tfliteをマルチコア（ラズパイ５）で動かした時の性能改善

# work on Flask server
# server will be activated only when client request is occured
#
#
from flask import Flask, jsonify
import cv2
import numpy as np
import tflite_runtime.interpreter as tflite
from picamera2 import Picamera2
from waitress import serve

app = Flask(__name__)

# モデル・ラベル初期化
interpreter = tflite.Interpreter(model_path="efficientdet_lite0.tflite", num_threads=4)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
with open("coco_labels.txt", "r") as f:
    labels = [line.strip() for line in f.readlines()]

# カメラ初期化
picam2 = Picamera2()
picam2.preview_configuration.main.size = (640, 480)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()

def preprocess_image(image):
    resized = cv2.resize(image, (320, 320))
    resized = resized[:, :, [2, 1, 0]]  # BGR→RGB
    return np.expand_dims(resized, axis=0).astype(np.uint8)

def postprocess_results(boxes, scores, classes, count, image_shape, labels):
    detections = []
    for i in range(count):
        if scores[i] > 0.4:
            ymin, xmin, ymax, xmax = boxes[i]
            left, right, top, bottom = (
                int(xmin * image_shape[1]),
                int(xmax * image_shape[1]),
                int(ymin * image_shape[0]),
                int(ymax * image_shape[0])
            )
            detections.append({
                'box': [left, top, right, bottom],
                'class_id': int(classes[i]),
                'score': float(scores[i]),
                'label': labels[int(classes[i])] if int(classes[i]) < len(labels) else f"id:{int(classes[i])}"
            })
    return detections

def detect_once():
    frame = picam2.capture_array()
    input_data = preprocess_image(frame)
    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    boxes = interpreter.get_tensor(output_details[0]['index'])[0]
    classes = interpreter.get_tensor(output_details[1]['index'])[0]
    scores = interpreter.get_tensor(output_details[2]['index'])[0]
    count = int(interpreter.get_tensor(output_details[3]['index'])[0])
    detections = postprocess_results(boxes, scores, classes, count, frame.shape, labels)
    return detections

@app.route('/detect', methods=['GET'])
def detect_route():
    detections = detect_once()
    return jsonify(detections)

if __name__ == '__main__':
	# when you limit access only from the local machine, use a loopback address instead of 0.0.0.0
    #app.run(host='0.0.0.0', port=5000)
    serve(app, host='0.0.0.0', port=5000)

#
# test script for image detect function(tflite server)
#

import requests
import time

if __name__ == '__main__':
    time1 = time.time()
    result = requests.get('http://rasp5.local:5000/detect').json()
    time2 = time.time()
    print('func1: {:.3f} sec'.format(time2 - time1))
    # 'label'が'person'を含んでいるかを判定
    person_detected = any(item['label'] == 'person' for item in result)
    
    if person_detected:
        print("Person detected!")
    else:
        print("No person detected.")