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Surveillance.NWB_final.py
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Surveillance.NWB_final.py
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# _____________________HEADER FILES______________________
import tkinter
from tkinter import*
from tkinter import ttk
from tkinter import filedialog
from imutils.video import FPS
from _cffi_backend import callback
from PIL import ImageTk, Image
import cv2
from cv2 import *
import numpy as np
import sys
import time
import argparse
import imutils
import keyboard
from pathlib import Path
from utils import *
import time
from skimage.restoration import wiener
from scipy.special import j1
# _____________________USER-DEFINED FUNCTIONS______________________
kernel_d = np.ones((3, 3), np.uint8)
kernel_e = np.ones((3, 3), np.uint8)
kernel_gauss = (3, 3)
is_blur = False # initializing_boolean_variables
is_close = True # initializing_boolean_variables
is_draw_ct = False # initializing_boolean_variables
fac = 2 # initializing_integer_variables
isVideoCaptureOpen = False # boolean flag to keep a check of the video capture
# ______________________OUTPUT_________________________________________
parent_dir = os.getcwd()
directory = "Results"
out_path = os.path.join(parent_dir, directory)
try:
os.mkdir(out_path)
except OSError as error:
pass
# ___________________INITALIZING THE GUI WINDOW______________________
window = Tk()
window.configure(background="grey64")
window.title("Surveillance System")
window.resizable(height=None, width=None)
window.geometry('1300x480')
# _______________SETTING VARIBALES TO CHECK STATE OF BUTTON (CHECKED OR UNCHECKED)______________________
current_value1 = IntVar()
current_value2 = IntVar()
# _______________________Global Variables__________________________
path = ""
flag_for_browse = False
save_video_flag = False
source_file = ""
capture = VideoCapture(0)
frame_width = 0
frame_height = 0
ROI_enhanced_arr = []
Combined_frames = []
object_frames = []
iterFPS = 0
top = None
fps = 0
fat = 0
def get_current_value1():
return int('{}'.format(current_value1.get()))
def slider_changed1(event):
value_label1.configure(text=get_current_value1())
slider_label1 = Label(window, text='Dilation', font=(
"Times New Roman", 12), fg="black", bg="grey64").place(x=832, y=52)
value_label1 = ttk.Label(window, text=get_current_value1())
slider1 = ttk.Scale(window, from_=0, to=20, orient='horizontal',
command=slider_changed1, variable=current_value1)
slider1.set(0)
slider1.place(x=890, y=50)
value_label1.place(x=995, y=52)
def get_current_value2():
return int('{}'.format(current_value2.get()))
def slider_changed2(event2):
value_label2.configure(text=get_current_value2())
slider_label2 = Label(window, text='Erosion', font=(
"Times New Roman", 12), fg="black", bg="grey64").place(x=832, y=82)
value_label2 = ttk.Label(window, text=get_current_value2())
slider2 = ttk.Scale(window, from_=0, to=20, orient='horizontal',
command=slider_changed2, variable=current_value2)
slider2.set(7)
slider2.place(x=890, y=82)
value_label2.place(x=995, y=82)
# _____________________CREATING BUTTONS______________________
title = Label(window, text="Surveillance System", font=(
"Times New Roman", 18, 'bold'), fg="black", bg="grey64").place(x=495, y=10)
label_file_explorer = Label(window, text="", fg="blue")
label_file_explorer.place(x=20, y=45)
# _____________________HEADER______________________
# title = Label(window, text="Surveillance System", font=(
# "Times New Roman", 18, 'bold'), fg="black", bg="grey64").place(x=495, y=10)
#label_file_explorer = Label(window, text = "", fg = "blue")
# label_file_explorer.place(x=20,y=60)
input_frame = LabelFrame(window.geometry('500x700'), text="Input", font=(
"Times New Roman", 18, 'bold'), bg="grey64")
input_frame.pack(side='left', expand='yes')
L1 = Label(input_frame, bg="grey64")
L1.pack()
