/
app.py
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/
app.py
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import cv2
import streamlit as st
from deep_list import *
import torch
def main():
st.title("Dashboard")
inference_msg = st.empty()
st.sidebar.title("Configuration")
input_source = st.sidebar.radio(
"Select input source",
('RTSP', 'Webcam', 'Local video'))
conf_thres = st.sidebar.text_input("Class confidence threshold", "0.25")
conf_thres_drift = st.sidebar.text_input("Class confidence threshold for drift dectection", "0.75")
fps_drop_warn_thresh = st.sidebar.text_input("FPS drop warning threshold", "8")
save_output_video = st.sidebar.radio("Save output video?",('Yes', 'No'))
if save_output_video == 'Yes':
nosave = False
display_labels = False
else:
nosave = True
display_labels = True
save_poor_frame = st.sidebar.radio("Save poor performing frames?",('Yes', 'No'))
if save_poor_frame == "Yes":
save_poor_frame__ = True
else:
save_poor_frame__ = False
# ------------------------- LOCAL VIDEO ------------------------------
if input_source == "Local video":
video = st.sidebar.file_uploader("Select input video", type=["mp4", "avi"], accept_multiple_files=False)
if st.sidebar.button("Start tracking"):
stframe = st.empty()
st.subheader("Inference Stats")
kpi1, kpi2, kpi3 = st.columns(3)
st.subheader("System Stats")
js1, js2, js3 = st.columns(3)
# Updating Inference results
with kpi1:
st.markdown("**Frame Rate**")
kpi1_text = st.markdown("0")
fps_warn = st.empty()
with kpi2:
st.markdown("**Detected objects in curret Frame**")
kpi2_text = st.markdown("0")
with kpi3:
st.markdown("**Total Detected objects**")
kpi3_text = st.markdown("0")
# Updating Jetson stats
with js1:
st.markdown("**Memory usage**")
js1_text = st.markdown("0")
with js2:
st.markdown("**CPU Usage**")
js2_text = st.markdown("0")
with js3:
st.markdown("**GPU Memory Usage**")
js3_text = st.markdown("0")
st.subheader("Inference Overview")
inf_ov_1, inf_ov_2, inf_ov_3, inf_ov_4 = st.columns(4)
with inf_ov_1:
st.markdown("**Poor performing classes (Conf < {0})**".format(conf_thres_drift))
inf_ov_1_text = st.markdown("0")
with inf_ov_2:
st.markdown("**No. of poor peforming frames**")
inf_ov_2_text = st.markdown("0")
with inf_ov_3:
st.markdown("**Minimum FPS**")
inf_ov_3_text = st.markdown("0")
with inf_ov_4:
st.markdown("**Maximum FPS**")
inf_ov_4_text = st.markdown("0")
detect(weights="yolov5s_people.pt", source=video.name, stframe=stframe, kpi1_text=kpi1_text, kpi2_text=kpi2_text, kpi3_text=kpi3_text, js1_text=js1_text, js2_text=js2_text, js3_text=js3_text, conf_thres=float(conf_thres),device='0', nosave=nosave, display_labels=display_labels, conf_thres_drift = float(conf_thres_drift), save_poor_frame__= save_poor_frame__, inf_ov_1_text=inf_ov_1_text, inf_ov_2_text=inf_ov_2_text, inf_ov_3_text=inf_ov_3_text, inf_ov_4_text=inf_ov_4_text, fps_warn=fps_warn, fps_drop_warn_thresh = float(fps_drop_warn_thresh))
inference_msg.success("Inference Complete!")
