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Speed_Estimation_&_License_Plate_Detection.py
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Speed_Estimation_&_License_Plate_Detection.py
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import cv2
import dlib
import time
import threading
import math
import datetime
import os
import openpyxl
from openpyxl import load_workbook
import matplotlib.pyplot as plt
import numpy as np
from skimage import util
from skimage.io import imread
from skimage.filters import threshold_otsu
from skimage import measure
from skimage.measure import regionprops
import matplotlib.patches as patches
import sklearn
import joblib
#import trial_23
SNo = int(1)
counter = 0
sheet = {}
now = datetime.datetime.now()
mainFilename = 'C:\\Users\SHREYA\Desktop\mainFileVehicleID.xlsx'
if not(os.path.isfile(mainFilename)):
mwb = openpyxl.Workbook() #main work book
#main_sheet = mwb.create_sheet('main_sheet')
#main_sheet = mwb.worksheets
main_sheet=mwb.active
#main_sheet = main_sheet.title
main_sheet.cell(row=1, column=1).value = 'VehicleID'
main_sheet.cell(row=1, column=2).value = 'Vehicle_Number'
sheet[0] = mwb.create_sheet('sheet%d' %counter)
# ws[counter] = mwb.worksheets[counter]
sheet[0] = mwb.active
a = 'Daily Vehicles'
day = now.day
b = str(str(a) + str(day) + '.' + str(now.month) + '.' + str(now.year))
sheet[0].title = b
sheet[0].cell(row=1, column=1).value = 'VehicleID'
sheet[0].cell(row=1, column=2).value = 'Date'
sheet[0].cell(row=1, column=3).value = 'Time'
sheet[0].cell(row=1, column=4).value = 'Camera'
sheet[0].cell(row=1, column=5).value = 'Speed'
sheet[0].cell(row=1, column=6).value = 'Number Plate'
mwb.save(mainFilename)
carCascade = cv2.CascadeClassifier('C:\\Users\\SHREYA\\Desktop\\myhaar.xml')
video = cv2.VideoCapture('C:\\Users\\SHREYA\\Desktop\\shreya\\Internship\\Videos\\a_1.avi')
WIDTH = 1280
HEIGHT = 720
######
def detection(image_1):
#print(img)
height = 900
width = 700
img = cv2.cvtColor(image_1, cv2.COLOR_RGB2GRAY)
car_image = cv2.resize(img, (width,height))
img_1 = cv2.resize(img, (width,height))
# car_image = imread("car.png", as_gray=True)
# it should be a 2 dimensional array
# the next line is not compulsory however, a grey scale pixel
# in skimage ranges between 0 & 1. multiplying it with 255
# will make it range between 0 & 255 (something we can relate better with
gray_car_image = car_image * 255
#fig, (ax1, ax2) = plt.subplots(1, 2)
#ax1.imshow(gray_car_image, cmap="gray")
threshold_value = threshold_otsu(gray_car_image)
binary_car_image = gray_car_image > threshold_value
#print(binary_car_image)
#ax2.imshow(binary_car_image, cmap="gray")
# ax2.imshow(gray_car_image, cmap="gray")
#plt.show()
# CCA (finding connected regions) of binary image
# this gets all the connected regions and groups them together
label_image = measure.label(binary_car_image)
#print(label_image.shape[0]) #height of the image
#print(label_image.shape[1]) #width of the image
# getting the maximum width, height and minimum width and height that a license plate can be
#plate_dimensions0 = (0.01*label_image.shape[0], 0.03*label_image.shape[0], 0.10*label_image.shape[1], 0.15*label_image.shape[1])
plate_dimensions = (0.06*label_image.shape[0], 0.13*label_image.shape[0], 0.085*label_image.shape[1], 0.2*label_image.shape[1])
plate_dimensions2 = (0.06*label_image.shape[0], 0.18*label_image.shape[0], 0.15*label_image.shape[1], 0.25*label_image.shape[1])
min_height, max_height, min_width, max_width = plate_dimensions
plate_objects_cordinates = []
plate_like_objects = []
#print(min_height, ' ', max_height, ' ', min_width, ' ', max_width)
