Hog Descriptor Opencv Python
Python programming language:
Python is an interactive and more accessible language than any other programming language. The python programming language uses a variety of libraries to perform the operations in a faster way. The python language can also be used in web development; Django and Flask are the frameworks used to create web applications using Python. In Python, indentation is the main concept; if we do not follow proper indentation, then the program will not run properly, and we will get an error in the output.
Python programming language contains methods or functions to reduce the size of the code, and the python programming language provides built-in functions and user-defined functions. We can import the functions in the python programming language through the libraries, which can be downloaded using the python package manager ( pip ). While working on the project and we want to develop the project using the python programming language.
Hog Descriptor
The HOG feature descriptor keeps track of how often gradient orientation appears in specific areas of a picture.With the aid of programmes like OpenCV, HOG can be implemented quite quickly. Since we have a preset function named hog in the skimage.feature library, it only requires a few lines of code. However, the calculation of these attributes is the main topic of this essay.
Oriented Gradients' Histogram Calculation Procedure (HOG):
We now have a fundamental understanding of what a HOG feature description is. It's time to explore this article's main thesis. Let's talk about how to calculate HOG step by step.
Take a look at the size image below (180 x 280). Let's examine in depth how the HOG characteristics for this image will be produced:
hog_feature
Step 1: Prepare the data (64 x 128):
The majority of you will be quite familiar with this phase. Preprocessing data is an essential step in any machine learning project, and working with photos is no exception.
The image needs to be preprocessed to reduce the width to height ratio to 1:2. TheThe image should ideally be 64 by 128 pixels in size. This is due to the fact that in order to extract the features, the image will be divided into 8*8 and 16*16 patches. All our calculations will be quite straightforward because of the predetermined size (64 x 128). This value was really used exactly in the original study.
Returning to our example, let's assume that the normal image size will be 64 x 128 for the time being. This is the resized picture:
hog_feature
Step 2: Calculating Gradients (direction x and y):
Calculating the gradient for each pixel in the image comes next. The subtle changes in the x and y directions are called gradients. I'm going to take a little patch from this here. Step 2: Gradient Calculation (direction x and y)
The gradient for each pixel in the image must then be calculated. The subtle shift in the x and y directions is known as a gradient.
When there is a sudden change in intensity, like around the edges, the magnitude would be greater.
The slopes in the x and y directions were calculated individually. For each pixel in the image, the same procedure is performed. The magnitude and orientation would then be determined using these values as the next step.
Step 3: Calculate the magnitude and orientation:
The magnitude and direction of each pixel value will now be determined using the gradients we computed in the previous phase.
Step 4: Perform an 8 by 8 cell (9 by 1) gradient histogram calculation.
The HOG feature descriptor does not generate histograms for the entire image. Instead, the image is split into 8 x 8 cells, with each cell's histogram of directional gradients being calculated.
Step 5: Normalize gradients in 16x16 cells (36 x 1).
It's crucial to comprehend why something is done in the first place before learning how it's done.
The gradients of the image are sensitive to the overall lighting, despite the fact that we have previously produced the HOG features for the image's 8x8 cells. This implies that given a specific image, some areas would be highly bright in comparison to other areas.
Step 6: The image's overall features.
The process of creating HOG features for the image is now complete. For the 1616 blocks of the image that we have built features for thus far. To obtain the features for the final image, we will now integrate all of these.