when i use google sheet image function,
then i found image's resoltion lower.
so i test 3 case.
case1 : image function use, hosting site 1(not google)
case2 : image function use, hosting site 2(not google)
case3 : google sheet image insert(insert -> image)
and i found only case3, there isn't low resoultion problem.
how can i do?
*here google sheet url that i tested
https://docs.google.com/spreadsheets/d/1IB9yMDXFrSZDUPbIGFy92BUWszd1Qzx2kdJEuLMpBe8/edit?usp=sharing
there are modes
mode – [ OPTIONAL – 1 by default ] – The sizing mode for the image
1 resizes the image to fit inside the cell, maintaining aspect ratio.
2 stretches or compresses the image to fit inside the cell, ignoring aspect ratio.
3 leaves the image at original size, which may cause cropping.
4 allows the specification of a custom size.
in your case, you should be using:
=IMAGE(B2, 3)
but keep in mind that the original size is 1266×420
so to get the original it would be:
=IMAGE(B2, 4, 420, 1266)
your C2 cell dimensions are 572×200 so for such a small cell it's best to use a smaller source. you can use https://andrew.hedges.name/experiments/aspect_ratio/
and some online resizing tool like https://picresize.com/
https://i.imgur.com/ptwnpnG.png
=IMAGE(B2, 4, 189, 570)
Related
I'm new to PDFlib. when use PDFlib to create PDF.I find that the dpi of the page(paper) was 72.and I want to set the dpi to 300 for print use,but i dont know how to use PDFlib to set.
enter image description here
as flomei already mentioned the PDF format itself do not have any kind of resolution. For placing the content or specifying the page dimension PDFlib use the PDF’s default coordinate system, which use the DTP points as unit. From the PDFlib 9.2 Tutorial, chapter 3.2.1.
PDF’s default coordinate system is used within PDFlib. The default coordinate system (or default user space) has the origin in the lower left corner of the page, and uses the DTP point as unit:
1 pt = 1/72 inch = 25.4/72 mm = 0.3528 mm
When you want to address the positions with a different unit, you can scale the coordinate system. Please check out the same PDFlib Tutorial chapter, section "Using metric coordinates":
p.scale(28.3465, 28.3465);
After this call PDFlib will interpret all coordinates (except for interactive features, see below) in centimeters since 72/2.54 = 28.3465.
of course you can use further scale values.
I followed multiple example, to train a custom object detector in TensorflowJS . The main problem I am facing every where it is using pretrained model.
Pretrained models are fine for general use cases, but custom scenario it fails. For example, take this this is example form official Tensorflowjs examples, here it is using mobilenet, and mobilenet and mobilenet has image size restriction 224x224 which defeats all the purpose, because my images are big and also not of same ratio so resizing is not an option.
I have tried multiple example, all follows same path oneway or another.
What I want ?
Any example by which I can train a custom objector from scratch in Tensorflow.js.
Although the answer sounds simple but trust me I searching for this for multiple days. Any help will be greatly appreciated. Thanks
Currently it is not yet possible to use tensorflow object detection api in nodejs. But the image size should not be a restriction. Instead of resizing, you can crop your image and keep only the part that contain your object to be detected.
One approach will be like partition the image in 224x224 and run for all partitions but what if the object is between two partitions
The image does not need to be partitioned for it. When labelling the image, you will need to know the x, y coordinates (from the top left) and the w, h of the detected box. You only need to crop a part of the image that will contain the box. Cropping at the coordinates x - (224-w)/2, y- (224-h)/2 can be a good start. There are two issues with these coordinates:
the detected boxes will always be in the center, so the training will be biaised. To prevent it, a randomn factor can be used. x - (224-w)/r , y- (224-h)/r where r can be randomly taken from [1-10] for instance
if the detected boxes are bigger than 224 * 224 maybe you might first choose to resize the video keeping it ratio before cropping. In this case the boxe size (w, h) will need to be readjusted according to the scale used for the resizing
Depending on which skin I use in the simulator, the result from the following method differs :
Display.getInstance().getDeviceDensity();
The results have nothing to do with the real device density since for a Xoom skin it outputs 30 (149 ppi in reality), for a an Iphone 6 it outputs 50 (329 in reality).
