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：
1. 训练模型的基础
2. 提高模型性能
3. 降低误判和误诊分险
4. 减少资源浪费
自动标注SAM的使用
模型切换
模型部署
代码语言：javascript复制# -*- 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/
