Hello everyone, I am Hao Ge! Today I want to share a very interesting topic with you — image recognition. With the development of artificial intelligence, image recognition technology has penetrated into all aspects of our lives. Whether it’s facial recognition for unlocking phones or object recognition in autonomous driving, this technology is indispensable. As Python enthusiasts, we can easily implement these cool features with simple code!
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OpenCV: The “Swiss Army Knife” of Computer Vision
OpenCV is arguably the most commonly used library for image processing. It not only reads and processes images but also provides a wealth of image processing algorithms. Let’s look at a simple example:
python run copy
import cv2
# Read image
img = cv2.imread('photo.jpg')
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Mark faces in the image
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
Tip: To install OpenCV, just type pip install opencv-python in the command line.
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TensorFlow: The “Superhero” of Deep Learning
TensorFlow is a deep learning framework developed by Google, which provides many pre-trained models that allow us to quickly implement image recognition:
python run copy
import tensorflow as tf
import tensorflow_hub as hub
# Load pre-trained model
model = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')
# Preprocess image
img = tf.keras.preprocessing.image.load_img('cat.jpg', target_size=(224, 224))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
# Make predictions
predictions = model(img_array)
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PyTorch: The “Rising Star” of Deep Learning
PyTorch’s syntax is closer to native Python style, making it particularly suitable for research and learning:
python run copy
import torch
from torchvision import models, transforms
# Load pre-trained ResNet model
model = models.resnet50(pretrained=True)
model.eval()
# Image preprocessing
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
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scikit-image: The “Good Helper” for Scientific Computing
scikit-image provides many advanced functions for image processing, making it particularly suitable for image preprocessing:
python run copy
from skimage import io, feature
from skimage.color import rgb2gray
# Read and process image
image = io.imread('sample.jpg')
# Edge detection
edges = feature.canny(rgb2gray(image))
# Feature extraction
corners = feature.corner_harris(rgb2gray(image))
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Pillow: The “Veteran” of Image Processing
Pillow is the basic library for image processing in Python, with simple and intuitive operations:
python run copy
from PIL import Image, ImageEnhance
# Open image
image = Image.open('test.jpg')
# Adjust brightness
enhancer = ImageEnhance.Brightness(image)
brightened_image = enhancer.enhance(1.5)
# Resize
resized_image = image.resize((500, 300))
Tip: When processing a large number of images, it is recommended to use batch processing to greatly improve efficiency.
Notes:
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Make sure to install the corresponding dependency packages before using these libraries -
Pay attention to memory usage when processing large images -
GPU acceleration can significantly improve the running speed of deep learning models
Friends, this concludes today’s Python learning journey! Remember to type the code yourself, and feel free to ask me any questions in the comments. I wish you all a pleasant learning experience, and may your Python skills improve day by day!