Pedestrian Crossing Radar Management System

FileName – fayl nomi
filter - tozalagich, filtr, filterlamoq, tozalamoq
find – topmoq
font – shrift
index - 1) ko`rsatkich, alfavit korsatkichi; 2) katalog, ro`yxat; indeks; 3) strelka (ko`rsatkich),
length - 1) uzunlik, masofa; 2) ketgan vaqt, davom etish muddati
modified - biror narsaning o`zgargan shakli, turlanish
no – yo`q
pate – qo`yish
share - (with, among, between) vt 1) taqsimlamoq, bo`l(ish)moq; 2) paylashmoq
ShowMessage – habarni ko`rsatmoq
simple - oson, oddiy, soda
size – hajm, kattalik, o`lchov
switch - birdan fikrini o`zgartirmoq
text – matn
type - ~ of n [c] 1) tur; 2) bosma harf; 3) tip, xil, nusxa
visible - 1) ko`zga ko`rinadigan, ko`z ilg`aydigan; 2) aniq, oshkora
yes – ha
true – rost
false – yolg‘on


Glossary

T/r	English	Uzbek
1	Sign In	Tizimga kiring
2	Processor Id	Protsessor identifikatori
3	Base Board Id	Bazaviy kengash identifikatori
4	BIOS Date	BIOS sanasi
5	Password	Maxfiy soʻz
6	Login	Kirish
7	Enter Password	Maxfiy soʻzni kiriting
8	Invalid Password	Maxfiy soʻz haqiqiy emas
9	Face Recognition Employee Attendance System	Yuzni tanib olish xodimlarining qatnashish tizimi
10	Reason Software Corporation	Reason dasturiy ta'minot korporatsiyasi
11	Developed by Sherzod Khoja Sulaiman Siddik	Sherzod Xo'ja Sulaymon Siddiq tomonidan ishlab chiqilgan
12	Register Employee	Xodimni ro'yxatdan o'tkazish
13	Clock In	Kirish soati
14	Clock Out	Chiqish soati
15	Report	Hisobot
16	Glossary	Glossariy
17	About	Dastur haqida
18	Logout	Tizimdan chiqish
19	Exit	Chiqish
20	Face Detection Using WebCamera	Web Camera yordamida yuzni aniqlash
21	Device	Uskuna(qurilma)
22	Start	Boshlash
23	Reset	Qayta tiklash(tozalash)
24	Pause	Toʻxtatish
25	Capture	Qoʻlga kiritish(rasmga olish)
26	Image Name	Rasmning nomi
27	File Name	Faylning nomi
28	Save	Saqlash
29	Employee Attendance Report Form	Xodimlarning qatnashish to'g'risidagi hisoboti shakli
30	Open CSV File	CSV faylini ochish
31	Employee Clock In	Xodimlar kirish soati
32	Employee Clock Out	Xodimlar chiqish soati
33	Open Excel File	Excel faylini ochish
34	File Name	Faylning nomi
35	Select Table	Jadvalni tanlash
36	Sheet	Varaq
37	Name	Nom
38	Time	Vaqt
39	Employee Clock In and Clock Out	Xodimlarning kirish va chiqish soati
40	Select Employee Clock In:	Xodimlar kirish soatini tanlang
41	Select Employee ClockOut:	Xodimlar chiqish soatini tanlang
42	Date	Sana
43	Monday	Dushanba
44	Tuesday	Seshanba
45	Wednesday	Chorshanba
46	Thursday	Payshanba
47	Friday	Juma
48	Saturday	Shanba
49	Sunday	Yakshanba
50	January	Yanvar
51	February	Fevral
52	March	Mart
53	April	Aprel
54	May	May
55	June	Iyun
56	July	Iyul
57	August	Avgust
58	September	Sentabr
59	October	Oktabr
60	November	Noyabr
61	December	Dekabr

Python dasturida “Pedestrian Crossing Radar Management System” dasturini yaratish

Asosiy Formaga:
Label
Timer
Panel
PictureBox
Button
komponentalarini tashlaymiz.

“Pedestrian Crossing Radar Management System” dasturining kodlari.

Umumiy kod: MainForms.py faylining kodlari:

import cv2 as cv
import numpy as np
import imutils
import time
import datetime
import os

t = time.strftime("%Y-%m-%d_%H-%M-%S")

#timeFolder = time.strftime("%Y-%m-%d")
dateTime = str(datetime.datetime.now())
#pathFolder = os.system(f'cmd /k "mkdir" folder_{timeFolder}')
img_counter = 0

# setting parameters

CONFIDENCE_THRESHOLD = 0.4
NMS_THRESHOLD = 0.3

#==================================================

timeFolder = time.strftime("%Y-%m-%d")

def createFolder(directory):

try:
if not os.path.exists(directory):
os.makedirs(directory)
except OSError:
print("Error: Creating directory. " + directory)

#createFolder('./test/')

createFolder(timeFolder)
#==================================================

#==================================================

import mediapipe as mp

width=640

height=480

mp_drawing = mp.solutions.drawing_utils

mp_face = mp.solutions.face_detection.FaceDetection(model_selection=1,min_detection_confidence=0.5)

def obj_data(img):

global img_counter, pathFolder
image_input = cv.cvtColor(img, cv.COLOR_BGR2RGB)
results = mp_face.process(image_input)
if not results.detections:
print("NO FACE")
else:
for detection in results.detections:
bbox = detection.location_data.relative_bounding_box
#print(bbox)
x, y, w, h = int(bbox.xmin*width), int(bbox.ymin * height), int(bbox.width*width),int(bbox.height*height)
start_point = (x-100, y-100)
end_point = (x+w+80, y+h+80)
#cv.rectangle(img,(x-100,y-100),(x+w+80,y+h+80),(0,0,255),2)
cv.rectangle(img, start_point, end_point,(0,0,255),2)
#print(cv.rectangle(img,(x-100,y-100),(x+w+80,y+h+80),(0,0,255),2))
#if yuz is True:
## img_name = f"{pahtFolder}/detect_"+t+"_{img_counter}.png"#.format(pathFolder, img_counter)
## cv.imwrite(img_name, frame)
## #print("screeenshot taken")
## img_counter += 1
#==================================================

