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Update workouts_monitoring solution (#16706)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-10-05 18:08:37 +05:00 • committed by GitHub
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166
ultralytics/solutions/ai_gym.py
View file

@ -1,127 +1,79 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import cv2
from ultralytics.utils.checks import check_imshow
from ultralytics.solutions.solutions import BaseSolution # Import a parent class
from ultralytics.utils.plotting import Annotator
class AIGym:
class AIGym(BaseSolution):
"""A class to manage the gym steps of people in a real-time video stream based on their poses."""
def __init__(
self,
kpts_to_check,
line_thickness=2,
view_img=False,
pose_up_angle=145.0,
pose_down_angle=90.0,
pose_type="pullup",
):
def __init__(self, **kwargs):
"""Initialization function for AiGYM class, a child class of BaseSolution class, can be used for workouts
monitoring.
"""
Initializes the AIGym class with the specified parameters.
# Check if the model name ends with '-pose'
if "model" in kwargs and "-pose" not in kwargs["model"]:
kwargs["model"] = "yolo11n-pose.pt"
elif "model" not in kwargs:
kwargs["model"] = "yolo11n-pose.pt"
super().__init__(**kwargs)
self.count = [] # List for counts, necessary where there are multiple objects in frame
self.angle = [] # List for angle, necessary where there are multiple objects in frame
self.stage = [] # List for stage, necessary where there are multiple objects in frame
# Extract details from CFG single time for usage later
self.initial_stage = None
self.up_angle = float(self.CFG["up_angle"]) # Pose up predefined angle to consider up pose
self.down_angle = float(self.CFG["down_angle"]) # Pose down predefined angle to consider down pose
self.kpts = self.CFG["kpts"] # User selected kpts of workouts storage for further usage
self.lw = self.CFG["line_width"] # Store line_width for usage
def monitor(self, im0):
"""
Monitor the workouts using Ultralytics YOLOv8 Pose Model: https://docs.ultralytics.com/tasks/pose/.
Args:
kpts_to_check (list): Indices of keypoints to check.
line_thickness (int, optional): Thickness of the lines drawn. Defaults to 2.
view_img (bool, optional): Flag to display the image. Defaults to False.
pose_up_angle (float, optional): Angle threshold for the 'up' pose. Defaults to 145.0.
pose_down_angle (float, optional): Angle threshold for the 'down' pose. Defaults to 90.0.
pose_type (str, optional): Type of pose to detect ('pullup', 'pushup', 'abworkout'). Defaults to "pullup".
im0 (ndarray): The input image that will be used for processing
Returns
im0 (ndarray): The processed image for more usage
"""
# Image and line thickness
self.im0 = None
self.tf = line_thickness
# Extract tracks
tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])[0]
# Keypoints and count information
self.keypoints = None
self.poseup_angle = pose_up_angle
self.posedown_angle = pose_down_angle
self.threshold = 0.001
if tracks.boxes.id is not None:
# Extract and check keypoints
if len(tracks) > len(self.count):
new_human = len(tracks) - len(self.count)
self.angle += [0] * new_human
self.count += [0] * new_human
self.stage += ["-"] * new_human
# Store stage, count and angle information
self.angle = None
self.count = None
self.stage = None
self.pose_type = pose_type
self.kpts_to_check = kpts_to_check
# Initialize annotator
self.annotator = Annotator(im0, line_width=self.lw)
# Visual Information
self.view_img = view_img
self.annotator = None
# Enumerate over keypoints
for ind, k in enumerate(reversed(tracks.keypoints.data)):
# Get keypoints and estimate the angle
kpts = [k[int(self.kpts[i])].cpu() for i in range(3)]
self.angle[ind] = self.annotator.estimate_pose_angle(*kpts)
im0 = self.annotator.draw_specific_points(k, self.kpts, radius=self.lw * 3)
# Check if environment supports imshow
self.env_check = check_imshow(warn=True)
self.count = []
self.angle = []
self.stage = []
def start_counting(self, im0, results):
"""
Function used to count the gym steps.
Args:
im0 (ndarray): Current frame from the video stream.
results (list): Pose estimation data.
"""
self.im0 = im0
if not len(results[0]):
return self.im0
if len(results[0]) > len(self.count):
new_human = len(results[0]) - len(self.count)
self.count += [0] * new_human
self.angle += [0] * new_human
self.stage += ["-"] * new_human
self.keypoints = results[0].keypoints.data
self.annotator = Annotator(im0, line_width=self.tf)
for ind, k in enumerate(reversed(self.keypoints)):
# Estimate angle and draw specific points based on pose type
if self.pose_type in {"pushup", "pullup", "abworkout", "squat"}:
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)
# Check and update pose stages and counts based on angle
if self.pose_type in {"abworkout", "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
elif self.pose_type in {"pushup", "squat"}:
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"
# Determine stage and count logic based on angle thresholds
if self.angle[ind] < self.down_angle:
if self.stage[ind] == "up":
self.count[ind] += 1
self.stage[ind] = "down"
elif self.angle[ind] > self.up_angle:
self.stage[ind] = "up"
# Display angle, count, and stage text
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])],
angle_text=self.angle[ind], # angle text for display
count_text=self.count[ind], # count text for workouts
stage_text=self.stage[ind], # stage position text
center_kpt=k[int(self.kpts[1])], # center keypoint for display
)
# Draw keypoints
self.annotator.kpts(k, shape=(640, 640), radius=1, kpt_line=True)
# Display the image if environment supports it and view_img is True
if self.env_check and self.view_img:
cv2.imshow("Ultralytics YOLOv8 AI GYM", self.im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
return
return self.im0
if __name__ == "__main__":
kpts_to_check = [0, 1, 2] # example keypoints
aigym = AIGym(kpts_to_check)
self.display_output(im0) # Display output image, if environment support display
return im0 # return an image for writing or further usage

12
ultralytics/solutions/solutions.py
View file

@ -4,11 +4,13 @@ from collections import defaultdict
from pathlib import Path
import cv2
from shapely.geometry import LineString, Polygon
from ultralytics import YOLO
from ultralytics.utils import yaml_load
from ultralytics.utils.checks import check_imshow
from ultralytics.utils import LOGGER, yaml_load
from ultralytics.utils.checks import check_imshow, check_requirements
check_requirements("shapely>=2.0.0")
from shapely.geometry import LineString, Polygon
DEFAULT_SOL_CFG_PATH = Path(__file__).resolve().parents[1] / "cfg/solutions/default.yaml"
@ -25,7 +27,7 @@ class BaseSolution:
# Load config and update with args
self.CFG = yaml_load(DEFAULT_SOL_CFG_PATH)
self.CFG.update(kwargs)
print("Ultralytics Solutions: ✅", self.CFG)
LOGGER.info(f"Ultralytics Solutions: ✅ {self.CFG}")
self.region = self.CFG["region"] # Store region data for other classes usage
self.line_width = self.CFG["line_width"] # Store line_width for usage
@ -54,6 +56,8 @@ class BaseSolution:
self.boxes = self.track_data.xyxy.cpu()
self.clss = self.track_data.cls.cpu().tolist()
self.track_ids = self.track_data.id.int().cpu().tolist()
else:
LOGGER.warning("WARNING ⚠️ tracks none, no keypoints will be considered.")
def store_tracking_history(self, track_id, box):
"""