ImageHash | Library containing perceptual hash algorithms | Hashing library
kandi X-RAY | ImageHash Summary
kandi X-RAY | ImageHash Summary
Library containing perceptual hash algorithms using the ImageSharp library
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ImageHash Key Features
ImageHash Examples and Code Snippets
Lines of Code : 33
License : No License
Copy# import the necessary packages
from PIL import Image
import imagehash
import argparse
import shelve
import glob
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True Community Discussions
Trending Discussions on ImageHash
QUESTION
I am using Perceptual hashing technique to find near-duplicate and exact-duplicate images. The code is working perfectly for finding exact-duplicate images. However, finding near-duplicate and slightly modified images seems to be difficult. As the difference score between their hashing is generally similar to the hashing difference of completely different random images.
To tackle this, I tried to reduce the pixelation of the near-duplicate images to 50x50 pixel and make them black/white, but I still don't have what I need (small difference score).
This is a sample of a near duplicate image pair:
Image 1 (a1.jpg):
Image 2 (b1.jpg):
The difference between the hashing score of these images is : 24
When pixeld (50x50 pixels), they look like this:
rs_a1.jpg
rs_b1.jpg
The hashing difference score of the pixeled images is even bigger! : 26
Below two more examples of near duplicate image pairs as requested by @ann zen:
Pair 1
Pair 2
The code I use to reduce the image size is this :
...ANSWER
Answered 2022-Mar-22 at 12:48Rather than using pixelisation to process the images before finding the difference/similarity between them, simply give them some blur using the cv2.GaussianBlur() method, and then use the cv2.matchTemplate() method to find the similarity between them:
QUESTION
Background
I am trying to plot an image noise using pytorch, however, when I reach to that point, the kernel dies. I am attempting the same code at Google Colab where I do get results
Result at Google Colab
Result at Jupyter
I do not think that it has something to do with the code itself, but I am posting the function to plot the grid:
...ANSWER
Answered 2022-Feb-28 at 22:25After a few days I was able to find the solution
Firstly, my code needed to be fixed to correctly call the params needed with the proper name
QUESTION
Sorry for my bad english, i have a small database that contains hashes of photos, when I try to find similar photos to the one below:
for which the following hash was calculated: "0f3f2764ecc482c2" using the method average_hash()
...ANSWER
Answered 2021-Oct-19 at 02:51You need to convert the hex strings to integers before doing xor operations
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
I have a state called "itemQty" where i want it to increase whenever the plus button is pressed and decrease whenever the minus button is pressed. However, now when i pressed the button, the state changes for every card in the page. I only one it to change in the card where the button is. I understand I have to use the key of the mapping. But I am not sure how to. Can someone help me? This is where my both my qty will change to the same. [![enter image description here][1]][1]
This is my state where the default is set to 1. But when i press one the plus button on the first card, both quantity becomes 2.
...ANSWER
Answered 2021-Jul-26 at 15:12So there are 2 options to do what you want:
- Make every card item a component with its own state, which will include quantity and change it on single item level
QUESTION
How can I properly install PyCaret in AWS Glue?
Methods I tried:
--additional-python-modulesand--python-modules-installer-optionPython library patheasy_installas described in Use AWS Glue Python with NumPy and Pandas Python Packages
I am using Glue Version 2.0. I used --additional-python-modules and set to pycaret as shown in the picture.
Then I got this error log.
...ANSWER
Answered 2021-Jul-08 at 17:01I reached out to AWS support. Meghana was in charge of this case.
Here is the reply:
QUESTION
I want to get fingerprints for images with the help of the imagehash function in Python but in order to apply
...ANSWER
Answered 2021-Jun-24 at 17:05You can use requests:
QUESTION
I'm creating a webapp which uses Solidity, Web3 and React. My issue is that I have a function in my smart contract to return two different arrays at the index submitted by a user from a form. Currently I've been able to save the two results from the contract methods call into a state variable which has two arrays.
State Constructor
...ANSWER
Answered 2021-May-20 at 05:53From your comment i believe you have an array with 2 more arrays in it and you need to iterate over those to display its value. Currently ur iterating over the outer array only. Try below code if it works:
QUESTION
I am trying to perform difference hashing with the python ImageHash library and keep getting a numpy error.
The error:
File "/Users/testuser/Desktop/test_folder/test_env/lib/python3.8/site-packages/imagehash.py", line 252, in dhash image = image.convert("L").resize((hash_size + 1, hash_size), Image.ANTIALIAS) AttributeError: 'numpy.ndarray' object has no attribute 'convert'
The code:
...ANSWER
Answered 2021-Apr-27 at 04:42as it is mentioned in imagehash library's document, @image must be a PIL instance.. so you cant set numpy array as input of the dshash function.if you want do some preprocess with opencv, you should convert it into PIL array before setting it into dhash, like this :
QUESTION
For a university project I have to recognize characters from a license plate. I have to do this using python 3. I am not allowed to use OCR functions or use functions that use deep learning or neural networks. I have reached the point where I am able to segment the characters from a license plate and transform them to a uniform format. A few examples of segmented characters are here.
The format of the segmented characters is very dependent on the input. However, I can easily convert this to uniform dimensions using opencv. Additionally, I have a set of template characters and numbers that I can use to predict what character / number it is.
I therefore need a metric to express the similarity between the segmented character and the reference image. In this way, I can say that the reference image with the highest similarity score matches the segmented character. I have tried the following ways to compute the similarity.
For these operations I have made sure that the reference characters and the segmented characters have the same dimensions.
- A bitwise XOR-operator
- Inverting the reference characters and comparing them pixel by pixel. If a pixel matches increment the similarity score, if a pixel does not match decrement the similarity score.
- hash both the segmented character and the reference character using 'imagehash'. Consequently comparing the hashes and see which ones are most similar.
None of these methods succeed to give me an accurate prediction for all characters. Most characters are usually correctly predicted. However, the program confuses characters like 8-B, D-0, 7-Z, P-R consistently.
Does anybody have an idea how to predict the segmented characters? I.e. defining a better similarity score.
Edit: Unfortunately, cv2.matchTemplate and cv2.matchShapes are not allowed for this assignment...
...ANSWER
Answered 2021-Mar-01 at 17:03The general procedure for comparing two images consists in the extraction of features from the two images and their subsequent comparison. What you are actually doing in the first two methods is considering the value of every pixel as a feature. The similarity measure is therefore a distance-computation on a space of very high dimension. This methods are, however, subject to noise and this requires very big datasets in order not to obtain acceptable results.
For this reason, usually one attempts to reduce the space dimensionality. I'm not familiar with the third method, but it seems to go in this direction.
A way to reduce the space dimensionality consists in defining some custom features meaningful for the problem you are facing.
A possibility for the character classification problem could be to define features that measure the response of the input image on strategic subshapes of the characters (an upper horizontal line, a lower one, a circle in the upper part of the image, a diagonal line, etc.). You could define a minimal set of shapes that, combined together, can generate every character. Then you should retrieve one feature for each shape, by measuring the response (i.e., integrating the signal of the input image inside the shape) of the original image on that particular shape. Finally, you should determine the class which the image belongs to by taking the nearest reference point in this, smaller, space of the features.
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