output_frame = LabelFrame(window.geometry('500x700'), text="Output", font=(
"Times New Roman", 18, 'bold'), bg="grey64")
output_frame.pack(side='right', expand='yes')
L2 = Label(output_frame, bg="grey64")
L2.pack()
dialog_box = Label(window, text="Pro Tips:", font=(
"Times New Roman", 14, 'bold'), fg="black", bg="grey64").place(x=1695, y=10)
info1 = Label(window, text="Press 'S' To Start Recording", font=(
"Times New Roman", 12), fg="black", bg="grey64").place(x=1695, y=40)
info2 = Label(window, text="Press 'Q' to Exit", font=(
"Times New Roman", 12), fg="black", bg="grey64").place(x=1695, y=60)
recordingVar = StringVar()
recordingVar.set(str('False'))
recordingLabel = Label(window, text= f"Recording: ", font=(
"Times New Roman", 12), fg="black", bg="grey64").place(x=1695, y=80)
recordingVar_label = Label(window, bg='grey64', textvariable=recordingVar,
font=("Times New Roman", 12))
recordingVar_label.place(x=1768, y=80)
displayVar = StringVar()
sample_text_fps = Label(window, bg='grey64', text="FPS: ",
font=("Times New Roman", 12, 'bold'))
sample_text_fps.place(x=40, y=85)
text_fps = Label(window, bg='grey64', textvariable=displayVar,
font=("Times New Roman", 12))
text_fps.place(x=75, y=85)
displayVarFAT = StringVar()
sample_text_fat = Label(window, bg='grey64', text="FAT: ",
font=("Times New Roman", 12, 'bold'))
sample_text_fat.place(x=40, y=115)
text_fat = Label(window, bg='grey64', textvariable=displayVarFAT,
font=("Times New Roman", 12))
text_fat.place(x=75, y=115)
displayVarPath = StringVar()
sample_text_path = Label(window, bg='grey64', text="Path To Output: ",
font=("Times New Roman", 12, 'bold'))
sample_text_path.place(x=40, y=145)
text_path = Label(window, bg='grey64', textvariable=displayVarPath,
font=("Times New Roman", 12))
text_path.place(x=160, y=145)
displayVar.set(str(0))
displayVarFAT.set(str(0))
un_entry = StringVar()
passwd_entry = StringVar()
ip_add_entry = StringVar()
# ___________________Object detection code___________________
def open_popup():
global top
top = Toplevel(window)
top.geometry("350x250")
top.resizable(0, 0)
top.title("Choose Video Source")
var = IntVar()
or_label = Label(top, text="OR", font=("Times New Roman", 12, 'bold')).place(x=20, y=125)
R1 = Radiobutton(top, text="Browse Files", font=("Times New Roman", 12, 'bold'),variable=var,
value=1, command=switch_flag_for_browse).place(x=10, y=160)
username = Label(top, text="Username",font=("Times New Roman", 12)).place(x=10, y=10)
password = Label(top, text="Password",font=("Times New Roman", 12)).place(x=10, y=50)
ip_address = Label(top, text="IP Address",font=("Times New Roman", 12)).place(x=10, y=90)
E1 = Entry(top, bd=2, textvariable=un_entry).place(x=100, y=10)
E2 = Entry(top, bd=2, textvariable=passwd_entry).place(x=100, y=50)
E3 = Entry(top, bd=2, textvariable=ip_add_entry).place(x=100, y=90)
B1=Button(top, text="Submit", font=("Times New Roman", 12,'bold'),command=submit)
B1.place(x=250, y=190)
def switch_flag_for_browse():
global flag_for_browse
flag_for_browse = True
print("FLAG FOR BROWSE SWITCHED")
def browseFiles():
source_file = filedialog.askopenfilename(
initialdir="/", title="Select a File", filetypes=[('All Files', '.*')], parent=window)
label_file_explorer.configure(text="File: "+source_file)
path = source_file
print(path)
return source_file
def submit():
global path
un = un_entry.get()
pw = passwd_entry.get()
ip = ip_add_entry.get()
# url = f'rtsp://{un}:{pw}@{ip}:554/Streaming/Channels/102'
url = f'rtsp://{un}:{pw}@{ip}/axis-media/media.amp?camera=1'
# print(url)
path = url
print(path)
un_entry.set("")
passwd_entry.set("")
ip_add_entry.set("")
top.destroy()
def loadVideo(videopath):
global frame_width, frame_height
ImagesSequence = []
i = 0
start = time.time()
capture = cv2.VideoCapture(path)
frame_width = int(capture.get(3))
frame_height = int(capture.get(4))
while(True):
ret, frame = capture.read()
i += 1
end = time.time()
if ret == True:
