[人工智能]【all_design代码自用】

【代码自用】
sift_ran.py

import numpy as np
import cv2
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 4

img1p= cv2.imread('train5.jpeg')
img2p = cv2.imread('5train5.jpeg')
img1= cv2.cvtColor(img1p, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img2p, cv2.COLOR_BGR2GRAY)
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1, des2, k=2)

# store all the good matches as per Lowe's ratio test.
good = []
for m, n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)



if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()

    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts,M)
    # plt.subplot(121)
    # plt.imshow(img2)
    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)

	# 校正后的图像
    imgOut = cv2.warpPerspective(img2p, M, (img1.shape[1], img1.shape[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
    plt.subplot(121)
    cv2.imwrite("imgOut.jpg",imgOut)
    plt.imshow(imgOut)
    plt.title('imgOut')
else:
    print("Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))
    matchesMask = None



draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)

# 对应关键点连接图
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.subplot(122)
plt.imshow(img3, 'gray')
plt.title('target<------>original picture')
plt.show()

0new_mian.py

import os

from PIL import Image
import cv2
import cv2 as cv
import numpy as np
from nsingle_numeracy_2 import zhizhen
from matplotlib import pyplot as plt
'''#1图像预处理'''
import numpy as np
import cv2
'''#4单表盘读数:剪掉圆盘区域再角点'''
# def  dushu_2(img,x1,y1,r,x2,y2)
    # # 绘制扇形  1.目标图片  2.椭圆圆心  3.长短轴长度  4.偏转角度  5.圆弧起始角度  6.终止角度  7.颜色  8.是否填充
    # cv2.ellipse(img, (x1, y1), (r, r), 36, 0, 36, (255, 255, 0), -1)

def  dushu(x1,y1,x2,y2):
    print("开始读数！")
    if x1 == x2:
        n =0
        result = 90
    elif y1 == y2 and x1<x2:
        n=2
        result=0
    elif y1 == y2 and x1 > x2:
        n = 7
        result = 180
    else:
        k = -(y2 - y1) / (x2 - x1)
        # 求反正切，再将得到的弧度转换为度
        result = np.arctan(k) * 57.29577
        # 234象限
        if x1 > x2 and y1 > y2:
            result += 180
        elif x1 > x2 and y1 < y2:
            result += 180
        elif x1 < x2 and y1 < y2:
            result += 360
        if  54<result<=90 :
            n=0
        elif 18<result<=54 :
            n = 1
        elif 0<=result<=18  or 360>=result>342:
            n = 2
        elif 306<result<=342 :
            n = 3
        elif 270<result<=306 :
            n = 4
        elif 234<result<=270 :
            n = 5
        elif 198<result<=234 :
            n = 6
        elif 162<result<=198 :
            n = 7
        elif 126<result<=162 :
            n = 8
        elif 90<result<=126:
            n = 9
        else:
            print("读数发生错误！")
    print("直线倾斜角度为：" + str(result) + "度,读数为", n)  # 得到倾斜角度
    return int(n),result

'''#3单表盘提取指针'''
def tiqu(src,a,b):
    img = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)
    cv2.imshow('HSV_img', img)
    cv2.waitKey(0)
    low_hsv = np.array([150, 43, 46])
    high_hsv = np.array([180, 255, 255])
    # 使用opencv的inRange函数提取颜色
    mask_Red1 = cv2.inRange(img, lowerb=low_hsv, upperb=high_hsv)

    # Red = cv2.bitwise_and(target, target, mask=mask)
    # cv2.imshow("Red1", mask_Red1)
    # cv2.waitKey(0)
    low_hsv = np.array([0, 43, 46])
    high_hsv = np.array([10, 255, 255])
    # 使用opencv的inRange函数提取颜色
    mask_Red2 = cv2.inRange( img, lowerb=low_hsv, upperb=high_hsv)
    maskRed = mask_Red1 + mask_Red2
    # maskRed = cv2.medianBlur(maskRed, 7)  # 进行中值模糊，去噪点
    # cv2.imshow("Red2", mask_Red2)
    cv2.imshow('Redmask1', mask_Red1)
    cv2.waitKey(0)
    # 腐蚀膨胀
    mask = cv2.erode(maskRed, None, iterations=3 )
    # maskRed = cv2.dilate(maskRed, None, iterations=1)
    cv2.imshow("Redmask2", mask)
    cv2.waitKey(0)
    circle = np.zeros(mask.shape[0:2], dtype="uint8")  # 创建圆
    j = 1
    while len(mask[mask == 255]) > 5:
        maskcircle = cv2.circle(circle, (a, b), j, 255, -1)  # 修改填充白色
        maskcircle = cv2.add(mask, np.zeros(np.shape(mask), dtype=np.uint8), mask=maskcircle)
        mask = mask - maskcircle
        j = j + 1
    cv2.imshow("point", mask)
    cv2.waitKey(0)


