工业视觉缺陷检测的工作流程

常用异常检测算法

面临的挑战及发展

图像分割的数据标注

数据标注准确的重要性：

[if !supportLists]1. [endif]训练模型的基础

[if !supportLists]2. [endif]提高模型性能

[if !supportLists]3. [endif]降低误判和误诊分险

[if !supportLists]4. [endif]减少资源浪费

自动标注SAM的使用

模型切换

模型部署

# -*- coding: UTF-8 -*-

import aidlite_gpu

import cv2

import os

import time

import numpy as np

from PIL import Image

import matplotlib.pyplot as plt

def mask_to_image(mask: np.ndarray):

    if mask.ndim == 2:

        return Image.fromarray((mask * 255).astype(np.uint8))

    elif mask.ndim == 3:

        return Image.fromarray((np.argmax(mask, axis=0) * 255 / mask.shape[0]).astype(np.uint8))

def aidlux_tflite_infer(model_path, img_path, save_path):

# step1:初始化aidlite类并创建aidlite对象

    aidlite = aidlite_gpu.aidlite()

    print('model initial success!!')

# step2:加载模型

    inp_shape = [256*256*1*4]

    out_shape = [256*256*2*4]

    value = aidlite.ANNModel(model_path, inp_shape, out_shape, 4, 0)

# step3:传入模型输入数据

    img = cv2.imread(img_path, 0)

    img = cv2.resize(img, (256, 256))

    img = img[np.newaxis, ...]

    img = img / 255.0

    img = np.expand_dims(img, axis=0)

    img = img.astype(dtype=np.float32)

    print("image shape is ", img.shape)

    aidlite.setInput_Float32(img)

# step4:执行推理

    start = time.time()

    aidlite.invoke()

    end = time.time()

    print("infer time(ms):{0}", 1000 * (end - start))

# step5:获取输出

    pred = aidlite.getOutput_Float32(0)

# step6:后处理

    pred = np.array(pred)

    pred = np.reshape(pred,(2,256,256))

    mask_img = mask_to_image(pred)

    mask_img.save(save_path)

    # mask_img = np.array(mask_img)  

    # cv2.imshow('mask_img', mask_img)

    # cv2.waitKey(0)

    # cv2.destroyAllWindows()

if __name__ == '__main__':

    model_path = "/home/dataset2aidlux/unetmodel_fp32.tflite"

    img_path = "/home/dataset2aidlux/test_imgs/0597.PNG"

    save_path = '/home/dataset2aidlux/test_imgs/result_0597.png'

    aidlux_tflite_infer(model_path, img_path, save_path)

效果视频：

基于Aidlux的语义分割模型转换：https://www.bilibili.com/video/BV1K64y1j7SB/

基于Aidlux的语义分割模型部署：https://www.bilibili.com/video/BV19u4y1c7k7/