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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]) |