【Python】键鼠操作、区域截图

1.跟踪鼠标位置

import time,os
import pyautogui as pag

try:
    while True:
        print("按下Ctrl + C 结束程序")
        x, y = pag.position()
        posStr = "当前鼠标位置:" + str(x).rjust(4) + ',' + str(y).rjust(4)
        print(posStr)
        time.sleep(1)
        os.system('cls')
except KeyboardInterrupt:
    print('已退出')

2.鼠标点击

import pyautogui
import time
counts=3
while counts>0:
    pyautogui.click(x=1671, y=90)
    time.sleep(2)
    #pyautogui.click(x=1181,y=539)
    break

3.屏幕截图

from pyautogui import screenshot
import time
from PIL import ImageGrab

def grab_screenshot():#全屏截图
    shot = screenshot()
    shot.save("my_screenshot.png")

def grab_screenshot_area():#指定区域截图
    area = (0,0,500,500)
    shot = ImageGrab.grab(area)
    shot.save("my_screenshot_area.png")

def grab_screenshot_delay():#延时全屏截图
    time.sleep(5)
    shot = screenshot()
    shot.save("my_screen_delay.png")


grab_screenshot_area()

4.图像相似度

import cv2
import numpy as np


# 均值哈希算法
def aHash(img):
    # 缩放为8*8
    img = cv2.resize(img, (8, 8))
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # s为像素和初值为0，hash_str为hash值初值为''
    s = 0
    hash_str = ''
    # 遍历累加求像素和
    for i in range(8):
        for j in range(8):
            s = s + gray[i, j]
    # 求平均灰度
    avg = s / 64
    # 灰度大于平均值为1相反为0生成图片的hash值
    for i in range(8):
        for j in range(8):
            if gray[i, j] > avg:
                hash_str = hash_str + '1'
            else:
                hash_str = hash_str + '0'
    return hash_str


# 差值感知算法
def dHash(img):
    # 缩放8*8
    img = cv2.resize(img, (9, 8))
    # 转换灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    hash_str = ''
    # 每行前一个像素大于后一个像素为1，相反为0，生成哈希
    for i in range(8):
        for j in range(8):
            if gray[i, j] > gray[i, j + 1]:
                hash_str = hash_str + '1'
            else:
                hash_str = hash_str + '0'
    return hash_str


# 感知哈希算法(pHash)
def pHash(img):
    # 缩放32*32
    img = cv2.resize(img, (32, 32))  # , interpolation=cv2.INTER_CUBIC

    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 将灰度图转为浮点型，再进行dct变换
    dct = cv2.dct(np.float32(gray))
    # opencv实现的掩码操作
    dct_roi = dct[0:8, 0:8]

    hash = []
    avreage = np.mean(dct_roi)
    for i in range(dct_roi.shape[0]):
        for j in range(dct_roi.shape[1]):
            if dct_roi[i, j] > avreage:
                hash.append(1)
            else:
                hash.append(0)
    return hash


# 通过得到RGB每个通道的直方图来计算相似度
def classify_hist_with_split(image1, image2, size=(256, 256)):
    # 将图像resize后，分离为RGB三个通道，再计算每个通道的相似值
    image1 = cv2.resize(image1, size)
    image2 = cv2.resize(image2, size)
    sub_image1 = cv2.split(image1)
    sub_image2 = cv2.split(image2)
    sub_data = 0
    for im1, im2 in zip(sub_image1, sub_image2):
        sub_data += calculate(im1, im2)
    sub_data = sub_data / 3
    return sub_data


# 计算单通道的直方图的相似值
def calculate(image1, image2):
    hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])
    hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])
    # 计算直方图的重合度
    degree = 0
    for i in range(len(hist1)):
        if hist1[i] != hist2[i]:
            degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))
        else:
            degree = degree + 1
    degree = degree / len(hist1)
    return degree


# Hash值对比
def cmpHash(hash1, hash2):
    n = 0
    # hash长度不同则返回-1代表传参出错
    if len(hash1)!=len(hash2):
        return -1
    # 遍历判断
    for i in range(len(hash1)):
        # 不相等则n计数+1，n最终为相似度
        if hash1[i] != hash2[i]:
            n = n + 1
    return n


img1 = cv2.imread('my_screenshot_area.png')  #  6------5 ----2--------0.84
img2 = cv2.imread('my_screenshot_area1.png')


hash1 = aHash(img1)
hash2 = aHash(img2)
n = cmpHash(hash1, hash2)
print('均值哈希算法相似度：', n)#不超过5，就说明两张图像很相似；如果大于10，就说明这是两张不同的图像

hash1 = dHash(img1)
hash2 = dHash(img2)
n = cmpHash(hash1, hash2)
print('差值哈希算法相似度：', n)

hash1 = pHash(img1)
hash2 = pHash(img2)
n = cmpHash(hash1, hash2)
print('感知哈希算法相似度：', n)

n = classify_hist_with_split(img1, img2)
print('三直方图算法相似度：', n)