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https://pyimagesearch.com/blog/ | Table of Contents Understanding Tasks in Diffusers: Part 3 Introduction Why Not Image-to-Image? ControlNet Models Configuring Your Development Environment Setup and Imports Installation Imports Utility Functions Canny ControlNet Setting Up Loading the Model Optimizing the Pipeline Image Generation Cleaning Up… |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Click here to download the source code to this pos
When it comes to learning new technology such as deep learning, configuring your development environment tends to be half the battle. Different operating systems, hardware, dependencies, and the actual libraries themselves can lead to many headaches before you’re even ... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | For the GPU install tutorial part of this series it is a requirement that you be on the metal — a VM just won’t cut it since it doesn’t have access to your physical GPU. Today, our blog post is broken down into four relatively easy steps:
Step #1: Install Ubuntu system dependencies
Step #2: Create your Python 3 virtua... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | To get started, you’ll want to have some time on your hands and access to an Ubuntu machine’s terminal — SSH is perfectly suitable if your box is in the cloud or elsewhere. Let’s begin! Step #1: Install Ubuntu system dependencies
The purpose of this step is to prepare your system with the dependencies necessary for Ope... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Step #2: Create your Python virtual environment
In this section we will setup a Python virtual environment on your system. Installing pip
We are now ready to start configuring our Python development environment for the build. The first step is to install pip , a Python package manager:
$ wget https://bootstrap.pypa.io/... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Again, let me reiterate that it’s standard practice in the Python community to be leveraging virtual environments of some sort, so I suggest you do the same:
$ sudo pip install virtualenv virtualenvwrapper
$ sudo rm -rf ~/.cache/pip get-pip.py
Once we have virtualenv and virtualenvwrapper installed, we need to updat... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | In past install tutorials, I’ve presented the choice of Python 2.7 or Python 3. At this point in the Python 3 development cycle, I consider it stable and the right choice. You may elect to use Python 2.7 if you have specific compatibility requirements, but for the purposes of my new deep learning book we will use Pytho... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | To install NumPy, ensure you are in the dl4cv virtual environment (otherwise NumPy will be installed into the system version of Python rather than the dl4cv environment). From there execute the following command:
$ pip install numpy
Step #3: Compile and Install OpenCV
In this section we will install and compile Open... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Make sure that your Python 3 section looks like the figure below:
Figure 3: Checking that Python 3 will be used when compiling OpenCV 3 for Ubuntu. Pay attention that the Interpreter points to our python3.5 binary located in the dl4cv virtual environment while numpy points to our NumPy install. In either case if you... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Secondly, your .so file (i.e., the actual OpenCV bindings) may be some variant of what is shown above, so be sure to use the appropriate file by double-checking the path. Testing your OpenCV 3.3 install
Now that we’ve got OpenCV 3.3 installed and linked, let’s do a quick sanity test to see if things work:
$ python
>>>... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Before you wrap up the install tutorial take a second to familiarize yourself with the ~/.keras/keras.json file:
{
"image_data_format": "channels_last",
"backend": "tensorflow",
"epsilon": 1e-07,
"floatx": "float32"
}
Ensure that image_data_format is set to channels_last and backend is tensorflow .... |
https://pyimagesearch.com/2017/09/25/configuring-ubuntu-for-deep-learning-with-python/ | Join the Newsletter! Website |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Click here to download the source code to this pos
This blog post is intended for readers who have purchased a copy of my new book, Deep Learning for Computer Vision with Python. Inside this tutorial you’ll learn how to:
Download the books, code, datasets, and any extras associated with your purchase. Obtain your emai... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | My goal is to ensure you receive a huge return on both your investment of time and finances. To ensure you get off on the right foot, this guide will help you get started with your brand new copy of Deep Learning for Computer Vision with Python. Downloading the files
