def extract(img_path, img_classes, param):
"""
Function that performs the extraction of an image using the CCOM
descriptor.
This function transforms the image being extracted to the desired image
format of the descriptor, performs the extraction of the image, and last,
transforms the output feature vector of the descriptor into the standard
output of the framework, a list of floats.
"""
print "Descriptor: CCOM"
#CONSTANTS
#Number of executions of the extraction
NUM_EXEC = 1
#PATHS
#Path for the folder containing the descriptor executable
##Not necessary in case that the extraction is present in the plugin code
descriptor_path = os.path.dirname(__file__)
#Path for the temporary folder, where the file with the feature vector
#extracted from the image will be saved
##Consider the path with the origin on the directory of the plugin
##(os.path.dirname(__file__)) to avoid problems with the directory where
##the framework is being executed
path_temp = os.path.join(os.path.dirname(__file__), "..", "..", "temp")
if not os.path.isdir(path_temp):
os.makedirs(path_temp)
print img_path, "being extract in the process ", os.getpid()
#Temporary name of the image in the desired image format
list_classes = ""
for name_class in img_classes:
list_classes += str(name_class) + "_"
list_names = img_path.split(os.sep)
img_name = list_classes + list_names[-2] + "_" + list_names[-1]
#Convert the image to the desired format of the descriptor
temp_img_path, converted = util.convert_desired_format(
img_path, img_name, "PPM")
if converted:
print "\tImage converted to PPM"
#Path of the file with the feature vector of an image
fv_path = os.path.join(path_temp, img_name + ".fv")
# Extraction of the feature vector
if not os.path.exists(fv_path):
system_platform = [platform.system(), platform.architecture()[0]]
if system_platform[0] == 'Linux':
if system_platform[1] == '32bit':
plugin_name = 'ccom_32l.so'
else:
plugin_name = 'ccom_64l.so'
else:
plugin_name = 'ccom_64l.so'
#Extraction of the feature vector
if not os.path.exists(fv_path):
setup = """
ctypes = __import__('ctypes')
plugin = "%s"
lib = ctypes.CDLL("%s%s" + plugin)
img_path = "%s"
fv_path = "%s"
""" % (plugin_name, descriptor_path, os.sep, temp_img_path, fv_path)
cmd = """
lib.Extraction(img_path, fv_path)
"""
t = timeit.Timer(stmt=cmd, setup=setup)
try:
t.timeit(number=NUM_EXEC)
print "\tFeature vector extracted"
except:
t.print_exc()
#Remove the temporary image
if converted:
os.remove(temp_img_path)
#Transforms the feature vector of the descriptor into the standard output
#of the framework
fv = fv_transform(fv_path)
return img_path, len(img_classes), img_classes, fv
def extract(img_path, img_classes, param):
"""
Function that performs the extraction of an image using the CEDD
descriptor.
This function transforms the image being extracted to the desired image
format of the descriptor, performs the extraction of the image, and last,
transforms the output feature vector of the descriptor into the standard
output of the framework, a list of floats.
"""
print "Descriptor: CEDD"
#CONSTANTS
#Number of executions of the extraction
NUM_EXEC = 1
#PATHS
sys.path.append(os.path.dirname(__file__))
#Path for the folder containing the descriptor executable
##Not necessary in case that the extraction is present in the plugin code
descriptor_path = os.path.dirname(__file__)
#Path for the temporary folder, where the file with the feature vector
#extracted from the image will be saved
##Consider the path with the origin on the directory of the plugin
##(os.path.dirname(__file__)) to avoid problems with the directory where
##the framework is being executed
path_temp = os.path.join(os.path.dirname(__file__), "..", "..", "temp")
if not os.path.isdir(path_temp):
os.makedirs(path_temp)
print img_path, "being extract in the process ", os.getpid()
#Temporary name of the image in the desired image format
list_classes = ""
for name_class in img_classes:
list_classes += str(name_class) + "_"
img_name = list_classes + img_path.split(os.sep)[-1]
#Convert the image to the desired format of the descriptor
temp_img_path, converted = util.convert_desired_format(
img_path, img_name, "JPG")
if converted:
print "\tImage converted to JPG"
#Path of the file with the feature vector of an image
#CEDD does not need a file to write the feature vector
#Extraction of the feature vector
#-------------------------------------------------------------------------
#Compile the .java files
# Change the current directory to the directory of the files
orig_dir = os.getcwd()
os.chdir(os.path.dirname(__file__))
os.system("javac CEDD.java")
print "\tJAVA files compiled"
#Call subprocess jython
jython_args = ['jython', 'jython_cedd.py', 'Extraction']
jython_args.append(temp_img_path)
jython_args.append(str(param))
import subprocess
cedd_process = subprocess.Popen(jython_args, stdout=subprocess.PIPE)
print "\t", cedd_process
cedd_extract = cedd_process.communicate()[0]
# cedd_process.wait()
fv = util.fv_string_to_list(cedd_extract)
# Return to the original directory
os.chdir(orig_dir)
#-------------------------------------------------------------------------
#Remove the temporary image
if converted:
os.remove(temp_img_path)
#Transforms the feature vector of the descriptor into the standard output
#of the framework
#Not necessary. The return of the getDoubleHistogram is already list of
#floats
return img_path, len(img_classes), img_classes, fv
def extract(img_path, img_classes, param):
"""
Function that performs the extraction of an image using the GIST
descriptor.
This function transforms the image being extracted to the desired image
format of the descriptor, performs the extraction of the image, and last,
transforms the output feature vector of the descriptor into the standard
output of the framework, a list of floats.
