handwriting/openvino_handwriting/predict.py

from openvino.inference_engine import IECore

import cv2 as cv
import numpy as np
import time

import sys
from util import ResizeImage,CropImage,ToTensor,NormalizeImage

model_xml = "IR/handwriting.xml"
model_bin = "IR/handwriting.bin"

onnx_model = "onnx/handwriting.onnx"
image_file = "test/test3.jpg"

top_k = 10 

# 图像预处理，转为Tensor张量
def preprocess(img):
    resize_op = ResizeImage(resize_short=256)
    img = resize_op(img)
    crop_op = CropImage(size=(224, 224))
    img = crop_op(img)
    img_mean = [0.485, 0.456, 0.406]
    img_std = [0.229, 0.224, 0.225]
    img_scale = 1.0 / 255.0
    normalize_op = NormalizeImage(
        scale=img_scale, mean=img_mean, std=img_std)
    img = normalize_op(img)
    tensor_op = ToTensor()
    img = tensor_op(img)
    return img

#标签文件
label_file = "labels.txt"

# 读取标签文件
def readLabels():
    index = 0
    labels=[]
    with open(label_file) as file_obj:
        for line in file_obj:
            labels.append(line.strip())
            index=index+1
    return labels

labels = readLabels()

DEVICE = 'CPU'
#初始化插件，输出插件版本号
ie = IECore()
devices = ie.available_devices
print(devices)
ver = ie.get_versions(DEVICE)[DEVICE]
print("{descr}: {maj}.{min}.{num}".format(descr=ver.description, maj=ver.major, min=ver.minor, num=ver.build_number))

#读取IR模型文件
#  net = ie.read_network(model=model_xml, weights=model_bin)
net = ie.read_network(model=onnx_model)

#准备输入输出张量MYRIAD
print("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = 1

#载入模型到AI推断计算设备
print("Loading IR to the plugin...")
exec_net = ie.load_network(network=net, num_requests=1, device_name=DEVICE)
frame = cv.imread(image_file)[:,:,::-1]
# frame = cv.resize(frame, (64,64))
# deal
n, c, h, w = net.inputs[input_blob].shape
initial_h, initial_w, channels = frame.shape
#按照AI模型要求放缩图片
image = cv.resize(frame, (w, h))  
image = preprocess(image)

print("Batch size is {}".format(n))
#执行推断计算
print("Starting inference in synchronous mode")
start = time.time()
res = exec_net.infer(inputs={input_blob: image})
end = time.time()
print("Infer Time:{}ms".format((end-start)*1000))
# 处理输出
print("Processing output blob")
res = res[out_blob]

result = zip(res[0].tolist(), labels)
result2 = sorted(result, reverse=True)

candidate = list(result2[0:top_k])
for c in candidate:
    print(c[1])