After you successfully checkout and purchase your co... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | File names including *_Code.zip contain your code/datasets associated for the bundle. For example, the file name SB_Code.zip contains all code/datasets associated with the Starter Bundle. The file name SB_Book.zip contains your PDF of the Starter Bundle. Finally, the VirtualMachine.zip file contains your pre-config... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Please go ahead and download these files at your earliest convenience. The service I use to handle payments and distribution of digital downloads automatically expires URLs after four days for security reasons. If your download ever expires, no problem at all, just refer to the “Reactivating an expired download” sectio... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Any other relevant information you may have (purchase number, whether the payment was made via credit card or PayPal, if a friend/colleague purchased for you etc.). From there I can double-check the database and ensure you receive your email receipt and downloads link. Accessing the companion website
Your purchase of D... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Posting an issue, bug report, or typo
The most important reason you should create your account on the companion website is to report an issue, bug, or typo. You can do this by clicking the “Issues” button in the header of the companion website:
Figure 4: If you encounter an error when using the book, please check the “... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Reactivating an expired download
The service I use to handle payments and distribution of digital downloads automatically expires URLs after four days for security reasons. If your URL ever expires, no problem at all — simply email me or send me a message and I can reactivate your purchase for you. Summary
In this tuto... |
https://pyimagesearch.com/2017/09/23/getting-started-deep-learning-computer-vision-python/ | Join the PyImageSearch Newsletter and Grab My FREE 17-page Resource Guide PDF
Enter your email address below to join the PyImageSearch Newsletter and download my FREE 17-page Resource Guide PDF on Computer Vision, OpenCV, and Deep Learning. Join the Newsletter! Website |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | Click here to download the source code to this pos
Updated: September 16th, 2019
Ever since I wrote the first PyImageSearch tutorial on installing OpenCV + Python on the Raspberry Pi B+ back in February 2015 it has been my dream to offer a downloadable, pre-configured Raspbian .img file with OpenCV pre-installed. Since... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | I haven’t conducted a similar survey for the Raspberry Pi 4, but my guess is that it would take most people about 4 hours to configure their Raspberry Pi 4. Clearly, the barrier to entry for many PyImageSearch readers trying to learn OpenCV and computer vision is getting OpenCV itself installed on their Raspberry Pi. I... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | If you have a question that is not covered in FAQ, please send me a message. Download and unpack the archive
When you receive the link to your purchase, be sure to download the book, code, videos, and Raspbian. Each file is in the form of a .zip. The Raspbian.zip contains the preconfigured images and a README.txt fil... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | I recommend the high quality Sandisk 32GB 98MB/s cards. They are available at Amazon and many online distributors. To write the pre-configured Raspbian .img to your card simply follow the official Raspberry Pi documentation. The recommended tool is BalenaEtcher (compatible with Mac, Linux, and Windows). BalenaEtcher ca... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | After the expansion has completed your Pi will reboot and you will be able to use it as normal (the expansion of the filesystem is only necessary on the first boot). Here is a screenshot of the disk utilization on my Pi after it has been auto-expanded:
Figure 3: After booting my Raspberry Pi for the first time your fil... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | It is convenient, but it is a security risk. While we do not recommend using this wireless network long term since this password is public (in nearly all deployment applications you should delete the network + password from your Pi), it is a great way to get connected if you just flashed a microSD. We also recommend ch... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | Furthermore, we can have Intel OpenVINO and Google Coral virtual environments. For a detailed look at Python virtual environments please refer to this tutorial. Python 2.7 support is deprecated
On January 1, 2020, Python.org will no longer be updating Python 2.7 (that goes for security updates too). Read Python 2.7’s s... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | Option 2: Use the source command
You can also just use the following command with the start scripts located in your home directory:
$ source ~/start_py3cv4.sh
Starting Python 3.7 with OpenCV 4.1.1 bindings...
