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pyspectrometer-v3.1.py
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pyspectrometer-v3.1.py
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#!/usr/bin/python3
'''
Python Picam/Webcam Spectometer Les Wright May 2021
Version 3.0 (Peak Hold Added)
**********************************************
V 3.1
Florian Ragwitz
So far I've only implemented a few small features to better support cameras that aren't the Pi Camera,
to support systems with more than one video device, to slightly improve usability in my particular setup,
and to add the ability to do absorption spectroscopy without having to use the CSV export and external post-processing.
**********************************************
https://www.youtube.com/leslaboratory
https://github.com/leswright1977
A simple Python Spectrometer!
To calibrate:
Expose 2 known wavelengths of light to your spectroscope (preferably lasers with KNOWN wavelengths) for example:
Any DPSS laser
Any gas Laser: He-Ne, Ar Ion etc
Click the corresponding peaks on the graph, the pixel values will appear in the oxes that say "Click graph to add point!"
In the boxes next to them, enter the wavelengths as intgers only.
Click the Red "Calibrate" button. This will turn Green, and rescale the graticule and the colormap for the graph.
Label Peak Width: Alters Minimum distance between each detected peak
Label Threshold: Alters the threshold for peak detetion
Peak Hold: Holds all peaks. This enables the recording of peak values of changin spectra, for example recording the tuning curve of a Dye Laser! This disables the savgol filter
Snapshot: Saves a snapshot of the graph, with a date and timestamp in the filename.
The display video and snapshot code is based on: https://solarianprogrammer.com/2018/04/21/python-opencv-show-video-tkinter-window/
The False color for the graph uses code from https://www.codedrome.com/exploring-the-visible-spectrum-in-python/
Note: Picam has a stripe on the right of the image! And it appears on our graph!
Graph is shrunk by 3px to remove it...
https://www.raspberrypi.org/forums/viewtopic.php?t=227701
Occasionaly the program throws a div0 warning because of the peak detector. You can safely ignore it, it is just a warning!
'''
import tkinter
import tkinter.font as tkFont
import cv2
import PIL.Image, PIL.ImageTk
import time
import numpy as np
from scipy.signal import savgol_filter
import peakutils
import base64
import argparse
