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pyNVscan_AT_CV_hot_spot.V3.1_o3.py
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pyNVscan_AT_CV_hot_spot.V3.1_o3.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jul 31 17:50:00 2016
pyNVscan Advanced Tools by OpenCV for hot spot
GUI by OpenCV , for Hough Test
@author: listen
"""
import matplotlib
matplotlib.use('Agg')
import cv2
import numpy as np
import matplotlib.pyplot as plt
from io import BytesIO
import sys
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=16) #默认宋体
#font = FontProperties(fname=r"c:\windows\fonts\msyh.ttc", size=16) #微软雅黑
png_io = BytesIO()
PZT_LIMIT_X = 100.0
PZT_LIMIT_Y = 100.0
def nothing(x):
pass
def read_CSV_Head(csv_head):
x0 = csv_head[0]
y0 = csv_head[1]
x1 = csv_head[2]
y1 = csv_head[3]
step_move = csv_head[4]
return x0, y0, x1, y1, step_move
#由区域扫描结果Data.csv，创建head
def creat_CSV_Head_File(my_matrix):
x0 = np.min(my_matrix[:,0])
x1 = np.max(my_matrix[:,0])
y0 = np.min(my_matrix[:,1])
y1 = np.max(my_matrix[:,1])
step_move = my_matrix[1,0] - my_matrix[0,0]
dx = x1 - x0
dy = y1 - y0
lx = np.size(my_matrix[:,0])
ldata = np.size(my_matrix[:,2])
if abs(lx-(dx/step_move+1.0)*(dy/step_move+1.0)) >= 0.01:
print "Error: creat large_scan_head.csv"
sys.exit(255)
elif x0<0 or y0<0 or x1>PZT_LIMIT_X or y1>PZT_LIMIT_Y:
print "Error: PZT XY LIMITs"
sys.exit(255)
elif x0>=x1 or y0>=y1:
print "Error: creat Large Scan"
sys.exit(255)
head = [x0, y0, x1, y1, step_move, lx, ldata]
return head
#最大公约数
def gcd(a, b):
if a < b:
a, b = b, a
while b != 0:
temp = a % b
a = b
b = temp
return a
#由csv绘制纯图像,返回比例尺值
def csv_to_PNG(my_matrix, csv_head, mode, contour_i, png_file_path):
# print csv_head
x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
# Becouse of numpy arange()
x2 = x1 + step_move * 0.5
y2 = y1 + step_move * 0.5
size_xy = int(csv_head[5])
progress_len = int(csv_head[6])
wave_data = my_matrix[:,2]
w_data = np.zeros(size_xy) # ready for one-photon_count
#progress_len
w_data[0:progress_len] = wave_data[0:progress_len] # Warning: NOT progress_len-1
X = np.arange(x0, x2, step_move)
Y = np.arange(y0, y2, step_move)
len_X = np.size(X)
len_Y = np.size(Y)
Z0 = w_data.reshape(-1,len_X)
Z0[1::2,:] = Z0[1::2,::-1]
extent = np.array([x0, x1, y0, y1]) + np.array([-step_move,step_move,-step_move,step_move])*0.5
#自适应去除matplotlib figure tight_layout PAD,放大到6*100像素的宽
fd = gcd(len_X,len_Y)
fx = len_X / fd
fy = len_Y / fd
if fx>(fy*0.5) and fx<=3:
fd = 6/fx
fx = 6
fy = fd*fy
plt.figure(figsize=(fx,fy))
axes = plt.subplot(111)
# axes.imshow(Z0, extent=extent, origin="lower",interpolation='nearest')
# imshow, contour mode,
#0 + -
#1 + +
#2 - +
if mode < 2 :
axes.imshow(Z0, extent=extent, cmap='gray',origin="lower")
if mode > 0 :
axes.contour(X, Y, Z0, contour_i, cmap='cool')
#for OpenCV ,NO AXEX,NO PAD
axes.set_xticks([])
axes.set_yticks([])
axes.spines['right'].set_color('none')
axes.spines['top'].set_color('none')
axes.spines['bottom'].set_color('none')
axes.spines['left'].set_color('none')
