person_tracking.py

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
#  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
# 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 =l1

    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')
        
    if authentication_status:
        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')
        app_mode = st.sidebar.selectbox(
                        'App Mode',
                        ['About','Application']
                        )
        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 = st.button('Use Webcam')
            # 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]
                                        
                                            
                                        
                                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
                                    
                                        
                                    
                                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
                                
                                    
                            
                            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()