# -------------------------- WEBCAM ----------------------------------
if input_source == "Webcam":
if st.sidebar.button("Start tracking"):
stframe = st.empty()
st.subheader("Inference Stats")
kpi1, kpi2, kpi3 = st.columns(3)
st.subheader("System Stats")
js1, js2, js3 = st.columns(3)
# Updating Inference results
with kpi1:
st.markdown("**Frame Rate**")
kpi1_text = st.markdown("0")
fps_warn = st.empty()
with kpi2:
st.markdown("**Detected objects in curret Frame**")
kpi2_text = st.markdown("0")
with kpi3:
st.markdown("**Total Detected objects**")
kpi3_text = st.markdown("0")
# Updating Jetson stats
with js1:
st.markdown("**Memory usage**")
js1_text = st.markdown("0")
with js2:
st.markdown("**CPU Usage**")
js2_text = st.markdown("0")
with js3:
st.markdown("**GPU Memory Usage**")
js3_text = st.markdown("0")
st.subheader("Inference Overview")
inf_ov_1, inf_ov_2, inf_ov_3, inf_ov_4 = st.columns(4)
with inf_ov_1:
st.markdown("**Poor performing classes (Conf < {0})**".format(conf_thres_drift))
inf_ov_1_text = st.markdown("0")
with inf_ov_2:
st.markdown("**No. of poor peforming frames**")
inf_ov_2_text = st.markdown("0")
with inf_ov_3:
st.markdown("**Minimum FPS**")
inf_ov_3_text = st.markdown("0")
with inf_ov_4:
st.markdown("**Maximum FPS**")
inf_ov_4_text = st.markdown("0")
detect(weights="yolov5s_people.pt", source='0', stframe=stframe, kpi1_text=kpi1_text, kpi2_text=kpi2_text, kpi3_text=kpi3_text, js1_text=js1_text, js2_text=js2_text, js3_text=js3_text, conf_thres=float(conf_thres),device='0', nosave=nosave, display_labels=display_labels, conf_thres_drift = float(conf_thres_drift), save_poor_frame__= save_poor_frame__, inf_ov_1_text=inf_ov_1_text, inf_ov_2_text=inf_ov_2_text, inf_ov_3_text=inf_ov_3_text, inf_ov_4_text=inf_ov_4_text, fps_warn=fps_warn, fps_drop_warn_thresh = float(fps_drop_warn_thresh))
# -------------------------- RTSP ------------------------------
if input_source == "RTSP":
rtsp_input = st.sidebar.text_input("IP Address", "rtsp://192.168.0.1")
if st.sidebar.button("Start tracking"):
stframe = st.empty()
st.subheader("Inference Stats")
kpi1, kpi2, kpi3 = st.columns(3)
st.subheader("System Stats")
js1, js2, js3 = st.columns(3)
# Updating Inference results
with kpi1:
st.markdown("**Frame Rate**")
kpi1_text = st.markdown("0")
fps_warn = st.empty()
with kpi2:
st.markdown("**Detected objects in curret Frame**")
kpi2_text = st.markdown("0")
with kpi3:
st.markdown("**Total Detected objects**")
kpi3_text = st.markdown("0")
# Updating Jetson stats
with js1:
st.markdown("**Memory usage**")
js1_text = st.markdown("0")
with js2:
st.markdown("**CPU Usage**")
js2_text = st.markdown("0")
with js3:
st.markdown("**GPU Memory Usage**")
js3_text = st.markdown("0")
st.subheader("Inference Overview")
inf_ov_1, inf_ov_2, inf_ov_3, inf_ov_4 = st.columns(4)
with inf_ov_1:
st.markdown("**Poor performing classes (Conf < {0})**".format(conf_thres_drift))
inf_ov_1_text = st.markdown("0")
with inf_ov_2:
st.markdown("**No. of poor peforming frames**")
inf_ov_2_text = st.markdown("0")
with inf_ov_3:
st.markdown("**Minimum FPS**")
inf_ov_3_text = st.markdown("0")
with inf_ov_4:
st.markdown("**Maximum FPS**")
inf_ov_4_text = st.markdown("0")
detect(weights="best.pt", source=rtsp_input, stframe=stframe, kpi1_text=kpi1_text, kpi2_text=kpi2_text, kpi3_text=kpi3_text, js1_text=js1_text, js2_text=js2_text, js3_text=js3_text, conf_thres=float(conf_thres),device='0', nosave=nosave, display_labels=display_labels, conf_thres_drift = float(conf_thres_drift), save_poor_frame__= save_poor_frame__, inf_ov_1_text=inf_ov_1_text, inf_ov_2_text=inf_ov_2_text, inf_ov_3_text=inf_ov_3_text, inf_ov_4_text=inf_ov_4_text, fps_warn=fps_warn, fps_drop_warn_thresh = float(fps_drop_warn_thresh))
torch.cuda.empty_cache()
if __name__ == "__main__":
try:
main()
except SystemExit:
pass