Python computer vision project practice: building image recognition applications from scratch

• Resize image
• Convert image format
• Filtering
• Enhancement

Feature extraction

Feature extraction is another important task of computer vision. It involves extracting discriminative information from images. Commonly used feature extraction methods include:

• Edge Detection
• Corner detection
• Color Histogram
• Local Binary Mode

Classification

Classification is the ultimate goal of computer vision. It involves assigning images to predefined categories. Commonly used classification methods include:

• KNearest Neighbor
• Support Vector Machines
• Decision Tree
• Neural Networks

Building an Image Recognition Application

Now that we understand the basics of computer vision, we can start building an image recognition application. We will use python and OpenCV to accomplish this task.

First, we need to import the necessary libraries:

import cv2
import numpy as np

Then, we need to load the image:

image = cv2.imread("image.jpg")

Next, we need to preprocess the image. We will resize the image, convert the image format and apply filtering:

image = cv2.resize(image, (224, 224))
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.GaussianBlur(image, (5, 5), 0)

Now we can extract the features of the image. We will use edge detection and corner detection:

edges = cv2.Canny(image, 100, 200)
corners = cv2.GoodFeaturesToTrack(image, 25, 0.01, 10)

Finally, we can classify the images. We will use K nearest neighbor classifier:

knn = cv2.ml.KNearest_create()
knn.train(train_data, train_labels)
result = knn.predict(image)

Summarize

This tutorial describes how to build an image recognition application from scratch. We cover all aspects of image preprocessing, feature extraction, and classification. You can use this tutorial to build your own image recognition applications for a variety of tasks such as object detection, face recognition, and medical diagnosis.

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