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(gray_car_image, cmap="gray")
flag =0
go = 0
# regionprops creates a list of properties of all the labelled regions
for region in regionprops(label_image):
#min_row, min_col, max_row, max_col = region.bbox
#print('')
#print('min_row:', min_row, 'max_row:', max_row, 'min_col:', min_col, 'max_col:', max_col)
#print(region.area)
#print('')
if (region.area < 500):
continue
# 50 -> 30
#print(region.area)
#min_row, min_col, max_row, max_col = region.bbox
#region_height = max_row - min_row
#region_width = max_col - min_col
#if the region is so small then it's likely not a license plate
# the bounding box coordinates
print(type(region.bbox))
print(region.bbox)
min_row, min_col, max_row, max_col = region.bbox
# print(min_row)
# print(min_col)
# print(max_row)
# print(max_col)
#print(yo+1)
region_height = max_row - min_row
region_width = max_col - min_col
#print('')
#print('region_height: ', region_height, 'region_width: ', region_width)
#print('min_row:', min_row, 'max_row:', max_row, 'min_col:', min_col, 'max_col:', max_col)
#print('region_width: ', region_width)
#print('')
# ensuring that the region identified satisfies the condition of a typical license plate
if (region_height >= min_height and region_height <= max_height and region_width >= min_width and region_width <= max_width and region_width > region_height):
flag = 1
go = go + 1
print(go)
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(gray_car_image, cmap="gray")
plate_like_objects.append(binary_car_image[min_row:max_row, min_col:max_col])
plate_objects_cordinates.append((min_row, min_col, max_row, max_col))
rectBorder = patches.Rectangle((min_col, min_row), max_col - min_col, max_row - min_row, edgecolor="red", linewidth=2, fill=False)
#ax1.add_patch(rectBorder)
#plt.show()
#cropped_image = gray_car_image[min_row:max_row, min_col:max_col]
#print(type(cropped_image))
#changed_color = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
#inverted_image = util.invert(cropped_image)
#threshold_value_license_plate = threshold_otsu(inverted_image)
#print(threshold_value_license_plate)
#thresholded_image = inverted_image > threshold_value_license_plate
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(thresholded_image, cmap='gray')
#plt.show()
# let's draw a red rectangle over those regions
if(flag == 1):
print(plate_like_objects[0])
#plt.show()
#print('flag=1')
if(flag == 0):
min_height, max_height, min_width, max_width = plate_dimensions2
plate_objects_cordinates = []
plate_like_objects = []
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(gray_car_image, cmap="gray")
# regionprops creates a list of properties of all the labelled regions
for region in regionprops(label_image):
if (region.area < 150): ## 50 -> 30
#if the region is so small then it's likely not a license plate
continue
# the bounding box coordinates
min_row, min_col, max_row, max_col = region.bbox
# print(min_row)
# print(min_col)
# print(max_row)
# print(max_col)
region_height = max_row - min_row
region_width = max_col - min_col
# print(region_height)
# print(region_width)
# ensuring that the region identified satisfies the condition of a typical license plate
if (region_height >= min_height and region_height <= max_height and region_width >= min_width and region_width <= max_width and region_width > region_height):
# print("hello")
plate_like_objects.append(binary_car_image[min_row:max_row, min_col:max_col])
plate_objects_cordinates.append((min_row, min_col, max_row, max_col))