I noticed that because I need to translate char height measured in Gimp (72 dpi) into the device world so that it looks alike on an image.
Any help on that topic would be appreciated!
Cheers
The JavaDocs for getDeviceDensity state:
Returns one of the density variables appropriate for this device,
notice that density doesn't always correspond to resolution and an
implementation might decide to change the density based on DPI
constraints.
Returns:
one of the DENSITY constants of Display
The DENSITY constants refers to one of these.
Notice you can also use convertToPixels which is probably a far better API to use. The density API is mostly used to pick the right multi image and should rarely be used in user code.
Given an image (i.e. newspaper, scanned newspaper, magazine etc), how do I detect the region containing text? I only need to know the region and remove it, don't need to do text recognition.
The purpose is I want to remove these text areas so that it will speed up my feature extraction procedure as these text areas are meaningless for my application. Anyone know how to do this?
BTW, it will be good if this can be done in Matlab!
Best!
You can use Stroke Width Transform (SWT) to highlight text regions.
Using my mex implementation posted here, you can
img = imread('http://i.stack.imgur.com/Eyepc.jpg');
[swt swtcc] = SWT( img, 0, 10 );
Playing with internal parameters of the edge-map extraction and image filtering in SWT.m can help you tweak the resulting mask to your needs.
To get this result:
I used these parameters for the edge map computation in SWT.m:
edgeMap = single( edge( img, 'canny', [0.05 0.25] ) );
Text detection in natural images is an active area of research in computer vision community. U can refer to ICDAR papers. But in your case I think it should be simple enough. As you have text from newspaper or magazines, it should be of fixed size and horizontally oriented.
So, you can apply scanning window of a fixed size, say 32x32. Train it on ICDAR 2003 training dataset for positive windows having text in it. U can use a small feature set of color and gradients and train an SVM which would give a positive or negative result for a window having text or not.
For reference go to http://crypto.stanford.edu/~dwu4/ICDAR2011.pdf . For code, you can try their homepages
This example in the Computer Vision System Toolbox in Matlab shows how to detect text using MSER regions.
If your image is well binarized and you know the usual size of the text you could use the HorizontalRunLengthSmoothing and VerticalRunLengthSmoothing algorithms. They are implemented in the open source library Aforge.Net but it should be easy to reimplement them in Matlab.
The intersection of the result image from these algorithm will give you a good indication that the region contains text, it is not perfect but it is fast.
I am now working on an eye tracking project. In this project I am tracking eyes in a webcam video (resolution if 640X480).
I can locate and track the eye in every frame, but I need to locate the pupil. I read a lot of papers and most of them refer to Alan Yuille's deformable template method to extract and track the eye features. Can anyone help me with the code of this method in any languages (matlab/OpenCV)?
I have tried with different thresholds, but due to the low resolution in the eye regions, it does not work very well. I will really appreciate any kind of help regarding finding pupil or even iris in the video.
What you need to do is to convert your webcam to a Near-Infrared Cam. There are plenty of tutorials online for that. Try this.
A Image taken from an NIR cam will look something like this -
You can use OpenCV then to threshold.
Then use the Erode function.
After this fill the image with some color takeing a corner as the seed point.
Eliminate the holes and invert the image.
Use the distance transform to the nearest non-zero value.
Find the max-value's coordinate and draw a circle.
If you're still working on this, check out my OptimEyes project: https://github.com/LukeAllen/optimeyes
It uses Python with OpenCV, and works fairly well with images from a 640x480 webcam. You can check out the "Theory Paper" and demo video on that page also. (It was a class project at Stanford earlier this year; it's not very polished but we made some attempts to comment the code.)
Depending on the application for tracking the pupil I would find a bounding box for the eyes and then find the darkest pixel within that box.
Some psuedocode:
box left_location = findlefteye()
box right_location = findrighteye()
image_matrix left = image[left_location]
image_matrix right = image[right_location]
image_matrix average = left + right
pixel min = min(average)
pixel left_pupil = left_location.corner + min
pixel right_pupil = right_location.corner + min
In the first answer suggested by Anirudth...
Just apply the HoughCirles function after thresholding function (2nd step).
Then you can directly draw the circles around the pupil and using radius(r) and center of eye(x,y) you can easily find out the Center of Eye..