(py3cv4) $
If you use OpenVINO, an additional Intel-provided script will be called automatically via the “sta... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | Which Raspbian images are compatible with which respective hardware? Here is the compatibility listing:
Raspbian3B_4B.img.gz :
Raspberry Pi 4B (1GB, 2GB, and 4GB models)
Raspberry Pi 3B+
Raspberry Pi 3B
RaspbianZeroW.img.gz :
Raspberry Pi Zero W
What if I want to install OpenCV + Python on my Raspberry Pi by my... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | I always encourage you to use my free tutorials on installing OpenCV on the Raspberry Pi, but if you want to save yourself time (and headaches), then definitely consider going with the pre-configured Raspbian .img. Which Practical Python and OpenCV bundles is the Raspbian image included in? The pre-configured Raspbian ... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | How did you reduce the size of the Raspbian image file? To start, I removed unneeded software such as Wolfram’s Mathematica and LibreOffice. Removing these two pieces of software alone saved nearly 1GB of space. From there, the size of the main partition was reduced by zeroing all bits and compressing the file to .gz f... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | What Python packages are installed on the Raspberry Pi? After accessing any virtual environment (see “Accessing a virtual environment”) run pip freeze to see a full list of Python packages installed. In short, I have included all necessary Python packages you will need to be successful executing the examples in Raspbe... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | To purchase your copy of Raspberry Pi for Computer Vision, simply click here, select your bundle (I recommend either the Hacker Bundle or the Complete Bundle if you really want to master the Raspberry Pi), and checkout. Can I purchase the .img as a standalone product? The .img files are intended to accompany my books a... |
https://pyimagesearch.com/2016/11/21/raspbian-opencv-pre-configured-and-pre-installed/ | All bundles come with the pre-configured Raspbian .img files. Or to purchase your copy of Practical Python and OpenCV, just use this link. You will find the pre-configured Raspbian .img files inside both the Quickstart Bundle and the Hardcopy Bundle (the Basic Bundle does not include the Raspbian .img). To see all the ... |
https://pyimagesearch.com/2015/02/04/train-custom-image-classifiers-object-detectors-object-trackers/ | Click here to download the source code to this pos
Did you watch the Super Bowl this past weekend? I did. Kind of. I spent Super Bowl Sunday (which is practically a holiday in the United States) at my favorite Indian bar. Pounding Kingfisher beers. Savoring a delicious dish of Tandoori chicken…
…all while hacking up a ... |
https://pyimagesearch.com/2015/02/04/train-custom-image-classifiers-object-detectors-object-trackers/ | Let’s start off with something simple, training your own face detector:
Figure 1: Inside PyImageSearch Gurus you’ll learn how to train your own custom object detector to detect faces in images. Here you can see that I have trained my custom object detector using the Histogram of Oriented Gradients descriptor and a Line... |
https://pyimagesearch.com/2015/02/04/train-custom-image-classifiers-object-detectors-object-trackers/ | Join PyImageSearch Gurus before the door closes…
As you can see, we’ll be learning a lot of actionable skills inside the PyImageSearch Gurus course. From custom image classifiers, to object detectors, to real-time object tracking, you’re guaranteed to become a computer vision master inside the PyImageSearch Gurus cours... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Click here to download the source code to this pos
I have some big news to announce today…
Besides writing a ton of blog posts about computer vision, image processing, and image search engines, I’ve been behind the scenes, working on a second book. And you may be thinking, hey, didn’t you just finish up Practical Pytho... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Read on and I’ll show you. What does this book cover? This book covers five main topics related to computer vision in the real world. Check out each one below, along with a screenshot of each. #1. Face detection in photos and video
Figure 1: Learn how to use OpenCV and Python to detect faces in images. By far, the most... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Another common question I get asked is “How can I track objects in video?” In this chapter, I discuss how you can use the color of an object to track its trajectory as it moves in the video. #3. Handwriting recognition with Histogram of Oriented Gradients (HOG)
Figure 3: Learn how to use HOG and a Linear Support Vector... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | You could take a picture of the phone number and have it automatically recognized and stored safely. In this chapter of my Case Studies book, you’ll learn how to use the Histogram of Oriented Gradients (HOG) descriptor and Linear Support Vector Machines to classify digits in an image. #4. Plant classification using col... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Using this piece of software, users could snap a photo of books they were interested in, and then have them automatically added to their cart and shipped to their doorstep — at a substantially cheaper price than your standard Barnes & Noble! Anyway, I guess Gregory had one too many beers, because guess what? He clued m... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Ideally, you have already read through Practical Python and OpenCV and have a strong grasp on the basics (if you haven’t had a chance to read Practical Python and OpenCV, definitely pick up a copy). I consider my new Case Studies book to be the next logical step in your journey to learn computer vision. You see, this b... |
https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/ | Website |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Click here to download the source code to this pos
