minimalistic inference

main.py

import cv2
import numpy as np
import time
from rknnpool import rknnPoolExecutor


def myFunc(rknn_lite, IMG):
    outputs = rknn_lite.inference(inputs=[IMG])
    return outputs


if __name__ == '__main__':
    ori_img = cv2.imread('./space_shuttle_224126.jpg')
    img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
    img = np.expand_dims(img, 0)
    modelPath = "DINOv2g.rknn"
    TPEs = 12
    pool = rknnPoolExecutor(
        rknnModel=modelPath,
        TPEs=TPEs,
        func=myFunc)
    
    frames, loopTime, initTime = 0, time.time(), time.time()
    for j in range(1000):
        pool.put(img)

    for j in range(1001):
        frame, flag = pool.get()
        if flag == False:
            break        
        if j % 100 == 0:
            print("100 images time:\t", (time.time() - loopTime), "sec")
            loopTime = time.time()
    pool.release()