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Optimize queue solution (#16246)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Muhammad Rizwan Munawar 2024-09-13 22:36:23 +05:00 • committed by GitHub
parent 6c3297623b
commit e309b6efab
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2 changed files with 23 additions and 76 deletions
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21
docs/en/guides/queue-management.md
View file

@ -56,15 +56,13 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
names=model.names,
reg_pts=queue_region,
line_thickness=3,
fontsize=1.0,
region_color=(255, 144, 31),
)
while cap.isOpened():
success, im0 = cap.read()
if success:
tracks = model.track(im0, show=False, persist=True, verbose=False)
tracks = model.track(im0, persist=True)
out = queue.process_queue(im0, tracks)
video_writer.write(im0)
@ -100,15 +98,13 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
names=model.names,
reg_pts=queue_region,
line_thickness=3,
fontsize=1.0,
region_color=(255, 144, 31),
)
while cap.isOpened():
success, im0 = cap.read()
if success:
tracks = model.track(im0, show=False, persist=True, verbose=False, classes=0) # Only person class
tracks = model.track(im0, persist=True, classes=0) # Only person class
out = queue.process_queue(im0, tracks)
video_writer.write(im0)
@ -126,19 +122,12 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
### Arguments `QueueManager`
| Name | Type | Default | Description |
| ------------------- | ---------------- | -------------------------- | ----------------------------------------------------------------------------------- |
| ---------------- | ---------------- | -------------------------- | -------------------------------------------------------------------------------- |
| `names` | `dict` | `model.names` | A dictionary mapping class IDs to class names. |
| `reg_pts` | `list of tuples` | `[(20, 400), (1260, 400)]` | Points defining the counting region polygon. Defaults to a predefined rectangle. |
| `line_thickness` | `int` | `2` | Thickness of the annotation lines. |
| `track_thickness` | `int` | `2` | Thickness of the track lines. |
| `view_img` | `bool` | `False` | Whether to display the image frames. |
| `region_color` | `tuple` | `(255, 0, 255)` | Color of the counting region lines (BGR). |
| `view_queue_counts` | `bool` | `True` | Whether to display the queue counts. |
| `draw_tracks` | `bool` | `False` | Whether to draw tracks of the objects. |
| `count_txt_color` | `tuple` | `(255, 255, 255)` | Color of the count text (BGR). |
| `track_color` | `tuple` | `None` | Color of the tracks. If `None`, different colors will be used for different tracks. |
| `region_thickness` | `int` | `5` | Thickness of the counting region lines. |
| `fontsize` | `float` | `0.7` | Font size for the text annotations. |
### Arguments `model.track`
@ -170,8 +159,6 @@ queue = solutions.QueueManager(
names=model.names,
reg_pts=queue_region,
line_thickness=3,
fontsize=1.0,
region_color=(255, 144, 31),
)
while cap.isOpened():
@ -223,8 +210,6 @@ queue_airport = solutions.QueueManager(
names=model.names,
reg_pts=queue_region_airport,
line_thickness=3,
fontsize=1.0,
region_color=(0, 255, 0),
)
```

66
ultralytics/solutions/queue_management.py
View file

@ -20,15 +20,8 @@ class QueueManager:
names,
reg_pts=None,
line_thickness=2,
track_thickness=2,
view_img=False,
region_color=(255, 0, 255),
view_queue_counts=True,
draw_tracks=False,
count_txt_color=(255, 255, 255),
track_color=None,
region_thickness=5,
fontsize=0.7,
):
"""
Initializes the QueueManager with specified parameters for tracking and counting objects.
@ -38,57 +31,35 @@ class QueueManager:
reg_pts (list of tuples, optional): Points defining the counting region polygon. Defaults to a predefined
rectangle.
line_thickness (int, optional): Thickness of the annotation lines. Defaults to 2.
track_thickness (int, optional): Thickness of the track lines. Defaults to 2.
view_img (bool, optional): Whether to display the image frames. Defaults to False.
region_color (tuple, optional): Color of the counting region lines (BGR). Defaults to (255, 0, 255).
view_queue_counts (bool, optional): Whether to display the queue counts. Defaults to True.
draw_tracks (bool, optional): Whether to draw tracks of the objects. Defaults to False.
count_txt_color (tuple, optional): Color of the count text (BGR). Defaults to (255, 255, 255).
track_color (tuple, optional): Color of the tracks. If None, different colors will be used for different
tracks. Defaults to None.
region_thickness (int, optional): Thickness of the counting region lines. Defaults to 5.
fontsize (float, optional): Font size for the text annotations. Defaults to 0.7.
