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‎Task_App/Output.mp4

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‎Task_App/app.py

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from email.mime import image
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from re import S
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from django.db import connection
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import streamlit as st
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import mediapipe as mp
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import cv2
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import numpy as np
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import tempfile
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import time
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from PIL import Image
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mp_drawing = mp.solutions.drawing_utils
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mp_face_mesh = mp.solutions.face_mesh
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DEMO_IMAGE = 'demo_image.jpg'
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DEMO_VIDEO = 'video.mp4'
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st.title('Face Mesh App')
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st.markdown(
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"""
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<style>
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
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width:350px
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
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width:350px
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margin-left: -350px
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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st.sidebar.title("App Sidebar")
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st.sidebar.subheader('parameters')
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@st.cache()
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def image_resize(image, width: None, height: None, inter=cv2.INTER_AREA):
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dim = None
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(h, w) = image.shape[:2]
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if width is None and height is None:
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return image
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if width is None:
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r = width/float(w)
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dim = (int(w*r), height)
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else:
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r = width/float(w)
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dim = (width, int(h*r))
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# resize the image
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resized = cv2.resize(image, dim, interpolation=inter)
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return resized
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app_mode = st.sidebar.selectbox('Choose the App mode',
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['About App', 'Run on Image', 'Run on Video']
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)
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if app_mode == 'About App':
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st.markdown(
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"In this application we are using **Mediapipe** for creating an App")
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st.markdown(
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"""
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<style>
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
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width:350px
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
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width:350px
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margin-left: -350px
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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st.video("https://youtu.be/TVWfKsFyiP0")
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elif app_mode == "Run on Image":
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drawing_spec = mp_drawing.DrawingSpec(thickness=2, circle_radius=1)
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st.sidebar.markdown("---")
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st.markdown(
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"""
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<style>
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
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width:350px
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
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width:350px
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margin-left: -350px
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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st.markdown("**Detected Faces**")
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kpil_text = st.markdown("0")
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max_faces = st.sidebar.number_input("Maximum Number of face", value=2, min_value=1)
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st.markdown("----")
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detection_confidence=st.sidebar.slider(
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"Min Detection Confidence", min_value=0.0, max_value=0.0, value=0.5)
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st.markdown("---")
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img_file_buffer=st.sidebar.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
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if img_file_buffer is not None:
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image=np.array(
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Image.open(img_file_buffer))
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else:
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demo_image=DEMO_IMAGE
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image=np.array(Image.open(demo_image))
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st.sidebar.text("Original Image")
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st.sidebar.image(image)
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face_count=0
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# Dashboard
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with mp_face_mesh.FaceMesh(static_image_mode=True,max_num_faces=max_faces,
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min_detection_confidence=detection_confidence) as face_mesh:
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results=face_mesh.process(image)
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out_image=image.copy()
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# face landmark drawing
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for face_landmarks in results.multi_face_landmarks:
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face_count += 1
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mp_drawing.draw_landmarks(
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image = out_image,
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landmark_list=face_landmarks,
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connections= mp_face_mesh.FACE_CONNECTIONS,
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landmark_drawing_spec = drawing_spec)
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kpil_text.write(f"<h1 style='text-align:center; color:red;'>{face_count}</h1>",unsafe_allow_html=True)
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st.subheader('Output Image')
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st.image(out_image,use_column_width=True)
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elif app_mode == "Run on Video":
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st.set_option('deprecation.showfileUploaderEncoding',False)
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use_webcam = st.sidebar.button('Use Webcam')
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record = st.sidebar.checkbox('Record Video')
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if record:
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st.checkbox("Recording",value=True)
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st.markdown(
