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contours.py
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contours.py
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"""
created by:maogu123@126.com
data:2021-01-06
功能：在视频中,抓取目标仪表的轮廓，按轮廓切割 ，然后霍夫直线得到指针的角度
"""
from typing import Optional, Any
import cv2
import numpy as np
import time
from matplotlib import pyplot as plt
# 获取面积最大的轮廓数据
def max_contours(contour):
area = map(cv2.contourArea, contour)
area_list = list(area)
get_area_max = max(area_list)
get_post = area_list.index(get_area_max)
return get_post, get_area_max
# 将直线延长与边界相交， 在图形中画出
def get_HImg(H_image, Lines):
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('theta:' + str(((Theta / np.pi) * 180)))
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 - H_image.shape[0] * np.sin(Theta)) / np.cos(Theta)), H_image.shape[0])
# 绘制一条线
cv2.line(H_image, Pt1, Pt2, 255, 1)
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 = (H_image.shape[1], int((Rho - H_image.shape[1] * np.cos(Theta)) / np.sin(Theta)))
# 绘制一条直线
cv2.line(H_image, Pt1, Pt2, 255, 1)
return H_image
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
while True:
ret, frame = cap.read()
if frame is None:
print('frame is none --continue')
continue
# 保存原图片
origin = frame
# 高斯除噪
kernel = np.ones((5, 5), np.float32) / 25
gray_cut_filter2D = cv2.filter2D(frame, -1, kernel)
# 转为灰度图。再二值化
img_gray = cv2.cvtColor(gray_cut_filter2D, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) # THRESH_BINARY_INV
# cv2.imshow('thresh', thresh)
# 查找轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE) # cv2.CHAIN_APPROX_NONE CHAIN_APPROX_SIMPLE
# 查找面积最大的轮廓
post, area_max = max_contours(contours)
print('area:' + str(area_max))
# 过滤面积小的区域
if area_max < 145000:
print('area_max is not enough --continue')
continue
else:
# 在原图上画出轮廓
C_img = cv2.drawContours(frame, contours, post, (0, 255, 0), 1)
# cv2.imshow('C_img', C_img)
# 新建空白图像，放入轮廓内的图像
cimg = np.zeros_like(frame)
cimg[:, :, :] = 255
cv2.drawContours(cimg, contours, post, (0, 0, 0), -1)
# 抓取后的图像
final = cv2.bitwise_or(frame, cimg)
# 高斯除噪 灰度图 二值化 边缘化检测
final_filter2D = cv2.filter2D(final, -1, kernel)
final_gray = cv2.cvtColor(final_filter2D, cv2.COLOR_BGR2GRAY)
ret, thresh1 = cv2.threshold(final_gray, 80, 255, cv2.THRESH_BINARY)
edges = cv2.Canny(thresh1, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi / 180, 60)
if lines is None or len(lines) < 1:
continue
Line = lines[0][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('------------------------')
print('Line:', Line)
print('------------------------')
lbael_text = 'distance:' + str(round(Rho)) + 'theta:' + str(round((Theta / np.pi) * 180 - 90, 2))
cv2.putText(frame, lbael_text, (0,25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
result = get_HImg(frame, lines) # edges
cv2.imshow('result', result)
gray_result = get_HImg(edges, lines)
cv2.imshow('gray_result', gray_result)
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