How to use image recognition technology in Python?

In the field of contemporary science and technology, image recognition technology is becoming more and more important. Image recognition technology helps us identify and classify entities extracted from digital images, which are then used in data analysis and prediction. Python is a very popular programming language that is also well suited for working with image recognition technology. In this article, we will learn how to use image recognition technology in Python and what we can do with it.

1. Image processing library

Before starting to use image recognition technology, it is best to understand some basic knowledge of image processing libraries. The most commonly used image processing libraries in Python include OpenCV, Pillow, and Scikit-image. In this article, we will focus on using two libraries, OpenCV and Scikit-image.

2. OpenCV

OpenCV is an open source computer vision library that can be used on different platforms. OpenCV provides a large number of algorithms and functions that can be used to implement digital image processing, analysis and computer vision. Here are the basic steps for using OpenCV for image recognition:

1. Install OpenCV

Before you start using OpenCV, you need to install it on your computer. The OpenCV library can be installed through pip and conda commands. On Windows, you can install it with the following command:

pip install opencv-python

Alternatively, you can use conda to install OpenCV:

conda install -c conda-forge opencv

2. Load the image

Next, you need to load the image you want to analyze Image. In Python, you can load a single image or multiple images using the OpenCV function cv2.imread().

import cv2

# load an image
image = cv2.imread("path/to/image")

3. Preprocessing images

Before using OpenCV, the image needs to be preprocessed. The following processing can be done on the image:

# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# apply a Gaussian blur to remove noise
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# apply edge detection to extract edges
edges = cv2.Canny(blurred, 50, 200)

4. Identify objects

Once the image has been preprocessed, objects can be identified using OpenCV’s algorithms and functions. Objects can be marked as rectangles or circles, etc.

# perform an object detection
(contours, _) = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
    # compute the bounding box of the object
    (x, y, w, h) = cv2.boundingRect(c)

    # draw the bounding box around the object
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

5. Display results

Use OpenCV to display the processed image.

# display the result
cv2.imshow("Object Detection", image)
cv2.waitKey(0)

3. Scikit-image

Scikit-image is an image processing library based on the Python language. It also provides many image processing algorithms and functions. The following are the basic steps for using Scikit-image for image recognition:

1. Install Scikit-image

You can use the following command to install the Scikit-image library:

pip install scikit-image

2 .Load Image

Similarly, before using Scikit-image, you need to load the image you want to analyze.

from skimage import io

# load the image
image = io.imread("path/to/image")

3. Preprocess the image

Before using Scikit-image, you also need to preprocess the image. The following processing can be done on the image:

from skimage.filters import threshold_local
from skimage.color import rgb2gray

# convert the image to grayscale
gray = rgb2gray(image)

# apply a threshold to the image
thresh = threshold_local(gray, 51, offset=10)

4. Identify objects

Use Scikit-image's algorithms and functions to identify objects and mark the objects as rectangles or circles, etc.

from skimage import measure
from skimage.color import label2rgb
from skimage.draw import rectangle

# find contours in the image
contours = measure.find_contours(thresh, 0.8)

# draw a rectangle around each object
for n, contour in enumerate(contours):
    row_min, col_min = contour.min(axis=0)
    row_max, col_max = contour.max(axis=0)
    rect = rectangle((row_min, col_min), (row_max, col_max), shape=image.shape)
    image[rect] = 0

5. Display results

Use Scikit-image to display the processed image.

io.imshow(image)
io.show()

Conclusion

Through this article, we learned how to use OpenCV and Scikit-image in Python for image recognition. These two libraries are one of the most popular image processing libraries in Python and can help us with image processing, analysis, and computer vision work. Using image recognition technology, invisible entities can be easily extracted from digital images and used in data analysis and prediction, for example, it can be applied to medicine, security and finance. Although this article provides some basic usage methods, image recognition technology is a very complex and varied field, and there are many other algorithms and techniques that can be used. Therefore, learning and exploring this field is a very interesting and worthwhile process.

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