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MarkZero.py
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MarkZero.py
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# -*- coding: utf-8 -*-
# @Time : 2020/5/3 15:27
# @Author : luyekang
# @Email : glasslucas00@gmail.com
# @File : meter.py
# @Software: PyCharm
import datetime
import pandas as pd
from random import sample
import cv2
from scipy import stats
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import random
from sympy import *
import math
class mential():
def get_max_point(self, cnt):
lmost = tuple(cnt[cnt[:, :, 0].argmin()][0])
rmost = tuple(cnt[cnt[:, :, 0].argmax()][0])
tmost = tuple(cnt[cnt[:, :, 1].argmin()][0])
bmost = tuple(cnt[cnt[:, :, 1].argmax()][0])
pmost = [lmost, rmost, tmost, bmost]
return pmost
def distance(self, pmost, centerpoint):
cx, cy = centerpoint
distantion = []
for point in pmost:
dx, dy = point
distantion.append((cx - dx) ** 2 + (cy - dy) ** 2)
index_of_max = distantion.index((max(distantion)))
return index_of_max
def ds_ofpoint(self, a, b):
x1, y1 = a
x2, y2 = b
distances = int(sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2))
return distances
def findline(self, cp, lines):
x, y = cp
cntareas = []
for line in lines:
x1, y1, x2, y2 = line[0]
aa = sqrt(min((x1 - x) ** 2 + (y1 - x) ** 2, (x2 - x) ** 2 + (y2 - x) ** 2))
if (aa < 50):
cntareas.append(line)
print(cntareas)
return cntareas
def angle(v1, v2):
dx1 = v1[2] - v1[0]
dy1 = v1[3] - v1[1]
dx2 = v2[2] - v2[0]
dy2 = v2[3] - v2[1]
angle1 = math.atan2(dy1, dx1)
angle1 = angle1 * 180 / math.pi
# print(angle1)
angle2 = math.atan2(dy2, dx2)
angle2 = angle2 * 180 / math.pi
# print(angle2)
if angle1 * angle2 >= 0:
included_angle = abs(angle1 - angle2)
else:
included_angle = abs(angle1) + abs(angle2)
# if included_angle > 180:
# included_angle = 360 - included_angle
return included_angle
def get_mode(arr):
while 0 in arr:
arr.remove(0)
mode = []
arr_appear = dict((a, arr.count(a)) for a in arr) # 统计各个元素出现的次数
if max(arr_appear.values()) == 1: # 如果最大的出现为1
arrs = np.array(arr)
oo = np.median(arrs)
return oo
else:
for k, v in arr_appear.items(): # 否则，出现次数最大的数字，就是众数
if v == max(arr_appear.values()):
mode.append(k)
return mode
def remove_diff(deg):
"""
:funtion :
:param b:
:param c:
:return:
"""
if (True):
# new_nums = list(set(deg)) #剔除重复元素
mean = np.mean(deg)
var = np.var(deg)
# print("原始数据共", len(deg), "个\n", deg)
'''
for i in range(len(deg)):
print(deg[i],'→',(deg[i] - mean)/var)
#另一个思路，先归一化，即标准正态化，再利用3σ原则剔除异常数据，反归一化即可还原数据
'''
# print("中位数:",np.median(deg))
percentile = np.percentile(deg, (25, 50, 75), interpolation='midpoint')
# print("分位数：", percentile)
# 以下为箱线图的五个特征值
Q1 = percentile[0] # 上四分位数
Q3 = percentile[2] # 下四分位数
IQR = Q3 - Q1 # 四分位距
ulim = Q3 + 2.5 * IQR # 上限 非异常范围内的最大值
llim = Q1 - 1.5 * IQR # 下限 非异常范围内的最小值
new_deg = []
uplim = []
for i in range(len(deg)):
if (llim < deg[i] and deg[i] < ulim):
new_deg.append(deg[i])
# print("清洗后数据共", len(new_deg), "个\n", new_deg)
new_deg = np.mean(new_deg)
return new_deg
# 图表表达
flag = 0
p0 = 0
def markzero(img):
# img = cv2.imread(path)
def on_EVENT_LBUTTONDOWN(event, x, y, flags, param):