In this tutorial, you will learn to perform both histogram equalization and adaptive histogram equalization with OpenCV. Histogram equalization is a basic image processing technique that adjusts the global contrast of an image by updating the image histogram’s pixel i... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Looking for the source code to this post? Jump Right To The Downloads Section
OpenCV Histogram Equalization and Adaptive Histogram Equalization (CLAHE)
In the first part of this tutorial, we’ll discuss what histogram equalization is and how we can apply histogram equalization with OpenCV. From there, we’ll configur... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | With histogram equalization, our goal is to spread these pixels to buckets that don’t have as many pixels binned to them. Mathematically, what this means is that we’re attempting to apply a linear trend to our cumulative distribution function (CDF):
Figure 2: The histogram equalization goal gives the output image a lin... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | The downside is that adaptive histogram equalization is by definition more computationally complex (but given modern hardware, both implementations are still quite speedy). How can we use OpenCV for histogram equalization? Figure 5: OpenCV provides two functions for histogram equalization: cv2.equalizeHist and cv2.crea... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | All that said, are you:
Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code right now on your Windows, macOS, or Linux system? Then join PyImageSearch Plus today! Gain acce... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Our images directory contains example images to which we will apply histogram equalization. Implementing standard histogram equalization with OpenCV
With our project directory structure reviewed, let’s move on to implementing basic histogram equalization with OpenCV. Open the simple_equalization.py file in your project... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | To perform histogram equalization on a multi-channel image, you would need to (1) split the image into its respective channels, (2) equalize each channel, and (3) merge the channels back together. The final step is to show our output images:
# show the original grayscale image and equalized image
cv2.imshow("Input", gr... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | The dog (left) appears washed out due to underexposure. By applying histogram equalization (right), we correct this effect and improve the dog’s contrast. The following image highlights one of the limitations of global contrast adjustment via histogram equalization:
$ python simple_equalization.py --image images/boston... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | To start, the fire in the fireplace is totally washed out. And if you study our faces, particularly mine, you’ll see that portions of my forehead are now totally washed out. To improve our results, we need to apply adaptive histogram equalization. Implementing adaptive histogram equalization with OpenCV
At this point, ... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | If you set the value too large, then effectively, what you’re doing is maximizing local contrast, which will, in turn, maximize noise (which is the opposite of what you want). Instead, try to keep this value as low as possible.--tile: The tile grid size for CLAHE. Conceptually, what we are doing here is dividing our in... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | On the left, we have our original input image. We then apply adaptive histogram equalization on the right — compare these results to that of Figure 4, where we applied basic histogram equalization. Notice how adaptive histogram equalization has improved the contrast of the input image. My wife and I are more visible. T... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Additionally, I acknowledge the contributors to Wikipedia’s page on histogram equalization. If you’re interested in more mathematical details behind histogram equalization, be sure to refer to that page. The example moon.png image was obtained from this article on EarthSky, while the dog.png image came from this page. ... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should ... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Basic histogram equalization aims to improve the global contrast of an image by “spreading out” pixel intensities often used in the image. But while simple histogram equalization is easy to apply and computationally efficient, the problem is that it can increase noise. What would be basic noise that could be easily fil... |
https://pyimagesearch.com/2021/02/01/opencv-histogram-equalization-and-adaptive-histogram-equalization-clahe/ | Website |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Click here to download the source code to this pos
In this tutorial, you will learn how to translate and shift images using OpenCV. Translation is the shifting of an image along the x- and y-axis. To translate an image using OpenCV, we must:
Load an image from diskDefine an affine transformation matrixApply the cv2.wa... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Defining a translation matrix with OpenCV
To perform image translation with OpenCV, we first need to define a 2 x 3 matrix called an affine transformation matrix:
Figure 1: To translate an image with OpenCV, we must first construct an affine transformation matrix. For the purposes of translation, all we care about are ... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | All that said, are you:
Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code right now on your Windows, macOS, or Linux system? Then join PyImageSearch Plus today! Gain acce... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Using translation, we can shift an image up, down, left, or right, along with any combination of the above. Mathematically, we define a translation matrix, M, that we can use to translate an image:
Figure 3: Defining an image translation matrix with OpenCV. This concept is better explained through some code:
# import ... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Let’s now load our image from disk and perform our first translation:
# load the image and display it to our screen
image = cv2.imread(args["image"])
cv2.imshow("Original", image)
