I am using spectral to view an hyperspectral image for a specific band in python. This is my code.
from spectral import *
img=open_image('flc1.lan')
view = imshow(img)
print(view)
gt=open_image('flc1.lan').read_band(0)
view1= imshow(classes=gt)
print(view1)
The image does pop up, but then closes. Is there any kind of function like waitkey to hold the window?
As an alternative you can use :
envi.save_image(give_file_name, image_name, ext='hdr')
to save the image locally and view it later, at your discretion
Related
I'm currently working on cloud removals from satellite data (I'm pretty new).
This is the image I'm working on (TIFF)
And this is the mask, where black pixels represent clouds (JPG)
I'm trying to remove the clouds from the TIFF, using the mask to identify the position of the cloud, and the cloudless image itself, like this (the area is the same, but the period is different):
I'm kindly ask how can I achieve that. A Python solution, with libraries like Rasterio or skimage is particularly appreciated.
Thanks in advance.
You can read the images with rasterio, PIL, OpenCV or tifffile, so I use OpenCV
import cv2
import numpy as np
# Load the 3 images
cloudy = cv2.imread('cloudy.png')
mask = cv2.imread('mask.jpg')
clear = cv2.imread('clear.png')
Then just use Numpy where() to choose whether you want the clear or cloudy image at each location according to the mask:
res = np.where(mask<128, clear, cloudy)
Note that if your mask was a single channel PNG rather than JPEG, or if it was read as greyscale like this:
mask = cv2.imread('mask.jpg', cv2.IMREAD_GRAYSCALE)
you would have to make it broadcastable to the 3 channels of the other two arrays by adding a new axis like this:
res = np.where(mask[...,np.newaxis]<128, clear, cloudy)
I am making a discord bot with discord.py. Currently, I'm working on a LaTeX printer with sympy.
I have the code:
#commands.command(name="latex",
aliases=["tex", "tx"])
async def texPrt(self, ctx, *, text):
Funcs.command_exec(ctx)
expre = sympify(text, evaluate=False)
preview(expre, viewer="file", filename="output.png")
await ctx.send(file=discord.File(f"./output.png", filename="LaTeX_output.png"))
This takes the equation that the user enters and outputs a png image:
The only issue is that the image that comes out is very small and low resolution. Is there a way to make it render the image larger and increase the font size.
Also if possible I would like to change the background colour to be similar to discord, but that's not the priority.
Set the density parameter in dvioptions:
preview(expr, viewer="file", filename="output.png", dvioptions=['-D','1200'])
I am trying to create a simple robot simulator with 3D + 2D(bird-eye view mini-map) like the below image.
My map file is just a list of vertices for polygon and center/radius for circles (all objects are heights of 1 where z = 0).
I found that python VTK plotter makes it really easy to visualize simple object but there is a lack of documentation for the multi-view windows. I also tried open-cv but it creates a 2D image in a separate window.
What would be the easiest way to achieve a simulator like below? There would be very few objects on the map so efficiency is not my concern.
My strategy for making a 2D mini-map overlay like this is to use glWindowPos2d and glDrawPixels, and I have found it to be very successful. You'll want to turn off common OpenGL features like texturing, lighting, and the depth test. In the following example, minimap_x and minimap_y are the window coordinates of the upper-left corner of the minimap.
For example:
glDisable(GL_TEXTURE_2D)
glDisable(GL_LIGHTING)
glDisable(GL_DEPTH_TEST)
glWindowPos2d(minimap_x, window_height - (minimap_y + minimap_height))
glDrawPixels(minimap_width, minimap_height, GL_RGBA, GL_UNSIGNED_BYTE, minimap_image)
glEnable(GL_TEXTURE_2D)
glEnable(GL_LIGHTING)
glEnable(GL_DEPTH_TEST)
You'll need to provide the minimap_image data.
In my applications, I'm typically using PyGame, and so the minimap is on a PyGame Surface. Converting the Surface to raw image data usable by glDrawPixels looks like this:
minimap_image = pygame.image.tostring(minimap_surface, "RGBA", True)
The goal is to identify that the input scanned image is passport or PAN card using Opencv.
I have used structural_similarity(compare_ssim) method of skimage to compare input scan image with the images of template of Passport and PAN card.
But in both cases i got low score.
Here is the code that i have tried
from skimage.measure import compare_ssim as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2enter code here
img1 = cv2.imread('PAN_Template.jpg', 0)
img2 = cv2.imread('PAN_Sample1.jpg', 0)
def prepare_img(im):
size = 300, 200
im = cv2.resize(im, size)
return im
img1 = prepare_img(img1)
img2 = prepare_img(img2)
def compare_images(imageA, imageB):
s = ssim(imageA, imageB)
return s
ssim = compare_images(img1, img2)
print(ssim)
Comparing the PAN Card Template with Passport i have got ssim score of 0.12
and Comparing the PAN Card template with a PAN Card the score was 0.20
Since both the score were very close i wast not able to distinguish between them through the code.
If anyone got any other solution or approach then please help.
Here is a sample image
PAN Scanned Image
You can also compare 2 images by the mean square error (MSE) of those 2 images.
def mse(imageA, imageB):
# the 'Mean Squared Error' between the two images is the
# sum of the squared difference between the two images;
# NOTE: the two images must have the same dimension
err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
err /= float(imageA.shape[0] * imageA.shape[1])
# return the MSE, the lower the error, the more "similar"
# the two images are
return err
As per my understanding Pan card and Passport images contain different text data, so i believe OCR can solve this problem.
