connected component labeling algorithm (bwconncomp) to label the different parts of a binary image (MATLAB). Now i need to calculate the area of different labels and remove the labels with smaller area. Can i use the default area finding command or is there any specific commands for that in matlab...Help..
From the documentation:
CC = bwconncomp(BW) returns the connected components CC found in BW.
The binary image BW can have any dimension. CC is a structure with
four fields...
The final field in CC is PixelIdxList, which is:
[a] 1-by-NumObjects cell array where the kth element in the cell array is
a vector containing the linear indices of the pixels in the kth object.
You can find the area of each label by looking at the length of the corresponding entry in the cell array. Something like:
areas_in_pixels = cellfun(#length, CC.PixelIdxList);
The PixelIdxList is a cell array, each member of which contains the linear indexes of the pixels present in that connected component. The line of code above finds the length of each cell in the cell array - i.e. the number of pixels in each connected component.
I've used cellfun to keep the code short and efficient. A different way of writing the same thing would be something like:
areas_in_pixels = nan(1, length(CC.PixelIdxList);
for i = 1:length(CC.PixelIdxList)
areas_in_pixels(i) = length(CC.PixelIdxList{i});
end
For each connected component, you can then find the size of that component in pixels by accessing an element in areas_in_pixels:
areas_in_pixels(34) %# area of connected component number 34
If you don't want to write lots of code like above just use built-in functions of MATLAB to detect the area. Label your components and from the properties of the component you can find out the area of that component. Suppose Bw is the binary image:
[B,L] = bwboundaries(Bw,'noholes');
stats = regionprops(L,'Area','perimeter');
for k = 1:length(B)
area(k)=stats.Area;
end
You can make this better still by avoiding the for loop with the following:
[B,L] = bwboundaries(Bw,'noholes');
stats = regionprops(L,'Area','perimeter');
area = [stats.Area];
Best,
-Will