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Login.py

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+import cv2
+import datetime
+import imutils
+import numpy as np
+from centroidtracker import CentroidTracker
+import pandas as pd 
+import torch 
+import streamlit as st
+import mediapipe as mp
+import cv2 as cv
+import numpy as np
+import tempfile
+import time
+from PIL import Image
+import pandas as pd
+import torch
+import base64
+import streamlit.components.v1 as components
+import csv
+import pickle
+from pathlib import Path
+import streamlit_authenticator as stauth
+import os
+import csv
+from streamlit_option_menu import option_menu
+#  x-x-x-x-x-x-x-x-x-x-x-x-x-x LOGIN FORM x-x-x-x-x-x-x-x-x
+
+                                
+import streamlit as st
+import pandas as pd
+import hashlib
+import sqlite3 
+#
+
+import pickle
+from pathlib import Path
+import streamlit_authenticator as stauth
+import pyautogui
+
+# print("Done !!!")
+
+data = ["student Count",'Date','Id','Mobile','Watch']
+with open('final.csv', 'w') as file:
+    writer = csv.writer(file)
+    writer.writerow(data)
+    
+
+# # l1 = []
+# # l2 = []
+# # if st.button('signup'):
+    
+    
+# #     usernames = st.text_input('Username')
+# #     pwd = st.text_input('Password') 
+# #     l1.append(usernames)
+# #     l2.append(pwd)
+
+# #     names = ["dmin", "ser"]
+# #     if st.button("signupsss"):
+# #         username =l1
+
+# #         password =l2
+
+# #         hashed_passwords =stauth.Hasher(password).generate()
+
+# #         file_path = Path(__file__).parent / "hashed_pw.pkl"
+
+# #         with file_path.open("wb") as file:
+# #             pickle.dump(hashed_passwords, file)
+            
+    
+# # elif st.button('Logins'):
+# names = ['dmin', 'ser']
+
+# username = []
+
+# file_path = Path(__file__).parent / 'hashed_pw.pkl'
+
+# with file_path.open('rb') as file:
+#     hashed_passwords = pickle.load(file)
+
+# authenticator = stauth.Authenticate(names,username,hashed_passwords,'Cheating Detection','abcdefg',cookie_expiry_days=180)
+
+# name,authentication_status,username= authenticator.login('Login','main')
+
+
+# if authentication_status == False:
+#     st.error('Username/Password is incorrect')
+    
+# if authentication_status == None:
+#     st.error('Please enter a username and password')
+
+@st.experimental_memo
+def get_img_as_base64(file):
+    with open(file, "rb") as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+
+#img = get_img_as_base64("/home/anas/PersonTracking/WebUI/attendence.jpg")
+
+page_bg_img = f"""
+<style>
+[data-testid="stAppViewContainer"] > .main {{
+background-image: url("https://www.xmple.com/wallpaper/blue-gradient-black-linear-1920x1080-c2-87cefa-000000-a-180-f-14.svg");
+background-size: 180%;
+background-position: top left;
+background-repeat: no-repeat;
+background-attachment: local;
+}}
+
+[data-testid="stHeader"] {{
+background: rgba(0,0,0,0);
+}}
+[data-testid="stToolbar"] {{
+right: 2rem;
+}}
+</style>
+"""
+
+st.markdown(page_bg_img, unsafe_allow_html=True)
+files = pd.read_csv('LoginStatus.csv')
+
+
+idS = list(files['Id'])
+Pwd = list(files['Password'].astype(str))
+
+# print(type(Pwd))
+ids = st.sidebar.text_input('Enter a username')
+Pswd = st.sidebar.text_input('Enter a password',type="password",key="password")
+
+# print('list : ',type(Pwd))
+
+    
+        
+if (ids in idS) and(str(Pswd) in Pwd):
+    
+        # st.empty()    
+        date_time = time.strftime("%b %d %Y %-I:%M %p")
+        date = date_time.split()
+        dates = date[0:3]
+        times = date[3:5]
+        # x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-xAPPLICACTION -x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x
+
+        def non_max_suppression_fast(boxes, overlapThresh):
+            try:
+                if len(boxes) == 0:
+                    return []
+
+                if boxes.dtype.kind == "i":
+                    boxes = boxes.astype("float")
+
+                pick = []
+
+                x1 = boxes[:, 0]
+                y1 = boxes[:, 1]
+                x2 = boxes[:, 2]
+                y2 = boxes[:, 3]
+
+                area = (x2 - x1 + 1) * (y2 - y1 + 1)
+                idxs = np.argsort(y2)
+
+                while len(idxs) > 0:
+                    last = len(idxs) - 1
+                    i = idxs[last]
+                    pick.append(i)
+
+                    xx1 = np.maximum(x1[i], x1[idxs[:last]])
+                    yy1 = np.maximum(y1[i], y1[idxs[:last]])
+                    xx2 = np.minimum(x2[i], x2[idxs[:last]])
+                    yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+                    w = np.maximum(0, xx2 - xx1 + 1)
+                    h = np.maximum(0, yy2 - yy1 + 1)
+
+                    overlap = (w * h) / area[idxs[:last]]
+
+                    idxs = np.delete(idxs, np.concatenate(([last],
+                                                        np.where(overlap > overlapThresh)[0])))
+
+                return boxes[pick].astype("int")
+            except Exception as e:
+                print("Exception occurred in non_max_suppression : {}".format(e))
+
+
+        protopath = "MobileNetSSD_deploy.prototxt"
+        modelpath = "MobileNetSSD_deploy.caffemodel"
+        detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
+        # Only enable it if you are using OpenVino environment
+        # detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+        # detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
+
+
+        CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
+                "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+                "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+                "sofa", "train", "tvmonitor"]
+
+        tracker = CentroidTracker(maxDisappeared=80, maxDistance=90)
+
+        st.markdown(
+            """
+            <style>
+            [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+                width: 350px
+            }
+            [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+                width: 350px
+                margin-left: -350px
+            }
+            </style>
+            """,
+            unsafe_allow_html=True,
+        )
+        hide_streamlit_style = """
+                <style>
+                #MainMenu {visibility: hidden;}
+                footer {visibility: hidden;}
+                </style>
+                """
+        st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+        # Resize Images to fit Container
+        @st.cache()
+        # Get Image Dimensions
+        def image_resize(image, width=None, height=None, inter=cv.INTER_AREA):
+            dim = None
+            (h,w) = image.shape[:2]
+
+            if width is None and height is None:
+                return image
+
+            if width is None:
+                r = width/float(w)
+                dim = (int(w*r),height)
+