#frame = cv2.flip(frame, 1)
ImagesSequence.append(cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY))
cv2.imshow('gray', cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY))
fps = i/(end-start)
displayVar.set(str(int(fps)))
if cv2.waitKey(25) & 0xFF == ord('q'):
break
capture.release()
cv2.destroyAllWindows()
return ImagesSequence
def write_video(frames_list, fps, type, detur=False):
if detur:
Frames_BGR = [cv2.cvtColor(Frame, cv2.COLOR_GRAY2BGR)
for Frame in frames_list]
path_to_out = f'{out_path}/{type}_{np.random.randint(0, 99)}'
displayVarPath.set(str(path_to_out)+'.mp4')
out = cv2.VideoWriter(f'{path_to_out}.mp4', cv2.VideoWriter_fourcc(*'MP4V'), fps, (frames_list[0].shape[1],
frames_list[0].shape[0]))
for i in range(len(frames_list)):
if detur:
out.write(Frames_BGR[i].astype(np.uint8))
else:
out.write(frames_list[i])
out.release()
def toggleCapture():
try:
global capture
global path
if flag_for_browse:
path = browseFiles()
print("OBJ DETECT PATH: ", path)
capture = VideoCapture(path)
except:
print("Exception Found")
def objdetect():
global iterFPS
global object_frames
global save_video_flag
isVideoCaptureOpen = True
recordingVar.set(str('False'))
i = 0
while(1):
start = time.time()
(ret_old, old_frame) = capture.read()
iterFPS += 1
gray_oldframe = cvtColor(old_frame, COLOR_BGR2GRAY)
if(is_blur):
gray_oldframe = GaussianBlur(gray_oldframe, kernel_gauss, 0)
oldBlurMatrix = np.float32(gray_oldframe)
accumulateWeighted(gray_oldframe, oldBlurMatrix, 0.003)
while True:
ret, frame = capture.read()
gray_frame = cvtColor(frame, COLOR_BGR2GRAY)
if(is_blur):
newBlur_frame = GaussianBlur(gray_frame, kernel_gauss, 0)
else:
newBlur_frame = gray_frame
newBlurMatrix = np.float32(newBlur_frame)
minusMatrix = absdiff(newBlurMatrix, oldBlurMatrix)
ret, minus_frame = threshold(minusMatrix, 60, 255.0, THRESH_BINARY)
accumulateWeighted(newBlurMatrix, oldBlurMatrix, 0.02)
inp = ImageTk.PhotoImage(Image.fromarray(frame))
L1['image'] = inp
# imshow('Input', frame)
# drawRectangle(frame, minus_frame)
if(is_blur):
minus_frame = GaussianBlur(minus_frame, kernel_gauss, 0)
minus_Matrix = np.float32(minus_frame)
if(is_close):
for i in range(get_current_value1()):
minus_Matrix = dilate(minus_Matrix, kernel_d)
for i in range(get_current_value2()):
minus_Matrix = erode(minus_Matrix, kernel_e)
minus_Matrix = np.clip(minus_Matrix, 0, 255)
minus_Matrix = np.array(minus_Matrix, np.uint8)
contours, hierarchy = findContours(
minus_Matrix.copy(), RETR_TREE, CHAIN_APPROX_SIMPLE)
for c in contours:
(x, y, w, h) = boundingRect(c)
rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if(is_draw_ct):
drawContours(frame, contours, -1, (0, 255, 255), 1)
end = time.time()
##### Saving Video
if save_video_flag == False:
if keyboard.is_pressed('s') and isVideoCaptureOpen == True:
save_video_flag = True
recordingVar.set(str('True'))
else:
object_frames.append(frame)
frame = ImageTk.PhotoImage(Image.fromarray(frame))
fps = iterFPS/(end-start)
# displayVar.set(str(int(np.random.randint(18, 20))))
displayVar.set(str(int(fps)))
L2['image'] = frame
window.update()
if keyboard.is_pressed('q') and isVideoCaptureOpen == True:
try:
isVideoCaptureOpen = False
capture.release()
L1.config(image='')
L2.config(image='')
displayVar.set(str(0))
if len(object_frames) > 0:
write_video(object_frames, 20, 'object_detect')
recordingVar.set(str('Stopped and Saved'))
else:
recordingVar.set(str('Stopped'))
print("Capture released")
return
except:
print("Some error has occured")
def deturbulence():
try:
global path
if flag_for_browse:
path = browseFiles()
print("deturbulenceT PATH: ", path)
except:
print("Exception Found")
global ROI_enhanced_arr
dataType = np.float32
N_FirstReference = 10
L = 11
patch_size = (L, L) # (y,x) [pixels]. isoplanatic region
patch_half_size = (
int((patch_size[0] - 1) / 2), int((patch_size[1] - 1) / 2))
patches_shift = 1 # when equals to one we get full overlap.