# shift the image 25 pixels to the right and 50 pixels down
M = np.float32([[1, 0, 25], [0, 1, 50]])
shifted = cv2.warpAffine(image, M, (im... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Notice how the image has clearly been “shifted” down and to the right. Let’s examine another example of image translation with OpenCV. # now, let's shift the image 50 pixels to the left and 90 pixels
# up by specifying negative values for the x and y directions,
# respectively
M = np.float32([[1, 0, -50], [0, 1, -90]])... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | Note: If you are interested in seeing the implementation of the imutils.translate function, simply refer to my GitHub repo. OpenCV image translation results
To perform image translation with OpenCV, be sure to access the “Downloads” section of this tutorial to retrieve the source code and example image. You can then ex... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should ... |
https://pyimagesearch.com/2021/02/03/opencv-image-translation/ | You accomplished this task by first defining an affine transformation matrix:
Figure 9: To translate an image with OpenCV, we must first construct an affine transformation matrix. You then specified how you wanted to shift the image:
Negative values for the value will shift the image to the leftPositive values for s... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Click here to download the source code to this pos
In this tutorial, you will learn how to perform connected component labeling and analysis with OpenCV. Specifically, we will focus on OpenCV’s most used connected component labeling function, cv2.connectedComponentsWithStats. Connected component labeling (also known a... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Looking for the source code to this post? Jump Right To The Downloads Section
OpenCV Connected Component Labeling and Analysis
In the first part of this tutorial, we’ll review the four (yes, four) functions OpenCV provides to perform connected component analysis. The most popular of these functions is cv2.connected... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | In the vast majority of situations, you will need the stats, so it’s worth simply using cv2.connectedComponentsWithStats instead. The third method, cv2.connectedComponentsWithAlgorithm, implements faster, more efficient algorithms for connected component analysis. If you have OpenCV compiled with parallel processing su... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Then join PyImageSearch Plus today! Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required. And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project st... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Open up the basic_connected_components.py file in your project folder, and let’s get to work:
# import the necessary packages
import argparse
import cv2
# construct the argument parser and parse the arguments
ap = argparse. ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
a... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | For each location in labels, we have an integer ID value that corresponds to the connected component where the pixel belongs. You’ll learn how to filter the labels matrix later in this section.stats: Statistics on each connected component, including the bounding box coordinates and area (in pixels).The centroids (i.e.,... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | OpenCV connected component analysis results
We are now ready to perform connected component labeling with OpenCV! Be sure to access the “Downloads” section of this tutorial to retrieve the source code and example image:
$ python basic_connected_components.py --image license_plate.png
[INFO] examining component 1/17 (ba... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | How to filter connected components with OpenCV
Our previous code example demonstrated how to extract connected components with OpenCV, but it didn’t demonstrate how to filter them. Let’s learn how we can filter connected components now:
# import the necessary packages
import numpy as np
import argparse
import cv2
# co... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Note: Wondering how I came up with these values? I used print statements to show the width, height, and area for each connected component while visualizing them individually to my screen. I noted the width, height, and area for the license plate characters and found their minimum/maximum values, with a bit of tolerance... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Bottom: Output after applying connected component filtering with OpenCV. Notice how we’ve been able to filter out just the characters from the license plate. Figure 5 displays the results of filtering our connected components. On the top, we have our original input image containing the license plate. The bottom has the... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | 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? That’s not the case. All you need to master computer vision and deep learning is for someone to explain things t... |
https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Inside PyImageSearch University you'll find:
✓ 84 courses on essential computer vision, deep learning, and OpenCV topics
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https://pyimagesearch.com/2021/02/22/opencv-connected-component-labeling-and-analysis/ | Download the code! Website |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Click here to download the source code to this pos
In this tutorial, you will learn how to defend against adversarial image attacks using Keras and TensorFlow. So far, you have learned how to generate adversarial images using three different methods:
Adversarial images and attacks with Keras and TensorFlowTargeted adv... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | If you’re deploying models into the real-world, then be sure you have procedures in place to defend against adversarial attacks. By following these tutorials, you can train your CNNs to make correct predictions even if they are presented with adversarial images. To learn how to train a CNN to defend against adversarial... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Defending against adversarial attacks has been and will continue to be an active research area. There is no “magic bullet” method that will make your model robust to adversarial attacks. Instead, you should reframe your thinking of adversarial attacks — it’s less of a “magic bullet” procedure and more like an arms race... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | For each action, there is an equal reaction. It’s not just physics, and it’s the way of the world. It would not be wise to assume that our computer vision and deep learning models exist in a vacuum, devoid of manipulation. They can (and are) manipulated. Just like our computers can contract viruses developed by hackers... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Typically, this type of adversarial inoculation is applied by either:
Training our model on a given dataset, generating a set of adversarial images, and then fine-tuning the model on the adversarial imagesGenerating mixed batches of both the original training images and adversarial images, followed by fine-tuning our n... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Additionally, if we train only on adversarial images then the model might not perform well on the regular images. This phenomenon is often referred to as catastrophic forgetting, and in the context of adversarial defense, means that the model has “forgotten” what a real image looks like. To mitigate this problem, we fi... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | All that said, are you:
Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code right now on your Windows, macOS, or Linux systems? Then join PyImageSearch University today! Ga... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Finally, train_adversarial_defense.py glues all these pieces together and will demonstrate:
How to train our CNN architectureHow to evaluate the CNN on our testing setHow to generate batches of image adversaries using our trained CNNHow to evaluate the accuracy of our CNN on the image adversariesHow to fine-tune our CN... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | If you open the fgsm.py file in the pyimagesearch module, you will find the following code:
# import the necessary packages
from tensorflow.keras.losses import MSE
import tensorflow as tf
def generate_image_adversary(model, image, label, eps=2 / 255.0):
# cast the image
image = tf.cast(image, tf.float32)
# record ... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | We then initialize two lists, perturbImages (to store the batch of adversarial images generated later in this while loop) and perturbLabels (to store the original class labels for the image). Lines 19 and 20 randomly sample a set of our images. We can now loop over the indexes of each of these randomly selected images:... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Notice that we’re importing our SimpleCNN architecture along with the generate_adverserial_batch function, which we just implemented. We then proceed to load the MNIST dataset and preprocess it:
# load MNIST dataset and scale the pixel values to the range [0, 1]
print("[INFO] loading MNIST dataset...")
(trainX, trainY)... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | As we’ll see in the next section, fine-tuning our CNN on these adversarial examples allows our model to make correct predictions for both the original images and images generated by adversarial techniques! Adversarial image defense results
We are now ready to train our CNN to defend against adversarial image attacks! S... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Accuracy has dropped on the testing set by 0.5%, but the good news is that we’re now hitting 99% accuracy when classifying our adversarial images, thereby implying that:
Our model can make correct predictions on the original, non-perturbed images from the MNIST dataset. We can also make accurate predictions on the gene... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | We recommend PyImageSearch University. Course information:
84 total classes • 114+ hours of on-demand code walkthrough videos • Last updated: February 2024
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I strongly believe that if you had the right teacher you could master computer vision and deep learning. Do... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects. Join me in computer vision mastery. Inside PyImageSearch University you'll find:
✓ 84 courses on essential computer vision, deep learning, and OpenCV topics
✓ 84 Certificates of Completion
✓ 114+ hours of ... |
https://pyimagesearch.com/2021/03/08/defending-against-adversarial-image-attacks-with-keras-and-tensorflow/ | Download the Source Code and FREE 17-page Resource Guide
Enter your email address below to get a .zip of the code and a FREE 17-page Resource Guide on Computer Vision, OpenCV, and Deep Learning. Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! Download the code! W... |
https://pyimagesearch.com/2021/03/15/mixing-normal-images-and-adversarial-images-when-training-cnns/ | Click here to download the source code to this pos
In this tutorial, you will learn how to generate image batches of (1) normal images and (2) adversarial images during the training process. Doing so improves your model’s ability to generalize and defend against adversarial attacks. Last week we learned a simple metho... |
https://pyimagesearch.com/2021/03/15/mixing-normal-images-and-adversarial-images-when-training-cnns/ | After each batch update, the model has improved by two factors. First, the model has ideally learned more discriminating patterns in the training data. Secondly, the model has learned to defend against adversarial examples that the model itself generated. Throughout an entire training procedure (tens to hundreds of epo... |
https://pyimagesearch.com/2021/03/15/mixing-normal-images-and-adversarial-images-when-training-cnns/ | Let’s get started! How can we mix normal images and adversarial images during training? Mixing training images with adversarial images is best explained visually. We start with both a neural network architecture and a training set:
Figure 1: To defend against adversarial attacks, we start with a neural network archite... |
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