+            else:
+                r = width/float(w)
+                dim = width, int(h*r)
+
+            # Resize image
+            resized = cv.resize(image,dim,interpolation=inter)
+
+            return resized
+                
+        # About Page
+        # authenticator.logout('Logout')
+        EXAMPLE_NO = 3
+
+
+        def streamlit_menu(example=1):
+            if example == 1:
+                # 1. as sidebar menu
+                with st.sidebar:
+                    selected = option_menu(
+                        menu_title="Main Menu",  # required
+                        options=["Home", "Projects", "Contact"],  # required
+                        icons=["house", "book", "envelope"],  # optional
+                        menu_icon="cast",  # optional
+                        default_index=0,  # optional
+                    )
+                return selected
+
+            if example == 2:
+                # 2. horizontal menu w/o custom style
+                selected = option_menu(
+                    menu_title=None,  # required
+                    options=["Home", "Projects", "Contact"],  # required
+                    icons=["house", "book", "envelope"],  # optional
+                    menu_icon="cast",  # optional
+                    default_index=0,  # optional
+                    orientation="horizontal",
+                )
+                return selected
+
+            if example == 3:
+                # 2. horizontal menu with custom style
+                selected = option_menu(
+                    menu_title=None,  # required
+                    options=["Home", "Projects", "Contact"],  # required
+                    icons=["house", "book", "envelope"],  # optional
+                    menu_icon="cast",  # optional
+                    default_index=0,  # optional
+                    orientation="horizontal",
+                    styles={
+                        "container": {"padding": "0!important", "background-color": "#eaeaea"},
+                        "icon": {"color": "#080602", "font-size": "18px"},
+                        "nav-link": {
+                            "font-size": "18px",
+                            "text-align": "left",
+                            "color": "#000000",
+                            "margin": "0px",
+                            "--hover-color": "#E1A031",
+                        },
+                        "nav-link-selected": {"background-color": "#ffffff"},
+                    },
+                )
+                return selected
+
+
+        selected = streamlit_menu(example=EXAMPLE_NO)
+
+        if selected == "Home":
+            st.title(f"You have selected {selected}")
+        # if selected == "Projects":
+        #     st.title(f"You have selected {selected}")
+        if selected == "Contact":
+            st.title(f"You have selected {selected}")
+        # app_mode = st.sidebar.selectbox(
+        #                 'App Mode',
+        #                 ['Application']
+        #                 )
+        if selected == 'Projects':
+        # 2. horizontal menu with custom style
+            # selected = option_menu(
+            #     menu_title=None,  # required
+            #     options=["Home", "Projects", "Contact"],  # required
+            #     icons=["house", "book", "envelope"],  # optional
+            #     menu_icon="cast",  # optional
+            #     default_index=0,  # optional
+            #     orientation="horizontal",
+            #     styles={
+            #         "container": {"padding": "0!important", "background-color": "#fafafa"},
+            #         "icon": {"color": "orange", "font-size": "25px"},
+            #         "nav-link": {
+            #             "font-size": "25px",
+            #             "text-align": "left",
+            #             "margin": "0px",
+            #             "--hover-color": "#eee",
+            #         },
+            #         "nav-link-selected": {"background-color": "blue"},
+            #     },
+            # )
+        # if app_mode == 'About':
+        #     st.title('About Product And Team')
+        #     st.markdown('''
+        #                 Imran Bhai Project
+        #     ''')
+        #     st.markdown(
+        #         """
+        #         <style>
+        #         [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+        #             width: 350px
+        #         }
+        #         [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+        #             width: 350px
+        #             margin-left: -350px
+        #         }
+        #         </style>
+        #         """,
+        #         unsafe_allow_html=True,
+        #     )
+
+            
+
+
+        # elif app_mode == 'Application':
+            
+            st.set_option('deprecation.showfileUploaderEncoding', False)
+
+            use_webcam = "pass"
+            # record = st.sidebar.checkbox("Record Video")
+
+            # if record:
+            #     st.checkbox('Recording', True)
+
+            # drawing_spec = mp.solutions.drawing_utils.DrawingSpec(thickness=2, circle_radius=1)
+
+            # st.sidebar.markdown('---')
+
+            # ## Add Sidebar and Window style
+            # st.markdown(
+            #     """
+            #     <style>
+            #     [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+            #         width: 350px
+            #     }
+            #     [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+            #         width: 350px
+            #         margin-left: -350px
+            #     }
+            #     </style>
+            #     """,
+            #     unsafe_allow_html=True,
+            # )
+
+            # max_faces = st.sidebar.number_input('Maximum Number of Faces', value=5, min_value=1)
+            # st.sidebar.markdown('---')
+            # detection_confidence = st.sidebar.slider('Min Detection Confidence', min_value=0.0,max_value=1.0,value=0.5)
+            # tracking_confidence = st.sidebar.slider('Min Tracking Confidence', min_value=0.0,max_value=1.0,value=0.5)
+            # st.sidebar.markdown('---')
+
+            ## Get Video
+            stframe = st.empty()
+            video_file_buffer = st.file_uploader("Upload a Video", type=['mp4', 'mov', 'avi', 'asf', 'm4v'])
+            temp_file = tempfile.NamedTemporaryFile(delete=False)
+
+            
+            if not video_file_buffer:
+                if use_webcam:
+                    video = cv.VideoCapture(0)
+                else:
+                    try:
+                        video = cv.VideoCapture(1)
+                        temp_file.name = video
+                    except:
+                        pass
+            else:
+                temp_file.write(video_file_buffer.read())
+                video = cv.VideoCapture(temp_file.name)
+
+            width = int(video.get(cv.CAP_PROP_FRAME_WIDTH))
