I have imported an image. I have parsed it to double precision and performed some filtering on it.
When I plot the result with imshow, the double image is too dark. But when I use imshowpair to plot the original and the final image, both images are correctly displayed.
I have tried to use uint8, im2uint8, multiply by 255 and then use those functions, but the only way to obtain the correct image is using imshowpair.
What can I do?
It sounds like a problem where the majority of your intensities / colour data are outside the dynamic range of what is accepted for imshow when showing double data.
I also see that you're using im2double, but im2double simply converts the image to double and if the image is already double, nothing happens. It's probably because of the way you are filtering the images. Are you doing some sort of edge detection? The reason why you're getting dark images is probably because the majority of your intensities are negative, or are hovering around 0. imshow whe displaying double type images assumes that the dynamic range of intensities is [0,1].
Therefore, one way to resolve your problem is to do:
imshow(im,[]);
This shifts the display so that range so the smallest value is mapped to 0, and the largest to 1.
If you'd like a more permanent solution, consider creating a new output variable that does this for you:
out = (im - min(im(:))) / (max(im(:)) - min(im(:)));
This will perform the same shifting that imshow does when displaying data for you. You can now just do:
imshow(out);
Related
I have a 2D CNN model where I perform a classification task. My images are all coming from a sensor data after conversion.
So, normally, my way is to convert them into images using the following approach
newsize = (9, 1000)
pic = acc_normalized[0]
img = Image.fromarray(np.uint8(pic*255), 'L')
img = img.resize(newsize)
image_path = "Images_Accel"
image_name = "D1." + str(2)
img.save(f"{image_path}/{image_name}.jpeg")
This is what I obtain:
However, their precision is sort of important. For instance, some of the numerical values are like:
117.79348187327987 or 117.76568758022673.
As you see in the above line, their difference is the digits, when I use uint8, it only takes 117 to when converting it into image pixels and it looks the same, right? But, I'd like to make them different. In some cases, the difference is even at the 8th or 10th digit.
So, when I try to use mode F and save them .jpeg in Image.fromarray line it gives me error and says that PIL cannot write mode F to jpeg.
Then, I tried to first convert them RGB like following;
img = Image.fromarray(pic, 'RGB')
I am not including np.float32 just before pic or not multiplying it by 255 as it is. Then, I convert this image to grayscale. This is what I got for RGB image;
After converting RGB into grayscale:
As you see, it seems that there is a critical different between the first pic and the last pic. So, what should be the proper way to use them in 2D CNN classification? or, should I convert them into RGB and choose grayscale in CNN implementation and a channel of 1? My image dimensions 1000x9. I can even change this dimension like 250x36 or 100x90. It doesn't matter too much. By the way, in the CNN network, I am able to get more than 90% test accuracy when I use the first-type of image.
The main problem here is using which image conversion method I'll be able to take into account those precision differences across the pixels. Would you give me some idea?
---- EDIT -----
Using .tiff format I made some quick comparisons.
First of all, my data looks like the following;
So, if I convert this first reading into an image using the following code where I use np.float64 and L gives me a grayscale image;
newsize = (9, 1000)
pic = acc_normalized[0]
img = Image.fromarray(np.float64(pic), 'L')
img = img.resize(newsize)
image_path = "Images_Accel"
image_name = "D1." + str(2)
img.save(f"{image_path}/{image_name}.tiff")
It gives me this image;
Then, the first 15x9 matrix seems like following image; The contradiction is that if you take a closer look at the numerical array, for instance (1,4) member, it's a complete black where the numerical array is equal to 0.4326132099074307. For grayscale images, black means that it's close to 0 cause it makes white if it's close to 1. However, if it's making a row operation, there is another value closer to 0 and I was expecting to see it black at (1,5) location. If it does a column operation, there is again something wrong. As I said, this data has been already normalized and varies within 0 and 1. So, what's the logic that it converts the array into an image? What kind of operation it does?
