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import streamlit as st
from PIL import Image
from utils.modules import detect, detectVideo, getDataframe
from utils.modules import getFlag, setFlag, resetFlag
from utils.modules import initial_setup
@st.cache_data
def convert_df(df):
"""
Reads the counts dataframe and
returns in CSV format.
"""
return df.to_csv().encode('utf-8')
def processImage():
"""
UI Part if the users chooses
to proceess a image.
"""
# threhold = st.slider('Choose a threshold value', 0.0, 1.0, 0.40)
image_file = st.file_uploader("Upload An Image",type=['png','jpeg','jpg'])
if image_file is not None:
file_details = {"FileName":image_file.name,"FileType":image_file.type}
file_type = (image_file.type).split('/')[1]
input_file_name = f"data/Input.{file_type}"
with open(input_file_name,mode = "wb") as f:
f.write(image_file.getbuffer())
first_process = int(getFlag())
count = detect(input_file_name, )
img_ = Image.open("data/result.jpg")
st.subheader(f"People Count = {count}")
st.image(img_)
with open("data/result.jpg", "rb") as file:
st.download_button(
label="Download image",
data=file,
file_name="Processed.jpg",
mime="image/jpg"
)
def processVideo():
"""
UI Part if the users chooses
to proceess a video.
"""
# threhold = st.slider('Choose a threshold value', 0.0, 1.0, 0.40)
uploaded_video = st.file_uploader("Upload a Video", type = ['mp4','mpeg','mov'])
if uploaded_video is not None :
file_type = (uploaded_video.type).split('/')[1]
input_file_name = f"data/Input.{file_type}"
if uploaded_video != None:
vid = input_file_name
with open(vid, mode='wb') as f:
f.write(uploaded_video.read())
st_video = open(vid,'rb')
video_bytes = st_video.read()
first_process = int(getFlag())
if first_process == 1:
with st.spinner('Processing the video ⌛️'):
detectVideo(vid, )
setFlag()
first_process = int(getFlag())
st_video = open('data/output.mp4','rb')
video_bytes = st_video.read()
st.video(video_bytes)
df = getDataframe()
st.markdown("<h3 style='text-align: center;'>People Visit Trend 📊</h3>", unsafe_allow_html=True)
col1, col2 = st.columns([3, 1])
col1.line_chart(data=df, x='Time', y='Count')
col2.dataframe(data=df, )
row1, row2, _ = st.columns([3, 3, 5])
with open("data/output.mp4", "rb") as file:
btn = row1.download_button(
label="Download video",
data=file,
file_name="Processed.mp4",
mime="video/mp4"
)
csv = convert_df(df)
row2.download_button(
label="Download data as CSV",
data=csv,
file_name='data/density.csv',
mime='text/csv',
)
def main():
"""
UI Part of the entire application.
"""
st.set_page_config(
page_title ="Track-X",
page_icon = "🧊",
menu_items={
'About': "# iKurious People Track-X"
}
)
st.markdown("<h1 style='text-align: center;'>People <span style='color: #9eeade;'>Track-X</span></h1>", unsafe_allow_html=True)
st.subheader("Artificial Intelligent System")
option = st.selectbox(
'What Type of File do you want to work with?',
('Images', 'Videos'))
if option == "Images":
st.title('Image Analysis')
processImage()
else:
st.title('Video Analysis')
st.button("Reset", on_click=resetFlag)
processVideo()
with st.expander("About People Track-X"):
st.markdown( '<p style="font-size: 30px;"><strong>Welcome to the People \
<span style="color: #9eeade;">Track-X</span> App!</strong></p>', unsafe_allow_html= True)
st.markdown('<p style = "font-size : 20px; color : white;">This application was \
built to analyse the <strong>People Density</strong> \
on a particular place.</p>', unsafe_allow_html=True)
if __name__ == '__main__':
__author__ = 'Mahimai Raja J'
__version__ = "1.0.0"
initial_setup()
main()
# 📌 NOTE :
# Do not modify the credits unless you have
# legal permission from the authorizing authority .
# Thank you for helping to maintain the integrity of the
# open source community by promoting fair and ethical
# use of open source software 💎. |