# (y,x). for each side: up/down/left/right
registration_interval = (15, 15)
R = 0.08 # iterativeAverageConstant
m_lambda0 = 0.55 * 10 ** -6
m_aperture = 0.06
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture
# 3 options: 1. via Lucky region for N_firstRef frames, 2. mean of N_firstRef frames 3. first frame.
cap = cv2.VideoCapture(path)
ImagesSequenceList = []
ROI_arr_to_save = []
ROI_enhanced_arr_to_save = []
itr_fps = 0
start = time.time()
# ImagesSequence = loadVideo(0)
while True:
ret, frame = cap.read()
itr_fps += 1
end = time.time()
if keyboard.is_pressed('esc') and isVideoCaptureOpen == True:
L1.config(image='')
L2.config(image='')
displayVar.set(str(0))
displayVarFAT.set(str(0))
cap.release()
return
if keyboard.is_pressed('q'):
L1.config(image='')
L2.config(image='')
displayVar.set(str(0))
displayVarFAT.set(str(0))
cap.release()
concatenatedVid = [np.hstack((ROI_arr_to_save[i], np.zeros(
(ROI_arr_to_save[0].shape[0], 10)), ROI_enhanced_arr_to_save[i])).astype(np.float32) for i in range(len(ROI_arr_to_save))]
write_video(concatenatedVid, 5, 'deturbulence', True)
return
if ret:
fps = itr_fps/(end-start)
displayVar.set(str(int(fps)))
#frame = cv2.flip(frame, 1)
# np.append(ImagesSequence, cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY))
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
if(len(ImagesSequenceList) > 10):
ImagesSequenceList.clear()
ImagesSequenceList.append(frame)
ImagesSequence = np.array(ImagesSequenceList).astype(dataType)
# roi = selectROI(ImagesSequence[0], resize_factor=2)
# print("LEN OF IMAGE SEQ:", len(ImagesSequence))
roi = (0, 0, ImagesSequence[0].shape[0],
ImagesSequence[0].shape[1])
ROI_coord = roi
ROI_coord = (ROI_coord[1], ROI_coord[0], patch_size[1] * int(ROI_coord[3] / patch_size[1]),
patch_size[0] * int(ROI_coord[2] / patch_size[0])) # now roi[0] - rows!
ROI_arr = []
enhancedFrames = []
# option 2: Mean of N_FirstReference frames.
ReferenceFrame = np.mean(ImagesSequence[:N_FirstReference], axis=0)
startRegistrationFrame = N_FirstReference
enhancedFrames.append(ReferenceFrame)
i = 0
if len(ImagesSequenceList) > 10:
for frame in ImagesSequence[startRegistrationFrame:]:
t = time.time()
enhancedFrame = np.copy(frame)
ROI = frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_arr.append(ROI*255.0/ROI.max())
ROI_arr_to_save.append(ROI*255.0/ROI.max())
no_rows_Cropped_Frame, no_cols_Cropped_Frame = \
(ROI_coord[2] + 2 * registration_interval[0],
ROI_coord[3] + 2 * registration_interval[1])
ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = \
(1 - R) * ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] + \
R * frame[ROI_coord[0]: ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_registered = ReferenceFrame[ROI_coord[0]:ROI_coord[0] +
ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
m_lambda0 = 0.55 * 10 ** -6
m_aperture_diameter = 0.055
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture_diameter
ROI_reg_norm = ROI_registered / 255
k = (2 * np.pi) / m_lambda0
Io = 1.0
L = 250
X = np.arange(-m_aperture_diameter/2,
m_aperture_diameter/2, m_aperture_diameter/70)
Y = X
XX, YY = np.meshgrid(X, Y)
AiryDisk = np.zeros(XX.shape)
# print("SHAPE: ", AiryDisk.shape)
q = np.sqrt((XX-np.mean(Y)) ** 2 + (YY-np.mean(Y)) ** 2)
beta = k * m_aperture_diameter * q / 2 / L
AiryDisk = Io * (2 * j1(beta) / beta) ** 2
AiryDisk_normalized = AiryDisk/AiryDisk.max()
deblurredROI_wiener = wiener(
ROI_reg_norm, psf=AiryDisk, balance=7)
deblurredROI = deblurredROI_wiener
deblurredROI = deblurredROI / deblurredROI.max() * 255.0
enhancedFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = np.abs(deblurredROI)
ROI_enhanced_arr.clear()
ROI_enhanced_arr.append(deblurredROI)
ROI_enhanced_arr_to_save.append(deblurredROI)
enhancedFrames.append(enhancedFrame)
displayVarFAT.set(str("{:.3f}".format(time.time() - t)))
# print("LEN OF ROI_arr: ", len(ROI_arr))
# print("LEN OF ROI_enhanced_arr: ", len(ROI_enhanced_arr))
try:
inp_roi = ImageTk.PhotoImage(
Image.fromarray(ROI_arr[i].astype(np.uint8)))
out_roi = ImageTk.PhotoImage(Image.fromarray(
ROI_enhanced_arr[i].astype(np.uint8)))
except:
L1.config(image='')
L2.config(image='')
return
L1['image'] = inp_roi
L2['image'] = out_roi
window.update()
i += 1
def endeturbulence():
global path
if flag_for_browse:
path = browseFiles()
print("deturbulenceT PATH: ", path)
dataType = np.float32
N_FirstReference = 10
L = 11
patch_size = (L, L)
patch_half_size = (
int((patch_size[0] - 1) / 2), int((patch_size[1] - 1) / 2))
patches_shift = 1
registration_interval = (15, 15)
R = 0.08
m_lambda0 = 0.55 * 10 ** -6
m_aperture = 0.06
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture
readVideo = 1
ReferenceInitializationOpt = 2
ImagesSequence = loadVideo(
'rtsp://Guest:srmguest123@192.168.1.64:554/Streaming/Channels/102')
ImagesSequence = np.array(ImagesSequence).astype(dataType)
roi = selectROI(ImagesSequence[0], resize_factor=2)
roi_plate_250 = (1092, 830, 564, 228)
roi_test = (310, 279, 200, 128)
if readVideo:
ROI_coord = roi
else:
ROI_coord = roi_plate_250
ROI_coord = (ROI_coord[1], ROI_coord[0], patch_size[1] * int(ROI_coord[3] / patch_size[1]),
patch_size[0] * int(ROI_coord[2] / patch_size[0])) # now roi[0] - rows!