    # # 边缘检测#调用Canny函数，指定最大和最小阈值，其中apertureSize默认为3。
    # cannyimage = cv2.Canny(maskRed, 50, 100)
    # cv2.imshow("Red", maskRed)
    # cv2.imshow("cannyimage", cannyimage)
    # cv2.waitKey(0)
    ## # Shi-Tomasi 算法是Harris 算法的改进，角点算法识别指针顶端
    cons = []
    #corners = cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize[,corners[, useHarrisDetector[, k]]])
    con = cv2.goodFeaturesToTrack(mask ,1, 0.9, 10)
    if con is not None and len(con) > 0:
        for x, y in np.float32(con).reshape(-1, 2):
            cons.append((x, y))
            cons_img = cv2.circle(src, (int(x), int(y)), 1, (0, 0, 255))

    # 输出角点
    print(cons)
    cv2.imshow('cons_img ', cons_img)
    cv2.waitKey(0)
    n, ang=dushu( a,b,cons[0][0], cons[0][1] )
    # linepic = cv2.line(img, (int(cons[0][0]), int(cons[0][1])), (int(i[0]), int(i[1])), (0, 0, 255))
    # # if i[0] is int:
    # n, ang = dushu(i[0], i[1], cons[0][0], cons[0][1])
    #
    #
    # cv2.imshow('linepic', linepic)
    # cv2.waitKey(0)
    # return (n)
    return n,ang

def tiqublack(img,a,b,r):
    # 使用opencv的inRange函数提取颜色
    # img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    # cv2.imshow('HSV_img', img)
    # cv2.waitKey(0)
    low_hsv = np.array([0, 0, 0])
    high_hsv = np.array([180, 255, 46])
    # 使用opencv的inRange函数提取颜色
    mask= cv2.inRange(img, lowerb=low_hsv, upperb=high_hsv)

    cv2.imshow('Blackmask1', mask)
    cv2.waitKey(0)
    # # 进行中值模糊，去噪点
    # mask= cv2.medianBlur(mask, 9)
    # 腐蚀膨胀
    mask = cv2.erode(mask, None, iterations=4)
    mask = cv2.dilate(mask, None, iterations=1)
    cv2.imshow("Blackmask2", mask )
    circle = np.zeros(mask.shape[0:2], dtype="uint8")  # 创建圆
    j = 1
    while len(mask[mask == 255]) >5:
        maskcircle = cv2.circle(circle, (a, b), j, 255, -1)  # 修改填充白色
        maskcircle = cv2.add(mask, np.zeros(np.shape(mask), dtype=np.uint8), mask=maskcircle)
        mask = mask - maskcircle
        j = j + 1
    cv2.imshow("point", mask)
    cv2.waitKey(0)
    # q,p =np.where(mask == 255)
    ## # Shi-Tomasi 算法是Harris 算法的改进，角点算法识别指针顶端
    cons = []
    # corners = cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize[,corners[, useHarrisDetector[, k]]])
    con = cv2.goodFeaturesToTrack(mask, 1, 0.001 , 1)
    if con is not None and len(con) > 0:
        for x, y in np.float32(con).reshape(-1, 2):
            cons.append((x, y))
            cons_img = cv2.circle(img, (int(x), int(y)), 1, (0, 0, 255))

    # 输出角点
    print(cons)
    cv2.imshow('cons_img ', cons_img)
    linepic = cv2.line(img, (int(cons[0][0]), int(cons[0][1])), (int(a), int(b)), (0, 0, 255))
    cv2.imshow('linepic', linepic)
    cv2.waitKey(0)
    n,ang=dushu(a,b,cons[0][0], cons[0][1])
    # print('q,p:',q,p)
    # n, ang = dushu(a, b, q,p)
    # linepic = cv2.line(img, (int(cons[0][0]), int(cons[0][1])), (int(i[0]), int(i[1])), (0, 0, 255))
    # # if i[0] is int:
    # n, ang = dushu(i[0], i[1], cons[0][0], cons[0][1])
    #
    #
    # cv2.imshow('linepic', linepic)
    # cv2.waitKey(0)
    # return (n)
    return n, ang

def dividing(img):
    src = img # 读取图片
    ROI = np.zeros(src.shape, np.uint8)   # 创建与原图同尺寸的空numpy数组，用来保存ROI信息
    # 灰度化
    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
    cv2.imshow("gray", gray)
    cv2.waitKey(0)
    # 自适应二值化
    binary= cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,19,10)#均值
    #blocksize过大会导致图像细节的丢失，过小虽然保存了图像细节，增加时间
    # binary2= cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,19,10)#高斯
    cv2.imshow("ADAPTIVE_THRESH_MEAN_C", binary)
    #cv2.imshow("ADAPTIVE_THRESH_GAUSSIAN_C",  binary2)

