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import streamlit as st
from ultralytics import YOLO
import cv2
import time
import numpy as np
import torch
from PIL import Image
import tempfile
import warnings
warnings.filterwarnings('ignore')
def get_direction(old_center, new_center, min_movement=10):
if old_center is None or new_center is None:
return "stationary"
dx = new_center[0] - old_center[0]
dy = new_center[1] - old_center[1]
if abs(dx) < min_movement and abs(dy) < min_movement:
return "stationary"
if abs(dx) > abs(dy):
return "right" if dx > 0 else "left"
else:
return "down" if dy > 0 else "up"
class ObjectTracker:
def __init__(self):
self.tracked_objects = {}
self.object_count = {}
def update(self, detections):
current_objects = {}
results = []
for detection in detections:
x1, y1, x2, y2 = detection[0:4]
center = ((x1 + x2) // 2, (y1 + y2) // 2)
class_id = detection[5]
object_id = f"{class_id}_{len(self.object_count.get(class_id, []))}"
min_dist = float('inf')
closest_id = None
for prev_id, prev_data in self.tracked_objects.items():
if prev_id.split('_')[0] == str(class_id):
dist = np.sqrt((center[0] - prev_data['center'][0])**2 +
(center[1] - prev_data['center'][1])**2)
if dist < min_dist and dist < 100:
min_dist = dist
closest_id = prev_id
if closest_id:
object_id = closest_id
else:
if class_id not in self.object_count:
self.object_count[class_id] = []
self.object_count[class_id].append(object_id)
prev_center = self.tracked_objects.get(object_id, {}).get('center', None)
direction = get_direction(prev_center, center)
current_objects[object_id] = {
'center': center,
'direction': direction,
'detection': detection
}
results.append((detection, object_id, direction))
self.tracked_objects = current_objects
return results
def main():
st.title("Real-time Object Detection with Direction")
# File uploader for video
uploaded_file = st.file_uploader("Choose a video file", type=['mp4', 'avi', 'mov'])
# Add start button
start_detection = st.button("Start Detection")
# Add stop button
stop_detection = st.button("Stop Detection")
if uploaded_file is not None and start_detection:
# Create a session state to track if detection is running
if 'running' not in st.session_state:
st.session_state.running = True
# Save uploaded file temporarily
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
# Load model
with st.spinner('Loading model...'):
model = YOLO('yolov8x.pt',verbose=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
tracker = ObjectTracker()
cap = cv2.VideoCapture(tfile.name)
direction_colors = {
"left": (255, 0, 0),
"right": (0, 255, 0),
"up": (0, 255, 255),
"down": (0, 0, 255),
"stationary": (128, 128, 128)
}
# Create placeholder for video frame
frame_placeholder = st.empty()
# Create placeholder for detection info
info_placeholder = st.empty()
st.success("Detection Started!")
while cap.isOpened() and st.session_state.running:
success, frame = cap.read()
if not success:
break
# Run detection
results = model(frame,
conf=0.25,
iou=0.45,
max_det=20,
verbose=False)[0]
detections = []
for box in results.boxes.data:
x1, y1, x2, y2, conf, cls = box.tolist()
detections.append([int(x1), int(y1), int(x2), int(y2), float(conf), int(cls)])
tracked_objects = tracker.update(detections)
# Dictionary to store detection counts
detection_counts = {}
for detection, obj_id, direction in tracked_objects:
x1, y1, x2, y2, conf, cls = detection
color = direction_colors.get(direction, (128, 128, 128))
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
label = f"{model.names[int(cls)]} {direction} {conf:.2f}"
# Increased font size and thickness
font_scale = 1.2
thickness = 3
text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)[0]
# Increased padding for label background
padding_y = 15
cv2.rectangle(frame,
(int(x1), int(y1) - text_size[1] - padding_y),
(int(x1) + text_size[0], int(y1)),
color, -1)
cv2.putText(frame, label,
(int(x1), int(y1) - 5),
cv2.FONT_HERSHEY_SIMPLEX,
font_scale,
(255, 255, 255),
thickness)
# Count detections by class
class_name = model.names[int(cls)]
detection_counts[class_name] = detection_counts.get(class_name, 0) + 1
# Convert BGR to RGB
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Update frame
frame_placeholder.image(frame_rgb, channels="RGB", use_column_width=True)
# Update detection info
info_text = "Detected Objects:\n"
for class_name, count in detection_counts.items():
info_text += f"{class_name}: {count}\n"
info_placeholder.text(info_text)
# Check if stop button is pressed
if stop_detection:
st.session_state.running = False
break
cap.release()
st.session_state.running = False
st.warning("Detection Stopped")
elif uploaded_file is None and start_detection:
st.error("Please upload a video file first!")
if __name__ == "__main__":
main() |