+            height = int(video.get(cv.CAP_PROP_FRAME_HEIGHT))
+            fps_input = int(video.get(cv.CAP_PROP_FPS))
+
+            ## Recording
+            codec = cv.VideoWriter_fourcc('a','v','c','1')
+            out = cv.VideoWriter('output1.mp4', codec, fps_input, (width,height))
+
+            # st.sidebar.text('Input Video')
+            # st.sidebar.video(temp_file.name)
+
+            fps = 0
+            i = 0
+
+            drawing_spec = mp.solutions.drawing_utils.DrawingSpec(thickness=2, circle_radius=1)
+
+            kpil, kpil2, kpil3,kpil4,kpil5, kpil6 = st.columns(6)
+
+            with kpil:
+                st.markdown('**Frame Rate**')
+                kpil_text = st.markdown('0')
+
+            with kpil2:
+                st.markdown('**detection ID**')
+                kpil2_text = st.markdown('0')
+
+            with kpil3:
+                st.markdown('**Mobile**')
+                kpil3_text = st.markdown('0')
+            with kpil4:
+                st.markdown('**Watch**')
+                kpil4_text = st.markdown('0')
+            with kpil5:
+                st.markdown('**Count**')
+                kpil5_text = st.markdown('0')
+            with kpil6:
+                st.markdown('**Img Res**')
+                kpil6_text = st.markdown('0')
+            
+
+
+            st.markdown('<hr/>', unsafe_allow_html=True)
+            # try:
+            def main():
+                db = {}
+                
+                # cap = cv2.VideoCapture('//home//anas//PersonTracking//WebUI//movement.mp4')
+                path='/usr/local/lib/python3.10/dist-packages/yolo0vs5/yolov5s-int8.tflite'
+                #count=0
+                custom = 'yolov5s'
+
+                model = torch.hub.load('/usr/local/lib/python3.10/dist-packages/yolovs5', custom, path,source='local',force_reload=True)
+
+                b=model.names[0] = 'person'
+                mobile = model.names[67] = 'cell phone'
+                watch = model.names[75] = 'clock'
+
+                fps_start_time = datetime.datetime.now()
+                fps = 0
+                size=416
+
+                count=0
+                counter=0
+
+
+                color=(0,0,255)
+
+                cy1=250
+                offset=6
+
+
+                pt1 = (120, 100)
+                pt2 = (980, 1150)
+                color = (0, 255, 0)
+
+                pt3 = (283, 103)
+                pt4 = (1500, 1150)
+                
+                cy2 = 500
+                color = (0, 255, 0)
+                total_frames = 0
+                prevTime = 0
+                cur_frame = 0
+                count=0
+                counter=0
+                fps_start_time = datetime.datetime.now()
+                fps = 0
+                total_frames = 0
+                lpc_count = 0
+                opc_count = 0
+                object_id_list = []
+                # success = True
+                if st.button("Detect"):
+                    try:
+                        while video.isOpened():
+                            
+                            ret, frame = video.read()
+                            frame = imutils.resize(frame, width=600)
+                            total_frames = total_frames + 1
+
+                            (H, W) = frame.shape[:2]
+
+                            blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
+
+                            detector.setInput(blob)
+                            person_detections = detector.forward()
+                            rects = []
+                            for i in np.arange(0, person_detections.shape[2]):
+                                confidence = person_detections[0, 0, i, 2]
+                                if confidence > 0.5:
+                                    idx = int(person_detections[0, 0, i, 1])
+
+                                    if CLASSES[idx] != "person":
+                                        continue
+
+                                    person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
+                                    (startX, startY, endX, endY) = person_box.astype("int")
+                                    rects.append(person_box)
+
+                            boundingboxes = np.array(rects)
+                            boundingboxes = boundingboxes.astype(int)
+                            rects = non_max_suppression_fast(boundingboxes, 0.3)
+
+                            objects = tracker.update(rects)
+                            for (objectId, bbox) in objects.items():
+                                x1, y1, x2, y2 = bbox
+                                x1 = int(x1)
+                                y1 = int(y1)
+                                x2 = int(x2)
+                                y2 = int(y2)
+
+                                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                                text = "ID: {}".format(objectId)
+                                # print(text)
+                                cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+                                if objectId not in object_id_list:
+                                    object_id_list.append(objectId)
+                            fps_end_time = datetime.datetime.now()
+                            time_diff = fps_end_time - fps_start_time
+                            if time_diff.seconds == 0:
+                                fps = 0.0
+                            else:
+                                fps = (total_frames / time_diff.seconds)
+
+                            fps_text = "FPS: {:.2f}".format(fps)
+
+                            cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+                            lpc_count = len(objects)
+                            opc_count = len(object_id_list)
+
+                            lpc_txt = "LPC: {}".format(lpc_count)
+                            opc_txt = "OPC: {}".format(opc_count)
+                            
+                            count += 1
+                            if count % 4 != 0:
+                                continue
+                            # frame=cv.resize(frame, (600,500))
+                            # cv2.line(frame, pt1, pt2,color,2)
+                            # cv2.line(frame, pt3, pt4,color,2)
+                            results = model(frame,size)
+                            components = results.pandas().xyxy[0]
+                            for index, row in results.pandas().xyxy[0].iterrows():
+                                x1 = int(row['xmin'])
+                                y1 = int(row['ymin'])
+                                x2 = int(row['xmax'])
+                                y2 = int(row['ymax'])
+                                confidence  = (row['confidence'])
+                                obj = (row['class'])
+
+                                
