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import tempfile
import cv2
import dlib
import numpy as np
from scipy.spatial import distance as dist
from imutils import face_utils
import gradio as gr
def detect_head_posture(video_path):
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("assets/models/shape_predictor_68_face_landmarks.dat")
cap = cv2.VideoCapture(video_path)
frame_width, frame_height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
with tempfile.NamedTemporaryFile(delete=False, suffix='.avi') as temp_file:
out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (frame_width, frame_height))
posture_data = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
for rect in detector(gray, 0):
shape = face_utils.shape_to_np(predictor(gray, rect))
jaw_width = dist.euclidean(shape[1], shape[15])
jaw_height = dist.euclidean(shape[8], (shape[1] + shape[15]) / 2)
posture = "Upright" if jaw_height / jaw_width > 0.5 else "Slumped"
posture_data.append(posture)
for (x, y) in shape:
cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
out.write(frame)
cap.release()
out.release()
posture_type = max(set(posture_data), key=posture_data.count)
return temp_file.name, posture_type
def create_head_posture_tab():
with gr.Row():
with gr.Column(scale=1):
input_video = gr.Video(label="Input Video")
with gr.Row():
clear_btn = gr.Button("Clear")
submit_btn = gr.Button("Analyze", elem_classes="submit")
with gr.Column(scale=1, elem_classes="dl4"):
output_video = gr.Video(label="Processed Video", elem_classes="video2")
output_posture = gr.Label(label="Posture Type")
submit_btn.click(detect_head_posture, inputs=input_video, outputs=[output_video, output_posture], queue=True)
clear_btn.click(lambda: (None, None, None), outputs=[input_video, output_video, output_posture], queue=True)
gr.Examples(["./assets/videos/fitness.mp4"], inputs=[input_video]) |