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ammeter.py
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ammeter.py
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"""
created by:[email protected]
data:2021-01-04
功能：在视频中寻找匹配的仪表，并识别指针的角度
"""
import numpy as np
import cv2
import time
from matplotlib import pyplot as plt
class C_ammerter:
def __init__(self,temp):
# 获取模板样本
self.template = temp
# 基于视频流
self.cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
# 获取模板的尺寸
self.w = self.template.shape[0]
self.h = self.template.shape[1]
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF',
'cv2.TM_SQDIFF_NORMED']
# 平方差 SQDIFF
# 相关匹配 CCORR
# 相关系数法 CCOEFF
self.method = cv2.TM_CCORR
def close(self):
self.cap.release()
cv2.destroyAllWindows()
# 获取匹配的图片位置
def get_match(self,img):
res = cv2.matchTemplate(img, self.template, self.method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
top_left = max_loc
print("----------------------------" )
print("min_val", min_val)
print("max_val", max_val)
print("min_loc", min_loc)
print("max_loc", max_loc)
print("----------------------------" )
bottom_right = (top_left[0] + self.w, top_left[1] + self.h)
cv2.rectangle(img, top_left, bottom_right, 255, 2)
c_x, c_y = ((np.array(top_left) + np.array(bottom_right)) / 2).astype(np.int)
# print(c_x, c_y)
return max_val,top_left, bottom_right
# return img[top_left[1]:top_left[1] + h, top_left[0]:top_left[0] + w]
def am_run(self):
while True:
ret, frame = self.cap.read()
if frame is None:
print('video picture is none --continue ')
continue
gray = frame.copy()
# cv2.imshow('origin', gray)
# 匹配模板 框出匹配区域
image = gray.copy()
maxval,t_left, b_right = self.get_match(gray)
if maxval < 16000000000: # 对匹配程度做判断
print("---------------------------------------")
print('matchTemplate is not enough --continue')
print("---------------------------------------")
result =frame
image=frame
else:
cv2.rectangle(image, t_left, b_right, 255, 2)
# 高斯除噪
kernel = np.ones((6,6), np.float32) / 36
gray_cut_filter2D = cv2.filter2D(image[t_left[1]:t_left[1] + self.h, t_left[0]:t_left[0] + self.w], -1, kernel)
# 灰度图 二值化
gray_img = cv2.cvtColor(gray_cut_filter2D, cv2.COLOR_BGR2GRAY)
ret, thresh1 = cv2.threshold(gray_img, 130, 255, cv2.THRESH_BINARY)
# 二值化后 分割主要区域 减小干扰 模板图尺寸371*369
tm = thresh1.copy()
test_main = tm[50:500, 50:500]
# 边缘化检测
edges = cv2.Canny(test_main, 50, 150, apertureSize=3)
# 霍夫直线
lines = cv2.HoughLines(edges, 1, np.pi / 180, 60)
if lines is None:
continue
result = edges.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
print('distance:' + str(rho), 'theta:' + str(((theta / np.pi) * 180)))
lbael_text = 'distance:' + str(round(rho))+ 'theta:' + str(round((theta / np.pi) * 180-90,2))
cv2.putText(image, lbael_text,(t_left[0],t_left[1]-12),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
if (theta > 3 * (np.pi / 3)) or (theta < (np.pi / 2)): # 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])
# 绘制一条白线
cv2.line(result, pt1, pt2,255, 1)
# print('theat >180 theta<90')
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)))
# 绘制一条直线
cv2.line(result, pt1, pt2, 255, 1)
# print('theat <180 theta > 90')
# 直线拟合
cv2.imshow('result', result)
cv2.imshow('rectangle', image)
if cv2.waitKey(1) & 0XFF == ord('q'):
break
# Terminal进入当前文件 命令生成exe: pyinstaller --console --onefile ammeter.py