树莓派使用PaddleX做物体分类

1.先使用百度AI运行代码。参考/aistudio/projectdetail/2160041链接网址，从而得到模型。但是paddlex运行得到的模型不能直接在树莓派上跑。所以进行第二步。

2.把模型转换成paddle-lite支持的模型。在百度studio，上一步的代码里运行

paddle_lite_opt --model_fie=你的模型途径

--param_file=你的权值途径

--valid_targets=arm

--optimize_out_type=naive_buffer

--optimize_out=你要的输出nb模型的途径和名称

3.执行以下分类代码，修改属于你的参数

from paddlelite.lite import *

import cv2

import numpy as np

import sys

import time

from PIL import Image

from PIL import ImageFont

from PIL import ImageDraw

# 加载模型

def create_predictor(model_dir):

config = MobileConfig()

config.set_model_from_file(model_dir)

predictor = create_paddle_predictor(config)

return predictor

#图像归一化处理

def process_img(image, input_image_size):

origin = image

img = origin.resize(input_image_size, Image.BILINEAR)

resized_img = img.copy()

if img.mode != 'RGB':

img = img.convert('RGB')

img = np.array(img).astype('float32').transpose((2, 0, 1)) # HWC to CHW

img -= 127.5

img *= 0.007843

img = img[np.newaxis, :]

return origin,img

# 预测

def predict(image, predictor, input_image_size):

#输入数据处理

input_tensor = predictor.get_input(0)

input_tensor.resize([1, 3, input_image_size[0], input_image_size[1]])

image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA))

origin, img = process_img(image, input_image_size)

image_data = np.array(img).flatten().tolist()

input_tensor.set_float_data(image_data)

#执行预测

predictor.run()

#获取输出

output_tensor = predictor.get_output(0)

print("output_tensor.float_data()[:] : ", output_tensor.float_data()[:])

res = output_tensor.float_data()[:]

return res

# 展示结果

def post_res(label_dict, res):

print(max(res))

target_index = res.index(max(res))

print("结果是:" + " " + label_dict[target_index])

if __name__ == '__main__':

# 初始定义

label_dict = {0:"metal", 1:"paper", 2:"plastic", 3:"glass"}

image = "./test_pic/images_orginal/glass/glass300.jpg"

model_dir = "./trained_model/ResNet50_trash_x86_model.nb"

image_size = (224, 224)

# 初始化

predictor = create_predictor(model_dir)

# 读入图片

image = cv2.imread(image)

# 预测

res = predict(image, predictor, image_size)

# 显示结果

post_res(label_dict, res)

cv2.imshow("image", image)

cv2.waitKey()