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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') | |
| 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 | |
| # 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() | |