rectBorder = patches.Rectangle((min_col, min_row), max_col - min_col, max_row - min_row, edgecolor="red", linewidth=2, fill=False)
#ax1.add_patch(rectBorder)
#plt.show()
#print('flag=0')
#cropped_image = gray_car_image[min_row:max_row, min_col:max_col]
#print(type(cropped_image))
#changed_color = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
#inverted_image = util.invert(cropped_image)
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(inverted_image, cmap='gray')
#plt.show()
# let's draw a red rectangle over those regions
#print(plate_like_objects[3])
#plt.show()
#print(flag)
#print(len(plate_like_objects))
#print(plate_like_objects[len(plate_like_objects)-1])
#print(plate_objects_cordinates[len(plate_like_objects)-1])
if (len(plate_like_objects)!=0):
print(len(plate_like_objects))
(r1, c1, r2, c2)=plate_objects_cordinates[len(plate_like_objects)-1]
cropped_image_1 = gray_car_image[r1:r2, c1:c2]
img_2 = img_1[r1:r2, c1:c2]
img_3 = cv2.medianBlur(img_2,5)
#print(cropped_image)
#grayscale = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
ret,th_1 = cv2.threshold(img_3,155,255,cv2.THRESH_BINARY)
th_2 = cv2.adaptiveThreshold(img_3,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)# this one is better after all the thresholdings
#th_3 = cv2.adaptiveThreshold(img_3,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
# cv2.THRESH_BINARY,11,2)
not_th_2 = cv2.bitwise_not(th_2)
#fig, (ax1,ax2) = plt.subplots(1,2)
#ax1.imshow(th_2, cmap='gray')
#ax2.imshow(not_th_2, cmap='gray')
#plt.show()
width_plate = 90
height_plate = 60
license_plate = cv2.resize(not_th_2, (width_plate,height_plate))
labelled_plate = measure.label(license_plate)
#print(license_plate.shape[0]) #height of the plate
character_dimensions = (0.1875*license_plate.shape[0], 0.480*license_plate.shape[0], 0.05*license_plate.shape[1], 0.40*license_plate.shape[1])
min_height, max_height, min_width, max_width = character_dimensions #from resized license plate
#print(character_dimensions)
characters = []
counter=0
column_list = []
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(license_plate, cmap="gray")
for regions in regionprops(labelled_plate):
y0, x0, y1, x1 = regions.bbox
region_height = y1 - y0
region_width = x1 - x0
#print(region.bbox)
#print('')
#print(region.bbox)
#print('')
#print('region_height', region_height, 'region_width', region_width)
#print('min_height', min_height, 'max_height', max_height, 'min_width', min_width, 'max_width', max_width)
if (region_height > min_height and region_height < max_height and region_width > min_width and region_width < max_width):
roi = license_plate[y0:y1, x0:x1]
#print('')
#print(roi)
#print('')
# draw a red bordered rectangle over the character.
rect_border = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, edgecolor="red", linewidth=2, fill=False)
#print(rect_border)
#ax1.add_patch(rect_border)
# resize the characters to 20X20 and then append each character into the characters list
resized_char = cv2.resize(roi, (20, 20))
characters.append(resized_char)
#print('')
#print(resized_char)
# this is just to keep track of the arrangement of the characters
column_list.append(x0)
#print(column_list)
# print(characters)
#plt.show()
#print("Loading model")
#filename = 'C:\\Users\\SHREYA\\Desktop\\shreya\\Internship\\finalized_model.sav' # write the full directory path and also put double slashes if required!
#model = pickle.load(open(filename, 'rb'))
#print('Model loaded. Predicting characters of number plate')
#print("Loading model")
filename = 'C:\\Users\\SHREYA\\Desktop\\finalized_model.sav' # write the full directory path and also put double slashes if required!
