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import argparse
import sys
import cv2
import numpy as np
from rich.console import Console
from rich.panel import Panel
from rich.align import Align
from rich.layout import Layout
from pyfiglet import Figlet
import mediapipe as mp
from PoseClassification.pose_embedding import FullBodyPoseEmbedding
from PoseClassification.pose_classifier import PoseClassifier
from PoseClassification.utils import EMADictSmoothing
from PoseClassification.visualize import PoseClassificationVisualizer
# For cross-platform compatibility
try:
import msvcrt # Windows
except ImportError:
import termios # Unix-like
import tty
def getch():
if sys.platform == "win32":
return msvcrt.getch().decode("utf-8")
else:
fd = sys.stdin.fileno()
old_settings = termios.tcgetattr(fd)
try:
tty.setraw(sys.stdin.fileno())
ch = sys.stdin.read(1)
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
return ch
def create_ascii_title(text):
f = Figlet(font="isometric2")
return f.renderText(text)
def main(input_source, display=False, output_file=None):
console = Console()
layout = Layout()
# Create ASCII title
ascii_title = create_ascii_title("YOGAI")
# Create the layout
layout.split(
Layout(Panel(Align.center(ascii_title), border_style="bold blue"), size=15),
Layout(name="main"),
)
is_live = input_source == "live"
if is_live:
layout["main"].update(
Panel(
"Processing live video from camera",
title="Video Classification",
border_style="bold blue",
)
)
else:
layout["main"].update(
Panel(
f"Processing video: {input_source}",
title="Video Classification",
border_style="bold blue",
)
)
console.print(layout)
# Initialize pose tracker, embedder, and classifier
mp_pose = mp.solutions.pose
pose_tracker = mp_pose.Pose()
pose_embedder = FullBodyPoseEmbedding()
pose_classifier = PoseClassifier(
pose_samples_folder="data/yoga_poses_csvs_out",
pose_embedder=pose_embedder,
top_n_by_max_distance=30,
top_n_by_mean_distance=10,
)
pose_classification_filter = EMADictSmoothing(window_size=10, alpha=0.2)
# Open the video source
if is_live:
video = cv2.VideoCapture(0)
fps = 30 # Assume 30 fps for live video
total_frames = float("inf") # Infinite frames for live video
else:
video = cv2.VideoCapture(input_source)
fps = video.get(cv2.CAP_PROP_FPS)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
# Initialize pose timings (use lowercase for keys)
pose_timings = {
"chair": 0,
"cobra": 0,
"dog": 0,
"plank": 0,
"goddess": 0,
"tree": 0,
"warrior": 0,
"no pose detected": 0,
"fallen": 0,
}
frame_count = 0
while True:
ret, frame = video.read()
if not ret:
if is_live:
console.print(
"[bold red]Error reading from camera. Exiting...[/bold red]"
)
break
# Process the frame
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result = pose_tracker.process(image=frame_rgb)
if result.pose_landmarks is not None:
# Draw landmarks on the frame
mp.solutions.drawing_utils.draw_landmarks(
frame, result.pose_landmarks, mp_pose.POSE_CONNECTIONS
)
frame_height, frame_width = frame.shape[0], frame.shape[1]
pose_landmarks = np.array(
[
[lmk.x * frame_width, lmk.y * frame_height, lmk.z * frame_width]
for lmk in result.pose_landmarks.landmark
],
dtype=np.float32,
)
# Classify the pose
pose_classification = pose_classifier(pose_landmarks)
pose_classification_filtered = pose_classification_filter(
pose_classification
)
# Update pose timings (only for the pose with highest confidence)
max_pose = max(
pose_classification_filtered, key=pose_classification_filtered.get
).lower()
pose_timings[max_pose] += 1 / fps
else:
pose_timings["no pose detected"] += 1 / fps
frame_count += 1
if frame_count % 30 == 0: # Update every 30 frames
panel_content = (
f"[bold]Chair:[/bold] {pose_timings['chair']:.2f}s\n"
f"[bold]Cobra:[/bold] {pose_timings['cobra']:.2f}s\n"
f"[bold]Dog:[/bold] {pose_timings['dog']:.2f}s\n"
f"[bold]Plank:[/bold] {pose_timings['plank']:.2f}s\n"
f"[bold]Goddess:[/bold] {pose_timings['goddess']:.2f}s\n"
f"[bold]Tree:[/bold] {pose_timings['tree']:.2f}s\n"
f"[bold]Warrior:[/bold] {pose_timings['warrior']:.2f}s\n"
f"---\n"
f"[bold]No pose detected:[/bold] {pose_timings['no pose detected']:.2f}s\n"
f"[bold]Fallen:[/bold] {pose_timings['fallen']:.2f}s"
)
if not is_live:
panel_content += f"\n\nProcessed {frame_count}/{total_frames} frames"
layout["main"].update(
Panel(
panel_content,
title="Classification Results",
border_style="bold green",
)
)
console.print(layout)
if display:
cv2.imshow("Video", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
video.release()
if display:
cv2.destroyAllWindows()
# Final results
final_panel_content = (
f"[bold]Chair:[/bold] {pose_timings['chair']:.2f}s\n"
f"[bold]Cobra:[/bold] {pose_timings['cobra']:.2f}s\n"
f"[bold]Dog:[/bold] {pose_timings['dog']:.2f}s\n"
f"[bold]Plank:[/bold] {pose_timings['plank']:.2f}s\n"
f"[bold]Goddess:[/bold] {pose_timings['goddess']:.2f}s\n"
f"[bold]Tree:[/bold] {pose_timings['tree']:.2f}s\n"
f"[bold]Warrior:[/bold] {pose_timings['warrior']:.2f}s\n"
f"---\n"
f"[bold]No pose detected:[/bold] {pose_timings['no pose detected']:.2f}s\n"
f"[bold]Fallen:[/bold] {pose_timings['fallen']:.2f}s"
)
layout["main"].update(
Panel(
final_panel_content,
title="Final Classification Results",
border_style="bold green",
)
)
console.print(layout)
if output_file:
console.print(f"[green]Output saved to: {output_file}[/green]")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Classify poses in a video file or from live camera."
)
parser.add_argument("input", help="Input video file or 'live' for camera feed")
parser.add_argument(
"--display", action="store_true", help="Display the video with detected poses"
)
parser.add_argument("--output", help="Output video file")
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(1)
args = parser.parse_args()
main(args.input, args.display, args.output)
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