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import gradio as gr
import cv2
import mediapipe as mp
import torch
import numpy as np
import tempfile
from transformers import pipeline
from PIL import Image
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
# Hugging Face pretrained model for action recognition
action_model = pipeline(
"image-classification",
model="rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224"
)
def detect_pose_and_activity(video_file):
"""
Process the uploaded video to detect human poses and classify activity.
Video is limited to 10 seconds. Returns annotated video and predicted action.
"""
try:
# Save uploaded video temporarily
temp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
temp_video.write(open(video_file, "rb").read())
temp_video.close()
cap = cv2.VideoCapture(temp_video.name)
if not cap.isOpened():
return None, "Error: Could not open video."
fps = cap.get(cv2.CAP_PROP_FPS)
if fps == 0:
fps = 30 # fallback
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
max_frames = int(min(total_frames/fps, 10) * fps) # limit 10s
output_frames = []
action_predictions = []
# Process frames
with mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5) as pose:
for _ in range(max_frames):
ret, frame = cap.read()
if not ret:
break
# Pose detection
image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(image_rgb)
if results.pose_landmarks:
mp.solutions.drawing_utils.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
output_frames.append(frame)
# Convert frame to PIL image for Hugging Face model
pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
pred = action_model(pil_image)
action_predictions.append(pred[0]['label'])
cap.release()
if len(output_frames) == 0:
return None, "Error: No frames to process."
# Take the most frequent predicted action
action_label = max(set(action_predictions), key=action_predictions.count)
# Save annotated video
output_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
height, width, _ = output_frames[0].shape
out = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
for f in output_frames:
out.write(f)
out.release()
return output_file, f"Predicted Action: {action_label}"
except Exception as e:
return None, f"Runtime Error: {str(e)}"
# Gradio Interface
iface = gr.Interface(
fn=detect_pose_and_activity,
inputs=gr.Video(label="Upload a Video (max 10s)"),
outputs=[gr.Video(label="Pose Detection Output"), gr.Textbox(label="Detected Action")],
title="Human Pose & Activity Recognition",
description="Upload a short video (max 10s). The app detects human poses and predicts the activity (e.g., dancing, cycling, running)."
)
iface.launch() |