73e6861d95
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
79 lines
3.7 KiB
Python
79 lines
3.7 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from ultralytics.solutions.solutions import BaseSolution # Import a parent class
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from ultralytics.utils.plotting import Annotator
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class AIGym(BaseSolution):
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"""A class to manage the gym steps of people in a real-time video stream based on their poses."""
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def __init__(self, **kwargs):
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"""Initialization function for AiGYM class, a child class of BaseSolution class, can be used for workouts
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monitoring.
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"""
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# Check if the model name ends with '-pose'
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if "model" in kwargs and "-pose" not in kwargs["model"]:
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kwargs["model"] = "yolo11n-pose.pt"
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elif "model" not in kwargs:
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kwargs["model"] = "yolo11n-pose.pt"
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super().__init__(**kwargs)
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self.count = [] # List for counts, necessary where there are multiple objects in frame
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self.angle = [] # List for angle, necessary where there are multiple objects in frame
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self.stage = [] # List for stage, necessary where there are multiple objects in frame
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# Extract details from CFG single time for usage later
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self.initial_stage = None
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self.up_angle = float(self.CFG["up_angle"]) # Pose up predefined angle to consider up pose
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self.down_angle = float(self.CFG["down_angle"]) # Pose down predefined angle to consider down pose
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self.kpts = self.CFG["kpts"] # User selected kpts of workouts storage for further usage
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self.lw = self.CFG["line_width"] # Store line_width for usage
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def monitor(self, im0):
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"""
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Monitor the workouts using Ultralytics YOLOv8 Pose Model: https://docs.ultralytics.com/tasks/pose/.
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Args:
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im0 (ndarray): The input image that will be used for processing
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Returns
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im0 (ndarray): The processed image for more usage
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"""
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# Extract tracks
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tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])[0]
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if tracks.boxes.id is not None:
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# Extract and check keypoints
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if len(tracks) > len(self.count):
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new_human = len(tracks) - len(self.count)
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self.angle += [0] * new_human
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self.count += [0] * new_human
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self.stage += ["-"] * new_human
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# Initialize annotator
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self.annotator = Annotator(im0, line_width=self.lw)
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# Enumerate over keypoints
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for ind, k in enumerate(reversed(tracks.keypoints.data)):
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# Get keypoints and estimate the angle
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kpts = [k[int(self.kpts[i])].cpu() for i in range(3)]
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self.angle[ind] = self.annotator.estimate_pose_angle(*kpts)
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im0 = self.annotator.draw_specific_points(k, self.kpts, radius=self.lw * 3)
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# Determine stage and count logic based on angle thresholds
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if self.angle[ind] < self.down_angle:
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if self.stage[ind] == "up":
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self.count[ind] += 1
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self.stage[ind] = "down"
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elif self.angle[ind] > self.up_angle:
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self.stage[ind] = "up"
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# Display angle, count, and stage text
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self.annotator.plot_angle_and_count_and_stage(
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angle_text=self.angle[ind], # angle text for display
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count_text=self.count[ind], # count text for workouts
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stage_text=self.stage[ind], # stage position text
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center_kpt=k[int(self.kpts[1])], # center keypoint for display
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)
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self.display_output(im0) # Display output image, if environment support display
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return im0 # return an image for writing or further usage
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