+                                # min':x1,'ymin':y1,'xmax':x2,'ymax':y2,'confidence':confidence,'Object':obj}
+                                # if lpc_txt is not None:
+                                # 	try:
+                                # 		db["student Count"] = [lpc_txt]
+                                # 	except:
+                                # 		db["student Count"] = ['N/A']
+                                if obj == 0:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(b), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    
+                                    db["student Count"] = [lpc_txt]
+                                    db['Date'] = [date_time]
+                                    db['id'] = ['N/A']
+                                    db['Mobile']=['N/A']
+                                    db['Watch'] = ['N/A']
+                                    if cy<(cy1+offset) and cy>(cy1-offset):
+                                        DB = []
+                                        counter+=1
+                                        DB.append(counter)
+
+                                        ff = DB[-1]
+                                        fx = str(ff)
+                                        # cv2.line(frame, pt1, pt2,(0, 0, 255),2)
+                                        # if cy<(cy2+offset) and cy>(cy2-offset):
+
+                                        # cv2.line(frame, pt3, pt4,(0, 0, 255),2)
+                                        font = cv2.FONT_HERSHEY_TRIPLEX
+                                        cv2.putText(frame,fx,(50, 50),font, 1,(0, 0, 255),2,cv2.LINE_4)
+                                        cv2.putText(frame,"Movement",(70, 70),font, 1,(0, 0, 255),2,cv2.LINE_4)
+                                        kpil2_text.write(f"<h5 style='text-align: left; color:red;'>{text}</h5>", unsafe_allow_html=True)
+                                        
+                                        
+                                        db['id'] = [text]
+                                        name = "/screenshot/"+str(date_time) + '.jpg'
+                                        print ('Creating...' + name)
+                                        cv2.imwrite(name, frame)
+                                        
+                                        # myScreenshot = pyautogui.screenshot()
+                                        # if st.buttn("Dowload ss"):
+                                        #     myScreenshot.save(r'name.png')   
+                                        # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                if obj == 67:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(mobile), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    cv2.putText(frame,'Mobile',(50, 50),cv2.FONT_HERSHEY_PLAIN, 1,(0, 0, 255),2,cv2.LINE_4)
+                                    kpil3_text.write(f"<h5 style='text-align: left; color:red;'>{mobile}{text}</h5>", unsafe_allow_html=True)
+                                    db['Mobile']=mobile+' '+text
+                                    name = "/screenshot/"+str(date_time) + '.jpg'
+                                    print ('Creating...' + name)
+                            
+                                    # writing the extracted images
+                                    cv2.imwrite(name, frame)
+  
+                                    # myScreenshot = pyautogui.screenshot()
+                                    # if st.buttn("Dowload ss"):
+                                    # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                    # myScreenshot.save(r'name.png')     
+                                    
+                                if obj == 75:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(watch), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    cv2.putText(frame,'Watch',(50, 50),cv2.FONT_HERSHEY_PLAIN, 1,(0, 0, 255),2,cv2.LINE_4)
+                                    kpil6_text.write(f"<h5 style='text-align: left; color:red;'>{watch}</h5>", unsafe_allow_html=True)
+                                    
+                                    
+                                    db['Watch']=watch
+                                    name = "/screenshot/"+str(date_time) + '.jpg'
+                                    print ('Creating...' + name)
+                                    cv2.imwrite(name, frame)
+                            
+                                    # writing the extracted images
+                                    
+                                    # myScreenshot = pyautogui.screenshot()
+                                    # if st.buttn("Dowload ss"):
+                                            # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                    # myScreenshot.save(r'name.png')  
+                                
+                                    
+                            
+                            kpil_text.write(f"<h5 style='text-align: left; color:red;'>{int(fps)}</h5>", unsafe_allow_html=True)
+                            kpil5_text.write(f"<h5 style='text-align: left; color:red;'>{lpc_txt}</h5>", unsafe_allow_html=True)
+                            kpil6_text.write(f"<h5 style='text-align: left; color:red;'>{width*height}</h5>",
+                                            unsafe_allow_html=True)
+                            
+            
+                            frame = cv.resize(frame,(0,0), fx=0.8, fy=0.8)
+                            frame = image_resize(image=frame, width=640)
+                            stframe.image(frame,channels='BGR', use_column_width=True)
+                            df = pd.DataFrame(db)
+                            df.to_csv('final.csv',mode='a',header=False,index=False)
+                    except:
+                        pass
+                    with open('final.csv') as f:
+                        st.download_button(label = 'Download Cheating Report',data=f,file_name='data.csv')
+                        
+                    os.remove("final.csv")
+            main()

screenshot/Login.py

@@ -0,0 +1,694 @@
+import cv2
+import datetime
+import imutils
+import numpy as np
+from centroidtracker import CentroidTracker
+import pandas as pd 
+import torch 
+import streamlit as st
+import mediapipe as mp
+import cv2 as cv
+import numpy as np
+import tempfile
+import time
+from PIL import Image
+import pandas as pd
+import torch
+import base64
+import streamlit.components.v1 as components
+import csv
+import pickle
+from pathlib import Path
+import streamlit_authenticator as stauth
+import os
+import csv
+from streamlit_option_menu import option_menu
+#  x-x-x-x-x-x-x-x-x-x-x-x-x-x LOGIN FORM x-x-x-x-x-x-x-x-x
+
+                                
+import streamlit as st
+import pandas as pd
+import hashlib
+import sqlite3 
+#
+
+import pickle
+from pathlib import Path
+import streamlit_authenticator as stauth
+import pyautogui
+
+# print("Done !!!")