current_dir = os.path.dirname(os.path.realpath(filename))
model_dir = os.path.join(current_dir, 'finalized_model.sav')
model = joblib.load(model_dir)
#print('Model loaded. Predicting characters of number plate')
classification_result = []
for each_character in characters:
# converts it to a 1D array
each_character = each_character.reshape(1, -1);
#print(each_character)
result = model.predict(each_character)
classification_result.append(result)
#print('Classification result')
#print(classification_result)
plate_string = ''
for eachPredict in classification_result:
plate_string += eachPredict[0]
#print('Predicted license plate')
#print(plate_string)
# it's possible the characters are wrongly arranged
# since that's a possibility, the column_list will be
# used to sort the letters in the right order
column_list_copy = column_list[:]
column_list.sort()
rightplate_string = ''
for each in column_list:
rightplate_string += plate_string[column_list_copy.index(each)]
#print('License plate')
#print(rightplate_string)
else:
plate_string = 'not able to detect'
return plate_string
######
def estimateSpeed(location1, location2, fps):
d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
# ppm = location2[2] / carWidht
ppm = 10 #$$$$
d_meters = d_pixels / ppm
#print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
#fps = 10#$$$$
print(fps)
speed = d_meters * 10 * 3.6 #3600/1000
return speed
def trackMultipleObjects():
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 1
cropped_image = {}
LPN = {}
carTracker = {}
carNumbers = {}
carLocation1 = {}
carLocation2 = {}
speed = [None] * 1000
counter = 0
a = 0
myuse = []
# Write output to video file
out = cv2.VideoWriter('C:\\Users\\SHREYA\\Desktop\\outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (WIDTH,HEIGHT))
while True:
now = datetime.datetime.now()
if (now.hour == 0 & now.minute == 0 & now.second == 0):
a = 1
counter = counter + 1
filename = 'C:\\Users\SHREYA\Desktop\mainFileVehicleID.xlsx'
mwb = load_workbook(filename)
sheet[counter] = mwb.create_sheet('sheet%d' %counter)
# ws[counter] = mwb.worksheets[counter]
sheet[counter] = mwb.active
a = 'Daily Vehicles'
day = now.day
b = str(str(a) + str(day) + '.' + str(now.month) + '.' + str(now.year))
sheet[counter].title = b
sheet[counter].cell(row=1, column=1).value = 'VehicleID'
sheet[counter].cell(row=1, column=2).value = 'Date'
sheet[counter].cell(row=1, column=3).value = 'Time'
sheet[counter].cell(row=1, column=4).value = 'Camera'
sheet[counter].cell(row=1, column=5).value = 'Speed'
sheet[counter].cell(row=1, column=6).value = 'Number Plate'
mwb.save(filename)
start_time = time.time()
rc, image_2 = video.read()
if type(image_2) == type(None):
break
image_1 = cv2.resize(image_2, (WIDTH, HEIGHT))
resultImage = image_1.copy()
height = image_1.shape[0]
width = image_1.shape[1]
image = image_1[0:height, 0:int(width/2)]
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(image)
if trackingQuality < 7: #7
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
#print ('Removing carID ' + str(carID) + ' from list of trackers.')
#print ('Removing carID ' + str(carID) + ' previous location.')
#print ('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
cropped_image.pop(carID,None)
LPN.pop(carID, None)
if not (frameCounter % 10): #it goes inside only when frameCounter is a multiple of 10
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
cars = list(cars)
#print(cars)
for i in range (len(cars)):
