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Add real-world projects in Ultralytics + guides in Docs (#6695)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2023-12-02 03:20:14 +05:00 • committed by GitHub
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130
ultralytics/solutions/ai_gym.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import cv2
from ultralytics.utils.plotting import Annotator
class AIGym:
"""A class to manage the gym steps of people in a real-time video stream based on their poses."""
def __init__(self):
"""Initializes the AIGym with default values for Visual and Image parameters."""
# Image and line thickness
self.im0 = None
self.tf = None
# Keypoints and count information
self.keypoints = None
self.poseup_angle = None
self.posedown_angle = None
self.threshold = 0.001
# Store stage, count and angle information
self.angle = None
self.count = None
self.stage = None
self.pose_type = 'pushup'
self.kpts_to_check = None
# Visual Information
self.view_img = False
self.annotator = None
def set_args(self,
kpts_to_check,
line_thickness=2,
view_img=False,
pose_up_angle=145.0,
pose_down_angle=90.0,
pose_type='pullup'):
"""
Configures the AIGym line_thickness, save image and view image parameters
Args:
kpts_to_check (list): 3 keypoints for counting
line_thickness (int): Line thickness for bounding boxes.
view_img (bool): display the im0
pose_up_angle (float): Angle to set pose position up
pose_down_angle (float): Angle to set pose position down
pose_type: "pushup", "pullup" or "abworkout"
"""
self.kpts_to_check = kpts_to_check
self.tf = line_thickness
self.view_img = view_img
self.poseup_angle = pose_up_angle
self.posedown_angle = pose_down_angle
self.pose_type = pose_type
def start_counting(self, im0, results, frame_count):
"""
function used to count the gym steps
Args:
im0 (ndarray): Current frame from the video stream.
results: Pose estimation data
frame_count: store current frame count
"""
self.im0 = im0
if frame_count == 1:
self.count = [0] * len(results[0])
self.angle = [0] * len(results[0])
self.stage = ['-' for _ in results[0]]
self.keypoints = results[0].keypoints.data
self.annotator = Annotator(im0, line_width=2)
for ind, k in enumerate(reversed(self.keypoints)):
if self.pose_type == 'pushup' or self.pose_type == 'pullup':
self.angle[ind] = self.annotator.estimate_pose_angle(k[int(self.kpts_to_check[0])].cpu(),
k[int(self.kpts_to_check[1])].cpu(),
k[int(self.kpts_to_check[2])].cpu())
self.im0 = self.annotator.draw_specific_points(k, self.kpts_to_check, shape=(640, 640), radius=10)
if self.pose_type == 'abworkout':
self.angle[ind] = self.annotator.estimate_pose_angle(k[int(self.kpts_to_check[0])].cpu(),
k[int(self.kpts_to_check[1])].cpu(),
k[int(self.kpts_to_check[2])].cpu())
self.im0 = self.annotator.draw_specific_points(k, self.kpts_to_check, shape=(640, 640), radius=10)
if self.angle[ind] > self.poseup_angle:
self.stage[ind] = 'down'
if self.angle[ind] < self.posedown_angle and self.stage[ind] == 'down':
self.stage[ind] = 'up'
self.count[ind] += 1
self.annotator.plot_angle_and_count_and_stage(angle_text=self.angle[ind],
count_text=self.count[ind],
stage_text=self.stage[ind],
center_kpt=k[int(self.kpts_to_check[1])],
line_thickness=self.tf)
if self.pose_type == 'pushup':
if self.angle[ind] > self.poseup_angle:
self.stage[ind] = 'up'
if self.angle[ind] < self.posedown_angle and self.stage[ind] == 'up':
self.stage[ind] = 'down'
self.count[ind] += 1
self.annotator.plot_angle_and_count_and_stage(angle_text=self.angle[ind],
count_text=self.count[ind],
stage_text=self.stage[ind],
center_kpt=k[int(self.kpts_to_check[1])],
line_thickness=self.tf)
if self.pose_type == 'pullup':
if self.angle[ind] > self.poseup_angle:
self.stage[ind] = 'down'
if self.angle[ind] < self.posedown_angle and self.stage[ind] == 'down':
self.stage[ind] = 'up'
self.count[ind] += 1
self.annotator.plot_angle_and_count_and_stage(angle_text=self.angle[ind],
count_text=self.count[ind],
stage_text=self.stage[ind],
center_kpt=k[int(self.kpts_to_check[1])],
line_thickness=self.tf)
self.annotator.kpts(k, shape=(640, 640), radius=1, kpt_line=True)
if self.view_img:
cv2.imshow('Ultralytics YOLOv8 AI GYM', self.im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
return
if __name__ == '__main__':
AIGym()

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ultralytics/solutions/object_counter.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import defaultdict
import cv2
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.plotting import Annotator, colors
check_requirements('shapely>=2.0.0')
from shapely.geometry import Polygon
from shapely.geometry.point import Point
class ObjectCounter:
"""A class to manage the counting of objects in a real-time video stream based on their tracks."""
def __init__(self):
"""Initializes the Counter with default values for various tracking and counting parameters."""
