laser point detection
laser point detection
本文主要目的是通过检测激光点，通过相机光轴、激光束和激光点之前形成的几何关系求解距离信息，达到单目相机测距的功能。
检测结果
参考
1.DIY专区一、 用摄像头及激光来测距(opencv)
2.opencv激光点追踪代码
3.opencv-python中文教程:颜色空间转换
4.github.com/Ankur/OpenCV-Laser-Tracker
5.OpenCV-Laser-Tracker
6. Laser pointer detect and track
7.stack overflow:laser curved line detection using opencv and python
8.Python视觉处理（四）HSV处理
9.用python3 opencv3 实现相机学生证的实时跟踪
10.Opencv中SimpleBlobDetector的使用（斑点检测）
11.【转】使用OpenCV进行斑点检测Blob Detection Using OpenCV ( Python, C++ )
12.重点参考该内容:pyimagesearch.com:Detecting multiple bright spots in an image with Python and OpenCV
13.OpenCV 形状分析（上）：计算轮廓中心
14.三角测距法的原理code
#!/usr/bin/python3
# import the necessary packages
from imutils import contours
from skimage import measure
import numpy as np
#import argparse
import imutils
import cv2
import math
CAM=2
cap = cv2.VideoCapture(CAM)
def nothing(x):
pass
#添加参数控制
cv2.namedWindow("params")
cv2.createTrackbar("manual","params",0,1,nothing)
cv2.createTrackbar("pixels_num","params",0,2000,nothing)
ret,frame=cap.read()
W=frame.shape[0]
H=frame.shape[1]
while True:
ret,frame=cap.read()
image=frame
# load the image, convert it to grayscale, and blur it
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (11, 11), 0)
# threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]
# perform a series of erosions and dilations to remove
# any small blobs of noise from the thresholded image
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=4)
# perform a connected component analysis on the thresholded
# image, then initialize a mask to store only the "large"
# components
labels = measure.label(thresh, neighbors=8, background=0)
mask = np.zeros(thresh.shape, dtype="uint8")
MANUAL=cv2.getTrackbarPos("manual","params")
PIXEL_THRESH=cv2.getTrackbarPos("pixels_num","params") if MANUAL==1 else 500
# loop over the unique components
for label in np.unique(labels):
# if this is the background label, ignore it
if label == 0:
continue
# otherwise, construct the label mask and count the
# number of pixels
labelMask = np.zeros(thresh.shape, dtype="uint8")
labelMask[labels == label] = 255
numPixels = cv2.countNonZero(labelMask)
# 通过斑点的像素面积确定激光斑点
#if numPixels > 300:
#print(numPixels) #debug
cv2.putText(image, "pixel_num:{}".format(numPixels), (10, 10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
if numPixels #亮斑过滤,可调参数149 ,500,1269
mask = cv2.add(mask, labelMask)
# find the contours in the mask, then sort them from left to
# right
cnts= cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#不同返回参数，不是一个算法！！？ 打包成一个对象了
#im,cnts,hierarchy = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) <2:
print("未检测到轮廓")
continue
cnts = cnts[0] if imutils.is_cv2() else cnts[1] #用以区分OpenCV2.4和OpenCV3
#cnts = contours.sort_contours(cnts)[0]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] #保留最大轮廓
# loop over the contours
for (i, c) in enumerate(cnts):
# draw the bright spot on the image
(x, y, w, h) = cv2.boundingRect(c)
((cX, cY), radius) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(cX), int(cY)), int(radius),(0, 255, 0), 3)
cv2.circle(image, (int(H/2), int(W/2)), int(3),(0, 0, 255), -1)
cv2.putText(image, "#{} at ({},{})".format(i + 1,x,y), (x, y - 15),cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
X=math.pow(abs(W/2-y),2) #|方向
X=X+math.pow(abs(H/2-x),2) #-方向
X1=abs(H/2-x)
X2=abs(W/2-y)
print("X_:",X1)
print("X|:",X2)
#X=math.sqrt(X)
X=X1
if X==0:
continue
print("X:",X)
D=797.292/X*3.5 #camL f=797.29
#D=832.52/X*3.5 #camR f=832.52
print("D:",D,"cm")
break
cv2.imshow("mask",mask)
cv2.imshow("image",image)
k=cv2.waitKey(40)&0xff
if k==27:
break
cap.release()
cv2.destoryAllWindows()