+
+data = ["student Count",'Date','Id','Mobile','Watch']
+with open('final.csv', 'w') as file:
+    writer = csv.writer(file)
+    writer.writerow(data)
+    
+
+# # l1 = []
+# # l2 = []
+# # if st.button('signup'):
+    
+    
+# #     usernames = st.text_input('Username')
+# #     pwd = st.text_input('Password') 
+# #     l1.append(usernames)
+# #     l2.append(pwd)
+
+# #     names = ["dmin", "ser"]
+# #     if st.button("signupsss"):
+# #         username =l1
+
+# #         password =l2
+
+# #         hashed_passwords =stauth.Hasher(password).generate()
+
+# #         file_path = Path(__file__).parent / "hashed_pw.pkl"
+
+# #         with file_path.open("wb") as file:
+# #             pickle.dump(hashed_passwords, file)
+            
+    
+# # elif st.button('Logins'):
+# names = ['dmin', 'ser']
+
+# username = []
+
+# file_path = Path(__file__).parent / 'hashed_pw.pkl'
+
+# with file_path.open('rb') as file:
+#     hashed_passwords = pickle.load(file)
+
+# authenticator = stauth.Authenticate(names,username,hashed_passwords,'Cheating Detection','abcdefg',cookie_expiry_days=180)
+
+# name,authentication_status,username= authenticator.login('Login','main')
+
+
+# if authentication_status == False:
+#     st.error('Username/Password is incorrect')
+    
+# if authentication_status == None:
+#     st.error('Please enter a username and password')
+
+@st.experimental_memo
+def get_img_as_base64(file):
+    with open(file, "rb") as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+
+#img = get_img_as_base64("/home/anas/PersonTracking/WebUI/attendence.jpg")
+
+page_bg_img = f"""
+<style>
+[data-testid="stAppViewContainer"] > .main {{
+background-image: url("https://www.xmple.com/wallpaper/blue-gradient-black-linear-1920x1080-c2-87cefa-000000-a-180-f-14.svg");
+background-size: 180%;
+background-position: top left;
+background-repeat: no-repeat;
+background-attachment: local;
+}}
+
+[data-testid="stHeader"] {{
+background: rgba(0,0,0,0);
+}}
+[data-testid="stToolbar"] {{
+right: 2rem;
+}}
+</style>
+"""
+
+st.markdown(page_bg_img, unsafe_allow_html=True)
+files = pd.read_csv('LoginStatus.csv')
+
+
+idS = list(files['Id'])
+Pwd = list(files['Password'].astype(str))
+
+# print(type(Pwd))
+ids = st.sidebar.text_input('Enter a username')
+Pswd = st.sidebar.text_input('Enter a password',type="password",key="password")
+
+# print('list : ',type(Pwd))
+
+    
+        
+if (ids in idS) and(str(Pswd) in Pwd):
+    
+        # st.empty()    
+        date_time = time.strftime("%b %d %Y %-I:%M %p")
+        date = date_time.split()
+        dates = date[0:3]
+        times = date[3:5]
+        # x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-xAPPLICACTION -x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x
+
+        def non_max_suppression_fast(boxes, overlapThresh):
+            try:
+                if len(boxes) == 0:
+                    return []
+
+                if boxes.dtype.kind == "i":
+                    boxes = boxes.astype("float")
+
+                pick = []
+
+                x1 = boxes[:, 0]
+                y1 = boxes[:, 1]
+                x2 = boxes[:, 2]
+                y2 = boxes[:, 3]
+
+                area = (x2 - x1 + 1) * (y2 - y1 + 1)
+                idxs = np.argsort(y2)
+
+                while len(idxs) > 0:
+                    last = len(idxs) - 1
+                    i = idxs[last]
+                    pick.append(i)
+
+                    xx1 = np.maximum(x1[i], x1[idxs[:last]])
+                    yy1 = np.maximum(y1[i], y1[idxs[:last]])
+                    xx2 = np.minimum(x2[i], x2[idxs[:last]])
+                    yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+                    w = np.maximum(0, xx2 - xx1 + 1)
+                    h = np.maximum(0, yy2 - yy1 + 1)
+
+                    overlap = (w * h) / area[idxs[:last]]
+
+                    idxs = np.delete(idxs, np.concatenate(([last],
+                                                        np.where(overlap > overlapThresh)[0])))
+
+                return boxes[pick].astype("int")
+            except Exception as e:
+                print("Exception occurred in non_max_suppression : {}".format(e))
+
+
+        protopath = "MobileNetSSD_deploy.prototxt"
+        modelpath = "MobileNetSSD_deploy.caffemodel"
+        detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
+        # Only enable it if you are using OpenVino environment
+        # detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+        # detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
+
+
+        CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
+                "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+                "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+                "sofa", "train", "tvmonitor"]
+
+        tracker = CentroidTracker(maxDisappeared=80, maxDistance=90)
+
+        st.markdown(
+            """
+            <style>
+            [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+                width: 350px
+            }
+            [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+                width: 350px
+                margin-left: -350px
+            }
+            </style>
+            """,
+            unsafe_allow_html=True,
+        )
+        hide_streamlit_style = """
+                <style>
+                #MainMenu {visibility: hidden;}
+                footer {visibility: hidden;}
+                </style>
+                """
+        st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+        # Resize Images to fit Container
+        @st.cache()
+        # Get Image Dimensions
+        def image_resize(image, width=None, height=None, inter=cv.INTER_AREA):
+            dim = None
+            (h,w) = image.shape[:2]
+
+            if width is None and height is None:
+                return image
+
+            if width is None:
+                r = width/float(w)
+                dim = (int(w*r),height)
+
+            else:
+                r = width/float(w)
+                dim = width, int(h*r)
+
+            # Resize image
+            resized = cv.resize(image,dim,interpolation=inter)
+
+            return resized
+                
+        # About Page
+        # authenticator.logout('Logout')
+        EXAMPLE_NO = 3
+
+
+        def streamlit_menu(example=1):
+            if example == 1:
+                # 1. as sidebar menu
+                with st.sidebar:
+                    selected = option_menu(
+                        menu_title="Main Menu",  # required
+                        options=["Home", "Projects", "Contact"],  # required