Secondly, if I first get an RGB image of the data and then convert it into a grayscale image, why I am not having exactly the same image as what I obtained first? Should the image coming from direct grayscale conversion (L method, np.float64) and the one coming from RGB-based (first I get RGB then convert it to grayscale) be the same? There is a difference in black-white pixels in those images. I do not know why we have it.
---- EDIT 2 ----
.tiff image with F mode and np.float32 gives the following;
I don't really understand your question, but you seem to want to store image differences that are less than 1, i.e. less than the resolution of integer values.
To do so, you need to use an image format that can store floats. JPEG, PNG, GIF, TGA and BMP cannot store floats. Instead, use TIFF, EXR or PFM formats which can handle floats.
Alternatively, you can create 16-bit PNG images wherein each pixel can store values in range 0..65535. So, say the maximum difference you wanted to store was 60 you could calculate the difference and multiply it by 1000 and round it to make an integer in range 0..60000 and store as 16-bit PNG.
You could record the scale factor as a comment within the image if it is variable.
Img is a dtype=float64 numpy data type. When I run this code:
Img2 = np.array(Img, np.uint8)
the background of my images turns white. How can I avoid this and still get an 8-bit image?
Edit:
Sure, I can give more info. The single image is compiled from a stack of 400 images. They are each coming from an .avi video file, and each image is converted into a NumPy array like this:
gray_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
A more complicated operation is performed on this whole stack, but does not involve creating new images. It's simply performing calculations on each 1D array to yield a single pixel.
The interpolation is most likely linear (the default in plotting images with matplotlib. The images were saved as .PNGs.
You probably see overflow. If you cast 257 to np.uint8, you will get 1. According to a google search, avi files contain images with a color depth of 15 - 24 bit. When you cast this depth to np.uint8, you will see white regions getting darkened and (if a normalization takes place somewhere) also dark regions getting white (-5 -> 251). For the regions that become bright, you could check whether you have negative pixel values in the original image Img.
The Docs say that sometimes you have to do some scaling to get a proper cast, and to rather use higher depth whenever possible to avoid artefacts.
The solution seems to be either working at higher depth, i.e. casting to np.uint16 or np.uint32, or to scale the pixel values before reducing the depth, i.e. with Img2 already being a numpy matrix
# make sure that values are between 0 and 255, i.e. within 8bit range
Img2 *= 255/Img2.max()
# cast to 8bit
Img2 = np.array(Img, np.uint8)
I converted a RGB iamge to gray and then to binary. I wanted that the white pixels of binary image be replaced by pixel value of gray image. Though the command window shows that all 1s are replaced with gray pixel value but same is not reflected in the image.
The binary image (bw) and the new image (newbw) looks exactly. Why so ?
clc;clear all;close all;
i = imread('C:\Users\asus\Documents\Academics 2014 (SEM 7)\DIP\matlabTask\im1.jpg');
igray = rgb2gray(i);
bw = im2bw(igray);
[m,n]=size(bw);
newbw = zeros(m,n);
for i=1:m
for j=1:n
if bw(i,j)==1
newbw(i,j)=igray(i,j);
else
newbw(i,j)=bw(i,j);
end
end
end
subplot(311),imshow(igray),subplot(312),imshow(bw),subplot(313),imshow(newbw)
The reason why is because when you are creating your new blank image, this is automatically created as double type. When doing imshow, if you provide a double type image, the dynamic range of the pixel intensities are expected to be between [0,1] where 0 is black and 1 is white. Anything less than 0 (negative) will be shown as black, and anything greater than 1 will be shown as white.
Because this is surely not the case in your image, and a lot of the values are going to be > 1, you will get an output of either black or white. I suspect your image is of type uint8, and so the dynamic range is between [0,255].
