Open up a new file, name it  In other words, you can look at the gradient image and still easily say there is a person in the picture.At every pixel, the gradient has a magnitude and a direction. width and height) than Image A, leaving me with a feature vector of size 512-d.How would I go about comparing these two feature vectors?Remember, our extracted feature vectors are supposed to characterize and represent the visual contents of an image. Just like in the Again, I won’t be performing a deep dive on the (very small amount of) machine learning we’ll be using in this lesson — we have the entire But before we dive deep into this project, let’s look at our dataset.Our car logo dataset consists of five brands of vehicles: After gathering the images from Google, I then went outside and took a stroll around the local parking lot, snapping seven photos of car logos. Why not 32×32 ? The scikit-image implementation is far more flexible, and thus we will primarily use the scikit-image implementation throughout this course.Here is an example of how to compute HOG descriptors using scikit-image:Later on in the PyImageSearch Gurus course, we’ll learn how to In the remainder of this lesson, I’ll demonstrate how we can use the Histogram of Oriented Gradients descriptor to characterize the logos of car brands. constant colored background ), but highlighted outlines. Gradients ( x and y derivatives ) of an image are useful because the magnitude of gradients is large around edges and corners ( regions of abrupt intensity changes ) and we know that edges and corners pack in a lot more information about object shape than flat regions.In this section, we will go into the details of calculating the HOG feature descriptor. However, since HOG captures local intensity gradients and edge directions, it also makes for a good texture descriptor.The HOG descriptor returns a real-valued feature vector. Let’s first focus on the pixel encircled in blue. You will notice that dominant direction of the histogram captures the shape of the person, especially around the torso and legs.Unfortunately, there is no easy way to visualize the HOG descriptor in OpenCV.I am a co-founder of TAAZ Inc where the scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. To illustrate each step, we will use a patch of an image.As mentioned earlier HOG feature descriptor used for pedestrian detection is calculated on a 64×128 patch of an image.

The cornerstone of the HOG descriptor algorithm is that appearance of an object can be modeled by the distribution of intensity gradients inside rectangular regions of an image:Implementing this descriptor requires dividing the image into small connected regions called cells, and then for each cell, computing a histogram of oriented gradients for the pixels within each cell. There are three main normalization methods that we can consider:In most cases, it’s best to start with either no normalization or square-root normalization. In fact, these are the exact same image gradients that we learned about in the We’ll be discussing the steps necessary to combine both HOG and a Linear SVM into an object classifier later in this course. Histogram of Oriented Gradients (and car logo recognition)Histogram of Oriented Gradients (and car logo recognition)# construct the argument parse and parse command line arguments# load the image, convert it to grayscale, and detect edges# find contours in the edge map, keeping only the largest one which# extract the logo of the car and resize it to a canonical width# extract Histogram of Oriented Gradients from the logo# load the test image, convert it to grayscale, and resize it to# extract Histogram of Oriented Gradients from the test image and# draw the prediction on the test image and display it Individual graidents may have noise, but a histogram over 8×8 patch makes the representation much less sensitive to noise.But why 8×8 patch ? You can see the histogram has a lot of weight near 0 and 180 degrees, which is just another way of saying that in the patch gradients are pointing either up or down.Before I explain how the histogram is normalized, let’s see how a vector of length 3 is normalized.Let’s say we have an RGB color vector [ 128, 64, 32 ].

But, it is very useful for tasks like image recognition and object detection. The histogram contains 9 bins corresponding to angles 0, 20, 40 … 160. But once you take the time to deconstruct them, the mystery is replaced by mastery and that is what we are after. HOG features were first introduced by Dalal and Triggs in their CVPR 2005 paper, Histogram of Oriented Gradients for Human Detection. It has an angle ( direction ) of 80 degrees and magnitude of 2. At the current time, this project supports calculating the following: Horizontal and vertical gradients. Dalal and Triggs report that using either Here is an example where we have taken an input region of an image, computed a gradient histogram for each cell, and then locally grouped the For each of the cells in the current block we concatenate their corresponding gradient histograms, followed by either L1 or L2 normalizing the entire concatenated feature vector. If your input region is 32We’ll be utilizing HOG descriptors later in this course for object classification, so if you’re a little confused on how to properly tune the parameters, don’t worry — this won’t be the last time you see these descriptors! While this multi-representation is redundant and wasteful of space, it actually increases performance of the descriptor.Finally, after all blocks are normalized, we take the resulting histograms, concatenate them, and treat them as our final feature vector.HOG descriptors are implemented inside the OpenCV and scikit-image library.

We can then accumulate these histograms across multiple cells to form our feature vector.Dalal and Triggs also demonstrated that we can perform Now, let’s review each of the steps for computing the HOG descriptor.This normalization step is entirely optional, but in some cases this step can improve performance of the HOG descriptor.