+                        icons=["house", "book", "envelope"],  # optional
+                        menu_icon="cast",  # optional
+                        default_index=0,  # optional
+                    )
+                return selected
+
+            if example == 2:
+                # 2. horizontal menu w/o custom style
+                selected = option_menu(
+                    menu_title=None,  # required
+                    options=["Home", "Projects", "Contact"],  # required
+                    icons=["house", "book", "envelope"],  # optional
+                    menu_icon="cast",  # optional
+                    default_index=0,  # optional
+                    orientation="horizontal",
+                )
+                return selected
+
+            if example == 3:
+                # 2. horizontal menu with custom style
+                selected = option_menu(
+                    menu_title=None,  # required
+                    options=["Home", "Projects", "Contact"],  # required
+                    icons=["house", "book", "envelope"],  # optional
+                    menu_icon="cast",  # optional
+                    default_index=0,  # optional
+                    orientation="horizontal",
+                    styles={
+                        "container": {"padding": "0!important", "background-color": "#eaeaea"},
+                        "icon": {"color": "#080602", "font-size": "18px"},
+                        "nav-link": {
+                            "font-size": "18px",
+                            "text-align": "left",
+                            "color": "#000000",
+                            "margin": "0px",
+                            "--hover-color": "#E1A031",
+                        },
+                        "nav-link-selected": {"background-color": "#ffffff"},
+                    },
+                )
+                return selected
+
+
+        selected = streamlit_menu(example=EXAMPLE_NO)
+
+        if selected == "Home":
+            st.title(f"You have selected {selected}")
+        # if selected == "Projects":
+        #     st.title(f"You have selected {selected}")
+        if selected == "Contact":
+            st.title(f"You have selected {selected}")
+        # app_mode = st.sidebar.selectbox(
+        #                 'App Mode',
+        #                 ['Application']
+        #                 )
+        if selected == 'Projects':
+        # 2. horizontal menu with custom style
+            # selected = option_menu(
+            #     menu_title=None,  # required
+            #     options=["Home", "Projects", "Contact"],  # required
+            #     icons=["house", "book", "envelope"],  # optional
+            #     menu_icon="cast",  # optional
+            #     default_index=0,  # optional
+            #     orientation="horizontal",
+            #     styles={
+            #         "container": {"padding": "0!important", "background-color": "#fafafa"},
+            #         "icon": {"color": "orange", "font-size": "25px"},
+            #         "nav-link": {
+            #             "font-size": "25px",
+            #             "text-align": "left",
+            #             "margin": "0px",
+            #             "--hover-color": "#eee",
+            #         },
+            #         "nav-link-selected": {"background-color": "blue"},
+            #     },
+            # )
+        # if app_mode == 'About':
+        #     st.title('About Product And Team')
+        #     st.markdown('''
+        #                 Imran Bhai Project
+        #     ''')
+        #     st.markdown(
+        #         """
+        #         <style>
+        #         [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+        #             width: 350px
+        #         }
+        #         [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+        #             width: 350px
+        #             margin-left: -350px
+        #         }
+        #         </style>
+        #         """,
+        #         unsafe_allow_html=True,
+        #     )
+
+            
+
+
+        # elif app_mode == 'Application':
+            
+            st.set_option('deprecation.showfileUploaderEncoding', False)
+
+            use_webcam = "pass"
+            # record = st.sidebar.checkbox("Record Video")
+
+            # if record:
+            #     st.checkbox('Recording', True)
+
+            # drawing_spec = mp.solutions.drawing_utils.DrawingSpec(thickness=2, circle_radius=1)
+
+            # st.sidebar.markdown('---')
+
+            # ## Add Sidebar and Window style
+            # st.markdown(
+            #     """
+            #     <style>
+            #     [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+            #         width: 350px
+            #     }
+            #     [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+            #         width: 350px
+            #         margin-left: -350px
+            #     }
+            #     </style>
+            #     """,
+            #     unsafe_allow_html=True,
+            # )
+
+            # max_faces = st.sidebar.number_input('Maximum Number of Faces', value=5, min_value=1)
+            # st.sidebar.markdown('---')
+            # detection_confidence = st.sidebar.slider('Min Detection Confidence', min_value=0.0,max_value=1.0,value=0.5)
+            # tracking_confidence = st.sidebar.slider('Min Tracking Confidence', min_value=0.0,max_value=1.0,value=0.5)
+            # st.sidebar.markdown('---')
+
+            ## Get Video
+            stframe = st.empty()
+            video_file_buffer = st.file_uploader("Upload a Video", type=['mp4', 'mov', 'avi', 'asf', 'm4v'])
+            temp_file = tempfile.NamedTemporaryFile(delete=False)
+
+            
+            if not video_file_buffer:
+                if use_webcam:
+                    video = cv.VideoCapture(0)
+                else:
+                    try:
+                        video = cv.VideoCapture(1)
+                        temp_file.name = video
+                    except:
+                        pass
+            else:
+                temp_file.write(video_file_buffer.read())
+                video = cv.VideoCapture(temp_file.name)
+
+            width = int(video.get(cv.CAP_PROP_FRAME_WIDTH))
+            height = int(video.get(cv.CAP_PROP_FRAME_HEIGHT))
+            fps_input = int(video.get(cv.CAP_PROP_FPS))
+
+            ## Recording
+            codec = cv.VideoWriter_fourcc('a','v','c','1')
+            out = cv.VideoWriter('output1.mp4', codec, fps_input, (width,height))
+
