File size: 2,745 Bytes
4fb6ca0 77123ea 4fb6ca0 77123ea 4fb6ca0 77123ea cb20121 77123ea 4fb6ca0 77123ea 4fb6ca0 77123ea 4fb6ca0 cb20121 4fb6ca0 77123ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import streamlit as st
import cv2
import tempfile
import numpy as np
from ultralytics import YOLO, solutions
# Load the YOLOv8 model
model = YOLO("yolov8n-pose.pt")
# Streamlit App
st.title("Workout Monitoring App")
st.write("Upload a video to monitor your ab workout.")
uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"])
if uploaded_file is not None:
# Save the uploaded video to a temporary file
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
tfile.close()
# Load the video with OpenCV
cap = cv2.VideoCapture(tfile.name)
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Initialize AIGym object
gym_object = solutions.AIGym(
line_thickness=2,
view_img=False, # Set to False since we are using Streamlit to display
pose_type="abworkout", # Use 'abworkout' as the pose type
kpts_to_check=[6, 8, 10],
)
# List to store processed frames
processed_frames = []
# Streamlit progress bar
progress_bar = st.progress(0)
frame_count = 0
# Process the video frame by frame
st.write("Analyzing video... Please wait.")
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
results = model.track(im0, verbose=False) # Tracking recommended
im0 = gym_object.start_counting(im0, results)
# Resize the frame to 320x320
im0_resized = cv2.resize(im0, (1024, 1024))
# Append the processed frame to the list
processed_frames.append(im0_resized)
# Update progress bar
frame_count += 1
progress_bar.progress(frame_count / total_frames)
cap.release()
cv2.destroyAllWindows()
# Create a temporary file to save the processed video
output_video_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
output_video_path = output_video_file.name
# Write the processed frames to a video file
video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (1024, 1024))
for frame in processed_frames:
video_writer.write(frame)
video_writer.release()
# Display the processed video in Streamlit
st.write("Analysis complete. Displaying processed video:")
st.video(output_video_path)
# Provide a download link for the processed video
st.write("Download the processed video:")
with open(output_video_path, "rb") as video_file:
st.download_button("Download", video_file, "workouts.mp4")
|