    #中值滤波
    binary= cv2.medianBlur(binary, 5)  # 进行中值模糊，去噪点(2、4不行)

    cv2.imshow("medianBlur", binary)
    # cv2.imshow("ADAPTIVE_THRESH_GAUSSIAN_C",  binary2)
    cv2.waitKey(0)
    '''#2多表盘分割'''
    circles = cv2.HoughCircles(binary, cv2.HOUGH_GRADIENT, 1, 50, param1=100, param2=1, minRadius=20, maxRadius=40)
    # 根据检测到圆的信息，画出每一个圆
    circles = np.uint16(np.around(circles))
    n=0
    x = [[] for i in range(8)]
    for i in circles[0, :]:
      if n<8:
        x[n].append(i[0])
        x[n].append(i[1])
        x[n].append(i[2])#存储圆的信息
        n = n + 1
        # # draw the outer circle
        # cv2.circle(src, (i[0], i[1]), i[2] + 1, (0, 255, 0), 2)
        # # draw the center of the circle
        # cv2.circle(src, (i[0], i[1]), 1, (0, 0, 255), 3)

      else :
        break
    cv2.imshow('circle', src)
    cv2.waitKey(0)
    print(x)
     #排序
    x=np.array(x)
    x=x[x[:,0].argsort()]#根据图上位置排序表盘
    print(x)
    n = 0
    ang = [0, 0, 0, 0, 0, 0, 0, 0]
    k= [0, 0, 0, 0, 0, 0, 0, 0]
    for i in range(len(x)):
        # draw the outer circle
        circle = np.zeros(ROI.shape[0:2], dtype="uint8")  # 创建圆
        maskcircle = cv2.circle(circle, (x[i][0], x[i][1]), x[i][2]-2, 255, -1)  # 修改填充白色

        mask = cv2.add(src, np.zeros(np.shape(src), dtype=np.uint8), mask=maskcircle)
        bg = np.ones_like(img, np.uint8) * 255


        cv2.bitwise_not(bg, bg, mask=maskcircle)  # bg的多边形区域为0，背景区域为255
        cv2.imshow('bg.jpg', bg)
        mask = mask + bg

        cv2.imshow('result.jpg', mask)
        cv2.waitKey(0)
        if n >= 0 and n <= 3:
            print(x[n])
            ang[n],k[n]=tiqublack(mask,x[n][0],x[n][1],x[n][2])

        elif 3 < n < 8:
            ang[n],k[n]=tiqu(mask,x[n][0],x[n][1] )
        n = n + 1

    return ang,k

if __name__ == '__main__':
     #img = "imgOut.jpg"
     img = "train5.jpeg"#可识别
     #img = "1second_template.jpg"
     #img = "train6.jpeg"#可识别，刻度处有问题

     #img = "train7.jpeg"
     img = cv2.imread(img)
#第一步：选出ROI区域,分割成单个圆盘
     ang,k=dividing(img)
     x=[]
     y=[]
     x.extend([ang[2],ang[0],ang[1],ang[3], ang[4],ang[6],ang[7],ang[5]])
     y.extend([k[2],k[0],k[1],k[3], k[4],k[6],k[7],k[5]])
     print("黑色指针部分水表读数为：", ang[2], ang[0], ang[1], ang[3])
     print("红色指针部分水表读数为：", ang[4], ang[6], ang[7], ang[5])
     print(x,y)


