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MeterReader.py
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'''
Author:CherryXuan
Email:[email protected]
Wechat:cherry19940614
File:MeterReader.py
Name:仪表识别类
Version:v0.0.1
Date:2019/6/14 12:30
'''
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
class METER(object):
def __init__(self,path):
self.path = path
# 读取图片
def readData(self):
imgs_path = []
for filename in os.listdir(self.path):
if filename.endswith('.jpg'):
filename = self.path + '/' + filename
# 图片归一化处理
#res = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_LINEAR) # 按照比例缩放，如x,y轴均缩小一倍
imgs_path.append(filename)
return imgs_path
# 图片归一化处理
def normalized_picture(self):
img = cv2.imread(self.path)
nor = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_LINEAR) # 按照比例缩放，如x,y轴均缩小一倍
cv2.imshow('Normalized picture',nor)
return nor
# 颜色空间转换：灰度化
def color_conversion(self,img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换为灰度图
cv2.imshow('Graying pictures', img_gray)
return img_gray
# 中值滤波去噪
def median_filter(self,img):
median = cv2.medianBlur(img, 1) # 中值滤波
cv2.imshow('Median filter', median)
return median
# 双边滤波去噪
def bilateral_filter(self,img):
bilateral = cv2.bilateralFilter(img, 9, 50, 50)
cv2.imshow('Bilateral filter', bilateral)
return bilateral
# 高斯滤波去噪
def gaussian_filter(self,img):
gaussian = cv2.GaussianBlur(img, (3, 3), 0)
cv2.imshow('Gaussian filter', gaussian)
return gaussian
# 图像二值化
def binary_image(self,img):
# 应用5种不同的阈值方法
# ret, th1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
# ret, th2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
# ret, th3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
# ret, th4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
# ret, th5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)
# titles = ['Gray', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
# images = [img_gray, th1, th2, th3, th4, th5]
# 使用Matplotlib显示
# for i in range(6):
# plt.subplot(2, 3, i + 1)
# plt.imshow(images[i], 'gray')
# plt.title(titles[i], fontsize=8)
# plt.xticks([]), plt.yticks([]) # 隐藏坐标轴
# plt.show()
# Otsu阈值
_, th = cv2.threshold(img, 0, 255, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
cv2.imshow('Binary image', th)
return th
# 边缘检测
def candy_image(self,img):
edges = cv2.Canny(img, 60, 143, apertureSize=3)
cv2.imshow('canny', edges)
return edges
# 开运算：先腐蚀后膨胀
def open_operation(self,img):
# 定义结构元素
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) # 矩形结构
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # 椭圆结构
# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5)) # 十字形结构
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) # 开运算
cv2.imshow('Open operation', opening)
return opening
# 霍夫圆变换：检测表盘
def detect_circles(self,gray,img):
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 100, param2=150, minRadius=160)
circles = np.uint16(np.around(circles)) # 把circles包含的圆心和半径的值变成整数
cir = img.copy()
for i in circles[0, :]:
cv2.circle(cir, (i[0], i[1]), i[2], (0, 255, 0), 2, cv2.LINE_AA) # 画圆
cv2.circle(cir, (i[0], i[1]), 2, (0, 255, 0), 2, cv2.LINE_AA) # 画圆心
cv2.imshow("circles", cir)
return cir
# 霍夫直线变换：检测指针
def detect_pointer(self,cir):
img = cv2.GaussianBlur(cir, (3, 3), 0)
edges = cv2.Canny(img, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi / 180, 118) # 这里对最后一个参数使用了经验型的值
result = cir.copy()
for line in lines[0]:
rho = line[0] # 第一个元素是距离rho
theta = line[1] # 第二个元素是角度theta