global flag, p0
if event == cv2.EVENT_LBUTTONDOWN:
xy = "%d,%d" % (x, y)
p0 = [x, y]
# print(x, y)
cv2.circle(img, (x, y), 2, (0, 0, 255), thickness=-1)
# cv2.putText(img, '*0*', (x - 30, y), 1,
# 2.0, (0, 0, 0), thickness=2)
# cv2.imshow("image", img)
elif event == cv2.EVENT_LBUTTONUP: # 鼠标左键fang
cv2.destroyWindow("image")
# print(p0)
cv2.namedWindow("image")
cv2.setMouseCallback("image", on_EVENT_LBUTTONDOWN)
cv2.imshow('image', img)
cv2.waitKey(5000)
return p0
# while (1):
# cv2.imshow("image", img)
# if cv2.waitKey(0)&0xFF>0:
# # if cv2.waitKey(500)|0xFF>0:
# print(flag)
# break
def cut_pic(path):
"""
:param pyrMeanShiftFiltering(input, 10, 100) 均值滤波
:param 霍夫概率圆检测
:param mask操作提取圆
:return: 半径，圆心位置
"""
input = cv2.imread(path)
dst = cv2.pyrMeanShiftFiltering(input, 10, 100)
cimage = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(cimage, cv2.HOUGH_GRADIENT, 1, 80, param1=100, param2=20, minRadius=80, maxRadius=0)
circles = np.uint16(np.around(circles)) # 把类型换成整数
r_1 = circles[0, 0, 2]
c_x = circles[0, 0, 0]
c_y = circles[0, 0, 1]
# print(input.shape[:2])
circle = np.ones(input.shape, dtype="uint8")
circle = circle * 255
cv2.circle(circle, (c_x, c_y), int(r_1), 0, -1)
# cv2.circle(circle, (c_x, c_y), int(r_1*0.65), (255,255,255), -1)
# cv2.imshow("circle", circle)
bitwiseOr = cv2.bitwise_or(input, circle)
# cv2.circle(bitwiseOr, (c_x, c_y), 2, 0, -1)
# cv2.imshow(pname+'_resize'+ptype, bitwiseOr)
cv2.imwrite(pname + '_resize' + ptype, bitwiseOr)
ninfo = [r_1, c_x, c_y]
return ninfo
def linecontours(cp_info):
"""
:funtion : 提取刻度线，指针
:param a: 高斯滤波 GaussianBlur，自适应二值化adaptiveThreshold，闭运算
:param b: 轮廓寻找 findContours，
:return:kb,new_needleset
"""
r_1, c_x, c_y = cp_info
img = cv2.imread(pname + '_resize' + ptype)
cv2.circle(img, (c_x, c_y), 20, (23, 28, 28), -1)
img = cv2.GaussianBlur(img, (3, 3), 0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('dds', img)
# ret, binary = cv2.threshold(~gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
binary = cv2.adaptiveThreshold(~gray, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 15, -10)
# cv2.circle(binary, (c_x, c_y), int(r_1*0.5), (0, 0, 0),5)
# 闭运算
# kernel = np.ones((3, 3), np.uint8)
# dilation = cv2.dilate(binary, kernel, iterations=1)
# kernel2 = np.ones((3, 3), np.uint8)
# erosion = cv2.erode(dilation, kernel2, iterations=1)
# ************************
# cv2.imshow('dds', binary)
contours, hier = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cntset = [] # 刻度线轮廓集合
cntareas = [] # 刻度线面积集合
needlecnt = [] # 指针轮廓集合
needleareas = [] # 指针面积集合
ca = (c_x, c_y)
incircle = [r_1 * 0.7, r_1 * 0.9]
# incircle = [r_1 * 0.1, r_1 * 1]
cv2.drawContours(img, contours, -1, (255, 90, 60), 2)
cv2.imshow("c ", img)
cv2.waitKey(0)
localtion = []
for xx in contours:
rect = cv2.minAreaRect(xx)
rect_box = cv2.boundingRect(xx)
# print(rect)
a, b, c = rect
w, h = b
w = int(w)
h = int(h)
''' 满足条件:“长宽比例”，“面积”'''
if h == 0 or w == 0:
pass
else:
dis = mential.ds_ofpoint(self=0, a=ca, b=a)
if (incircle[0] < dis and incircle[1] > dis):
localtion.append(dis)
if h / w > 2 or w / h > 2: # 4
cntset.append(xx)
cntareas.append(w * h)