"""
# Mouse events state
self.is_drawing = False
self.selected_point = None
# Region & Line Information
self.reg_pts = reg_pts if reg_pts is not None else [(20, 60), (20, 680), (1120, 680), (1120, 60)]
self.counting_region = (
Polygon(self.reg_pts) if len(self.reg_pts) >= 3 else Polygon([(20, 60), (20, 680), (1120, 680), (1120, 60)])
)
self.region_color = region_color
self.region_thickness = region_thickness
# Image and annotation Information
self.im0 = None
# annotation Information
self.tf = line_thickness
self.view_img = view_img
self.view_queue_counts = view_queue_counts
self.fontsize = fontsize
self.names = names # Class names
self.annotator = None # Annotator
self.window_name = "Ultralytics YOLOv8 Queue Manager"
# Object counting Information
self.counts = 0
self.count_txt_color = count_txt_color
# Tracks info
self.track_history = defaultdict(list)
self.track_thickness = track_thickness
self.draw_tracks = draw_tracks
self.track_color = track_color
# Check if environment supports imshow
self.env_check = check_imshow(warn=True)
def extract_and_process_tracks(self, tracks):
def extract_and_process_tracks(self, tracks, im0):
"""Extracts and processes tracks for queue management in a video stream."""
# Initialize annotator and draw the queue region
self.annotator = Annotator(self.im0, self.tf, self.names)
annotator = Annotator(im0, self.tf, self.names)
self.counts = 0 # Reset counts every frame
if tracks[0].boxes.id is not None:
boxes = tracks[0].boxes.xyxy.cpu()
@ -98,7 +69,7 @@ class QueueManager:
# Extract tracks
for box, track_id, cls in zip(boxes, track_ids, clss):
# Draw bounding box
self.annotator.box_label(box, label=f"{self.names[cls]}#{track_id}", color=colors(int(track_id), True))
annotator.box_label(box, label=self.names[cls], color=colors(int(track_id), True))
# Update track history
track_line = self.track_history[track_id]
@ -108,10 +79,10 @@ class QueueManager:
# Draw track trails if enabled
if self.draw_tracks:
self.annotator.draw_centroid_and_tracks(
annotator.draw_centroid_and_tracks(
track_line,
color=self.track_color or colors(int(track_id), True),
track_thickness=self.track_thickness,
color=colors(int(track_id), True),
track_thickness=self.line_thickness,
)
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
@ -125,21 +96,16 @@ class QueueManager:
# Display queue counts
label = f"Queue Counts : {str(self.counts)}"
if label is not None:
self.annotator.queue_counts_display(
annotator.queue_counts_display(
label,
points=self.reg_pts,
region_color=self.region_color,
txt_color=self.count_txt_color,
region_color=(255, 0, 255),
txt_color=(104, 31, 17),
)
self.display_frames()
def display_frames(self):
"""Displays the current frame with annotations."""
if self.env_check and self.view_img:
self.annotator.draw_region(reg_pts=self.reg_pts, thickness=self.region_thickness, color=self.region_color)
cv2.namedWindow(self.window_name)
cv2.imshow(self.window_name, self.im0)
annotator.draw_region(reg_pts=self.reg_pts, thickness=self.tf * 2, color=(255, 0, 255))
cv2.imshow("Ultralytics YOLOv8 Queue Manager", im0)
# Close window on 'q' key press
if cv2.waitKey(1) & 0xFF == ord("q"):
return
@ -152,12 +118,8 @@ class QueueManager:
im0 (ndarray): Current frame from the video stream.
tracks (list): List of tracks obtained from the object tracking process.
"""
self.im0 = im0 # Store the current frame
self.extract_and_process_tracks(tracks) # Extract and process tracks
if self.view_img:
self.display_frames() # Display the frame if enabled
return self.im0
self.extract_and_process_tracks(tracks, im0) # Extract and process tracks
return im0
if __name__ == "__main__":