mxnet导入图像数据

图像的标签在一个json文件中。

%matplotlib inline
import json
import gluonbook as gb
import mxnet as mx
from mxnet import autograd, gluon, image, init, nd
from mxnet.gluon import data as gdata, loss as gloss, utils as gutils
import sys
from time import time

train_Pedestrian_url = []
train_Cyclist_url = []
train_Others_url = []

with open('instances.json',encoding='utf-8') as f:
    for _ in range(100000):
        if len(train_Pedestrian_url) + len(train_Cyclist_url) + len(train_Others_url) >= 300:
            break
        line = f.readline()
        js = json.loads(line)
        if js['attrs']['ignore']=='yes' or js['attrs']['occlusion']=='heavily_occluded' or js['attrs']['occlusion']=='invisible':
            continue
        if js['attrs']['type'] == 'Pedestrian':
            if len(train_Pedestrian_url) >=100:
                continue
            train_Pedestrian_url.append(js['thumbnail_path'])
        elif js['attrs']['type'] == 'Cyclist':
            if len(train_Cyclist_url) >=100:
                continue
            train_Cyclist_url.append(js['thumbnail_path'])
        elif js['attrs']['type'] == 'Others':
            if len(train_Others_url) >=100:
                continue
            train_Others_url.append(js['thumbnail_path'])
        # img = image.imread(url)

    f.close()

print(train_Cyclist_url)
print(len(train_Pedestrian_url),len(train_Cyclist_url),len(train_Others_url))

img = image.imread('/mnt/hdfs-data-4/data/'+train_Cyclist_url[0])
img.astype('float32')

labels = nd.zeros(shape=(30000,))
labels[10000:20000] = 1
labels[20000:] = 2

数据整理就差不多了，然后就是建网络，跑模型了。

转载于:https://www.cnblogs.com/TreeDream/p/10059551.html