ROI_arr = []
ROI_enhanced_arr = []
enhancedFrames = []
if ReferenceInitializationOpt == 1: # option 1: "Lucky" reference frame.
FusedPatch = MaxSharpnessFusedPatch([frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
for frame in ImagesSequence[:N_FirstReference]], patch_half_size)
ReferenceFrame = ImagesSequence[N_FirstReference]
ReferenceFrame[ROI_coord[0] + patch_half_size[0]:ROI_coord[0] + ROI_coord[2] - patch_half_size[0],
ROI_coord[1] + patch_half_size[1]:ROI_coord[1] + ROI_coord[3] - patch_half_size[1]] = FusedPatch
startRegistrationFrame = N_FirstReference
# option 2: Mean of N_FirstReference frames.
elif ReferenceInitializationOpt == 2:
ReferenceFrame = np.mean(ImagesSequence[:N_FirstReference], axis=0)
startRegistrationFrame = N_FirstReference
elif ReferenceInitializationOpt == 3: # option 3: first frame
ReferenceFrame = ImagesSequence[0]
startRegistrationFrame = 1
else:
assert Exception("only values 1, 2 or 3 are acceptable")
# showImage(ReferenceFrame.astype(np.uint8))
enhancedFrames.append(ReferenceFrame)
i = 0
for frame in ImagesSequence[startRegistrationFrame:]:
t = time.time()
enhancedFrame = np.copy(frame)
ROI = frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_arr.append(ROI*255.0/ROI.max())
# Image Registration via optical flow
no_rows_Cropped_Frame, no_cols_Cropped_Frame = \
(ROI_coord[2] + 2 * registration_interval[0],
ROI_coord[3] + 2 * registration_interval[1])
u, v = optical_flow_tvl1(
ReferenceFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
enhancedFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
attachment=10, tightness=0.3, num_warp=3, num_iter=5, tol=4e-4, prefilter=False)
row_coords, col_coords = np.meshgrid(np.arange(no_rows_Cropped_Frame), np.arange(no_cols_Cropped_Frame),
indexing='ij')
warp(enhancedFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
np.array([row_coords + v, col_coords + u]), mode='nearest', preserve_range=True).astype(dataType)
# Iterative averaging ROI
ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = \
(1 - R) * ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] + \
R * frame[ROI_coord[0]: ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_registered = ReferenceFrame[ROI_coord[0]:ROI_coord[0] +
ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
m_lambda0 = 0.55 * 10 ** -6
m_aperture_diameter = 0.055
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture_diameter
ROI_reg_norm = ROI_registered / 255
k = (2 * np.pi) / m_lambda0 # wavenumber of light in vacuum
Io = 1.0 # relative intensity
L = 250 # distance of screen from aperture
X = np.arange(-m_aperture_diameter/2, m_aperture_diameter /
2, m_aperture_diameter/70) # pupil coordinates
Y = X
XX, YY = np.meshgrid(X, Y)
AiryDisk = np.zeros(XX.shape)
q = np.sqrt((XX-np.mean(Y)) ** 2 + (YY-np.mean(Y)) ** 2)
beta = k * m_aperture_diameter * q / 2 / L
AiryDisk = Io * (2 * j1(beta) / beta) ** 2
AiryDisk_normalized = AiryDisk/AiryDisk.max()
deblurredROI_wiener = wiener(ROI_reg_norm, psf=AiryDisk, balance=7)
deblurredROI = deblurredROI_wiener
deblurredROI = deblurredROI / deblurredROI.max() * 255.0
enhancedFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = np.abs(deblurredROI)
ROI_enhanced_arr.append(deblurredROI)
enhancedFrames.append(enhancedFrame)
frame1 = ImageTk.PhotoImage(
Image.fromarray(ROI_arr[i].astype(np.uint8)))
L1['image'] = frame1
window.update()
frame2 = ImageTk.PhotoImage(Image.fromarray(
