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# To run use
# $ streamlit run yolor_streamlit_demo.py
from yolo_v7 import names, load_yolov7_and_process_each_frame
import tempfile
import cv2
from models.models import *
from utils.datasets import *
from utils.general import *
import streamlit as st
def main():
#title
st.title('Object Tracking Dashboard YOLOv7-tiny')
#side bar title
st.sidebar.title('Settings')
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,
)
use_webcam = st.sidebar.checkbox('Use Webcam')
st.sidebar.markdown('---')
confidence = st.sidebar.slider('Confidence',min_value=0.0, max_value=1.0, value = 0.25)
st.sidebar.markdown('---')
save_img = st.sidebar.checkbox('Save Video')
enable_GPU = st.sidebar.checkbox('enable GPU')
custom_classes = st.sidebar.checkbox('Use Custom Classes')
assigned_class_id = []
if custom_classes:
assigned_class = st.sidebar.multiselect('Select The Custom Classes',list(names),default='person')
for each in assigned_class:
assigned_class_id.append(names.index(each))
video_file_buffer = st.sidebar.file_uploader("Upload a video", type=[ "mp4", "mov",'avi','asf', 'm4v' ])
DEMO_VIDEO = 'test.mp4'
tfflie = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
##We get our input video here
if not video_file_buffer:
if use_webcam:
vid = cv2.VideoCapture(0, cv2.CAP_ARAVIS)
tfflie.name = 0
else:
vid = cv2.VideoCapture(DEMO_VIDEO)
tfflie.name = DEMO_VIDEO
dem_vid = open(tfflie.name,'rb')
demo_bytes = dem_vid.read()
st.sidebar.text('Input Video')
st.sidebar.video(demo_bytes)
else:
tfflie.write(video_file_buffer.read())
# print("No Buffer")
dem_vid = open(tfflie.name,'rb')
demo_bytes = dem_vid.read()
st.sidebar.text('Input Video')
st.sidebar.video(demo_bytes)
print(tfflie.name)
# vid = cv2.VideoCapture(tfflie.name)
stframe = st.empty()
st.markdown("<hr/>", unsafe_allow_html=True)
kpi1, kpi2, kpi3 = st.beta_columns(3) #st.columns(3)
# stframe.image(im0,channels = 'BGR',use_column_width=True)
with kpi1:
st.markdown("**Frame Rate**")
kpi1_text = st.markdown("0")
with kpi2:
st.markdown("**Tracked Objects**")
kpi2_text = st.markdown("0")
with kpi3:
st.markdown("**Total Count**")
kpi3_text = st.markdown("0")
st.markdown("<hr/>", unsafe_allow_html=True)
# call yolor
# load_yolor_and_process_each_frame(tfflie.name, enable_GPU, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe)
load_yolov7_and_process_each_frame('yolov7-tiny', tfflie.name, enable_GPU, save_img, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe)
st.text('Video is Processed')
if __name__ == '__main__':
try:
main()
except SystemExit:
pass
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