#print(cars, 'HAKUNA MATATA')
if (cars[i][0] < 195) or (cars[i][2] < 58) or (cars[i][3] < 58) or ((cars[i][1] > 40) and (cars[i][2] < 68)) or ((cars[i][1] > 268) and (cars[i][2] < 250)) or ((cars[i][1] > 268) and (cars[i][3] < 250)) :
myuse.append(i)
#print('1st', len(cars), ' ', myuse)
#print('SDJVFVJKS')
#if (cars[i][2] < 58) or (cars[i][3] < 58):
#myuse.append(i)
#if (cars[i][0] > 180) and (cars[i][3] < 150):
#myuse.append(i)
#print(cars, 'djsidSHJiiiIINHDHNJ')
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
cars.pop(myuse[i])
#print(cars)
myuse = []
for i in range(len(cars)):
if (cars[i][1] > 40) and (cars[i][2] < 70):
myuse.append(i)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
cars.pop(myuse[i])
#print(cars)
myuse = []
for i in range(len(cars)):
if (cars[i][1] > 110) and (cars[i][2] < 120):
myuse.append(i)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
cars.pop(myuse[i])
#print(cars)
myuse = []
for i in range(len(cars)):
if (cars[i][1] > 268) and (cars[i][2] < 250):
myuse.append(i)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
cars.pop(myuse[i])
#print(cars)
myuse = []
#print(cars)
#for x in range(len(cars)):
#x_1 = cars[x][0]
#y_1 = cars[x][1]
#w_1 = cars[x][2]
#h_1 = cars[x][3]
#for y in range(len(cars)):
#if (x!=y):
#x_2 = cars[y][0]
#y_2 = cars[y][1]
#w_2 = cars[y][2]
#h_2 = cars[y][3]
#if ((x_2 > (x_1 + 5) > (x_2 + w_2)) and (x_2 > (x_1 + w_1 - 5) > (x_2 + w_2)) and (y_2 > (y_1 + h_1 + 5) > (y_2 + h_2))) :
#myuse.append(x)
#print('HEY THERE IM RIGHT HERE BONJOIRs')
#if myuse != []:
#print(cars)
#for i in range(len(myuse)-1, -1, -1):
#cars.pop(myuse[i])
#myuse = []
#for i in range(len(cars)):
#if (cars[i][2] < 58) or (cars[i][3] < 58):
#myuse.append(i)
#print('2nd', len(cars), ' ', myuse)
#if myuse != []:
#for yo in myuse:
#if (yo < len(cars)):
#cars.pop(yo)
#myuse = []
#for i in range(len(cars)):
#for (_x, _y, _w, _h) in cars:
#print('yobaby')
#print(_x, _y, _w, _h)
#if (_h > int(height/2)):
#cv2.imshow()
#for (try_x, try_y, try_w, try_h) in cars:
#if (((_x > try_x) and (_x < (try_x + try_w)) and ((_y + _h) > try_y) and ((_y + _h) < (try_y + try_h))) or ((_x > try_x) and (_x < (try_x + try_w)) and (_y > try_y) and (_y < (try_y + try_h))) or (((_x + _w) > try_x) and ((_x + _w) < try_x) and (_y > try_y) and (_y < (try_y + try_h))) or (((_x + _w) > try_x) and ((_x + _w) < (try_x + try_w)) and ((_y + _h) > try_y) and ((_y + _h) < (try_y + try_h)))):
#print('HEY THERE IM RIGHT HERE BONJOIRs')
#if ((_w * _h) < (try_w * try_h)):
#for i in range(len(cars)):
#print(cars[i][0], 'EHFHREHHSHJFGNF')
#print('BIENTOT MASALA')
#if (_x == cars[i][0]) and (_y == cars[i][1]) and (_w == cars[i][2]) and (_h == cars[i][3]):
#print('oui, a FILLE')
#if ([_x _y _w _h] == cars[i]):
#a.append(i)
#print('MERA PIYA GHAR AAYA OO RAMJI')
#for i in range(len(cars)):
#if
for (_x, _y, _w, _h) in cars:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print ('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
currentCarID = currentCarID + 1
#cv2.line(resultImage,(0,480),(1280,480),(255,0,0),5)
myuse = []
#print(cars)
#print(carTracker, 'BEFOREEEE')
###1
for carID in carTracker.keys():
x_1 = carTracker[carID].get_position().left()
y_1 = carTracker[carID].get_position().top()
w_1 = carTracker[carID].get_position().width()
h_1 = carTracker[carID].get_position().height()
for carID_2 in carTracker.keys():
x_2 = carTracker[carID_2].get_position().left()
y_2 = carTracker[carID_2].get_position().top()