ROI_enhanced_arr[i].astype(np.uint8)))
L2['image'] = frame2
window.update()
if cv2.waitKey(25) & 0xFF == ord('q'):
break
i += 1
cv2.destroyAllWindows()
def endeturbulence_old_version():
source_file = browseFiles()
dataType = np.float32
N_FirstReference = 10
L = 11
patch_size = (L, L)
patch_half_size = (
int((patch_size[0] - 1) / 2), int((patch_size[1] - 1) / 2))
patches_shift = 1
registration_interval = (15, 15)
R = 0.08
m_lambda0 = 0.55 * 10 ** -6
m_aperture = 0.06
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture
readVideo = 1
ReferenceInitializationOpt = 2
video_path = source_file
ImagesSequence = loadVideo(video_path)
# ImagesSequence = loadVideo()
ImagesSequence = np.array(ImagesSequence).astype(dataType)
roi = selectROI(ImagesSequence[0], resize_factor=2)
roi_plate_250 = (1092, 830, 564, 228)
roi_test = (310, 279, 200, 128)
if readVideo:
ROI_coord = roi
else:
ROI_coord = roi_plate_250
ROI_coord = (ROI_coord[1], ROI_coord[0], patch_size[1] * int(ROI_coord[3] / patch_size[1]),
patch_size[0] * int(ROI_coord[2] / patch_size[0])) # now roi[0] - rows!
ROI_arr = []
ROI_enhanced_arr = []
enhancedFrames = []
if ReferenceInitializationOpt == 1: # option 1: "Lucky" reference frame.
FusedPatch = MaxSharpnessFusedPatch([frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
for frame in ImagesSequence[:N_FirstReference]], patch_half_size)
ReferenceFrame = ImagesSequence[N_FirstReference]
ReferenceFrame[ROI_coord[0] + patch_half_size[0]:ROI_coord[0] + ROI_coord[2] - patch_half_size[0],
ROI_coord[1] + patch_half_size[1]:ROI_coord[1] + ROI_coord[3] - patch_half_size[1]] = FusedPatch
startRegistrationFrame = N_FirstReference
# option 2: Mean of N_FirstReference frames.
elif ReferenceInitializationOpt == 2:
ReferenceFrame = np.mean(ImagesSequence[:N_FirstReference], axis=0)
startRegistrationFrame = N_FirstReference
elif ReferenceInitializationOpt == 3: # option 3: first frame
ReferenceFrame = ImagesSequence[0]
startRegistrationFrame = 1
else:
assert Exception("only values 1, 2 or 3 are acceptable")
# showImage(ReferenceFrame.astype(np.uint8))
enhancedFrames.append(ReferenceFrame)
i = 0
for frame in ImagesSequence[startRegistrationFrame:]:
t = time.time()
enhancedFrame = np.copy(frame)
ROI = frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_arr.append(ROI*255.0/ROI.max())
# Image Registration via optical flow
no_rows_Cropped_Frame, no_cols_Cropped_Frame = \
(ROI_coord[2] + 2 * registration_interval[0],
ROI_coord[3] + 2 * registration_interval[1])
u, v = optical_flow_tvl1(
ReferenceFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
enhancedFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
attachment=10, tightness=0.3, num_warp=3, num_iter=5, tol=4e-4, prefilter=False)
row_coords, col_coords = np.meshgrid(np.arange(no_rows_Cropped_Frame), np.arange(no_cols_Cropped_Frame),
indexing='ij')
warp(enhancedFrame[ROI_coord[0] - registration_interval[0]:ROI_coord[0] + ROI_coord[2] + registration_interval[0],
ROI_coord[1] - registration_interval[1]:ROI_coord[1] + ROI_coord[3] + registration_interval[1]],
np.array([row_coords + v, col_coords + u]), mode='nearest', preserve_range=True).astype(dataType)
# Iterative averaging ROI
ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = \
(1 - R) * ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] + \
R * frame[ROI_coord[0]: ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_registered = ReferenceFrame[ROI_coord[0]:ROI_coord[0] +
ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
m_lambda0 = 0.55 * 10 ** -6
m_aperture_diameter = 0.055
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture_diameter
ROI_reg_norm = ROI_registered / 255
k = (2 * np.pi) / m_lambda0 # wavenumber of light in vacuum
Io = 1.0 # relative intensity
L = 250 # distance of screen from aperture
X = np.arange(-m_aperture_diameter/2, m_aperture_diameter /
2, m_aperture_diameter/70) # pupil coordinates
Y = X
XX, YY = np.meshgrid(X, Y)
AiryDisk = np.zeros(XX.shape)
q = np.sqrt((XX-np.mean(Y)) ** 2 + (YY-np.mean(Y)) ** 2)
beta = k * m_aperture_diameter * q / 2 / L
AiryDisk = Io * (2 * j1(beta) / beta) ** 2
AiryDisk_normalized = AiryDisk/AiryDisk.max()
deblurredROI_wiener = wiener(ROI_reg_norm, psf=AiryDisk, balance=7)
deblurredROI = deblurredROI_wiener
deblurredROI = deblurredROI / deblurredROI.max() * 255.0
enhancedFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = np.abs(deblurredROI)
ROI_enhanced_arr.append(deblurredROI)
enhancedFrames.append(enhancedFrame)
print('Frame analysis time: ', time.time() - t)
cv2.imshow('Input', ROI_arr[i].astype(np.uint8))
cv2.imshow('Output', ROI_enhanced_arr[i].astype(np.uint8))
if cv2.waitKey(20) & 0xFF == ord('q'):
break
i += 1
cv2.destroyAllWindows()
def deturbWithObjDetec():
try:
global path
if flag_for_browse:
path = browseFiles()
print("deturbulenceT PATH: ", path)
except:
print("Exception Found")
dataType = np.float32
N_FirstReference = 10
L = 11
patch_size = (L, L) # (y,x) [pixels]. isoplanatic region
patch_half_size = (
int((patch_size[0] - 1) / 2), int((patch_size[1] - 1) / 2))
patches_shift = 1 # when equals to one we get full overlap.
# (y,x). for each side: up/down/left/right
registration_interval = (15, 15)
R = 0.08 # iterativeAverageConstant
m_lambda0 = 0.55 * 10 ** -6
m_aperture = 0.06
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture
cap = cv2.VideoCapture(path)
ImagesSequenceList = []
ROI_arr_to_save = []
ROI_enhanced_arr_to_save = []
Combined_frames = []
Combined_frames_to_save = []
itr_fps = 0
start = time.time()
# ImagesSequence = loadVideo(0)
while True:
ret, frame = cap.read()
print(ret)
itr_fps += 1
end = time.time()
if keyboard.is_pressed('esc') and isVideoCaptureOpen == True:
L1.config(image='')
L2.config(image='')
displayVar.set(str(0))
displayVarFAT.set(str(0))
cap.release()
return
if keyboard.is_pressed('q'):
L1.config(image='')
L2.config(image='')
displayVar.set(str(0))
displayVarFAT.set(str(0))
cap.release()
concatenatedVid = [np.hstack((ROI_arr_to_save[i], np.zeros(
(ROI_arr_to_save[0].shape[0], 10)), Combined_frames[i])).astype(np.float32) for i in range(len(ROI_arr_to_save))]
write_video(concatenatedVid, 20, 'combined', True)
return
if ret:
#frame = cv2.flip(frame, 1)
fps = itr_fps/(end-start)
displayVar.set(str(int(fps)))
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
if(len(ImagesSequenceList) > 10):
ImagesSequenceList.clear()
ImagesSequenceList.append(frame)
ImagesSequence = np.array(ImagesSequenceList).astype(dataType)
# roi = selectROI(ImagesSequence[0], resize_factor=2)
roi = (0, 0, ImagesSequence[0].shape[0],
ImagesSequence[0].shape[1])
ROI_coord = roi
ROI_coord = (ROI_coord[1], ROI_coord[0], patch_size[1] * int(ROI_coord[3] / patch_size[1]),
patch_size[0] * int(ROI_coord[2] / patch_size[0])) # now roi[0] - rows!
ROI_arr = []
ROI_enhanced_arr = []
enhancedFrames = []
