Python image processing: from basic to advanced
As an open source, efficient, and easy-to-learn programming language, Python is widely used in the field of image processing. In this article, we will introduce step by step what Python can do, how to do, and why you should use Python for image processing, from basic image processing methods to advanced machine learning applications.
Python Imaging Library
First, we need to understand Python’s image library. Python has many powerful image libraries, the most commonly used of which are PIL and OpenCV. PIL (Python Imaging Library) is a third-party image processing library for Python, mainly used for image format conversion, image enhancement, image filtering, geometric transformation and other operations. OpenCV is an open source computer vision library that can implement image processing, machine vision, deep learning and many other functions.
Using these image libraries, we can easily implement some simple image processing tasks, such as image rotation, scaling and cropping:
from PIL import Image
# open an image
img = Image.open('test.jpg')
# image rotation
img. rotate(90)
# image scaling
img. resize((500, 500))
# Image cropping
img. crop((0, 0, 100, 100))
Python filter effect
In addition to basic image processing, we can also apply the Python image library to achieve some interesting filter effects. Such as black and white filter, mosaic effect, blur, etc.:
from PIL import Image
from PIL import ImageFilter
# open an image
img = Image.open('test.jpg')
# black and white filter
img = ImageOps. grayscale(img)
# mosaic effect
img = img.resize((16, 16), resample=Image.BOX).resize((500, 500), resample=Image.NEAREST)
# blur effect
img = img. filter(ImageFilter. BLUR)
These filter effects can be applied to pictures to make them more interesting and visual.
Python image processing application
Finally, let’s look at some advanced Python image processing applications. In these application examples, we will use the Python machine learning library scikit-learn for classification, segmentation, and clustering, and the Deep Learning library Keras for image recognition.
Python Image Classification
Image classification is a common task in machine learning. Its goal is to map an input image to one of the predefined categories, such as classifying a picture into categories such as animals, food, and vehicles.
For the application of Python image classification, we can use the scikit-learn library to implement a support vector machine based classifier:
from sklearn import datasets from sklearn import svm # Load the iris dataset iris = datasets. load_iris() # SVM classifier classifier = svm.SVC(gamma=0.01, C=100) # train the classifier classifier.fit(iris.data[:-1], iris.target[:-1]) # forecast result classifier. predict(iris. data[-1])
Python image segmentation
Image segmentation is a basic task of image processing, and its purpose is to divide a picture into multiple parts or regions. This technology is widely used in UAV image processing, medical image processing and other fields.
For the application of Python image segmentation, we can use the K-means clustering algorithm in the scikit-learn library to achieve:
from sklearn.cluster import KMeans
from matplotlib import pyplot as plt
import cv2
# read an image
img = cv2.imread('test.jpg', 0)
# K-means segmentation
kmeans = KMeans(n_clusters=2, random_state=0).fit(img.reshape(-1, 1))
new_img = kmeans.cluster_centers_[kmeans.labels_].reshape(img.shape)
# Visualize the results
plt.imshow(new_img, cmap=plt.cm.gray)
Python image recognition
Finally, let’s look at a Python-based image recognition application. Image recognition is an important task in computer vision. Its purpose is to analyze, understand, identify and describe images through computer programs, so as to extract useful information in images.
For the application of Python image recognition, we can use the Keras library and deep neural network to implement a handwritten digit recognition model based on the MNIST dataset:
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
# Load the mnist dataset
(X_train, y_train), (X_test, y_test) = mnist. load_data()
# data processing
X_train = X_train.reshape(60000, 784).astype('float32')
X_test = X_test.reshape(10000, 784).astype('float32')
X_train /= 255
X_test /= 255
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
# Build a deep neural network
model = Sequential()
model.add(Dense(units=128, input_dim=784, activation='relu'))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# train the model
model.fit(X_train, y_train,
batch_size=128,
epochs=10,
validation_data=(X_test, y_test))
This simple model can recognize handwritten digits in the MNIST dataset with high recognition accuracy.
Conclusion
Python is a versatile programming language that can be used in many different fields. With the Python image processing library and machine learning library, we can easily perform image processing, filter effects, image classification, segmentation, and recognition. With the development of computer vision and deep learning technology, the application of Python will become more and more extensive.
The last last
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