w_2 = carTracker[carID_2].get_position().width()
h_2 = carTracker[carID_2].get_position().height()
#print(x_1, x_2, ' ', y_1, y_2, ' ', w_1, w_2, ' ', h_1, h_2)
if (carID != carID_2):
#print(x_2, x_1+10, x_2+w_2, ' ', x_2, x_1+w_1-10, x_2+w_2, ' ', y_2, y_1+h_1+10, y_2+h_2, 'SEEE THIS BRO')
if (x_2 < (x_1 + 10)) and ((x_1 + 10) < (x_2 + w_2)) and ((x_1 + w_1 - 10) > x_2) and ((x_1 + w_1 - 10) < (x_2 + w_2)) and ((y_1 + h_1 + 10) > y_2) and ((y_1 + h_1 +10) < (y_2 + h_2)):
myuse.append(carID)
#print('HAKUNAAAAA')
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
carTracker.pop(myuse[i])
carLocation1.pop(myuse[i])
#currentCarID = currentCarID - 1
#print(cars)
myuse = []
###2
for carID in carTracker.keys():
x_1 = carTracker[carID].get_position().left()
y_1 = carTracker[carID].get_position().top()
w_1 = carTracker[carID].get_position().width()
h_1 = carTracker[carID].get_position().height()
for carID_2 in carTracker.keys():
x_2 = carTracker[carID_2].get_position().left()
y_2 = carTracker[carID_2].get_position().top()
w_2 = carTracker[carID_2].get_position().width()
h_2 = carTracker[carID_2].get_position().height()
#print(x_1, x_2, ' ', y_1, y_2, ' ', w_1, w_2, ' ', h_1, h_2)
if (carID != carID_2):
#print(x_2, x_1+10, x_2+w_2, ' ', x_2, x_1+w_1-10, x_2+w_2, ' ', y_2, y_1+h_1+10, y_2+h_2, 'SEEE THIS BRO')
if ((x_1 + w_1 + 10) > x_2) and ((x_1 + w_1 + 10) < (x_2 + w_2)) and ((y_1 + 10) > y_2) and ((y_1 + 10) < (y_2 + h_2)) and ((y_1 + h_1 - 10) > y_2) and ((y_1 + h_1 - 10) < (y_2 + h_2)) :
myuse.append(carID)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
carTracker.pop(myuse[i])
carLocation1.pop(myuse[i])
#currentCarID = currentCarID - 1
#print(cars)
myuse = []
###3
for carID in carTracker.keys():
x_1 = carTracker[carID].get_position().left()
y_1 = carTracker[carID].get_position().top()
w_1 = carTracker[carID].get_position().width()
h_1 = carTracker[carID].get_position().height()
for carID_2 in carTracker.keys():
x_2 = carTracker[carID_2].get_position().left()
y_2 = carTracker[carID_2].get_position().top()
w_2 = carTracker[carID_2].get_position().width()
h_2 = carTracker[carID_2].get_position().height()
#print(x_1, x_2, ' ', y_1, y_2, ' ', w_1, w_2, ' ', h_1, h_2)
if (carID != carID_2):
#print(x_2, x_1+10, x_2+w_2, ' ', x_2, x_1+w_1-10, x_2+w_2, ' ', y_2, y_1+h_1+10, y_2+h_2, 'SEEE THIS BRO')
if ((x_1 + 10) > x_2) and ((x_1 + 10) < (x_2 + w_2)) and ((x_1 + w_1 - 10) > x_2) and ((x_1 + w_1 - 10) < (x_2 + w_2)) and ((y_1 - 10) > y_2) and ((y_1 - 10) < (y_2 + h_2)):
myuse.append(carID)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
carTracker.pop(myuse[i])
carLocation1.pop(myuse[i])
#currentCarID = currentCarID - 1
#print(cars)
myuse = []
###4
for carID in carTracker.keys():
x_1 = carTracker[carID].get_position().left()
y_1 = carTracker[carID].get_position().top()
w_1 = carTracker[carID].get_position().width()
h_1 = carTracker[carID].get_position().height()
for carID_2 in carTracker.keys():
x_2 = carTracker[carID_2].get_position().left()
y_2 = carTracker[carID_2].get_position().top()
w_2 = carTracker[carID_2].get_position().width()
h_2 = carTracker[carID_2].get_position().height()
#print(x_1, x_2, ' ', y_1, y_2, ' ', w_1, w_2, ' ', h_1, h_2)
if (carID != carID_2):
#print(x_2, x_1+10, x_2+w_2, ' ', x_2, x_1+w_1-10, x_2+w_2, ' ', y_2, y_1+h_1+10, y_2+h_2, 'SEEE THIS BRO')
if ((x_1 - 10) > x_2) and ((x_1 - 10) < (x_2 + w_2)) and ((y_1 + 10) > y_2) and ((y_1 + 10) < (y_2 + h_2)) and ((y_1 + h_1 - 10) > y_2) and ((y_1 + h_1 - 10) < (y_2 + h_2)) :