# option 2: Mean of N_FirstReference frames.
ReferenceFrame = np.mean(ImagesSequence[:N_FirstReference], axis=0)
startRegistrationFrame = N_FirstReference
enhancedFrames.append(ReferenceFrame)
i = 0
# print("LEN OF IMAGES SEQUENCE : ", len(ImagesSequence))
if len(ImagesSequenceList) > 10:
for frame in ImagesSequence[startRegistrationFrame:]:
t = time.time()
enhancedFrame = np.copy(frame)
ROI = frame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_arr.append(ROI*255.0/ROI.max())
ROI_arr_to_save.append(ROI*255.0/ROI.max())
no_rows_Cropped_Frame, no_cols_Cropped_Frame = \
(ROI_coord[2] + 2 * registration_interval[0],
ROI_coord[3] + 2 * registration_interval[1])
ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = \
(1 - R) * ReferenceFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] + \
R * frame[ROI_coord[0]: ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
ROI_registered = ReferenceFrame[ROI_coord[0]:ROI_coord[0] +
ROI_coord[2], ROI_coord[1]:ROI_coord[1] + ROI_coord[3]]
m_lambda0 = 0.55 * 10 ** -6
m_aperture_diameter = 0.055
m_focal_length = 250 * 10 ** -3
fno = m_focal_length / m_aperture_diameter
ROI_reg_norm = ROI_registered / 255
k = (2 * np.pi) / m_lambda0
Io = 1.0
L = 250
X = np.arange(-m_aperture_diameter/2,
m_aperture_diameter/2, m_aperture_diameter/70)
Y = X
XX, YY = np.meshgrid(X, Y)
AiryDisk = np.zeros(XX.shape)
q = np.sqrt((XX-np.mean(Y)) ** 2 + (YY-np.mean(Y)) ** 2)
beta = k * m_aperture_diameter * q / 2 / L
AiryDisk = Io * (2 * j1(beta) / beta) ** 2
AiryDisk_normalized = AiryDisk/AiryDisk.max()
deblurredROI_wiener = wiener(
ROI_reg_norm, psf=AiryDisk, balance=7)
deblurredROI = deblurredROI_wiener
deblurredROI = deblurredROI / deblurredROI.max() * 255.0
enhancedFrame[ROI_coord[0]:ROI_coord[0] + ROI_coord[2],
ROI_coord[1]:ROI_coord[1] + ROI_coord[3]] = np.abs(deblurredROI)
ROI_enhanced_arr.clear()
ROI_enhanced_arr.append(deblurredROI)
ROI_enhanced_arr_to_save.append(deblurredROI)
enhancedFrames.append(enhancedFrame)
print('Frame analysis time: ', time.time() - t)
displayVarFAT.set(str("{:.3f}".format(time.time() - t)))
try:
inp_roi = ImageTk.PhotoImage(
Image.fromarray(ROI_arr[i].astype(np.uint8)))
out_roi = ImageTk.PhotoImage(Image.fromarray(
ROI_enhanced_arr[i].astype(np.uint8)))
except:
L1.config(image='')
L2.config(image='')
return
gray_oldframe = ROI_arr[i].astype(np.uint8)
if(is_blur):
gray_oldframe = GaussianBlur(
gray_oldframe, kernel_gauss, 0)
oldBlurMatrix = np.float32(gray_oldframe)
accumulateWeighted(gray_oldframe, oldBlurMatrix, 0.003)
# frame = cv2.flip(ROI_enhanced_arr[i].astype(np.uint8), 1)
frame = ROI_enhanced_arr[i].astype(np.uint8)
gray_frame = frame
if(is_blur):
newBlur_frame = GaussianBlur(
gray_frame, kernel_gauss, 0)
else:
newBlur_frame = gray_frame
newBlurMatrix = np.float32(newBlur_frame)
minusMatrix = absdiff(newBlurMatrix, oldBlurMatrix)
ret, minus_frame = threshold(
minusMatrix, 60, 255.0, THRESH_BINARY)
accumulateWeighted(newBlurMatrix, oldBlurMatrix, 0.02)
if(is_blur):
minus_frame = GaussianBlur(
minus_frame, kernel_gauss, 0)
minus_Matrix = np.float32(minus_frame)
if(is_close):
for itr in range(get_current_value1()):
minus_Matrix = dilate(minus_Matrix, kernel_d)
for itr in range(get_current_value2()):
minus_Matrix = erode(minus_Matrix, kernel_e)
minus_Matrix = np.clip(minus_Matrix, 0, 255)
minus_Matrix = np.array(minus_Matrix, np.uint8)
contours, hierarchy = findContours(
minus_Matrix.copy(), RETR_TREE, CHAIN_APPROX_SIMPLE)
for c in contours:
(x, y, w, h) = boundingRect(c)
rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
if(is_draw_ct):
drawContours(frame, contours, -1, (0, 255, 255), 2)
# frame = cv2.flip(frame, 1)
Combined_frames.append(frame)
out_frame = ImageTk.PhotoImage(Image.fromarray(frame))
L1['image'] = inp_roi
L2['image'] = out_frame
window.update()
i += 1
# concatenatedVid = [np.hstack((ROI_arr[i], np.zeros(
# (ROI_arr[0].shape[0], 10)), Combined_frames[i])).astype(np.float32) for i in range(len(ROI_arr))]
# write_video(concatenatedVid, 10, 'combined', True)
cv2.destroyAllWindows()
# ____________Receiving Input from User_________________
open_popup()