# Mouse events
self.is_drawing = False
self.selected_point = None
# Region Information
self.reg_pts = None
self.counting_region = None
self.region_color = (255, 255, 255)
# Image and annotation Information
self.im0 = None
self.tf = None
self.view_img = False
self.names = None # Classes names
self.annotator = None # Annotator
# Object counting Information
self.in_counts = 0
self.out_counts = 0
self.counting_list = []
# Tracks info
self.track_history = defaultdict(list)
self.track_thickness = 2
self.draw_tracks = False
def set_args(self,
classes_names,
reg_pts,
region_color=None,
line_thickness=2,
track_thickness=2,
view_img=False,
draw_tracks=False):
"""
Configures the Counter's image, bounding box line thickness, and counting region points.
Args:
line_thickness (int): Line thickness for bounding boxes.
view_img (bool): Flag to control whether to display the video stream.
reg_pts (list): Initial list of points defining the counting region.
classes_names (dict): Classes names
region_color (tuple): color for region line
track_thickness (int): Track thickness
draw_tracks (Bool): draw tracks
"""
self.tf = line_thickness
self.view_img = view_img
self.track_thickness = track_thickness
self.draw_tracks = draw_tracks
self.reg_pts = reg_pts
self.counting_region = Polygon(self.reg_pts)
self.names = classes_names
self.region_color = region_color if region_color else self.region_color
def mouse_event_for_region(self, event, x, y, flags, params):
"""
This function is designed to move region with mouse events in a real-time video stream.
Args:
event (int): The type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).
x (int): The x-coordinate of the mouse pointer.
y (int): The y-coordinate of the mouse pointer.
flags (int): Any flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY,
cv2.EVENT_FLAG_SHIFTKEY, etc.).
params (dict): Additional parameters you may want to pass to the function.
"""
# global is_drawing, selected_point
if event == cv2.EVENT_LBUTTONDOWN:
for i, point in enumerate(self.reg_pts):
if isinstance(point, (tuple, list)) and len(point) >= 2:
if abs(x - point[0]) < 10 and abs(y - point[1]) < 10:
self.selected_point = i
self.is_drawing = True
break
elif event == cv2.EVENT_MOUSEMOVE:
if self.is_drawing and self.selected_point is not None:
self.reg_pts[self.selected_point] = (x, y)
self.counting_region = Polygon(self.reg_pts)
elif event == cv2.EVENT_LBUTTONUP:
self.is_drawing = False
self.selected_point = None
def extract_and_process_tracks(self, tracks):
boxes = tracks[0].boxes.xyxy.cpu()
clss = tracks[0].boxes.cls.cpu().tolist()
track_ids = tracks[0].boxes.id.int().cpu().tolist()
self.annotator = Annotator(self.im0, self.tf, self.names)
self.annotator.draw_region(reg_pts=self.reg_pts, color=(0, 255, 0))
for box, track_id, cls in zip(boxes, track_ids, clss):
self.annotator.box_label(box, label=self.names[cls], color=colors(int(cls), True)) # Draw bounding box
# Draw Tracks
track_line = self.track_history[track_id]
track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
track_line.pop(0) if len(track_line) > 30 else None
if self.draw_tracks:
self.annotator.draw_centroid_and_tracks(track_line,
color=(0, 255, 0),
track_thickness=self.track_thickness)
# Count objects
if self.counting_region.contains(Point(track_line[-1])):
if track_id not in self.counting_list:
self.counting_list.append(track_id)
if box[0] < self.counting_region.centroid.x:
self.out_counts += 1
else:
self.in_counts += 1
if self.view_img:
incount_label = 'InCount : ' + f'{self.in_counts}'
outcount_label = 'OutCount : ' + f'{self.out_counts}'
self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
cv2.namedWindow('Ultralytics YOLOv8 Object Counter')
cv2.setMouseCallback('Ultralytics YOLOv8 Object Counter', self.mouse_event_for_region,
{'region_points': self.reg_pts})
cv2.imshow('Ultralytics YOLOv8 Object Counter', self.im0)
# Break Window
if cv2.waitKey(1) & 0xFF == ord('q'):
return
def start_counting(self, im0, tracks):
"""
Main function to start the object counting process.
Args:
im0 (ndarray): Current frame from the video stream.
tracks (list): List of tracks obtained from the object tracking process.
"""
self.im0 = im0 # store image
if tracks[0].boxes.id is None:
return
self.extract_and_process_tracks(tracks)
if __name__ == '__main__':
ObjectCounter()