imdata = 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KdKp4gPWfmC8y9WranFdV9j/bQgz/iT3Iv5xo4B75BLWREb1bXUqJnanHLaAuEZuLx0nEr45mfFlGaYq+fd4eKxNk3ZlqzBoae6gn5vthHi/xGUyQ0Bl83l7QamIkQk6AXA9Dv+VoIBfI4FR0jbfBjOiM/CU0PJtJBHH6hPSH6UHinR7gc6fUHlAmWQzwhXNm8e0NjBi5n34l2IMExNQXdLUiScg1jrZhkiN+TZkf9YKTO+L73vwSsVIRLr6D+dUCR7t2MJK2qcQT9UF05ADhHJEYdV8/er6I2WAwkbh0tAdFLQUquBbdqtjjwnizksqtc8dIOdNFctMOE3ZNm20+UX4WUb+nzDgQmMU+jJILucyH0/AfmpD/4URoLp6qKhLEL8bh+4ioGI2DwmKXEh3FtjVwQfRY9fXgJF2s0yMrPaLNORsn92BunA12wTFGRFcSGEzycCfYCtKhJdijK0ddKgWXHvwRPZNmkp6+zRCuiPzB2eRrAnOJAwTVCFvZIPyjG3bRV0UEbdYS+xfupOCzp+6AwIsjHjTPgTl+CAKGlauebHX7NubwbNsxBDhZ5BjHNB4eX3uOh5WGiz4j48QujMe3zwC509KHf1ra0kQUFT/kk/WIQrsWxHhtrbMAWSG0CLMcGBYEvSPMnbAoC4XOk1XOZ6dw0aACH0MoyfcynibThfOv+aKA+Bjk/oGGALLATQZhiCfGBDH6dkJQ08/h3qbXO2pBZBGdFTTYHyGtK84CvA7S7w7f8kEt5vXocjIdBX2bCqKW8uxRW31EVZI2nn9PPYrpdPUyeRjiwld7U7pGPATmejarhz5N1KdhxBuSmrCkrA8vQQLnjjgPtqXp8UNMhMfy7XvcnNE10poR3/ovliKL/NKI//krkY9AcONGv6p4G3S1UOr7tuGoL8wEuBVTGxylBwP6uwbmrRxWNaHvqq520+OGhH8ncXBq2QIfIWmLEanLrcIkpO2cO5KlB6w5xa0/JsiRSvdyFxjSk1fm/zshzozBh9OpRgtUnVtOs6IUpC1EHepSjt2UgVV1LCXiSxBmXDgIfsXGaZdtUrYEd/ovqh01kz6q9o6I2kJjKdo0WtWYq0i/ufPBKYkqXHgzep8tbEP7adiDXfXMqIWjhUdQwz2rHIpB0+aG/b7lo9q4Y0ALHzZ+qkfP4xLVpDKkCMoI0e4CIGcmzCL/+D5y62jk2as4wnhxp9iArb8DwEn2SuJl4HBA8i2R3Jzvke7ChMEpFm5p6jkY0vK0zW4Uf8tVLrVRG9iUrhIGcEj6q46XjqI/QnJe6veyTAtS3V0/UGc9VWnnZD0VzBka0la8azuukN6dIGvQHL56UT4mRDePTycCLA/ai67YSL2onTE9+k0DhsAGkKj8KLmQsmGFo623c1yPzjL2GIHS8XA7FQyoVcQXa7sEEOKsvv106ygS93YPd71sUpZCMMdhY/1ooLgY1aKuvMOA7gkKvTIPDeqT187apsFt0NoCnC8eFDxQ1Ixvk+1t2Hg9SCPplrMVx5IEJmzKzDgcQeKN/RB6x61PxRbEDz/OINxs4jrBQbsx6d2vCEt31fych/OnF4aN2KQX3qUnjwRMqOgOZW+cLPdABiMDV6S965Erik60JaaVk8hWegb4YlBvLWGXskFFhi1+4ybtmlJy8h2NZISG8mYE5Jh6JBMfFpWAC5HP9Thv7aBtAIwyMAFPLEjbBHrc1i4I7PqzJTJ1WnqQThu5RQDAIh+KNzcjg13ruJ3D9D+YJGmHvR4YBDnRn1WhTZXMioJDePprlHdVFacKII0eUtWmvyOCjjvS2AJu3DUIYk5ZIcwDUWlmYlC3sysZuUJPNIRWGtekjdohIAsU8gjuPWHhrQeyyun6MxlurrrarrkmVPEKwNYDGz33uBkdE114inIib5JHsGGkTjqCN9OtEXwnShjlyfA9EYCA/y0nlvVEQRoawbo2fWDn4XKxyietYA+/DcN0A4UDCHF1zIP20EEBNsfEDrvjibUjPJ72cs9oj5EDbprYxYGfuNMQprEGGWFBCYhCtUzDEqejGowvZNgxWrAvaqfHJuIjYJSINoVw4bAAGqztlRz+Q/bo3zkRuZ+7Dv8fdcmR+Jk5Y0MAAABhelRYdFJhdyBwcm9maWxlIHR5cGUgaXB0YwAAeNo9ibsNgEAMQ3tPwQj5CZJ1SBo6CvYX1klgy1Hsh+t+GttSGDzDomIk6F862mJ+8E03F0Z5nclFmuRkK5Kde8HmqyJ4ATH+FOweWRCRAAAPVGlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNC40LjAtRXhpdjIiPgogPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgeG1sbnM6aXB0Y0V4dD0iaHR0cDovL2lwdGMub3JnL3N0ZC9JcHRjNHhtcEV4dC8yMDA4LTAyLTI5LyIKICAgIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvbW0vIgogICAgeG1sbnM6c3RFdnQ9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZUV2ZW50IyIKICAgIHhtbG5zOnBsdXM9Imh0dHA6Ly9ucy51c2VwbHVzLm9yZy9sZGYveG1wLzEuMC8iCiAgICB4bWxuczpHSU1QPSJodHRwOi8vd3d3LmdpbXAub3JnL3htcC8iCiAgICB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iCiAgICB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iCiAgIHhtcE1NOkRvY3VtZW50SUQ9ImdpbXA6ZG9jaWQ6Z2ltcDphMGRlNjBiNS03MjY4LTRkYjItYTU0Mi00OWFiNjg1YmUzNjIiCiAgIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6NWVlYTYxZWUtMDczMS00MmEyLWJjNTktYzkyZmI4ZDkwOGM4IgogICB4bXBNTTpPcmlnaW5hbERvY3VtZW50SUQ9InhtcC5kaWQ6MjdlMTBhMTAtMWQ1MC00MzdkLWJhNmMtNTEwMzIzNzEwZGFhIgogICBHSU1QOkFQST0iMi4wIgogICBHSU1QOlBsYXRmb3JtPSJXaW5kb3dzIgogICBHSU1QOlRpbWVTdGFtcD0iMTYxNTE5NTYwMTYxNzE0OCIKICAgR0lNUDpWZXJzaW9uPSIyLjEwLjgiCiAgIGRjOkZvcm1hdD0iaW1hZ2UvcG5nIgogICB4bXA6Q3JlYXRvclRvb2w9IkdJTVAgMi4xMCI+CiAgIDxpcHRjRXh0OkxvY2F0aW9uQ3JlYXRlZD4KICAgIDxyZGY6QmFnLz4KICAgPC9pcHRjRXh0OkxvY2F0aW9uQ3JlYXRlZD4KICAgPGlwdGNFeHQ6TG9jYXRpb25TaG93bj4KICAgIDxyZGY6QmFnLz4KICAgPC9pcHRjRXh0OkxvY2F0aW9uU2hvd24+CiAgIDxpcHRjRXh0OkFydHdvcmtPck9iamVjdD4KICAgIDxyZGY6QmFnLz4KICAgPC9pcHRjRXh0OkFydHdvcmtPck9iamVjdD4KICAgPGlwdGNFeHQ6UmVnaXN0cnlJZD4KICAgIDxyZGY6QmFnLz4KICAgPC9pcHRjRXh0OlJlZ2lzdHJ5SWQ+CiAgIDx4bXBNTTpIaXN0b3J5PgogICAgPHJkZjpTZXE+CiAgICAgPHJkZjpsaQogICAgICBzdEV2dDphY3Rpb249InNhdmVkIgogICAgICBzdEV2dDpjaGFuZ2VkPSIvIgogICAgICBzdEV2dDppbnN0YW5jZUlEPSJ4bXAuaWlkOmE2NjEyMzgzLWIzZGYtNDBmNS04NTFlLWUzOTY3NjYwMzA0MCIKICAgICAgc3RFdnQ6c29mdHdhcmVBZ2VudD0iR2ltcCAyLjEwIChXaW5kb3dzKSIKICAgICAgc3RFdnQ6d2hlbj0iMjAyMS0wMy0wOFQwOToyNjo0MSIvPgogICAgPC9yZGY6U2VxPgogICA8L3htcE1NOkhpc3Rvcnk+CiAgIDxwbHVzOkltYWdlU3VwcGxpZXI+CiAgICA8cmRmOlNlcS8+CiAgIDwvcGx1czpJbWFnZVN1cHBsaWVyPgogICA8cGx1czpJbWFnZUNyZWF0b3I+CiAgICA8cmRmOlNlcS8+CiAgIDwvcGx1czpJbWFnZUNyZWF0b3I+CiAgIDxwbHVzOkNvcHlyaWdodE93bmVyPgogICAgPHJkZjpTZXEvPgogICA8L3BsdXM6Q29weXJpZ2h0T3duZXI+CiAgIDxwbHVzOkxpY2Vuc29yPgogICAgPHJkZjpTZXEvPgogICA8L3BsdXM6TGljZW5zb3I+CiAgPC9yZGY6RGVzY3JpcHRpb24+CiA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA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'
#https://solarianprogrammer.com/2018/04/21/python-opencv-show-video-tkinter-window/
class App:
DEFAULT_CALIBRATION = ((72, 405), (304, 532))
def __init__(self, args, window, window_title, video_source=0):
self.window = window
self.window.geometry("660x570")
self.window.resizable (width = False, height = False)
self.window.title(window_title)
self.video_source = video_source
self.def_font = tkinter.font.nametofont("TkDefaultFont")
self.def_font.config(size=9)
self.vid = MyVideoCapture(args.calibration or App.DEFAULT_CALIBRATION, self.video_source)
#set up the graph points as str initially.
