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a00e120 711ee3f a00e120 711ee3f a00e120 | 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 | import joblib
import cv2
import numpy as np
import mediapipe as mp
import gradio as gr
import matplotlib.pyplot as plt
import io
from PIL import Image
model = joblib.load("image_model.pkl")
action_labels = {0: "Weightlifting", 1: "Soccer", 2: "Handball"}
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5)
mp_drawing = mp.solutions.drawing_utils
def process_frame(frame):
image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(image_rgb)
if results.pose_landmarks:
mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
resized = cv2.resize(image_rgb, (128, 128))
input_data = resized.flatten().reshape(1, -1)
prediction_proba = model.predict_proba(input_data)[0]
predicted_class = np.argmax(prediction_proba)
prediction = action_labels.get(predicted_class, "Unknown")
confidence = prediction_proba[predicted_class] * 100
label = f'{prediction} ({confidence:.2f}%)'
# 그래프 생성
fig, ax = plt.subplots()
ax.bar(action_labels.values(), prediction_proba, color='skyblue')
ax.set_ylim([0, 1])
ax.set_ylabel("Confidence")
ax.set_title("Prediction Probabilities")
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
graph_img = Image.open(buf).convert("RGB")
plt.close()
cv2.putText(frame, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
return frame, label, graph_img
demo = gr.Interface(
fn=process_frame,
inputs=gr.Image(source="webcam", streaming=True),
outputs=[
gr.Image(label="Pose + Prediction"),
gr.Textbox(label="Predicted Action"),
gr.Image(label="Confidence Graph")
],
live=True,
title="Pose Action Classifier",
description="Real-time action prediction using MediaPipe and a trained model."
)
demo.launch() |