cv2.rectangle(img, (rect_box[0], rect_box[1]),
(rect_box[0] + rect_box[2], rect_box[1] + rect_box[3]),(0, 255, 0), 1)
else:
if w > r_1 / 2 or h > r_1 / 2:
needlecnt.append(xx)
needleareas.append(w * h)
cv2.rectangle(img, (rect_box[0], rect_box[1]),
(rect_box[0] + rect_box[2], rect_box[1] + rect_box[3]), (0, 0, 255), 2)
cv2.imshow('kedu', img)
cv2.waitKey(0)
cntareas = np.array(cntareas)
nss = remove_diff(cntareas) # 中位数，上限区
new_cntset = []
# 面积
for i, xx in enumerate(cntset):
if (cntareas[i] <= nss * 1.5 and cntareas[i] >= nss * 0.8):
new_cntset.append(xx)
kb = [] # 拟合线集合
for xx in new_cntset:
rect = cv2.minAreaRect(xx)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.polylines(img, [box], True, (0, 255, 0), 1) # pic
output = cv2.fitLine(xx, 2, 0, 0.001, 0.001)
k = output[1] / output[0]
k = round(k[0], 2)
b = output[3] - k * output[2]
b = round(b[0], 2)
x1 = 1
x2 = gray.shape[0]
y1 = int(k * x1 + b)
y2 = int(k * x2 + b)
cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 1)
kb.append([k, b]) # 求中心点的点集[k,b]
############################################################
r = np.mean(localtion)
mask = np.zeros(img.shape[0:2], np.uint8)
# for cnt in needlecnt:
# cv2.fillConvexPoly(mask,cnt , 255)
mask = cv2.drawContours(mask, needlecnt, -1, (255, 255, 255), -1) # 生成掩膜
cv2.imshow('da', mask)
# cv2.waitKey(0)
cv2.imwrite(pname + '_scale' + ptype, img)
cv2.imwrite(pname + '_needle' + ptype, mask)
return kb, r, mask
def needle(img, r, cx, cy,x0,y0):
oimg = cv2.imread(pname + ptype)
# circle = np.ones(img.shape, dtype="uint8")
# circle = circle * 255
circle = np.zeros(img.shape, dtype="uint8")
cv2.circle(circle, (cx, cy), int(r), 255, -1)
mask = cv2.bitwise_and(img, circle)
cv2.imshow('m', mask)
kernel = np.ones((3, 3), np.uint8)
mask = cv2.dilate(mask, kernel, iterations=1)
# erosion = cv2.erode(mask, kernel, iterations=1)
cv2.imshow('1big', mask)
cv2.waitKey(0)
lines = cv2.HoughLinesP(mask, 1, np.pi / 180, 30, minLineLength=int(r / 2), maxLineGap=2)
nmask = np.zeros(img.shape, np.uint8)
# lines = mential.findline(self=0, cp=[x, y], lines=lines)
# print('lens', len(lines))
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(nmask, (x1, y1), (x2, y2), 100, 1, cv2.LINE_AA)
x1, y1, x2, y2 = lines[0][0]
d1 = (x1 - cx) ** 2 + (y1 - cy) ** 2
d2 = (x2 - cx) ** 2 + (y2 - cy) ** 2
if d1 > d2:
axit = [x1, y1]
else:
axit = [x2, y2]
nmask = cv2.erode(nmask, kernel, iterations=1)
# cv2.imshow('2new', nmask)
cnts, hier = cv2.findContours(nmask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
areass = [cv2.contourArea(x) for x in cnts]
# print(len(areass))
i = areass.index(max(areass))
# print('contours[i]',contours[i])
# cv2.drawContours(img, contours[i], -1, (10,20,250), 1)
# cv2.imshow('need_next', img)
cnt = cnts[i]
output = cv2.fitLine(cnt, 2, 0, 0.001, 0.001)
k = output[1] / output[0]
k = round(k[0], 2)
b = output[3] - k * output[2]
b = round(b[0], 2)
x1 = cx
x2 = axit[0]
y1 = int(k * x1 + b)
y2 = int(k * x2 + b)
cv2.line(oimg, (x1, y1), (x2, y2), (0, 23, 255), 1, cv2.LINE_AA)
cv2.line(oimg, (x1, y1), (x0,y0), (0, 23, 255), 1, cv2.LINE_AA)
cv2.circle(oimg, (x1,y1), 2, (0, 123, 255), -1)