#They will become ints later and we can check!
self.point1 = 'x'
self.point2 = 'x'
if args.calibration is not None:
self.point1 = args.calibration[0][0]
self.point2 = args.calibration[1][0]
def select_points(event):
#detect graph clicks and set the x vals in the labels
if self.marker1.cget("text") == "Click graph to add point!":
self.marker1.configure(text=event.x)
self.point1 = int(event.x)
elif self.marker2.cget("text") == "Click graph to add point!":
self.marker2.configure(text=event.x)
self.point2 = int(event.x)
def clear_points():
#just reset the interface, not the calibration
self.marker1.configure(text="Click graph to add point!")
self.marker2.configure(text="Click graph to add point!")
self.txt1.delete(0,tkinter.END)
self.txt2.delete(0,tkinter.END)
self.calbutton.configure(text="Calibrate",fg="yellow", bg="red", activebackground='red')
def peakwidth(event):
setattr(self.vid,'mindist',event) #set object value when peakwidth slider moved.
def peakthresh(event):
setattr(self.vid,'thresh',event) #set object value when threshold slider moved.
def savfilter(event):
setattr(self.vid,'savpoly',event) #set object value when threshold slider moved.
def snapshot():
# Get a frame from the graph, and write it to disk
ret, graphdata = self.vid.get_graph()
if ret:
now = time.strftime("%d-%m-%Y-%H:%M:%S")
cv2.imwrite("spectrum-" + now + ".jpg", cv2.cvtColor(graphdata[0], cv2.COLOR_RGB2BGR))
#print(graphdata[1]) #wavelengths
#print(graphdata[2]) #intensities
f = open(now+'.csv','w')
f.write('Wavelength,Intensity\r\n')
for x in zip(graphdata[1],graphdata[2]):
f.write(str(x[0])+','+str(x[1])+'\r\n')
f.close()
def peakhold():
if self.peakholdbtn.cget("bg") == 'yellow':
self.peakholdbtn.configure(fg="yellow", bg="red",activebackground='red', activeforeground="yellow")
setattr(self.vid,'holdpeaks',True) #set holdpeaks true
self.filt.configure(state="disabled")
else:
self.peakholdbtn.configure(fg="black", bg="yellow",activebackground='yellow',activeforeground="black")
setattr(self.vid,'holdpeaks',False) #set holdpeaks true
self.filt.configure(state="active")
# Create frames
self.top_frame = tkinter.Frame(window, width=640, height=240)
self.top_frame.grid(row=0, column=0, padx=10, pady=5)
self.bottom_frame = tkinter.Frame(window, width=640, height=255)
self.bottom_frame.grid(row=1, column=0, padx=10, pady=5)
#put elements in the frames
#control frame within frame
self.control_frame = tkinter.Frame(self.top_frame, width=324)
self.control_frame.grid(row=0, column=0, padx=0, pady=0)
self.decoration = tkinter.Canvas(self.control_frame, width = 300, height = 146,borderwidth=2,relief="raised")
self.decoration.grid(row=0, column=0, columnspan=3, padx=0, pady=0)
#control panel items:
# load the image file
self.decorate = tkinter.PhotoImage(data=imdata)
self.decoration.create_image(2, 2,image=self.decorate, anchor=tkinter.NW)
#Wavelength labels
self.lbl1 = tkinter.Label(self.control_frame ,text = "Wavelength 1:")
self.lbl1.grid(row = 1,column = 0)
self.lbl2 = tkinter.Label(self.control_frame ,text = "Wavelength 2:")
self.lbl2.grid(row = 2,column = 0)
#wavelength entry boxes
self.txt1 = tkinter.Entry(self.control_frame, width=5)
self.txt1.grid(row = 1,column = 1,sticky='w')
self.txt2 = tkinter.Entry(self.control_frame, width=5)
self.txt2.grid(row = 2,column = 1,sticky='w')
#click notifiers
self.marker1 = tkinter.Label(self.control_frame,text = "Click graph to add point!", width=19,borderwidth=2,relief="sunken")
self.marker1.grid(row = 1,column = 2,padx=0, pady=5)
self.marker2 = tkinter.Label(self.control_frame,text = "Click graph to add point!", width=19,borderwidth=2,relief="sunken")
self.marker2.grid(row = 2,column = 2,padx=0, pady=5)
#calibrate button
self.calbutton = tkinter.Button(self.control_frame ,text="Calibrate",width=6,fg="yellow",bg="red", activebackground='red', command=self.calibrate)
self.calbutton.grid(row=4,column=0)
#clear button
self.clrbutton = tkinter.Button(self.control_frame ,text="Clear", command=clear_points)
self.clrbutton.grid(row=4,column=2)
# display calibration data if provided
if args.calibration is not None:
self.marker1.configure(text=str(args.calibration[0][0]))
self.marker2.configure(text=str(args.calibration[1][0]))
self.txt1.insert(0, str(args.calibration[0][1]))
self.txt2.insert(0, str(args.calibration[1][1]))
self.calibrate() # just to update the botton state, really
# Create a canvas that can fit the above video source size
#weird, we have to sub 3 pixels to have it fit?
