detection

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# StrictVersion：版本号比较
# A version number consists of two or three dot-separated numeric components, with an optional "pre-release" tag on the end. The pre-release tag consists of the letter 'a' or 'b' followed by a number.  
# If the numeric components of two version numbers are equal, then one with a pre-release tag will always be deemed earlier (lesser) than one without.
# 由小到大的版本号：
    0.4       0.4.0  (these two are equivalent)
    0.4.1
    0.5a1
    0.5b3
    0.5
    0.9.6
    1.0
    1.0.4a3
    1.0.4b1
    1.0.4
>>> from distutils.version import StrictVersion
>>> import tensorflow as tf
>>> StrictVersion(tf.__version__) < StrictVersion('1.9.0')  # True/False

# sys.path：a list of strings that determines the interpreter’s search path for modules. 
>>> import sys
>>> sys.path.append("..")  # 将上一级目录添加至搜索路径

# urllib & tarfile
>>> import six.moves.urllib as urllib
>>> import tarfile
>>> import os
>>> opener = urllib.request.URLopener()
>>> opnner.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
>>> tar_file = tarfile.open(MODEL_FILE)
>>> for file in tar_file.getmembers():
        file_name = os.path.basename(file.name)
		if 'frozen_inference_graph.pb' in file_name：
		    tar_file.extract(file, os.getcwd())  # 当前工作路径
# os.path.split(path) >>>  (head, tail)
# os.path.split(home/wenny/aaa) >>> (home/wenny, aaa)
# os.path.split(home/wenny/aaa/) >>> (home/wenny/aaa, '')
# os.path.dirname(path) >>> os.path.split(path)的第一个部分head
# os.path.basename(path) >>> os.path.split(path)的第二个部分tail

# GraphDef：how the other languages transform the Graph to C++? They use a tool called protobuf which can generate specific language stubs, that's where the GraphDef come from. It's a serialized version of Graph.
>>> detection_graph = tf.Graph()
>>> with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
		with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
		    serialized_graph = fid.read()
			od_graph_def.ParseFromString(serialized_graph)
			tf.import_graph_def(od_graph_def, name='')
			
# numpy.expand_dims(a, axis) 增加一维
# Insert a new axis, corresponding to a given position in the array shape.
>>> x = np.array([1,2])
>>> x.shape
(2,)
>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1,2]])
>>> y.shape
(1,2)

# PIL.Image
>>> from PIL import Image
>>> import numpy as np
>>> image = Image.open(image_path)
>>> (im_width, im_height) = image.size
>>> if image.format == 'PNG':
        image = image.convert('RGB')
>>> image_np = np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
>>> image_np_expanded = np.expand_dims(image_np, axis=0)