myuse.append(carID)
if myuse != []:
#print(cars)
for i in range(len(myuse)-1, -1, -1):
carTracker.pop(myuse[i])
carLocation1.pop(myuse[i])
#currentCarID = currentCarID - 1
#print(cars)
myuse = []
#print(carTracker, 'AFTERRRRRRRR')
#b = 0
#c = []
#for carValue in carTracker.values():
#c.append(carValue)
#carTracker
#for
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4)
#print(t_x, ' ', t_y, ' ', t_w, ' ', t_h)
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(resultImage)
#plt.show()
# speed estimation
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
if not (end_time == start_time):
fps = 1.0/(end_time - start_time)
#print(fps)
#cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
for i in carLocation1.keys():
if frameCounter % 1 == 0: #it will always work
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
carLocation1[i] = [x2, y2, w2, h2]
# print 'new previous location: ' + str(carLocation1[i])
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
if (speed[i] == None or speed[i] == 0): #275.. 285 and y1 >= 250 and y1 <= 260.. this is to give command to the code to only estimate speed when the vehicle is between 275 and 285!
speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2], fps)
print('1st', x1, y1, w1, h1, ' ', '2nd', x2, y2, w2, h2, ' ', speed[i], ' ', 'fps:', fps)
#print('')
#if y1 > 275 and y1 < 285:
if (speed[i] != None and (x2 > x1+5 or x2 < x1-5) and (y2 > y1+5 or y2 < y1-5)): #so that even if the driver opens his seat, the code doesn't detect speed in that
cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 0, 0), 2)
print(speed[i], 'YOOOOOOOOOOOO')
print(datetime.datetime.now())
#if (x1 < 300):
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(resultImage)
#plt.show()
#mwb.save(filename)
#Img = cv2.imread(image)
cropped_image[i] = image[y1:y1+h1+25, x1:x1+w1+25]
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(cropped_image[i])
#plt.show()
LPN[i] = detection(cropped_image[i])
print(LPN[i])
a = int(a) + int(1)
a = int(a)
c = datetime.datetime.now()
d = c.strftime('%d') + '/' + c.strftime('%b') + '/' + c.strftime('%Y')
e = c.strftime('%H') + ':' + c.strftime('%M') + ':' + c.strftime('%S')
filename = 'C:\\Users\SHREYA\Desktop\mainFileVehicleID.xlsx'
mwb = load_workbook(filename)
sheet[counter] = mwb.active
max_row_sheet = sheet[counter].max_row
max_row_excel = int(max_row_sheet) + 1
sheet[counter].cell(row=max_row_excel, column=1).value = int(max_row_sheet)
sheet[counter].cell(row=max_row_excel, column=2).value = d
sheet[counter].cell(row=max_row_excel, column=3).value = e
sheet[counter].cell(row=max_row_excel, column=4).value = 'Cam1'
sheet[counter].cell(row=max_row_excel, column=5).value = speed[i]
sheet[counter].cell(row=max_row_excel, column=6).value = LPN[i]
mwb.save(filename)
#print(type(cropped_image[i]))
#fig, (ax1) = plt.subplots(1)
#ax1.imshow(cropped_image, cmap='gray')
#plt.show()
#print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
#else:
# cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
#print ('CarID ' + str(i) + ' Location1: ' + str(carLocation1[i]) + ' Location2: ' + str(carLocation2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
cv2.imshow('result', resultImage)
# Write the frame into the file 'output.avi'
#out.write(resultImage)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
if __name__ == '__main__':
trackMultipleObjects()