+            # st.sidebar.text('Input Video')
+            # st.sidebar.video(temp_file.name)
+
+            fps = 0
+            i = 0
+
+            drawing_spec = mp.solutions.drawing_utils.DrawingSpec(thickness=2, circle_radius=1)
+
+            kpil, kpil2, kpil3,kpil4,kpil5, kpil6 = st.columns(6)
+
+            with kpil:
+                st.markdown('**Frame Rate**')
+                kpil_text = st.markdown('0')
+
+            with kpil2:
+                st.markdown('**detection ID**')
+                kpil2_text = st.markdown('0')
+
+            with kpil3:
+                st.markdown('**Mobile**')
+                kpil3_text = st.markdown('0')
+            with kpil4:
+                st.markdown('**Watch**')
+                kpil4_text = st.markdown('0')
+            with kpil5:
+                st.markdown('**Count**')
+                kpil5_text = st.markdown('0')
+            with kpil6:
+                st.markdown('**Img Res**')
+                kpil6_text = st.markdown('0')
+            
+
+
+            st.markdown('<hr/>', unsafe_allow_html=True)
+            # try:
+            def main():
+                db = {}
+                
+                # cap = cv2.VideoCapture('//home//anas//PersonTracking//WebUI//movement.mp4')
+                path='/usr/local/lib/python3.10/dist-packages/yolo0vs5/yolov5s-int8.tflite'
+                #count=0
+                custom = 'yolov5s'
+
+                model = torch.hub.load('/usr/local/lib/python3.10/dist-packages/yolovs5', custom, path,source='local',force_reload=True)
+
+                b=model.names[0] = 'person'
+                mobile = model.names[67] = 'cell phone'
+                watch = model.names[75] = 'clock'
+
+                fps_start_time = datetime.datetime.now()
+                fps = 0
+                size=416
+
+                count=0
+                counter=0
+
+
+                color=(0,0,255)
+
+                cy1=250
+                offset=6
+
+
+                pt1 = (120, 100)
+                pt2 = (980, 1150)
+                color = (0, 255, 0)
+
+                pt3 = (283, 103)
+                pt4 = (1500, 1150)
+                
+                cy2 = 500
+                color = (0, 255, 0)
+                total_frames = 0
+                prevTime = 0
+                cur_frame = 0
+                count=0
+                counter=0
+                fps_start_time = datetime.datetime.now()
+                fps = 0
+                total_frames = 0
+                lpc_count = 0
+                opc_count = 0
+                object_id_list = []
+                # success = True
+                if st.button("Detect"):
+                    try:
+                        while video.isOpened():
+                            
+                            ret, frame = video.read()
+                            frame = imutils.resize(frame, width=600)
+                            total_frames = total_frames + 1
+
+                            (H, W) = frame.shape[:2]
+
+                            blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
+
+                            detector.setInput(blob)
+                            person_detections = detector.forward()
+                            rects = []
+                            for i in np.arange(0, person_detections.shape[2]):
+                                confidence = person_detections[0, 0, i, 2]
+                                if confidence > 0.5:
+                                    idx = int(person_detections[0, 0, i, 1])
+
+                                    if CLASSES[idx] != "person":
+                                        continue
+
+                                    person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
+                                    (startX, startY, endX, endY) = person_box.astype("int")
+                                    rects.append(person_box)
+
+                            boundingboxes = np.array(rects)
+                            boundingboxes = boundingboxes.astype(int)
+                            rects = non_max_suppression_fast(boundingboxes, 0.3)
+
+                            objects = tracker.update(rects)
+                            for (objectId, bbox) in objects.items():
+                                x1, y1, x2, y2 = bbox
+                                x1 = int(x1)
+                                y1 = int(y1)
+                                x2 = int(x2)
+                                y2 = int(y2)
+
+                                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                                text = "ID: {}".format(objectId)
+                                # print(text)
+                                cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+                                if objectId not in object_id_list:
+                                    object_id_list.append(objectId)
+                            fps_end_time = datetime.datetime.now()
+                            time_diff = fps_end_time - fps_start_time
+                            if time_diff.seconds == 0:
+                                fps = 0.0
+                            else:
+                                fps = (total_frames / time_diff.seconds)
+
+                            fps_text = "FPS: {:.2f}".format(fps)
+
+                            cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+                            lpc_count = len(objects)
+                            opc_count = len(object_id_list)
+
+                            lpc_txt = "LPC: {}".format(lpc_count)
+                            opc_txt = "OPC: {}".format(opc_count)
+                            
+                            count += 1
+                            if count % 4 != 0:
+                                continue
+                            # frame=cv.resize(frame, (600,500))
+                            # cv2.line(frame, pt1, pt2,color,2)
+                            # cv2.line(frame, pt3, pt4,color,2)
+                            results = model(frame,size)
+                            components = results.pandas().xyxy[0]
+                            for index, row in results.pandas().xyxy[0].iterrows():
+                                x1 = int(row['xmin'])
+                                y1 = int(row['ymin'])
+                                x2 = int(row['xmax'])
+                                y2 = int(row['ymax'])
+                                confidence  = (row['confidence'])
+                                obj = (row['class'])
+
+                                