# cv2.imshow('msss', oimg)
cv2.imwrite(pname +'_result'+ ptype,oimg)
cv2.imwrite(pname + '_needleline' + ptype, nmask)
return x1, y1, x2, y2, oimg
def findpoint(kb,path):
img = cv2.imread(path)
w, h, c = img.shape
point_list = []
print('kb length: ', len(kb))
if len(kb) > 2:
# print(len(kb))
random.shuffle(kb)
lkb = int(len(kb) / 2)
kb1 = kb[0:lkb]
kb2 = kb[lkb:(2 * lkb)]
# print('len', len(kb1), len(kb2))
kb1sample = sample(kb1, int(len(kb1) / 2))
kb2sample = sample(kb2, int(len(kb2) / 2))
else:
kb1sample = kb[0]
kb2sample = kb[1]
for i, wx in enumerate(kb1sample):
# for wy in kb2:
for wy in kb2sample:
k1, b1 = wx
k2, b2 = wy
# print('kkkbbbb',k1[0],b1[0],k2[0],b2[0])
# k1-->[123]
try:
if (b2 - b1) == 0:
b2 = b2 - 0.1
if (k1 - k2) == 0:
k1 = k1 - 0.1
x = (b2 - b1) / (k1 - k2)
y = k1 * x + b1
x = int(round(x))
y = int(round(y))
except:
x = (b2 - b1 - 0.01) / (k1 - k2 + 0.01)
y = k1 * x + b1
x = int(round(x))
y = int(round(y))
# x,y=solve_point(k1, b1, k2, b2)
if x < 0 or y < 0 or x > w or y > h:
break
point_list.append([x, y])
cv2.circle(img, (x, y), 2, (122, 22, 0), 2)
# print('point_list',point_list)
if len(kb) > 2:
# cv2.imshow(pname+'_pointset',img)
cv2.imwrite(pname + '_pointset' + ptype, img)
return point_list
def countpoint(pointlist,path):
# pointlist=[[1,2],[36,78],[36,77],[300,300],[300,300]]
img = cv2.imread(path, 0)
h, w = img.shape
pic_list = np.zeros((h, w))
for point in pointlist:
# print('point',point)
x, y = point
if x < w and y < h:
pic_list[y][x] += 1
# print(pic_list)
cc = np.where(pic_list == np.max(pic_list))
# print(cc,len(cc))
y, x = cc
cc = (x[0], y[0])
cv2.circle(img, cc, 2, (32, 3, 240), 3)
# cv2.imshow(pname + '_center_point', img)
cv2.imwrite(pname + '_center_point' + ptype, img)
return cc
import datetime
pname, ptype=0,0
def decter(path,opoint):
x0=opoint[0]
y0=opoint[1]
global pname, ptype
pname, ptype = path.split('.')
ptype = '.' + ptype
start = datetime.datetime.now()
ninfo = cut_pic(path) # 2.截取表盘
kb, r, mask = linecontours(ninfo)
point_list = findpoint(kb, path)
cx, cy = countpoint(point_list, path)
# print('半径，圆心', r, cx, cy)
da, db, dc, de,oimg = needle(mask, r, cx, cy, x0, y0)
# da,db,dc,de=needle_line(lines,new_needleset,cx,cy)
# print(da,db,dc,de)
distinguish = 100 / 360
OZ = [da, db, x0, y0]
OP = [da, db, dc, de]
ang1 = angle(OZ, OP)
output=ang1 * distinguish
print("AB和CD的夹角", output)
# print()
# output=str(output)
end = datetime.datetime.now()
print(end - start)
cv2.waitKey(0)
cv2.destroyAllWindows()
return output, oimg
if __name__ == '__main__':
file = 'test_images/pic009.jpg'
opint = [172, 146]
file = '5.jpg'
opint = [300, 90]
file = 'test_images/pic002.jpg'
opint = [88, 265]
# file = 'test_images/pic001.jpg'
# opint = [326, 477]
#
# file = 'test_images/pic003.jpg'
# opint = [62, 306]
#
# file = 'test_images/pic004.jpg'
# opint = [164, 299]
#
# file = 'test_images/pic005.jpg'
# opint = [50, 85]
#
# file = 'test_images/pic010.jpg'
# opint = [180, 350]
#
# file = 'test_images/pic011.jpg'
# opint = [139, 360]
#
# file = 'test_images/pic012.jpg'
# opint = [248, 257]
file = '5.jpg'
opint=markzero(file)
ang1 = decter(file, opint)