def __init__(self):
self.refuse_classification = RefuseClassification()
self.init_classify_image_model()
self.node_lookup = NodeLookup(
uid_chinese_lookup_path=
'./data/imagenet_2012_challenge_label_chinese_map.pbtxt',
model_dir='/tmp/imagenet')
class RafuseRecognize():
def __init__(self):
self.refuse_classification = RefuseClassification()
self.init_classify_image_model()
self.node_lookup = NodeLookup(
uid_chinese_lookup_path=
'./data/imagenet_2012_challenge_label_chinese_map.pbtxt',
model_dir='/tmp/imagenet')
def init_classify_image_model(self):
create_graph('/tmp/imagenet')
self.sess = tf.Session()
self.softmax_tensor = self.sess.graph.get_tensor_by_name('softmax:0')
def recognize_image(self, image_data):
predictions = self.sess.run(self.softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-5:][::-1]
result_list = []
for node_id in top_k:
human_string = self.node_lookup.id_to_string(node_id)
# print(human_string)
human_string = ''.join(
list(set(human_string.replace('，', ',').split(','))))
# print(human_string)
classification = self.refuse_classification.predict(human_string)
result_list.append('%s => %s' % (human_string, classification))
return '\n'.join(result_list)
class RafuseRecognize():
def __init__(self):
self.refuse_classification = RefuseClassification()
self.init_classify_image_model()
self.node_lookup = NodeLookup(uid_chinese_lookup_path='./data/imagenet_2012_challenge_label_chinese_map.pbtxt',
model_dir = '/tmp/imagenet')#将中文对照表转换为人类可读ID
def init_classify_image_model(self):
create_graph('/tmp/imagenet')#从保存的GraphDef文件创建一个图形并返回一个保存程序。
self.sess = tf.Session()#建立会话
self.softmax_tensor = self.sess.graph.get_tensor_by_name('softmax:0')
def recognize_image(self, image_data):
predictions = self.sess.run(self.softmax_tensor,#执行该变量
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)#从数组的形状中删除单维度条目，即把shape中为1的维度去掉
top_k = predictions.argsort()[-5:][::-1]#从小到大排序
result_list = []
for node_id in top_k:
human_string = self.node_lookup.id_to_string(node_id)#转换为string类型
#print(human_string)
human_string = ''.join(list(set(human_string.replace('，', ',').split(','))))#添加分隔符
#print(human_string)
classification = self.refuse_classification.predict(human_string)#对用户输入的进行分类
result_list.append('%s => %s' % (human_string, classification))#多种结果产生的列表
return '\n'.join(result_list)
def text():
test = RefuseClassification()
res = test.predict(entry2.get())
a2.set(res)
def main1():
test = RefuseClassification()
res = test.predict(entry_user_pd.get())
var_user_pd.set(res)
