import random# 生成随机坐标x = random.randint(0, 100)y = random.randint(0, 100)print("随机坐标为：({}, {})".format(x, y))

import random是Python中的一个模块，用于生成随机数和随机序列。可以通过该模块中的函数来实现随机数的生成，如random.random()、random.randint()等。同时，该模块还提供了一些其他的功能，如打乱序列、从序列中随机选择元素等。

相关问题

image=np.array(grayImage,dtype=float) percent=0.001 num=int(percent*image.shape[0]*image.shape[1]) for i in range(num): temp1=np.random.randint(image.shape[0]) temp2=np.random.randint(image.shape[1]) scale=150 noise=np.random.poisson(scale,1) image[temp1][temp2]+=noise out=image if out.min()<0: low_clip=-1. else: low_clip=0. out=np.clip(out,low_clip,255) expon_image=np.uint8(out) print(expon_image.shape) cv2.imshow("expon_image",expon_image) k=cv2.waitKey(0)优化这段代码的for循环

可以使用numpy的随机函数生成随机的坐标，然后使用numpy的切片和花式索引方式对图像进行操作，可以避免使用循环。

以下是优化后的代码：

import numpy as np
import cv2

grayImage = cv2.imread('test.jpg', 0)

# 转换为浮点型
image = grayImage.astype(float)

percent = 0.001
num = int(percent * image.shape[0] * image.shape[1])

# 生成随机坐标
rand_x = np.random.randint(0, image.shape[0], num)
rand_y = np.random.randint(0, image.shape[1], num)

scale = 150
noise = np.random.poisson(scale, num)

# 对图像进行操作
image[rand_x, rand_y] += noise

# 调整像素值范围
out = np.clip(image, 0, 255)

# 转换为8位整型
expon_image = out.astype(np.uint8)

print(expon_image.shape)
cv2.imshow("expon_image", expon_image)
k = cv2.waitKey(0)

使用numpy的切片和花式索引方式，可以避免使用循环，提高代码的效率。同时，也需要注意对图像进行操作时，需要保证图像的类型一致，避免出现类型错误的问题。

def En(image): x, y,_ = image.shape # 获取图片大小 radius = np.random.randint(10, int(min(x, y)), 1) # pos_x = np.random.randint(0, (min(x, y) - radius), 1) # 获取人脸光照区域的中心点坐标 pos_y = np.random.randint(0, (min(x, y) - radius), 1) # 获取人脸光照区域的中心坐标 pos_x = int(pos_x[0]) pos_y = int(pos_y[0]) radius = int(radius[0]) strength = 100 for j in range(pos_y - radius, pos_y + radius): for i in range(pos_x-radius, pos_x+radius): distance = math.pow((pos_x - i), 2) + math.pow((pos_y - j), 2) distance = np.sqrt(distance) if distance < radius: result = 1 - distance / radius result = result*strength # print(result) image[i, j, 0] = min((image[i, j, 0] + result),255) image[i, j, 1] = min((image[i, j, 1] + result),255) image[i, j, 2] = min((image[i, j, 2] + result),255) image = image.astype(np.uint8) return image对此函数进行文件夹内部图像处理

您可以使用以下代码对文件夹内的所有图像进行处理：

import os
import cv2
import numpy as np
import math

def En(image):
    x, y,_ = image.shape
    radius = np.random.randint(10, int(min(x, y)), 1)
    pos_x = np.random.randint(0, (min(x, y) - radius), 1)
    pos_y = np.random.randint(0, (min(x, y) - radius), 1)
    pos_x = int(pos_x[0])
    pos_y = int(pos_y[0])
    radius = int(radius[0])
    strength = 100
    for j in range(pos_y - radius, pos_y + radius):
        for i in range(pos_x-radius, pos_x+radius):
            distance = math.pow((pos_x - i), 2) + math.pow((pos_y - j), 2)
            distance = np.sqrt(distance)
            if distance < radius:
                result = 1 - distance / radius
                result = result*strength
                image[i, j, 0] = min((image[i, j, 0] + result),255)
                image[i, j, 1] = min((image[i, j, 1] + result),255)
                image[i, j, 2] = min((image[i, j, 2] + result),255)
    image = image.astype(np.uint8)
    return image

img_folder_path = "YOUR_FOLDER_PATH"  # 修改为你的文件夹路径
for filename in os.listdir(img_folder_path):
    if filename.endswith(".jpg") or filename.endswith(".png"):  # 处理jpg和png格式的图像
        img_path = os.path.join(img_folder_path, filename)
        img = cv2.imread(img_path)
        img_en = En(img)
        cv2.imwrite(img_path, img_en)

该代码会遍历指定文件夹内的所有图像，对每一张图像都进行处理并保存。您只需要将 `YOUR_FOLDER_PATH` 修改为您的文件夹路径即可。