99kishihua,py

```python
# # # 创建GUI窗口打开图像 并显示在窗口中
# #
# # from PIL import Image, ImageTk # 导入图像处理函数库
# # import tkinter as tk           # 导入GUI界面函数库
# # # 创建窗口 设定大小并命名
# # window = tk.Tk()
# # window.title('图像显示界面')
# # window.geometry('600x500')
# # global img_png           # 定义全局变量 图像的
# # var = tk.StringVar()    # 这时文字变量储存器
# #
# # # 创建打开图像和显示图像函数
# # def Open_Img():
# #     global img_png
# #     var.set('已打开')
# #     Img = Image.open('E:\\Python_3_7\\a.jpg')
# #     img_png = ImageTk.PhotoImage(Img)
# #
# # def Show_Img():
# #     global img_png
# #     var.set('已显示')   # 设置标签的文字为 'you hit me'
# #     label_Img = tk.Label(window, image=img_png)
# #     label_Img.pack()
# # # 创建文本窗口，显示当前操作状态
# # Label_Show = tk.Label(window,
# #     textvariable=var,   # 使用 textvariable 替换 text, 因为这个可以变化
# #     bg='blue', font=('Arial', 12), width=15, height=2)
# # Label_Show.pack()
# # # 创建打开图像按钮
# # btn_Open = tk.Button(window,
# #     text='打开图像',      # 显示在按钮上的文字
# #     width=15, height=2,
# #     command=Open_Img)     # 点击按钮式执行的命令
# # btn_Open.pack()    # 按钮位置
# # # 创建显示图像按钮
# # btn_Show = tk.Button(window,
# #     text='显示图像',      # 显示在按钮上的文字
# #     width=15, height=2,
# #     command=Show_Img)     # 点击按钮式执行的命令
# # btn_Show.pack()    # 按钮位置
# #
# # # 运行整体窗口
# # window.mainloop()
#
#
#
#
# from tkinter import *
# from tkinter import filedialog
#
# window = Tk()
# window.title("亲情导出")
# window.geometry('400x300')
# import tkinter.filedialog
#
# e1 = Label(window, text="路径：")  # 这是标签
# e1.grid(row=1, column=0)
# g = Entry(window, width=40)  # 这是输入框
# g.grid(row=1, column=1, columnspan=1)
#
#
# def se():  # 这是获取路径函数
#     g.delete(0, "end")
#     path = filedialog.askopenfilename()
#     path = path.replace("/", "\\\\")  # 通过replace函数替换绝对文件地址中的/来使文件可被程序读取 #注意：\\转义后为\，所以\\\\转义后为\\
#     g.insert('insert', path)
#
#
# b1 = Button(window, text="查询", command=se)  # 这是按键
# b1.grid(row=1, column=3)
#
# window.mainloop()
import cv2

from lunwen_000 import dividing
from tkinter import *
from tkinter.filedialog import askopenfilename
# from tkinter.messagebox import showinfo
frameT = Tk()
frameT.geometry('500x200+400+200')
frameT.title('选择需要输入处理的文件')
frame = Frame(frameT)
frame.pack(padx=10, pady=10)  # 设置外边距
frame_1 = Frame(frameT)
frame_1.pack(padx=10, pady=10)  # 设置外边距
frame1 = Frame(frameT)
frame1.pack(padx=10, pady=10)
v1 = StringVar()
v2 = StringVar()
ent = Entry(frame, width=50, textvariable=v1).pack(fill=X, side=LEFT)  # x方向填充,靠左
ent = Entry(frame_1, width=50, textvariable=v2).pack(fill=X, side=LEFT)  # x方向填充,靠左

global i,num
def fileopen():
    file_sql = askopenfilename()
    print(file_sql)
    global i
    i=file_sql
    if file_sql:
        v1.set(file_sql)

def number():
    # img = "train5.jpeg"#可识别
    # img = "1second_template.jpg"
    # img = "train6.jpeg"
    # img = "train7.jpeg"
    global i,num
    img = cv2.imread(i)
    # 第一步：选出ROI区域,分割成单个圆盘
    ang = dividing(img)
    print("黑色指针部分水表读数为：", ang[2], ang[0], ang[1], ang[3])
    print("红色指针部分水表读数为：", ang[4], ang[6], ang[7], ang[5])
    num = 1000 * ang[2] + 100 * ang[0] + 10 * ang[1] + 1 * ang[3] + 0.1 * ang[4] + 0.01 * ang[6] + 0.001 * ang[
        7] + 0.0001 * ang[5]
    if num:
        v2.set(num)
    cv2.destroyAllWindows()

btn = Button(frame, width=20, text='总文件', font=("宋体", 14), command=fileopen).pack(fil=X, padx=10)
# btn_1 = Button(frame_1, width=20, text='匹配文件', font=("宋体", 14), command=fileopen_1).pack(fil=X, padx=10)
ext = Button(frame1, width=10, text='退出', font=("宋体", 14), command=frameT.quit).pack(fill=X, side=LEFT)
etb = Button(frame_1, width=10, text='读数', font=("宋体", 14), command=number).pack(fill=X, padx=10)
frameT.mainloop()