rtheta = theta * (180 / np.pi)
print('θ1:', rtheta)
if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
# 该直线与第一行的交点
pt1 = (int(rho / np.cos(theta)), 0)
# 该直线与最后一行的焦点
pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
a = int(
int(int(rho / np.cos(theta)) + int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta))) / 2)
b = int(result.shape[0] / 2)
pt3 = (a, b)
pt4 = (int(int(int(rho / np.cos(theta)) + a) / 2), int(b / 2))
# 绘制一条白线
cv2.putText(result, 'theta1={}'.format(int(rtheta)), pt4, cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 1)
cv2.line(result, pt3, pt4, (0, 0, 255), 2, cv2.LINE_AA)
else: # 水平直线
# 该直线与第一列的交点
pt1 = (0, int(rho / np.sin(theta)))
# 该直线与最后一列的交点
pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
a = int(
int(int(rho / np.cos(theta)) + int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta))) / 2)
b = int(result.shape[0] / 2)
pt3 = (a, b)
pt4 = (int(int(int(rho / np.cos(theta)) + a) / 2), int(b / 2))
# 绘制一条直线
cv2.line(result, pt3, pt4, (0, 0, 255), 2, cv2.LINE_AA)
for line in lines[2]:
rho = line[0] # 第一个元素是距离rho
theta = line[1] # 第二个元素是角度theta
rtheta = theta * (180 / np.pi)
print('θ2:', - rtheta - 90)
if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
# 该直线与第一行的交点
pt1 = (int(rho / np.cos(theta)), 0)
# 该直线与最后一行的焦点
pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
a = int(
int(int(rho / np.cos(theta)) + int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta))) / 2)
b = int(result.shape[0] / 2)
pt3 = (a, b)
pt4 = (int(int(int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)) + a) / 2),
int(int(int(b + result.shape[0]) / 2)))
# 绘制一条白线
cv2.putText(result, 'theta2={}'.format(int(- rtheta - 90)), pt4, cv2.FONT_HERSHEY_COMPLEX, 0.5,
(0, 0, 255), 1)
cv2.line(result, pt3, pt4, (255, 0, 0), 2, cv2.LINE_AA)
else: # 水平直线
# 该直线与第一列的交点
pt1 = (0, int(rho / np.sin(theta)))
# 该直线与最后一列的交点
pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
a = int(
int(int(rho / np.cos(theta)) + int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta))) / 2)
b = int(result.shape[0] / 2)
pt3 = (a, b)
pt4 = (int(int(int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)) + a) / 2),
int(int(int(b + result.shape[0]) / 2)))
# 绘制一条直线
cv2.line(result, pt3, pt4, (255, 0, 0), 2, cv2.LINE_AA)
# cv2.imshow('Canny', edges)
cv2.imshow('Result', result)
return result
def iden_pic(self):
image = METER(self.path)
nor = image.normalized_picture()
gray = image.color_conversion(nor)
binary = image.binary_image(gray)
median = image.median_filter(binary)
#bilateral = image.bilateral_filter(median)
gaussian = image.gaussian_filter(median)
candy = image.candy_image(median)
cir = image.detect_circles(gray,nor)
pointer = image.detect_pointer(cir)
nor = cv2.cvtColor(nor, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
gaussian = cv2.cvtColor(gaussian, cv2.COLOR_BGR2RGB)
median = cv2.cvtColor(median, cv2.COLOR_BGR2RGB)
binary = cv2.cvtColor(binary, cv2.COLOR_BGR2RGB)
candy = cv2.cvtColor(candy, cv2.COLOR_BGR2RGB)
cir = cv2.cvtColor(cir, cv2.COLOR_BGR2RGB)
pointer = cv2.cvtColor(pointer, cv2.COLOR_BGR2RGB)
titles = ['Original', 'Gray', 'Gaussian', 'Mdian', 'Binary', 'Candy', 'Circle', 'Pointer']
images = [nor, gray, gaussian, median, binary, candy, cir, pointer]
# 使用Matplotlib显示
for i in range(8):
plt.subplot(2, 4, i + 1)
plt.imshow(images[i])
plt.title(titles[i], fontsize=8)
plt.xticks([]), plt.yticks([]) # 隐藏坐标轴
plt.show()
cv2.waitKey(0)