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from flask import Flask, request, jsonify
import cv2
import joblib
import mediapipe as mp
import numpy as np
import tempfile
app = Flask(__name__)
# Load model and label encoder
model = joblib.load("pose_classifier.joblib")
label_encoder = joblib.load("label_encoder.joblib")
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
def predict_pose_from_image(image_bytes):
# Convert image bytes to numpy array
nparr = np.frombuffer(image_bytes, np.uint8)
frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if frame is None:
return None, "Invalid image"
# Convert to RGB
img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Run MediaPipe Pose
results = pose.process(img_rgb)
if results.pose_landmarks:
landmarks = results.pose_landmarks.landmark
pose_data = [j.x for j in landmarks] + [j.y for j in landmarks] + \
[j.z for j in landmarks] + [j.visibility for j in landmarks]
pose_data = np.array(pose_data).reshape(1, -1)
y_pred = model.predict(pose_data)
predicted_label = label_encoder.inverse_transform(y_pred)[0]
return predicted_label, None
else:
return None, "No pose detected"
@app.route('/predict-pose', methods=['POST'])
def predict_pose():
if 'frame' not in request.files:
return jsonify({"error": "No image frame uploaded"}), 400
file = request.files['frame']
img_bytes = file.read()
label, error = predict_pose_from_image(img_bytes)
if error:
return jsonify({"error": error}), 400
return jsonify({"predicted_pose": label})
if __name__ == "__main__":
app.run(debug=True, port=5007)
# curl -X POST -F "frame=@your_image.jpg" http://localhost:5007/predict-pose |