self.canvas0 = tkinter.Canvas(self.top_frame, width = 317, height = 238,borderwidth=2,relief="sunken")
self.canvas0.grid(row=0, column=2, padx=(10,0))
#botttom canvas
# Create a canvas that can fit the above video source size
#weird, we have to sub 3 pixels to have it fit?
self.canvas1 = tkinter.Canvas(self.bottom_frame, width = 634, height = 252,borderwidth=2,relief="sunken", cursor="tcross")
self.canvas1.bind("<Button-1>", select_points)
self.canvas1.grid(row=0, column=0, padx=0, pady=0, columnspan=8)
#peaks label
self.lbpeak = tkinter.Label(self.bottom_frame ,text = "Peak Width:")
self.lbpeak.grid(row = 1,column = 0, pady=20, sticky="nw")
#slider for peak width
self.peakwidth = tkinter.Scale(self.bottom_frame,from_=0, to=100, orient="horizontal", command=peakwidth)
self.peakwidth.grid(row=1, column=1, padx=0, pady=2, sticky="n")
self.peakwidth.set(50)
#threshold label
self.lbthresh = tkinter.Label(self.bottom_frame ,text = "Threshold:")
self.lbthresh.grid(row = 1,column = 2, pady=20, sticky="n")
#slider for threshold
self.thresh = tkinter.Scale(self.bottom_frame, from_=0, to=100, orient="horizontal", command=peakthresh)
self.thresh.grid(row=1, column=3, padx=0, pady=2, sticky="n")
self.thresh.set(20)
#Filter label
self.lbfilt = tkinter.Label(self.bottom_frame ,text = "Filter:")
self.lbfilt.grid(row = 1,column = 4, pady=20, sticky="n")
#Slider for filter
self.filt = tkinter.Scale(self.bottom_frame, from_=0, to=16, orient="horizontal", command=savfilter)
self.filt.grid(row=1, column=5, padx=0, pady=2, sticky="n")
self.filt.set(7)
#Peak hold
self.peakholdbtn = tkinter.Button(self.bottom_frame, text="Peak Hold", width=6,fg="black", bg="yellow",activebackground='yellow', command=peakhold)
self.peakholdbtn.grid(row=1, column=6, padx=0, pady=0)
#Snapshot the graph
self.snapshotbtn = tkinter.Button(self.bottom_frame, text="Snapshot", width=6, command=snapshot)
self.snapshotbtn.grid(row=1, column=7, padx=0, pady=0)
# After it is called once, the update method will be automatically called every delay milliseconds
self.delay = 15
self.update()
self.window.mainloop()
def calibrate(self):
#check the data is set, and modify the vars in self.vid...
if isinstance(self.point1,int) and isinstance(self.point2,int):
calibration = ((self.point1, int(self.txt1.get())), (self.point2, int(self.txt2.get())))
self.vid.recalibrate(calibration)
self.calbutton.configure(text="Calibrated", fg="black", bg="yellow",activebackground='yellow')
def update(self):
# Get a frame from the video source
ret, frame = self.vid.get_frame()
ret2, graphdata = self.vid.get_graph()
if ret:
self.photo = PIL.ImageTk.PhotoImage(image = PIL.Image.fromarray(frame))
self.canvas0.create_image(0, 0, image = self.photo, anchor = tkinter.NW)
if ret2:
self.photo2 = PIL.ImageTk.PhotoImage(image = PIL.Image.fromarray(graphdata[0]))
self.canvas1.create_image(0, 0, image = self.photo2, anchor = tkinter.NW)
self.window.after(self.delay, self.update)
class MyVideoCapture:
def __init__(self, calibration, video_source=0):
self.calibration = calibration
#settings for peak detect
self.mindist = 50 #minumum distance between peaks
self.thresh = 20 #Threshold
self.savpoly = 7 #savgol filter polynomial
self.intensity = [0] * 636 #array for intensity data...full of zeroes
self.holdpeaks = False
# Open the video source
self.vid = cv2.VideoCapture(video_source)
#Settings
'''
0. CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds.
1. CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.
2. CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file
3. CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.
4. CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.
5. CV_CAP_PROP_FPS Frame rate.
6. CV_CAP_PROP_FOURCC 4-character code of codec.
7. CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.
8. CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .
9. CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.
10. CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).
11. CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).
12. CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).
13. CV_CAP_PROP_HUE Hue of the image (only for cameras).
14. CV_CAP_PROP_GAIN Gain of the image (only for cameras).
15. CV_CAP_PROP_EXPOSURE Exposure (only for cameras).
16. CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.
17. CV_CAP_PROP_WHITE_BALANCE Currently unsupported
18. CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)
'''
self.vid.set(cv2.CAP_PROP_FRAME_WIDTH,640)
self.vid.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
self.vid.set(cv2.CAP_PROP_FPS, 25)
if not self.vid.isOpened():
raise ValueError("Unable to open video source", video_source)
# Get video source width and height
self.width = self.vid.get(cv2.CAP_PROP_FRAME_WIDTH)
self.height = self.vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
#print(self.width)
def recalibrate(self, calibration):
self.calibration = calibration
def get_frame(self):
if self.vid.isOpened():
ret, frame = self.vid.read()
if ret:
# Return a boolean success flag and the current frame converted to BGR
frame = cv2.resize(frame, (320, 240)) #resize the live image
cv2.line(frame,(0,120),(320,120),(255,255,255),1)
return (ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
else:
return (ret, None)
else:
return (ret, None)
def wavelength_to_rgb(self,nm):
#from: https://www.codedrome.com/exploring-the-visible-spectrum-in-python/
#returns RGB vals for a given wavelength
gamma = 0.8
max_intensity = 255
factor = 0
rgb = {"R": 0, "G": 0, "B": 0}
if 380 <= nm <= 439:
rgb["R"] = -(nm - 440) / (440 - 380)
rgb["G"] = 0.0
rgb["B"] = 1.0
elif 440 <= nm <= 489:
rgb["R"] = 0.0
rgb["G"] = (nm - 440) / (490 - 440)
rgb["B"] = 1.0
elif 490 <= nm <= 509:
rgb["R"] = 0.0
rgb["G"] = 1.0
rgb["B"] = -(nm - 510) / (510 - 490)
elif 510 <= nm <= 579:
rgb["R"] = (nm - 510) / (580 - 510)
rgb["G"] = 1.0
rgb["B"] = 0.0
elif 580 <= nm <= 644:
rgb["R"] = 1.0
rgb["G"] = -(nm - 645) / (645 - 580)
rgb["B"] = 0.0
elif 645 <= nm <= 780:
rgb["R"] = 1.0
rgb["G"] = 0.0
rgb["B"] = 0.0
if 380 <= nm <= 419:
factor = 0.3 + 0.7 * (nm - 380) / (420 - 380)
elif 420 <= nm <= 700:
factor = 1.0
elif 701 <= nm <= 780:
factor = 0.3 + 0.7 * (780 - nm) / (780 - 700)
if rgb["R"] > 0:
rgb["R"] = int(max_intensity * ((rgb["R"] * factor) ** gamma))
else:
rgb["R"] = 0
if rgb["G"] > 0:
rgb["G"] = int(max_intensity * ((rgb["G"] * factor) ** gamma))
else:
rgb["G"] = 0
if rgb["B"] > 0:
rgb["B"] = int(max_intensity * ((rgb["B"] * factor) ** gamma))
else:
rgb["B"] = 0
return (rgb["R"], rgb["G"], rgb["B"])
def get_graph(self):
if self.vid.isOpened():
ret, frame = self.vid.read()
if ret:
#Process the data...