plt.tight_layout(pad=0)
plt.savefig(png_file_path)
png_ruler = (x1 - x0 + step_move) / (fx * 100.0) # 保存图像的比例尺：1个像素代表的微米
return png_ruler
#图片像素坐标[左上角(0,0)],变换到PZT坐标(微米)[左下角(0,0)] ，(dx,dy)偏移比例
def pngXY_to_pztXY(csv_head, gray, png_x, png_y):
x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
Lx,Ly = gray.shape
# pzt_x = png_x / Lx * (x1 - x0 + step_move) + x0 - step_move*0.5
# pzt_y = (Ly - png_y) / Ly * (y1 - y0 + step_move) + y0 - step_move*0.5
pzt_x = png_x / (Lx - 1.0) * (x1 - x0 + step_move) + x0 - step_move*0.5
pzt_y = (Ly - png_y) / (Ly - 1.0) * (y1 - y0 + step_move) + y0 - step_move*0.5
if pzt_x<0:
pzt_x = 0;
elif pzt_x>PZT_LIMIT_X:
pzt_x =PZT_LIMIT_X
elif pzt_y<0:
pzt_y = 0;
elif pzt_y>PZT_LIMIT_Y:
pzt_y = PZT_LIMIT_Y
return pzt_x, pzt_y
#PZT坐标(微米)变换到large_scan.png图片像素坐标
def pztXY_to_pngXY(csv_head, gray, pzt_x, pzt_y):
x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
Lx,Ly = gray.shape
# png_x = (pzt_x - x0 + step_move*0.5) / (x1 - x0 + step_move) * Lx
# png_y = Ly - (pzt_y - y0 + step_move*0.5) / (y1 - y0 + step_move) * Ly
png_x = (pzt_x - x0 + step_move*0.5) / (x1 - x0 + step_move) * (Lx - 1.0)
png_y = Ly - (pzt_y - y0 + step_move*0.5) / (y1 - y0 + step_move) * (Ly - 1.0)
# png_x = np.uint16(round(png_x))
# png_y = np.uint16(round(png_y))
return png_x, png_y
#筛选: 识别圆心坐标(浮点小数像素坐标系)，返回最靠近坐标的浮点像素坐标值
def select_Hot_Spot(circles,png_x,png_y):
num_circles = np.size(circles[:,0])
r_d = np.zeros(num_circles)
r_d = r_d.reshape(-1,1)
r_d[:,0] = (circles[:,0] - png_x)**2 + (circles[:,1] - png_y)**2
amin_r = np.argmin(r_d[:])
png_x = circles[amin_r,0]
png_y = circles[amin_r,1]
png_r = circles[amin_r,2]
return png_x, png_y, png_r
#png高斯模糊后，霍夫圆变换，centers的XY像素坐标列表,返回最靠近坐标的浮点像素坐标值
def mpl_Hough(csv_head, img, gray, png_x, png_y, hot_r, png_ruler, lowThreshold, higThreshold):
min_Dist, sml_Radius, big_Radius = hot_R_Hough(hot_r, png_ruler)
circles1 = cv2.HoughCircles(gray,cv2.HOUGH_GRADIENT ,1,
minDist=min_Dist,param1=lowThreshold,param2=higThreshold,
minRadius=sml_Radius,maxRadius=big_Radius)
img0 = np.array(img)
w, h = gray.shape
line_x = np.uint16(round(png_x))
line_y = np.uint16(round(png_y))
cv2.line(img0,(line_x,0),(line_x,h),(255,0,0),2)
cv2.line(img0,(0,line_y),(w,line_y),(255,0,0),2)
if circles1 is None:
cv2.imshow('mpl_Hough',img0)
return 0,0,0,0
else:
circles = circles1[0,:,:]#三维提取为二维,非常易出Bug，返回值为NoneType或数组
png_x, png_y, png_r = select_Hot_Spot(circles,png_x,png_y)
circles = np.uint16(np.around(circles))#四舍五入，取整
for i in circles[:]:
cv2.circle(img0,(i[0],i[1]),i[2],(0,0,255),2)#画圆
cv2.circle(img0,(i[0],i[1]),2,(0,0,255),2)#画圆心
box_x0, box_y0, box_x1, box_y1 = lock_Box_Draw(png_x, png_y, png_r)
cv2.rectangle(img0,(box_x0,box_y0),(box_x1,box_y1),(0,255,0),2)
pzt_x, pzt_y = pngXY_to_pztXY(csv_head, gray, png_x, png_y)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img0,"({:.2f}, {:.2f})".format(pzt_x, pzt_y),(10,30), font, 0.8,(255,255,255),2)
cv2.imshow('mpl_Hough',img0)
return 1, png_x, png_y, png_r #浮点像素坐标值
