我使用了Google的一个示例模型，并使用我的数据集对其进行了训练，数据集中的图像与使用该模型的图像相同，但是并不能很好地识别它们。
我为此使用的模型位于this link上。
我已经添加了更多照片，但是没有用。
在这里，我粘贴了用于对图像进行分类的代码。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import serial
import time
from camara import tomarfoto
from db import update_metal,update_papel, update_basura, update_carton, update_carton, update_vidrio,update_plastico, update_reciclable
import argparse
import sys
import time
import numpy as np
import tensorflow as tf
#arduino connection
#arduinoData = serial.Serial('COM3', 115200)
#object recognition function
def reconocer():
respuestaSensor = 0
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.compat.v1.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
input_mean=0, input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.io.read_file(file_name, input_name)
if file_name.endswith(".png"):
image_reader = tf.image.decode_png(file_reader, channels = 3,
name='png_reader')
elif file_name.endswith(".gif"):
image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
name='gif_reader'))
elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
else:
image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
name='jpeg_reader')
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0);
resized = tf.compat.v1.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.compat.v1.Session()
result = sess.run(normalized)
return result
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.io.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
if __name__ == "__main__":
model_file = "tf_files/retrained_graph.pb"
label_file = "tf_files/retrained_labels.txt"
input_height = 299
input_width = 299
input_mean = 128
input_std = 128
input_layer = "Mul"
output_layer = "final_result"
parser = argparse.ArgumentParser()
##parser.add_argument("--image", help="image to be processed")
##parser.add_argument("--graph", help="graph/model to be executed")
##parser.add_argument("--labels", help="name of file containing labels")
parser.add_argument("--input_height", type=int, help="input height")
parser.add_argument("--input_width", type=int, help="input width")
parser.add_argument("--input_mean", type=int, help="input mean")
parser.add_argument("--input_std", type=int, help="input std")
parser.add_argument("--input_layer", help="name of input layer")
parser.add_argument("--output_layer", help="name of output layer")
args = parser.parse_args()
tomarfoto()
##if args.graph:
model_file = ('./../tf_files/retrained_graph.pb')
##if args.image:
file_name = ('./foto.jpg')
##if args.labels:
label_file = ('./../tf_files/retrained_labels.txt')
if args.input_height:
input_height = args.input_height
if args.input_width:
input_width = args.input_width
if args.input_mean:
input_mean = args.input_mean
if args.input_std:
input_std = args.input_std
if args.input_layer:
input_layer = args.input_layer
if args.output_layer:
output_layer = args.output_layer
graph = load_graph(model_file)
t = read_tensor_from_image_file(file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name);
output_operation = graph.get_operation_by_name(output_name);
with tf.compat.v1.Session(graph=graph) as sess:
start = time.time()
results = sess.run(output_operation.outputs[0],
{input_operation.outputs[0]: t})
end=time.time()
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
template = "{} (score={:0.5f})"
out_classes = np.array([])
count = 0
for i in top_k:
classes = (labels[i], results[i])
out_classes = np.append(out_classes, classes)
count += 1
out_classes = out_classes.reshape(count, 2)
print(out_classes)
length = len(out_classes)-1
clase = out_classes[0,0]
if(clase == "basura"):
tipo = "b"
#send to the arduino
#arduinoData.write(b'b')
update_basura()
print("El objeto es: " + clase + " , "+ tipo )
else:
tipo = "r"
#manda al arduino
#arduinoData.write(b'r')
update_reciclable()
print("El objeto es: " + clase + " , "+ tipo )
if clase == "carton":
update_carton()
elif clase == "papel":
update_papel()
elif clase == "vidrio":
update_vidrio()
elif clase == "plastico":
update_plastico()
elif clase == "metal":
update_metal()
time.sleep(1)
while True:
#respuestaSensor = str(arduinoData.readline().decode().strip("\r\n"))
#print(respuestaSensor)
#if (respuestaSensor == "Apretado"):
#reconocer()
respuestaSensor = input()
if (respuestaSensor == "si"):
reconocer()
elif (respuestaSensor == "end"):
break