Perceptual image hashing

Added: Marin Smoot - Date: 21.01.2022 14:34 - Views: 15926 - Clicks: 4011

The remaining introduction to this blog post is very personal and covers events that happened in my life five years ago, nearly to this very day. But while PyImageSearch is a computer vision and deep learning blog, I am a very real human that writes it. Some of you may recognize this name — it appears in the dedication of all my books and publications. Josie was a Perceptual image hashing, a perfect, loving, caring beagle, that my dad got for me when I was 11 years old.

She was the last thread that tied my childhood to my adulthood. Any unadulterated feelings of childhood innocence were tied to that thread. I grew up in a broken home. My mother suffered and still does from bipolar schizophrenia, depression, severe anxiety, and a host of other mental afflictions, too many for Perceptual image hashing to enumerate. And when she did accept help, if often did not go well. My childhood consisted of a seemingly endless parade of visitations to the psychiatric hospital followed by nearly catatonic interactions with my mother.

When she came out of the catatonia, my home life often descended into turmoil and havoc. My dad, who must have recognized the potentially disastrous trajectory my early years were on and how it could have a major impact on my well-being as an adultbrought home a beagle puppy for me when I was 11 years old, most likely to help me hold on to a piece of my childhood.

As a kid, there is no better feeling than holding a puppy, feeling its heartbeat against yours, playfully squirming and wiggling in and out Perceptual image hashing your arms, only to fall asleep on your lap five minutes later. And whenever my home life turned into screaming, yelling, and utterly incomprehensible shrieks of tortured mental illness, Josie was always right there next to me.

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As Perceptual image hashing grew into those awkward mid-to-late teenage years, I started to suffer from Perceptual image hashing issues myself, a condition, I later learned, all too common for kids growing up in these circumstances. Despite my own issues as a teenager, trying to grow up and somehow grasp what was going on with myself and my family, Josie always laid next to me, keeping me company, and reminding me of what it was like to be a. The following year was brutal. I was finishing up my final semester of classes for my PhD, about to start my dissertation. I was working full-time.

And I even had some side projects going on…. However, soon after Josie died I found a bit of solace in collecting and organizing all the photos my family had of her. This therapeutic, nostalgic task involved scanning physical photos, going through old SD cards for digital cameras, and even digging through packed away boxes to find long forgotten cellphones that had pictures on their memory cards. It was tedious, manual work but that was just the work I needed. I needed to take a break for my own mental well-being.

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And I find it so perfectly eloquent that I can apply computer vision, my passion, to finish a task that means so much to me. By utilizing image hashing algorithms we can find near-identical images in constant time, or at worst, O lg n time when utilizing the proper data structures. Readers with backgrounds in cryptography or file verification i. The problem here lies in the very Perceptual image hashing of cryptographic hashing algorithms: changing a single bit in the file will result in a different hash.

An example of this is seen in Figure 2 above. Here I take an input image and compute the md5 hash. I then resize the image to have a width of pixels rather than pixels — no other alterations to the image were made. I then recompute the md5 hash.

Notice how the hash values have changed even though the visual contents of the image have not! In the case of image hashing and perceptual hashing, we actually want similar images to have similar hashes as well. Therefore, we actually seek some hash collisions if images are similar. The goal of this project is to help me develop a computer vision application that can using the needle and haystack analogy :.

My haystack in this case is my collection of photos in iPhotos — the name of this directory is Masters :. As we can see from the screenshots, my Masters directory contains 11, photos, totaling My goal is to determine which directories and images have already been imported into iPhoto and which directories I still need to import into iPhoto and organize. The image hashing algorithm Perceptual image hashing will be implementing for this blog post is called difference hashing or simply dHash for short. The first step in our image hashing algorithm is to convert Perceptual image hashing input image to grayscale and discard any color information.

If, for whatever reason, you are especially interested in color you can run the hashing algorithm on each channel independently and then combine at the end although this will result in a 3x larger hash. Well, keep in mind the name of the algorithm we are implementing: difference hash. The difference hash algorithm works by computing the difference i. If we take an input image with 9 pixels per row and compute the difference between adjacent column pixels, we end up with 8 differences. Eight rows of eight differences i. If this point is confusing, no worries, it will all become clear once we start actually looking at some code.

The final step is to as bits and build the resulting hash. To accomplish this, we use a simple binary test. In this case, we are testing if the left pixel is brighter than the right pixel. If the left pixel Perceptual image hashing brighter we set the output value to one.

Otherwise, if the left pixel is darker we set the output value to zero. Typically we use the Hamming distance to compare hashes.

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The Hamming distance measures the of bits in two hashes that are different. In practice you may need to tune these thresholds for your own applications and corresponding datasets. I will leave optimizing the search to compute Hamming differences for a future tutorial here on PyImageSearch. My implementation of image hashing and difference hashing is inspired by the imagehash library on GitHubbut tweaked to 1 use OpenCV instead of PIL and 2 correctly in my opinion utilize the full bit hash rather than compressing it.

Because of this, we need to rely on our perceptual hashing algorithm that can handle these slight variations to the input images. To get started, make sure you have installed my imutils packagea series of convenience functions to make working with OpenCV easier and make sure you access your Python virtual environment, assuming you are using one :.

Lines handle importing our required Python packages. Make sure you have imutils installed to have access to the paths submodule. Our dhash function requires an input image along with an optional hashSize. Given the resized image we can compute the binary diff on Line 16which tests if adjacent pixels are brighter or darker Step 3.

Finally, Line 19 builds the hash by converting the boolean values into a bit Perceptual image hashing Step 4. Our goal is to determine whether each image in --needles exists in --haystack or not. When implementing this script, a of images in my dataset had spaces in their filenames. User beware. Line 43 grabs the subdirectory names inside needlePaths — I need these subdirectory names to determine which folders have already been added to the haystack and Perceptual image hashing subdirectories I still need to examine. Line 44 then initializes haystacka dictionary that will map image hashes to respective filenames.

We are now ready to extract image hashes for our haystackPaths :. On Line 48 we are Perceptual image hashing over all image paths in haystackPaths. For each image we load it from disk Line 50 and check to see if the image is None Lines 54 and If the image is None then the image could not be properly read from disk, likely due to an issue with the image encoding a phenomenon you can about hereso we skip the image.

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Lines 58 and 59 compute the imageHash while Lines maintain a list of file paths that map to the Perceptual image hashing hash value. We can then move on to extracting the hash values from our needlePaths :. The difference is that we are no longer storing the hash value in haystack.

Instead, we now check the haystack dictionary to see if there are any image paths that have the same hash value Line If there are images with the same hash value, then I know I have already manually examined this particular subdirectory of images and added them to iPhoto.

Instead, I have included sample images from the UKBench dataset that you can play with. To determine which directories i. This library includes a of image hashing implementations, including difference hashing, average hashing, and others. I strongly believe that if you had the right teacher you could master computer vision and deep learning.

Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science? All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. My mission is to change education and how complex Artificial Intelligence topics are taught. If Perceptual image hashing serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today.

Perceptual image hashing

email: [email protected] - phone:(768) 142-6447 x 4573

Perceptual hashing