#Why 636 pixels? see notes on picam at beginning of file!
piwidth = 636
image = frame
bwimage = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
rows,cols = bwimage.shape
#create a blank image for the data
graph = np.zeros([255,piwidth,3],dtype=np.uint8)
graph.fill(255) #fill white
#Display a graticule calibrated with cal data
#calculate the ranges
pxrange = abs(self.calibration[0][0]-self.calibration[1][0]) #how many px between points 1 and 2?
nmrange = abs(self.calibration[0][1]-self.calibration[1][1]) #how many nm between points 1 and 2?
#how many pixels per nm?
pxpernm = pxrange/nmrange
#how many nm per pixel?
nmperpx = nmrange/pxrange
#how many nm is zero on our axis
zero = self.calibration[0][1]-(self.calibration[0][0]/pxpernm)
scalezero =zero #we need this unchanged duplicate of zero for later!
prevposition = 0
textoffset = 12
font =cv2.FONT_HERSHEY_SIMPLEX
#Graticule
#vertical lines
for i in range(piwidth):
position = round(zero)
if position != prevposition: #because of rounding, sometimes we draw twice. Lets fix tht!
# we could have grey lines for subdivisions???S
if position%10==0:
cv2.line(graph,(i,15),(i,255),(200,200,200),1)
if position%50==0:
cv2.line(graph,(i,15),(i,255),(0,0,0),1)
cv2.putText(graph,str(position)+'nm',(i-textoffset,12),font,0.4,(0,0,0),1, cv2.LINE_AA)
zero += nmperpx
prevposition = position
#horizontal lines
for i in range (255):
if i!=0 and i%51==0: #suppress the first line then draw the rest...
cv2.line(graph,(0,i),(piwidth,i),(100,100,100),1)
#now process the data...
halfway =int(rows/2) #halfway point to select a row of pixels from
#pull out single row of data and store in a self.intensity array
#Why -4 pixels? see notes on picam at beginning of file!
for i in range(cols-4):
data = bwimage[halfway,i]
if self.holdpeaks == True:
if data > self.intensity[i]:
self.intensity[i] = data
else:
self.intensity[i] = data
if self.holdpeaks == False:
#do a little smoothing of the data
self.intensity = savgol_filter(self.intensity,17,int(self.savpoly))
self.intensity = self.intensity.astype(int)
#now draw the graph
#for each index, plot a verital line derived from int
#use waveleng_to_rgb to false color the data.
self.wavelengthdata = []
index=0
for i in self.intensity:
wavelength = (scalezero+(index/pxpernm))
wavelengthdata = round(wavelength,1)
wavelength = round(wavelength)
self.wavelengthdata.append(wavelengthdata)
rgb=self.wavelength_to_rgb(wavelength)
r = rgb[0]
g = rgb[1]
b = rgb[2]
#(start x,y) (end x,y) (color) thickness
#origin is top left.
cv2.line(graph, (index,255), (index,255-i), (r,g,b), 1)
cv2.line(graph, (index,254-i), (index,255-i), (0,0,0), 1,cv2.LINE_AA)
index+=1
#find peaks and label them
thresh = int(self.thresh) #make sure the data is int.
indexes = peakutils.indexes(self.intensity, thres=thresh/max(self.intensity), min_dist=self.mindist)
for i in indexes:
height = self.intensity[i]
height = 245-height
wavelength = int(scalezero+(i/pxpernm))
cv2.rectangle(graph,((i-textoffset)-2,height+3),((i-textoffset)+45,height-11),(255,255,0),-1)
cv2.rectangle(graph,((i-textoffset)-2,height+3),((i-textoffset)+45,height-11),(0,0,0),1)
cv2.putText(graph,str(wavelength)+'nm',(i-textoffset,height),font,0.4,(0,0,0),1, cv2.LINE_AA)
#################################################################
graphdata = []
graphdata.append(graph)
graphdata.append(self.wavelengthdata)
graphdata.append(self.intensity)
return (ret, graphdata)
else:
return (ret, None)
else:
return (ret, None)
# Release the video source when the object is destroyed
def __del__(self):
if self.vid.isOpened():
self.vid.release()
def arg_parser() -> argparse.ArgumentParser:
def parse_calibration(s):
return tuple([ tuple(map(int, p.split(':', 1))) for p in s.split(',', 1) ])
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--calibration', type=parse_calibration)
return parser
# Create a window and pass it to the Application object
App(arg_parser().parse_args(), tkinter.Tk(), "PySpectrometer V3.0")