#由亮点的PZT坐标系半径值，和比例尺，求像素坐标系的霍夫圆变换部分参数
def hot_R_Hough(hot_r, png_ruler):
hot_r_png = hot_r / png_ruler * 1.0
min_Dist = np.uint16(round(hot_r_png * 2.5))
sml_Radius = np.uint16(round(hot_r_png * 0.3))
big_Radius = np.uint16(round(hot_r_png * 2.0))
return min_Dist, sml_Radius, big_Radius
def lock_Box_Draw(png_x, png_y, png_r):
box_x0 = np.uint16(round(png_x - png_r - 1.0))
box_x1 = np.uint16(round(png_x + png_r + 1.0))
box_y0 = np.uint16(round(png_y - png_r - 1.0))
box_y1 = np.uint16(round(png_y + png_r + 1.0))
return box_x0, box_y0, box_x1, box_y1
def gui_CV_Number(hot_x_100, hot_y_100, hot_r_100):
return hot_x_100/100.0, hot_y_100/100.0, hot_r_100/100.0
##########################################
######## main() ####################
##########################################
def main():
lowThreshold = 100
higThreshold = 12
max_lowThreshold = 300
max_higThreshold = 200
mode = 1
contour_i = 10
hot_x_100 = 5000
hot_y_100 = 5000
hot_r_100 = 40
csv_filepath_large_data = 'pyNVscan_AT_CV_V3.1.csv'
cv2.namedWindow('mpl_Hough',cv2.WINDOW_NORMAL)
cv2.namedWindow('param',cv2.WINDOW_NORMAL)
cv2.createTrackbar('Low threshold','param',100, max_lowThreshold,nothing)
cv2.createTrackbar('Hig threshold','param',12, max_higThreshold,nothing)
# cv2.createTrackbar('Matplotlib Mode','param',0, 2, nothing)
# cv2.createTrackbar('Contour_i','param',10, 100, nothing)
cv2.createTrackbar('Hot_X_100','param',5000, 10000, nothing)
cv2.createTrackbar('Hot_Y_100','param',5000, 10000, nothing)
cv2.createTrackbar('Hot_R_100','param',40, 10000, nothing)
with open(csv_filepath_large_data,"rb") as f:
my_matrix = np.loadtxt(f, delimiter=",", skiprows=0)
csv_head_large = creat_CSV_Head_File(my_matrix)
png_ruler_large = csv_to_PNG(my_matrix, csv_head_large, mode, contour_i, png_io)
img = cv2.imdecode(np.fromstring(png_io.getvalue(), dtype=np.uint8), 1) #读内存中的二进制图像数据
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gaussian_gray = cv2.GaussianBlur(gray,(3,3),0)
pzt_x, pzt_y, pzt_r = gui_CV_Number(hot_x_100, hot_y_100, hot_r_100)
png_x, png_y = pztXY_to_pngXY(csv_head_large, gray, pzt_x, pzt_y)
flag_hough, png_x1, png_y1, png_r1 = mpl_Hough(csv_head_large, img, gaussian_gray, png_x, png_y, pzt_r, png_ruler_large, lowThreshold, higThreshold) # initialization
while(1):
k=cv2.waitKey(20)&0xFF
if k==27:
break
lowThreshold = cv2.getTrackbarPos('Low threshold','param')
higThreshold = cv2.getTrackbarPos('Hig threshold','param')
# mode = cv2.getTrackbarPos('Matplotlib Mode','param')
# contour_i = cv2.getTrackbarPos('Contour_i','param')
hot_x_100 = cv2.getTrackbarPos('Hot_X_100','param')
hot_y_100 = cv2.getTrackbarPos('Hot_Y_100','param')
hot_r_100 = cv2.getTrackbarPos('Hot_R_100','param')
pzt_x, pzt_y, pzt_r = gui_CV_Number(hot_x_100, hot_y_100, hot_r_100)
png_x, png_y = pztXY_to_pngXY(csv_head_large, gray, pzt_x, pzt_y)
flag_hough, png_x1, png_y1, png_r1 = mpl_Hough(csv_head_large,img, gaussian_gray, png_x, png_y, pzt_r, png_ruler_large, lowThreshold, higThreshold)
cv2.destroyAllWindows()
main()