Python与计算机视觉的精彩碰撞：从图像理解到人工智能的无限可能

import numpy as np
import cv2

# 读取图像
image = cv2.imread("image.jpg")

# 图像灰度化
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 图像边缘检测
edges = cv2.Canny(gray_image, 100, 200)

# 显示图像
cv2.imshow("Original Image", image)
cv2.imshow("Gray Image", gray_image)
cv2.imshow("Edges", edges)
cv2.waiTKEy(0)
cv2.destroyAllwindows()

import numpy as np
import tensorflow as tf

# 加载数据
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 归一化数据
x_train = x_train / 255.0
x_test = x_test / 255.0

# 构建模型
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation="softmax")
])

# 编译模型
model.compile(optimizer="adam",
loss="sparse_cateGorical_crossentropy",
metrics=["accuracy"])

# 训练模型
model.fit(x_train, y_train, epochs=10)

# 评估模型
model.evaluate(x_test, y_test)