Computer vision is rapidly expanding in many different applications as traditional techniques, such as image thresholding, filtering, and edge detection, have been augmented by deep learning methods. TensorFlow is a widely used, powerful machine learning tool created by Google. It has user configurable APIs available to train and build complex neural network model in your local PC or in the cloud and optimize and deploy at scale in edge devices.
In this chapter, you will gain an understanding of advanced computer vision concepts using TensorFlow. This chapter discusses the foundational concepts of computer vision and TensorFlow to prepare you for the later, more advanced chapters of this book. We will look at how to perform image hashing and filtering. Then, we will learn about various methods of feature extraction and image retrieval...
If you have not done so already, install Anaconda from https://www.anaconda.com. Anaconda is a package manager for Python. You also need to install OpenCV for all of the computer vision work you will be carrying out, using pip install opencv-python. OpenCV is a library of built-in programming functions for computer vision work.
Image hashing is a method used to find similarity between images. Hashing involves modifying an input image to a fixed size of binary vector through transformation. There are different algorithms for image hashing using different transformations:
- Perpetual hash (phash): A cosine transformation
- Difference hash (dhash): The difference between adjacent pixels
After a hash transformation, images can be compared quickly with the Hamming distance. The Python code for applying a hash transformation is shown in the following code. A hamming distance of 0 shows an identical image (duplicate), whereas a larger hamming distance shows that the images are different from each other. The following snippet imports Python packages, such as PIL, imagehash, and distance. imagehash is a Python package that supports various types of hashing algorithms...
Once we know how to detect edges, the next task is to detect features. Many edges combine to form features. Feature extraction is the process of recognizing visual patterns in an image and extracting any discriminating local features that match with the image of an unknown object. Before performing feature extraction, it is important to understand the image histogram. An image histogram is the distribution of the color intensity of the image.
An image feature matches with the test image if the histograms are similar. The following is the Python code used to create an image histogram of the car:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from PIL import Image
image = Image.open('../car.png')
image_arr = np.asarray(image) # convert image to numpy array
Contours are closed regions within an image that has a similar shape. In this section, we will use Contours to classify and detect simple objects within an image. The image we will use consists of apples and oranges and we will use the Contour and the Canny edge detection method to detect the object and write the image class name on the bounding box. The code for this section can be found at https://github.com/PacktPublishing/Mastering-Computer-Vision-with-TensorFlow-2.0/blob/master/Chapter01/Chapter1_contours_opencv_object_detection_HOG.ipynb.
The methodology is described in the following subsections.
We first need to import the image and then use the...
In the previous sections, we covered the basics of computer vision techniques, such as image conversion, image filtering, convolution using a kernel, edge detection, histograms, and feature matching. This understanding and its various applications should develop a solid foundation for the advanced concept of deep learning, which will be introduced later on in this book.
Deep learning in computer vision is the cumulative learning of many different image features (such as edges, colors, boundaries, shapes, and so on) through a convolution operation of many intermediate (hidden) layers to gain a complete understanding of the image type. Deep learning augments computer vision techniques because it stacks many layers of calculations about how neurons behave. This is done by combining various inputs to produce outputs based...
In this chapter, we learned how image filtering modifies the input image through a convolution operation to produce an output that detects a portion of a feature called an edge. This is fundamental to computer vision. As you will learn in the following chapters, subsequent application of image filtering will transform the edges to a higher-level pattern, such as features.
We also learned how to calculate an image histogram, perform image matching using SIFT, and use contour and the HOG detector to draw a bounding box. We learned how to use OpenCV's bounding box color and size method to segregate one class from another. The chapter concluded with an introduction to TensorFlow, which will provide a foundation for the remaining chapters of this book.
In the next chapter, we will learn about a different type of computer vision technique, called pattern recognition, and...