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File size: 5,442 Bytes
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import streamlit as st
import cv2
import tempfile
import torch
import numpy as np
from ultralytics import YOLO
from deep_sort_realtime.deepsort_tracker import DeepSort
import warnings
import os
import platform
# Suppress ScriptRunContext warnings from threads
warnings.filterwarnings("ignore", message=".*missing ScriptRunContext.*")
# Check if running in headless environment
IS_HEADLESS = platform.system() == 'Linux'
# Initialize YOLO model
@st.cache_resource
def load_yolo_model():
try:
return YOLO("best.pt")
except Exception as e:
st.error(f"Error loading YOLO model: {str(e)}")
return None
# Main app
st.title("📦 Inventory Management")
# Settings
st.sidebar.header("Settings")
CONF_THRESHOLD = st.sidebar.slider(
"Confidence Threshold",
min_value=0.0,
max_value=1.0,
value=0.4,
help="Higher values mean more confident detections but might miss objects"
)
FRAME_SKIP = st.sidebar.slider(
"Frame Skip",
min_value=0,
max_value=10,
value=2,
help="Process every Nth frame (higher values = faster processing but may miss objects)"
)
# Load YOLO model
yolo_model = load_yolo_model()
if yolo_model is None:
st.error("Failed to load YOLO model. Please check if the model file exists.")
st.stop()
# File uploader
uploaded_file = st.sidebar.file_uploader(
"Upload Video",
type=["mp4", "avi", "mov"],
help="Supported formats: MP4, AVI, MOV"
)
if uploaded_file is not None:
try:
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
video_path = tfile.name
if st.sidebar.button("Start Processing"):
tracker = DeepSort(
embedder="mobilenet",
embedder_gpu=torch.cuda.is_available(),
max_age=30 # Increase max_age for longer tracking retention
)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
st.error("Error opening video file")
st.stop()
# Get video properties for processing
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
counted_objects = set()
frame_placeholder = st.empty()
status_text = st.sidebar.empty()
progress_bar = st.progress(0)
# Counter for frame skipping
frame_counter = 0
while True:
ret, frame = cap.read()
if not ret:
break
frame_counter += 1
current_position = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
progress = current_position / frame_count
progress_bar.progress(progress)
# Skip frames based on user setting
if FRAME_SKIP > 0 and frame_counter % (FRAME_SKIP + 1) != 0:
continue
try:
# Resize frame for faster processing (if needed)
# h, w = frame.shape[:2]
# if w > 1280: # Only resize if the frame is large
# frame = cv2.resize(frame, (1280, int(h * 1280 / w)))
results = yolo_model(frame, verbose=False) # Turn off verbose output for speed
detections = []
for result in results:
for box in result.boxes.data.tolist():
x1, y1, x2, y2, score, class_id = box
if score > CONF_THRESHOLD:
detections.append([[x1, y1, x2 - x1, y2 - y1], score, int(class_id)])
tracks = tracker.update_tracks(detections, frame=frame)
for track in tracks:
if not track.is_confirmed():
continue
track_id = track.track_id
ltrb = track.to_ltrb()
x1, y1, x2, y2 = map(int, ltrb)
counted_objects.add(track_id)
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, f"ID: {track_id}", (x1, y1-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.putText(frame, f"Total Objects: {len(counted_objects)}",
(20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_placeholder.image(frame_rgb, channels="RGB", use_container_width=True)
status_text.info(f"Processing... Current count: {len(counted_objects)}")
# Remove sleep to maximize performance
# time.sleep(0.01)
except Exception as e:
st.error(f"Error processing frame: {str(e)}")
continue
cap.release()
progress_bar.progress(1.0)
st.sidebar.success(f"Final count: {len(counted_objects)} objects")
st.balloons()
except Exception as e:
st.error(f"Error processing video: {str(e)}")
finally:
if 'tfile' in locals():
tfile.close() |