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"""
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<style>
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
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width:350px
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
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width:350px
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margin-left: -350px
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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max_faces = st.sidebar.number_input("Maximum Number of face", value=5, min_value=1)
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st.markdown("----")
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detection_confidence=st.sidebar.slider(
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"Min Detection Confidence", min_value=0.0, max_value=1.0, value=0.5)
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tracking_confidence = st.sidebar.slider(
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"Min Tracking Confidence", min_value=0.0, max_value=1.0, value=0.5)
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st.markdown("---")
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st.markdown("## Output")
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stframe = st.empty()
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video_file_buffer = st.sidebar.file_uploader("Upload a Video",type=['mp4','mov','avi','asf','m4v'])
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tffile = tempfile.NamedTemporaryFile(delete=False)
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## Input Video
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if not video_file_buffer:
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if use_webcam:
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vid = cv2.VideoCapture(0)
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else:
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vid = cv2.VideoCapture(DEMO_VIDEO)
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tffile.name = DEMO_VIDEO
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else:
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tffile.write(video_file_buffer.read())
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vid = cv2.VideoCapture(tffile.name)
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width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps_input = int(vid.get(cv2.CAP_PROP_FPS))
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# Recording part
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codec= cv2.VideoWriter_fourcc('M','J','P','G')
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out = cv2.VideoWriter('Output.mp4',codec,fps_input,(width,height))
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st.sidebar.text("Input Video")
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st.sidebar.video(tffile.name)
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fps = 0
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i = 0
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drawing_spec = mp_drawing.DrawingSpec(thickness=2, circle_radius=1)
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kpi1,kpi2,kpi3 = st.beta_columns(3)
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with kpi1:
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st.markdown("**Frame Rate**")
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kpi1_text = st.markdown("0")
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with kpi2:
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st.markdown("**Detected Faces**")
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kpi2_text = st.markdown("0")
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with kpi3:
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st.markdown("**Image Width**")
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kpi3_text = st.markdown("0")
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st.markdown("<hr/>",unsafe_allow_html=True)
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# Predictor
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with mp_face_mesh.FaceMesh(static_image_mode=True,max_num_faces=max_faces,
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min_detection_confidence=detection_confidence, min_tracking_confidence=tracking_confidence) as face_mesh:
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prevTime = 0
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while vid.isOpened():
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i+=1
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ret,frame= vid.read()
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if not ret:
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continue
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results = face_mesh.process(frame)
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frame.flags.writeable = True
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face_count = 0
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if results.multi_face_landmarks:
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for face_landmarks in results.multi_face_landmarks:
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face_count += 1
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mp_drawing.draw_landmarks(
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image=frame,
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landmark_list=face_landmarks,
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connections=mp_face_mesh.FACE_CONNECTIONS,
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landmark_drawing_spec=drawing_spec,
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connection_drawing_spec= drawing_spec)
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# FPS Counter Logic
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currTime = time.time()
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fps= 1/(currTime-prevTime)
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prevTime = currTime
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if record:
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out.write(frame)
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# Dashboard
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kpi1_text.write(
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f"<h1 style='text-align:center; color:red;'>{int(fps)}</h1>", unsafe_allow_html=True)
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kpi2_text.write(
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f"<h1 style='text-align:center; color:red;'>{face_count}</h1>", unsafe_allow_html=True)
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kpi3_text.write(
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f"<h1 style='text-align:center; color:red;'>{width}</h1>", unsafe_allow_html=True)
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frame = cv2.resize(frame,(0,0),fx=0.8,fy=0.8)
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frame = image_resize(image=frame,width=640,height=100)
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stframe.image(frame,channels='BGR',use_column_width=True)
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# results=face_mesh.process(image)
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# out_image=image.copy()
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# face landmark drawing
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st.subheader('Output Image')
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#st.image(frame,use_column_width=True)
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‎Task_App/demo_image.jpg

56 KB
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‎Task_App/reqs.txt

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opencv_python_headless==4.5.2.54
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streamlit == 0.82.0
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mediapipe==0.8.4.2
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numpy==1.18.5
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Pillow==8.2.0
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Django==3.2.13

‎Task_App/requirements.txt

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Django==4.0.4
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mediapipe==0.8.10
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numpy==1.22.0
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opencv_python==4.5.5.62
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Pillow==9.1.0
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streamlit==1.8.1

‎Task_App/video.mp4

23.4 MB
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