Python Guide: Embark on a Knowledge Expedition in Computer Vision

Embark on a knowledge expedition of computer vision, python is your indispensable partner. Computer vision is an exciting subject that focuses on making computers "see" the world.

With the help of Python, computer vision becomes easier to implement. In the world of computer vision, Python, with its powerful libraries and tools, allows you to easily process images, detect objects, recognize faces, and even let the computer "see" your gestures.

1. Image Processing:

The NumPy and SciPy libraries in Python are powerful tools for image processing. NumPy provides an efficient arrayprocessingframework, while SciPy provides various image processing algorithms. Using these libraries, you can easily perform image scaling, rotation, cropping, brightness adjustment, and more.

Demo code:

import numpy as np
from scipy.misc import imread, imsave

# 加载图像
image = imread("image.jpg")

# 图像缩放
scaled_image = np.array(Image.fromarray(image).resize((32, 32)))

# 图像旋转
rotated_image = np.array(Image.fromarray(image).rotate(45))

# 图像裁剪
cropped_image = image[100:200, 100:200]

# 图像亮度调整
adjusted_image = np.array(Image.fromarray(image).point(lambda x: x * 1.5))

# 保存图像
imsave("scaled_image.jpg", scaled_image)
imsave("rotated_image.jpg", rotated_image)
imsave("cropped_image.jpg", cropped_image)
imsave("adjusted_image.jpg", adjusted_image)

2. Object detection:

The OpenCV library in Python is a powerful tool for object detection. OpenCV provides a series of object detection algorithms out of the box, such as Haar cascade classifier and HOG detector. You can use these algorithms to easily detect faces, cars, pedestrians, and more from images.

Demo code:

import cv2

# 加载图像
image = cv2.imread("image.jpg")

# Haar级联分类器检测人脸
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(image, 1.1, 4)

# HOG检测器检测行人
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
people = hog.detectMultiScale(image, winStride=(8, 8), padding=(32, 32), scale=1.05)

# 绘制检测结果
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

for (x, y, w, h) in people:
cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

# 显示检测结果
cv2.imshow("Image", image)
cv2.waiTKEy(0)
cv2.destroyAllwindows()

3. face recognition:

The dlib library in Python is facial recognition

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