Tensorflow + ImageNet Inception-v3 视频图像识别
Tensorflow + ImageNet Inception-v3 摄像头视频图像识别
准备工作
编程环境：python 3.6.1, win7, x64
材料准备：
- Google基于2012年ImageNet
Challenge训练的模型：inception-2015-12-05.tgz - 非官方的python库（官方的python3.6版本还没有，如果是更旧一点的python版本可以查找一下官方的pip或者conda包）http://www.lfd.uci.edu/~gohlke/pythonlibs/。在里面下载：
Pillow-4.2.1-cp36-cp36m-win_amd64.whl
和
VideoCapture-0.9.5-cp36-cp36m-win_amd64.whl
安装
命令行进入到两个包所在目录，安装PIL和VideoCapture
pip install Pillow-4.2.1-cp36-cp36m-win_amd64.whl
pip install VideoCapture-0.9.5-cp36-cp36m-win_amd64.whl可能出现：
PermissionError:[WinError 5] Access is denied:...
以python模块方法调用pip可以解决：
python -m pip install Pillow-4.2.1-cp36-cp36m-win_amd64.whl
python -m pip install VideoCapture-0.9.5-cp36-cp36m-win_amd64.whl将inception-2015-12-05.tgz解压后放在当前目录的一个新的文件夹下，我这里取名model，如果自己起另外的目录名，脚本中对应的FLAGS调用地址也要做出修改。
model 中一共包含5个文件：
classify_image_graph_def.pb
cropped_panda.jpg
imagenet_2012_challenge_label_map_proto.pbtxt
imagenet_synset_to_human_label_map.txt
LICENSE
在python中测试摄像机的拍照、导出照片到jpg格式文件的功能：
from VideoCapture import Device
cam=Device()
cam.saveSnapshot('image_cap.jpg')可能会出现以下错误：
Exception: fromstring() has been removed. Please call frombytes() instead.这个最标准的解决方法应该是更新PIL包？？。。。但是。。我的难道不是最新的吗？？
所以组织上已经决定了，直接改源代码，在VideoCapture包的这个文件里，在报错信息上可以找到：
C:\Anaconda3\lib\site-packages\VideoCapture__init__.py：
......
def getImage(self, timestamp=0, boldfont=0, textpos=default_textpos):
......
if timestamp:
#text = now()
text = time.asctime(time.localtime(time.time()))
buffer, width, height = self.getBuffer()
if buffer:
#im = Image.fromstring
im = Image.frombytes(
'RGB', (width, height), buffer, 'raw', 'BGR', 0, -1)
if timestamp:
......
return im
......
上面这段getImage代码的14行原本是im=Image.fromstring(...)，改成im=Image.frombytes(...)就行
识别程序
这个识别程序原本是tensorflow用来测试用ImageNet训练完成的模型效果的官方测试文件，原本还包含了下载官方训练模型等函数。现将其用不到的部分剔除，并且加上摄像头拍摄的代码：
classify_image.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import re
from VideoCapture import Device
import numpy as np
import tensorflow as tf
import warnings
warnings.filterwarnings("ignore")
FLAGS = tf.app.flags.FLAGS
# classify_image_graph_def.pb:
# Binary representation of the GraphDef protocol buffer.
# imagenet_synset_to_human_label_map.txt:
# Map from synset ID to a human readable string.
# imagenet_2012_challenge_label_map_proto.pbtxt:
# Text representation of a protocol buffer mapping a label to synset ID.
tf.app.flags.DEFINE_string(
'model_dir', 'model',
"""Path to classify_image_graph_def.pb, """
"""imagenet_synset_to_human_label_map.txt, and """
"""imagenet_2012_challenge_label_map_proto.pbtxt.""")
#tf.app.flags.DEFINE_string('image_file', '',
# """Absolute path to image file.""")
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
"""Display this many predictions.""")可以看到程序有2个输入flag:
- model_dir: 存放imageNet训练结果模型的目录，此处为model目录
- num_top_predictions: 输出结果的条数，即图片最有可能匹配的对象输出数目
# pylint: disable=line-too-long
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# pylint: enable=line-too-long
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self,
label_lookup_path=None,
uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
if not uid_lookup_path:
uid_lookup_path = os.path.join(
FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
def load(self, label_lookup_path, uid_lookup_path):
"""Loads a human readable English name for each softmax node.
Args:
label_lookup_path: string UID to integer node ID.
uid_lookup_path: string UID to human-readable string.
Returns:
dict from integer node ID to human-readable string.
"""
if not tf.gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not tf.gfile.Exists(label_lookup_path):
tf.logging.fatal('File does not exist %s', label_lookup_path)
# Loads mapping from string UID to human-readable string
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
p = re.compile(r'[n\d]*[ \S,]*')
for line in proto_as_ascii_lines:
parsed_items = p.findall(line)
uid = parsed_items[0]
human_string = parsed_items[2]
uid_to_human[uid] = human_string
# Loads mapping from string UID to integer node ID.
node_id_to_uid = {}
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
for line in proto_as_ascii:
if line.startswith(' target_class:'):
target_class = int(line.split(': ')[1])
if line.startswith(' target_class_string:'):
target_class_string = line.split(': ')[1]
node_id_to_uid[target_class] = target_class_string[1:-2]
# Loads the final mapping of integer node ID to human-readable string
node_id_to_name = {}
for key, val in node_id_to_uid.items():
if val not in uid_to_human:
tf.logging.fatal('Failed to locate: %s', val)
name = uid_to_human[val]
node_id_to_name[key] = name
return node_id_to_name
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
def run_inference_on_image(image):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
def main(_):
cam=Device()
try:
while True:
cam.saveSnapshot('image_cap.jpg')
image = 'image_cap.jpg'
run_inference_on_image(image)
print('===========================')
except KeyboardInterrupt:
pass
if __name__ == '__main__':
tf.app.run()测试
运行classify_image.py，默认情况下程序会将抓取到的图像保存到当前目录的image_cap.jpg中，并不断覆盖。识别函数会调用这个不断被更新的图片，并输出识别到前5个可能性的类别。
识别的实时性取决于识别算法的运行时间，这个就和计算机的性能有关了，我跑这个程序的这台电脑也没有GPU，用一句话来形容就是卡成PPT。
输出实例：
===========================
computer keyboard, keypad (score = 0.62623)
space bar (score = 0.30992)
typewriter keyboard (score = 0.02321)
mouse, computer mouse (score = 0.00303)
notebook, notebook computer (score = 0.00076)
===========================对应时间。。我把我的破键盘举到了摄像头前。。。
识别还是可以的，不过我试了试正对我本人的识别，就没有什么好的识别结果了，可能这样的图片没有在训练集中出现吧。（最接近的分类是浴帽，摔！）
参考资料
https://stackoverflow.com/questions/38497924/capturing-image-from-webcam-using-python-on-windows
https://stackoverflow.com/questions/31172719/pip-install-access-denied-on-windows
Python 调用摄像头并保存图片 王小涛_同學