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from ultralytics import YOLO
from ultralytics import YOLOv10
import cv2
import time
import numpy as np
import torch
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():
# Use YOLOv8x with optimizations
# model = YOLO('yolov8x.pt')
model = YOLOv10.from_pretrained("Ultralytics/YOLOv8")
# Enable GPU if available and set half precision
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
if device.type != 'cpu':
torch.backends.cudnn.benchmark = True
tracker = ObjectTracker()
video_path = "test2.mp4"
cap = cv2.VideoCapture(video_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
cv2.namedWindow("YOLOv8x Detection with Direction", cv2.WINDOW_NORMAL)
cv2.resizeWindow("YOLOv8x Detection with Direction", 1280, 720)
direction_colors = {
"left": (255, 0, 0),
"right": (0, 255, 0),
"up": (0, 255, 255),
"down": (0, 0, 255),
"stationary": (128, 128, 128)
}
# FPS calculation
fps_start_time = time.time()
fps_counter = 0
fps_display = 0
# Process every 2nd frame for better performance
frame_skip = 2
frame_count = 0
print(f"Running on device: {device}")
while cap.isOpened():
success, frame = cap.read()
if not success:
break
frame_count += 1
if frame_count % frame_skip != 0:
continue
# Update FPS
fps_counter += 1
if time.time() - fps_start_time > 1:
fps_display = fps_counter * frame_skip # Adjust for skipped frames
fps_counter = 0
fps_start_time = time.time()
# Optimize inference
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)
# Draw FPS
cv2.putText(frame, f"FPS: {fps_display}",
(10, 30), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 0), 2)
# Draw total detections
cv2.putText(frame, f"Detections: {len(tracked_objects)}",
(10, 70), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 0), 2)
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}"
text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)[0]
cv2.rectangle(frame,
(int(x1), int(y1) - text_size[1] - 10),
(int(x1) + text_size[0], int(y1)),
color, -1)
cv2.putText(frame, label,
(int(x1), int(y1) - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
cv2.imshow("YOLOv8x Detection with Direction", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main() |