# colors for object detected

COLORS = [(0,255,255), (255,255,0), (0,255,0), (255,0,0), (255,0,255), (255,0,0)]
GREEN = (0,255,0)
RED = (0,0,255)
PINK = (147,20,255)
ORANGE = (0,69,255)
BLACK = (0,255,0)
# defining fonts
fonts = cv.FONT_HERSHEY_COMPLEX

# reading class name from text file

class_names = []

with open("dnn_model/classes.txt", "r") as f:

class_names = [cname.strip() for cname in f.readlines()]

# setting up opencv net

yoloNet = cv.dnn.readNet('dnn_model/yolov4-tiny.weights', 'dnn_model/yolov4-tiny.cfg')

yoloNet.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)

yoloNet.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA_FP16)

model = cv.dnn_DetectionModel(yoloNet)

model.setInputParams(size=(416,416), scale=1/255, swapRB=True)

def color_detect():

# color detection
hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
lower_red = np.array([0,50,120])
upper_red = np.array([10,255,255])
mask3 = cv.inRange(hsv, lower_red, upper_red)
cnts3 = cv.findContours(mask3, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
cnts3 = imutils.grab_contours(cnts3)

for c in cnts3:

area3 = cv.contourArea(c)
if area3 > 5000:

cv.drawContours(frame, [c], -1, (0,255,0), 3)

M = cv.moments(c)

cx = int(M["m10"]/M["m00"])

cy = int(M["m01"]/M["m00"])

cv.circle(frame, (cx, cy), 7, (255,255,255), -1)

cv.putText(frame, "Red", (cx-20, cy-20), cv.FONT_HERSHEY_SIMPLEX, 2.5, (255,255,255), 3)

# setting camera

#def ObjectDetector(image):
## classes, scores, boxes = model.detect(image, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
##
## # creating empty list to add objects data
## data_list = [] #[classid[0], classid[67]]
## for (classid, score, box) in zip(classes, scores, boxes):
## img_counter = 0
## # define color of each, object based on ito class id
## color = COLORS[int(classid) % len(COLORS)]
## foiz = (score*100)
## label = "%s : %f" % (class_names[classid], foiz)
## #lbl = 100 * label
##
## # draw rectangle on and label on object
## #if (classid == 0 and classid == 67):
## cv.rectangle(image, box, color, 2)
## cv.putText(image, label, (box[0], box[1]-14), fonts, 0.5, color, 2)
## for timeFrame in t:
## timeFrame = cv.putText(image, t, (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 1)
##
## if (classid == 67):
## cv.rectangle(image, box, color, 2)
## cv.putText(image, label, (box[0], box[1]-14), fonts, 0.5, color, 2)
##
## img_name = "detect_person_image/detect_"+t+"_{}.png".format(img_counter)
## cv.imwrite(img_name, image)
## #print("screeenshot taken")
## img_counter += 1
##
## elif (classid == 9):
## cv.rectangle(image, box, color, 2)
## cv.putText(image, label, (box[0], box[1]-14), fonts, 0.5, color, 2)
## color_detect()
##
## img_name = "detect_person_image/detect_"+t+"_{}.png".format(img_counter)
## cv.imwrite(img_name, image)
## #print("screeenshot taken")
## img_counter += 1
## # getting the data
# 1: class name 2: object width in pixels, 3: position where have to draw text(distance)
## if classid == 0 and 67: #person class id
## data_list.append([class_names[classid[0]], box[2], (box[0], box[1]-2)])
## elif classid == 9:
## data_list.append([class_names[classid[0]], box[2], (box[0], box[1]-2)])
## # return list
## return data_list

# load the cascade

face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')

cap = cv.VideoCapture(0)

while(cap.isOpened()):
ret, frame = cap.read()
frame = cv.putText(frame, dateTime, (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 1)
#ObjectDetector(frame)
classes, scores, boxes = model.detect(frame, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)

# creating empty list to add objects data

data_list = [] #[classid[0], classid[67]]
for (classid, score, box) in zip(classes, scores, boxes):
# define color of each, object based on ito class id
color = COLORS[int(classid) % len(COLORS)]

## foiz = round(score, 2)*100

## label = "%s : %f" % (class_names[classid], foiz)
label = class_names[classid]
foiz = round(score, 2)

# draw rectangle on and label on object

#if (classid == 0 and classid == 67):
cv.rectangle(frame, box, color, 2)
cv.putText(frame, label + " {:.2f}".format(foiz*100)+"%", (box[0], box[1]-14), fonts, 0.5, color, 2)

# convert to grayscale

gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)

# Detect the faces

faces = face_cascade.detectMultiScale(gray, 1.1, 4)

## def facesDetect():

## for (x,y,w,h) in faces:
## cv.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
## img_name = "detect_person_image/detect_"+t+"_{}.png".format(img_counter)
## cv.imwrite(img_name, frame)
## #print("screeenshot taken")
## img_counter += 1
print(box)
#print(score)

if (classid == 67):

cv.rectangle(frame, box, color, 2)
cv.putText(frame, label, (box[0], box[1]-14), fonts, 0.5, color, 2)
cv.imshow('Person Radar | Developed by Sherzod Khoja Sulaiman Siddik', frame)

key = cv.waitKey(1)

if key == ord('q') or key == 27:
break
cap.release()
cv.destroyAllWindows()