+                                # min':x1,'ymin':y1,'xmax':x2,'ymax':y2,'confidence':confidence,'Object':obj}
+                                # if lpc_txt is not None:
+                                # 	try:
+                                # 		db["student Count"] = [lpc_txt]
+                                # 	except:
+                                # 		db["student Count"] = ['N/A']
+                                if obj == 0:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(b), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    
+                                    db["student Count"] = [lpc_txt]
+                                    db['Date'] = [date_time]
+                                    db['id'] = ['N/A']
+                                    db['Mobile']=['N/A']
+                                    db['Watch'] = ['N/A']
+                                    if cy<(cy1+offset) and cy>(cy1-offset):
+                                        DB = []
+                                        counter+=1
+                                        DB.append(counter)
+
+                                        ff = DB[-1]
+                                        fx = str(ff)
+                                        # cv2.line(frame, pt1, pt2,(0, 0, 255),2)
+                                        # if cy<(cy2+offset) and cy>(cy2-offset):
+
+                                        # cv2.line(frame, pt3, pt4,(0, 0, 255),2)
+                                        font = cv2.FONT_HERSHEY_TRIPLEX
+                                        cv2.putText(frame,fx,(50, 50),font, 1,(0, 0, 255),2,cv2.LINE_4)
+                                        cv2.putText(frame,"Movement",(70, 70),font, 1,(0, 0, 255),2,cv2.LINE_4)
+                                        kpil2_text.write(f"<h5 style='text-align: left; color:red;'>{text}</h5>", unsafe_allow_html=True)
+                                        
+                                        
+                                        db['id'] = [text]
+                                        name = "/screenshot/"+str(date_time) + '.jpg'
+                                        print ('Creating...' + name)
+                                        cv2.imwrite(name, frame)
+                                        
+                                        # myScreenshot = pyautogui.screenshot()
+                                        # if st.buttn("Dowload ss"):
+                                        #     myScreenshot.save(r'name.png')   
+                                        # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                if obj == 67:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(mobile), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    cv2.putText(frame,'Mobile',(50, 50),cv2.FONT_HERSHEY_PLAIN, 1,(0, 0, 255),2,cv2.LINE_4)
+                                    kpil3_text.write(f"<h5 style='text-align: left; color:red;'>{mobile}{text}</h5>", unsafe_allow_html=True)
+                                    db['Mobile']=mobile+' '+text
+                                    name = "/screenshot/"+str(date_time) + '.jpg'
+                                    print ('Creating...' + name)
+                            
+                                    # writing the extracted images
+                                    cv2.imwrite(name, frame)
+  
+                                    # myScreenshot = pyautogui.screenshot()
+                                    # if st.buttn("Dowload ss"):
+                                    # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                    # myScreenshot.save(r'name.png')     
+                                    
+                                if obj == 75:
+                                    cv2.rectangle(frame,(x1,y1),(x2,y2),(0,0,255),2)
+                                    rectx1,recty1 = ((x1+x2)/2,(y1+y2)/2)
+                                    rectcenter = int(rectx1),int(recty1)
+                                    cx = rectcenter[0]
+                                    cy = rectcenter[1]
+                                    cv2.circle(frame,(cx,cy),3,(0,255,0),-1)
+                                    cv2.putText(frame,str(watch), (x1,y1), cv2.FONT_HERSHEY_PLAIN,2,(255,255,255),2)
+                                    cv2.putText(frame,'Watch',(50, 50),cv2.FONT_HERSHEY_PLAIN, 1,(0, 0, 255),2,cv2.LINE_4)
+                                    kpil6_text.write(f"<h5 style='text-align: left; color:red;'>{watch}</h5>", unsafe_allow_html=True)
+                                    
+                                    
+                                    db['Watch']=watch
+                                    name = "/screenshot/"+str(date_time) + '.jpg'
+                                    print ('Creating...' + name)
+                                    cv2.imwrite(name, frame)
+                            
+                                    # writing the extracted images
+                                    
+                                    # myScreenshot = pyautogui.screenshot()
+                                    # if st.buttn("Dowload ss"):
+                                            # myScreenshot.save(r'/home/anas/PersonTracking/AIComputerVision-master/pages/name.png')
+                                    # myScreenshot.save(r'name.png')  
+                                
+                                    
+                            
+                            kpil_text.write(f"<h5 style='text-align: left; color:red;'>{int(fps)}</h5>", unsafe_allow_html=True)
+                            kpil5_text.write(f"<h5 style='text-align: left; color:red;'>{lpc_txt}</h5>", unsafe_allow_html=True)
+                            kpil6_text.write(f"<h5 style='text-align: left; color:red;'>{width*height}</h5>",
+                                            unsafe_allow_html=True)
+                            
+            
+                            frame = cv.resize(frame,(0,0), fx=0.8, fy=0.8)
+                            frame = image_resize(image=frame, width=640)
+                            stframe.image(frame,channels='BGR', use_column_width=True)
+                            df = pd.DataFrame(db)
+                            df.to_csv('final.csv',mode='a',header=False,index=False)
+                    except:
+                        pass
+                    with open('final.csv') as f:
+                        st.download_button(label = 'Download Cheating Report',data=f,file_name='data.csv')
+                        
+                    os.remove("final.csv")
+            main()