I am trying to de-noise an image that I've made in order to read the numbers on it using Tesseract.
Noisy image.
Is there any way to do so?
I am kind of new to image manipulation.
from PIL import ImageFilter
im1 = im.filter(ImageFilter.BLUR)
im2 = im.filter(ImageFilter.MinFilter(3))
im3 = im.filter(ImageFilter.MinFilter)
The Pillow library provides the ImageFilter module that can be used to enhance images. Per the documentation:
The ImageFilter module contains definitions for a pre-defined set of filters, which can be be used with the Image.filter() method.
These filters work by passing a window or kernel over the image, and computing some function of the pixels in that box to modify the pixels (usually the central pixel)
The MedianFilter seems to be widely used and resembles the description given in nishthaneeraj's answer.
You have to read Python pillow Documentation
Python pillow Documentation link:
https://pillow.readthedocs.io/en/stable/
Pillow image module:
https://pillow.readthedocs.io/en/stable/reference/ImageFilter.html#module-PIL.ImageFilter
How do you remove noise from an image in Python?
The mean filter is used to blur an image in order to remove noise. It involves determining the mean of the pixel values within a n x n kernel. The pixel intensity of the center element is then replaced by the mean. This eliminates some of the noise in the image and smooths the edges of the image.
Related
I'm looking for a method to understand when an image has a tear in the data -
All I could think of is running vertically pixel by pixel and "understanding" major changes in data
tearing in image:
tears are always horizontal
Any suggestion will be helpful
To solve a similar issue I was having with my images, I was able to filter the images using the standard deviation of the laplacian. If your untorn images are similar to the torn images you may be able to differentiate between them and discard the images with a standard deviation above some value. Other edge detection algorithms such as Canny may work as well.
A simple implementation to import an image and calculate the standard deviation of the laplacian can be done using opencv in python.
import cv2 as cv
ddepth = cv.CV_16S # desired depth of destination image
kernel_size = 3 # Aperture size used to compute the second-derivative filters
path = r"C:\Your\filepath\here" # Directory to get files from
img = cv.imread(path, cv.IMREAD_COLOR) # Read image
std = cv.Laplacian(img, ddepth,
ksize=kernel_size).std() # Get sd of laplacian. If std is too high, maybe image tearing
I am sure there are better approaches to this problem, so hopefully you will get other answers as well.
I have two images:
Exuse the different resolution but that's not the point. On the left I have a "large" blob due to a camera reflection. I want to get rid of that blob, so closing the blob. But on the right I have smaller blobs that are valuable information that I need to keep.
Both of these image need to undergo the same algorithm.
If I use a simple opening the smaller blobs will be gone, too. Is there an easy way to implement this in Python with skimage or/and PIL?
In a perfect world the left image should just create a white circle, where the right image should have the black dots within the white circle. It is okay to change the size of the black dots on the right image.
Here is an image which should describe the problem at the image directly
Ok. So before I answer. I have to tell you that this is a hackish way and has no scientific background.
from skimage import io, measure
import numpy as np
img = io.imread('img.png', as_grey=True)
img = np.invert(img>0)
labeled_img = measure.label(img)
labels = np.unique(labeled_img)
newimg = np.zeros((img.shape[0],img.shape[1]))
for label in labels:
if np.sum(labeled_img==label) < 250:
newimg = newimg + (labeled_img==label)
io.imshow(newimg)
io.show()
Since this is a hackish way, I know I should have commented rather answered, but I don't have enough points to comment.
I need to extract a object of interest (a vehicle ) from a large picture, now I know the 4 coordinates of this vehicle in the picture. How could I crop the image of this vehicle in the picture and then rotate it to 90 degree as shown below
I need to program it in python, but I don’t know which library to use for this functionality ?
You can use PIL (http://www.pythonware.com/products/pil/)
from PIL import Image
im = Image.open("img.jpg")
im.rotate(45)
You also have a crop method ...
You could use PIL and do it like here :
Crop the image using PIL in python
You could use OpenCV and do it like here:
How to crop an image in OpenCV using Python
For the rotation you could use OpenCV's cv::transpose().
Rotating using PIL: http://matthiaseisen.com/pp/patterns/p0201/
I have an image of 300*300 pixels size and want to convert it into 1500*1500 pixels size using python. I need this because i have to georeferenced this image with 1500*1500 pixel raster image. Any python library function or basic fundamental how to do this are welcome.
You should try using Pillow (fork of PIL = Python Image Library)
Simple like this:
from PIL import Image
img = Image.open("my_image.png")
img.resize((1500, 1500, ))
img.save("new_image.png")
Here i have one RGB image where i need want extract plane of intensity.
I have tried HSL, in this i took L Luminosity but its not similar with Intensity, and tried RGB2GRAY but this also little bit similar but not actual.
so is there any special code to get intensity of the image? or is there any calculation of Intensity?
Try to use BGR2GRAY(and so on - BGR2HSL etc) instead of RGB2GRAY - OpenCV usually use BGR channel order, not RGB.
The default format of RGB in OpenCV is BGR. So, you can get the intensity of your image using OpenCV like below:
intensity_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV);
intensity_image[:,:,2] is the value image of your original image
Hope this helps.