As such, what you need to do is cast your output image so that it is of the same type as your input image. Once you do this, you should be able to see the gray values displayed properly. All you have to do now is simply change your newbw statement so that the variable is of the same class as the input image. In other words:
newbw = zeros(m,n,class(igray));
Your code should now work. FWIW, you're not the first one to encounter this problem. Almost all of my questions that I answer when using imshow are due to the fact that people forget that putting in an image of type double and type uint* behave differently.
Minor note
For efficiency purposes, I personally would not use a for loop. You can achieve the above behaviour by using indexing with your boolean array. As such, I would replace your for loop with this statement:
newbw(bw) = igray(bw);
Whichever locations in bw are true or logical 1, you copy those locations from igray over to newbw.
I am trying to convert an array of RGB values into an array of HSV values in Matlab. I am running the following code:
pic = imread('ColoradoMountains.jpg');
pic = rgb2hsv(pic);
imwrite(pic,'pic.jpg')
But the image that gets written has completely different colors. I've been trying to set the color map to hsv, but I'm not sure if that even makes sense. Nothing on the internet comes up with my keywords, can someone please point me in the right direction?
You can specify the colormap that imwrite is supposed to use. Try this:
imwrite(pic,colormap('HSV'),'pic.png');
Here's the documentation for imwrite: http://www.mathworks.com/help/matlab/ref/imwrite.html
In Matlab you have to distinguish between an indexed image and an 3-channel image. An indexed image is a n*m*1 image where each value of the [0,1] range is associated to a colour. This association is called colour map, which could be for example a unit circle in HSV or RGB. This can be written using imwrite with the map parameter, but this is not what you want.
What you obviously want is an HSV-encoded image, which means the three rgb-channels are converted to three hsv channels. This is (as far as I know) not possible. If you take a look into the documentation of imwrite, you can see how CMYK-Encoded images are written, this is done implicit passing a n*m*4 image. Is there any of the standard file formats which supports HSV? If so I'll take a closer look at this format.
I am using imagesc to get an integral image. However, I only manage to display it and then I have to save it by hand, I can't find a way to save the image from the script with imwrite or imsave. Is it possible at all?
The code:
image='C:\image.jpg';
in1= imread((image));
in=rgb2gray(in1);
in_in= cumsum(cumsum(double(in)), 2);
figure, imagesc(in_in);
You can also use the print command. For instance if you are running over multiple images and want to serialize them and save them, you can do something like:
% Create a new figure
figure (fig_ct)
% Plot your figure
% save the figure to your working directory
eval(['print -djpeg99 ' num2str(fig_ct)]);
% increment the counter for the next figure
fig_ct = fig_ct+1;
where fig_ct is just a counter. If you are interested in saving it in another format different than jpeg take a look at the documentation, you can do tiff, eps, and many more.
Hope this helps
I believe your problem may be with the fact that you are saving a double matrix that is not on the range of [0 1]. If you read the documentation, you'll see that
If the input array is of class double, and the image is a grayscale or
RGB color image, imwrite assumes the dynamic range is [0,1] and
automatically scales the data by 255 before writing it to the file as
8-bit values.
You can convert it yourself to a supported type (that's logical, uint8, uint16, or double) or get it in the range [0 1] by, for example, dividing it by the max:
imwrite (in_in / max (in_in(:)), 'out.jpg');
You may still want to further increase the dynamic range of the image you saved. For example, subtract the mininum before dividing by the max.
in_in = in_in - min (in_in(:));
in_in = in_in / max (in_in(:));
imwrite (in_in, 'out.jpg');
If you want exactly what imagesc displays
The imagesc function scales image data to the full range of the current colormap.
I don't know what exactly does it mean exactly but call imagesc requesting with 1 variable, and inspect the image handle to see the colormap and pass it to imwrite().
I'm a very new programmer, so apologies in advance if this isn't very helpful, but I just had the same problem and managed to figure it out. I used uint8 to convert it like this:
imwrite(uint8(in_in), 'in_in.jpg', 'jpg');