All you need to do is- extract the text data from the images using any OCR library like Tesseract and look for a few predefined key words in the text data to differentiate the images.
Here is simple Python script showing the image pre-processing and OCR using pyteseract module:
img = cv2.imread("D:/pan.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv2.imwrite('filterImg.png', th1)
pilImg = Image.open('filterimg.png')
text = pytesseract.image_to_string(pilImg)
print(text.encode("utf-8"))
Below is the binary image used for OCR:
I got the below string data after doing the OCR on the above image:
esraax fram EP aca ae
~ INCOME TAX DEPARTMENT Ld GOVT. OF INDIA
wrtterterad sg
Permanent Account Number. Card \xe2\x80\x98yf
KFWPS6061C
PEF vom ; ae
Reviavs /Father's Name. e.
SUDHIR SINGH : . ,
Though this text data contains noises but i believe it is more than enough to get the job done.
Another OCR solution is to use TextCleaner ImageMagick script from Fred's Scripts. A tutorial which explain how to install and use it (on Windows) is available here.
Script used:
C:/cygwin64/bin/textcleaner -g -e normalize -f 20 -o 20 -s 20 C:/Users/Link/Desktop/id.png C:/Users/Link/Desktop/out.png
Result:
I applied OCR on this with Tesseract (I am using version 4) and that's the result:
fart
INCOME TAX DEPARTMENT : GOVT. OF INDIA
wort cra teat ears -
Permanent Account Number Card
KFWPS6061C
TT aa
MAYANK SUDHIR SINGH el
far aT ary /Father's Name
SUDHIR SINGH
Wa RT /Date of Birth den. +
06/01/1997 genge / Signature
Code for OCR:
import cv2
from PIL import Image
import tesserocr as tr
number_ok = cv2.imread("C:\\Users\\Link\\Desktop\\id.png")
blur = cv2.medianBlur(number_ok, 1)
cv2.imshow('ocr', blur)
pil_img = Image.fromarray(cv2.cvtColor(blur, cv2.COLOR_BGR2RGB))
api = tr.PyTessBaseAPI()
try:
api.SetImage(pil_img)
boxes = api.GetComponentImages(tr.RIL.TEXTLINE, True)
text = api.GetUTF8Text()
finally:
api.End()
print(text)
cv2.waitKey(0)
Now, this don't answer at your question (passport or PAN card) but it's a good point where you can start.
Doing OCR might be a solution for this type of image classification but it might fail for the blurry or not properly exposed images. And it might be slower than newer deep learning methods.
You can use Object detection (Tensorflow or any other library) to train two separate class of image i.e PAN and Passport. For fine-tuning pre-trained models, you don't need much data too. And as per my understanding, PAN and passport have different background color so I guess it will be really accurate.
Tensorflow Object Detection: Link
Nowadays OpenCV also supports object detection without installing any new libraries(i.e.Tensorflow, caffee, etc.). You can refer this article for YOLO based object detection in OpenCV.
We can use:
Histogram Comparison - Simplest & fastest methods, using this we will get the similarity between histograms.
Template Matching - Searching and finding the location of a template image, using this we can find smaller image parts in a bigger one. (like some common patterns in PAN card).
Feature Matching - Features extracted from one image and the same feature will be recognised in another image even if the image rotated or skewed.
I am a beginner in VTK ITK, I am trying to read a DICOM series with ITK and display with VTK but I had pictures upside down, I tried to read a single image (JPG) with ITK and visualuser with VTK it is the same problem, so I had the idea of treating the image on photoshop ie I applied to the original image rotation (vertical symmetry of the work area) and I did the reading with ITK and display with VTK, the image is displayed in the correct orientation, infact ITK keeps the orientation of the image, but the problem is at VTK, it is which displays the image upside down, I searched all over the internet I have not found a solution or a method or not even an idea, I encountered the same problem in many forums but there is no response, I count on your help, I can not apply any image processing to find a solution to this problem.
Please Help! thank you in advance
Ideally you should re-orient your camera in VTK so that it is suited for medical image visualization. (The default camera in VTK uses the computer graphics conventions).
If you want a quick hack, you can copy-paste the following code in ITK:
FlipFilterType::Pointer flipperImage = FlipFilterType::New();
bool flipAxes[3] = { false, true, false };
flipperImage = FlipFilterType::New();
flipperImage->SetFlipAxes(flipAxes);
flipperImage->SetInput( image );
flipperImage->Update();
I use a rapid way to set the orientation:
imageActor->SetOrientation(180,0,0);
No need to add filter.
Here's an example of how I would do it. I'm not sure what classes you are using, so I cannot be specific.
vtkSmartPointer<vtkImageData> result = vtkSmartPointer<vtkIMageData>::New();
result->DeepCopy(YourImage); //DeepCopy your image to result
rImage->Update();
double val;
int i = 0;
for(vtkIdType f = result->GetNumberOfPoints()-1; f > -1; f--)
{
val = YourImage->GetPointData()->GetScalars()->GetTuple1(f);
result->GetPointData()->GetScalars->SetTuple1(i,val);
i++;
}
result->Update();
//Now Visualize your image