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ultralytics 8.3.16 PyTorch 2.5.0 support (#16998)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
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Glenn Jocher 2024-10-18 13:54:45 +02:00 • committed by GitHub
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104
ultralytics/solutions/object_counter.py
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@ -1,18 +1,40 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from shapely.geometry import LineString, Point
from ultralytics.solutions.solutions import BaseSolution # Import a parent class
from ultralytics.solutions.solutions import BaseSolution
from ultralytics.utils.plotting import Annotator, colors
class ObjectCounter(BaseSolution):
"""A class to manage the counting of objects in a real-time video stream based on their tracks."""
"""
A class to manage the counting of objects in a real-time video stream based on their tracks.
This class extends the BaseSolution class and provides functionality for counting objects moving in and out of a
specified region in a video stream. It supports both polygonal and linear regions for counting.
Attributes:
in_count (int): Counter for objects moving inward.
out_count (int): Counter for objects moving outward.
counted_ids (List[int]): List of IDs of objects that have been counted.
classwise_counts (Dict[str, Dict[str, int]]): Dictionary for counts, categorized by object class.
region_initialized (bool): Flag indicating whether the counting region has been initialized.
show_in (bool): Flag to control display of inward count.
show_out (bool): Flag to control display of outward count.
Methods:
count_objects: Counts objects within a polygonal or linear region.
store_classwise_counts: Initializes class-wise counts if not already present.
display_counts: Displays object counts on the frame.
count: Processes input data (frames or object tracks) and updates counts.
Examples:
>>> counter = ObjectCounter()
>>> frame = cv2.imread("frame.jpg")
>>> processed_frame = counter.count(frame)
>>> print(f"Inward count: {counter.in_count}, Outward count: {counter.out_count}")
"""
def __init__(self, **kwargs):
"""Initialization function for Count class, a child class of BaseSolution class, can be used for counting the
objects.
"""
"""Initializes the ObjectCounter class for real-time object counting in video streams."""
super().__init__(**kwargs)
self.in_count = 0 # Counter for objects moving inward
@ -26,14 +48,23 @@ class ObjectCounter(BaseSolution):
def count_objects(self, track_line, box, track_id, prev_position, cls):
"""
Helper function to count objects within a polygonal region.
Counts objects within a polygonal or linear region based on their tracks.
Args:
track_line (dict): last 30 frame track record
box (list): Bounding box data for specific track in current frame
track_id (int): track ID of the object
prev_position (tuple): last frame position coordinates of the track
cls (int): Class index for classwise count updates
track_line (Dict): Last 30 frame track record for the object.
box (List[float]): Bounding box coordinates [x1, y1, x2, y2] for the specific track in the current frame.
track_id (int): Unique identifier for the tracked object.
prev_position (Tuple[float, float]): Last frame position coordinates (x, y) of the track.
cls (int): Class index for classwise count updates.
Examples:
>>> counter = ObjectCounter()
>>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]}
>>> box = [130, 230, 150, 250]
>>> track_id = 1
>>> prev_position = (120, 220)
>>> cls = 0
>>> counter.count_objects(track_line, box, track_id, prev_position, cls)
"""
if prev_position is None or track_id in self.counted_ids:
return
@ -42,7 +73,7 @@ class ObjectCounter(BaseSolution):
dx = (box[0] - prev_position[0]) * (centroid.x - prev_position[0])
dy = (box[1] - prev_position[1]) * (centroid.y - prev_position[1])
if len(self.region) >= 3 and self.r_s.contains(Point(track_line[-1])):
if len(self.region) >= 3 and self.r_s.contains(self.Point(track_line[-1])):
self.counted_ids.append(track_id)
# For polygon region
if dx > 0:
@ -52,7 +83,7 @@ class ObjectCounter(BaseSolution):
self.out_count += 1
self.classwise_counts[self.names[cls]]["OUT"] += 1
elif len(self.region) < 3 and LineString([prev_position, box[:2]]).intersects(self.l_s):
elif len(self.region) < 3 and self.LineString([prev_position, box[:2]]).intersects(self.r_s):
self.counted_ids.append(track_id)
# For linear region
if dx > 0 and dy > 0:
@ -64,20 +95,34 @@ class ObjectCounter(BaseSolution):
def store_classwise_counts(self, cls):
"""
Initialize class-wise counts if not already present.
Initialize class-wise counts for a specific object class if not already present.
Args:
cls (int): Class index for classwise count updates
cls (int): Class index for classwise count updates.
This method ensures that the 'classwise_counts' dictionary contains an entry for the specified class,
initializing 'IN' and 'OUT' counts to zero if the class is not already present.
Examples:
>>> counter = ObjectCounter()
>>> counter.store_classwise_counts(0) # Initialize counts for class index 0
>>> print(counter.classwise_counts)
{'person': {'IN': 0, 'OUT': 0}}
"""
if self.names[cls] not in self.classwise_counts:
self.classwise_counts[self.names[cls]] = {"IN": 0, "OUT": 0}
def display_counts(self, im0):
"""
Helper function to display object counts on the frame.
Displays object counts on the input image or frame.
Args:
im0 (ndarray): The input image or frame
im0 (numpy.ndarray): The input image or frame to display counts on.
Examples:
>>> counter = ObjectCounter()
>>> frame = cv2.imread("image.jpg")
>>> counter.display_counts(frame)
"""
labels_dict = {
str.capitalize(key): f"{'IN ' + str(value['IN']) if self.show_in else ''} "
@ -91,12 +136,21 @@ class ObjectCounter(BaseSolution):
def count(self, im0):
"""
Processes input data (frames or object tracks) and updates counts.
Processes input data (frames or object tracks) and updates object counts.
This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates
object counts, and displays the results on the input image.
Args:
im0 (ndarray): The input image that will be used for processing
Returns
im0 (ndarray): The processed image for more usage
im0 (numpy.ndarray): The input image or frame to be processed.
Returns:
(numpy.ndarray): The processed image with annotations and count information.
Examples:
>>> counter = ObjectCounter()
>>> frame = cv2.imread("path/to/image.jpg")
>>> processed_frame = counter.count(frame)
"""
if not self.region_initialized:
self.initialize_region()
@ -122,7 +176,9 @@ class ObjectCounter(BaseSolution):
)
# store previous position of track for object counting
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
prev_position = None
if len(self.track_history[track_id]) > 1:
prev_position = self.track_history[track_id][-2]
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
self.display_counts(im0) # Display the counts on the frame