"""
print "Descriptor: GIST"
#CONSTANTS
#Number of executions of the extraction
NUM_EXEC = 1
#PARAMETERS
cell_size = param["Cell Size"]
orientations = param["Orientations"]
#PATHS
#Path for the folder containing the descriptor executable
##Not necessary in case that the extraction is present in the plugin code
descriptor_path = os.path.dirname(__file__)
#Path for the temporary folder, where the file with the feature vector
#extracted from the image will be saved
##Consider the path with the origin on the directory of the plugin
##(os.path.dirname(__file__)) to avoid problems with the directory where
##the framework is being executed
path_temp = os.path.join(os.path.dirname(__file__), "..", "..", "temp")
if not os.path.isdir(path_temp):
os.makedirs(path_temp)
print img_path, "being extract in the process ", os.getpid()
#Temporary name of the image in the desired image format
list_classes = ""
for name_class in img_classes:
list_classes += str(name_class) + "_"
img_name = list_classes + img_path.split(os.sep)[-1]
#Convert the image to the desired format of the descriptor
temp_img_path, converted = util.convert_desired_format(
img_path, img_name, "PPM")
if converted:
print "\tImage converted to PPM"
#Path of the file with the feature vector of an image
fv_path = os.path.join(path_temp, img_name + ".fv")
#Extraction of the feature vector
if not os.path.exists(fv_path):
cmd = 'cd "%s"; ./compute_gist "%s" "%s" -nblocks %d \
-orientationsPerScale "%s"' % (descriptor_path, temp_img_path,
fv_path, cell_size, orientations)
subprocess.call(cmd, shell=True)
#Remove the temporary image
if converted:
os.remove(temp_img_path)
#Transforms the feature vector of the descriptor into the standard output
#of the framework
fv = fv_transform(fv_path)
return img_path, len(img_classes), img_classes, fv
def extract(img_path, img_classes, param):
"""
Function that performs the extraction of an image using the EXAMPLE
descriptor.
This function transforms the image being extracted to the desired image
format of the descriptor, performs the extraction of the image, and last,
transforms the output feature vector of the descriptor into the standard
output of the framework, a list of floats.
"""
print "Descriptor: EXAMPLE"
#CONSTANTS
#Number of executions of the extraction
NUM_EXEC = 1
#PATHS
#Path for the folder containing the descriptor executable
##Not necessary in case that the extraction is present in the plugin code
descriptor_path = os.path.dirname(__file__)
#Path for the temporary folder, where the file with the feature vector
#extracted from the image will be saved
##Consider the path with the origin on the directory of the plugin
##(os.path.dirname(__file__)) to avoid problems with the directory where
##the framework is being executed
path_temp = os.path.join(os.path.dirname(__file__), "..", "..", "temp")
if not os.path.isdir(path_temp):
os.makedirs(path_temp)
print img_path, "being extract in the process ", os.getpid()
#Temporary name of the image in the desired image format
list_classes = ""
for name_class in img_classes:
list_classes += name_class + "_"
img_name = list_classes + img_path.split(os.sep)[-1]
#Convert the image to the desired format of the descriptor
temp_img_path, converted = util.convert_desired_format(img_path, img_name,
"EXAMPLE")
if converted:
print "\tImage converted to EXAMPLE"
#Path of the file with the feature vector of an image
fv_path = os.path.join(path_temp, img_name + ".fv")
#Extraction of the feature vector
if not os.path.exists(fv_path):
#Example:
# setup = """
#ctypes = __import__('ctypes')
#plugin = "EXAMPLE.so"
#lib = ctypes.CDLL("%s%s" + plugin)
#img_path = "%s"
#fv_path = "%s"
# """%(descriptor_path, os.sep, temp_img_path, fv_path)
#
# cmd = """
#lib.Extraction(img_path, fv_path)
# """
#
# t = timeit.Timer(stmt=cmd, setup=setup)
# try:
# t.timeit(number=NUM_EXEC)
# print "\tFeature vector extracted"
# except:
# t.print_exc()
#Remove the temporary image
if converted:
os.remove(temp_img_path)
#Transforms the feature vector of the descriptor into the standard output
#of the framework
fv = fv_transform(fv_path)
return img_path, len(img_classes), img_classes, fv
def fv_transform(fv_path):
"""
Receive the path with the feature vector in the descriptor output and
return the feature vector in the framework standard output, a list of
floats.
"""
list_fv = []
#Open the file created by the descriptor, save the feature vector in the
#standard output, remove the file and return the new feature vector
try:
file_fv = open(fv_path, "rb")
except IOError:
print "ERROR"
sys.exit(1)
#Performs the necessary operations to transform the feature vector into
#the standard output
file_fv.close()
os.remove(fv_path)
print "\tFeature vector transformed in the standard output"
return list_fv
def distance(fv1, fv2):
"""
Performs the calculation of distance between the two feature vectors,
according to the Distance function of the executable.
Inputs:
- fv1 (list of floats): First feature vector
- fv2 (list of floats): Second feature vector
Output:
- distance (double): Distance between the two feature vectors
"""
#Imports
import ctypes
descriptor_path = os.path.dirname(__file__)
plugin_name = "EXAMPLE.so"
plugin_path = os.path.join(descriptor_path, plugin_name)
plugin = ctypes.CDLL(plugin_path)
#Descriptor exclusive
#-------------------------------------------------------------------------
#Creating class requested by the Distance function
class Class_Example(ctypes.Structure):
#Example: Pointer to a float vector and an integer
_fields_ = [("first_field", ctypes.POINTER(ctypes.c_double)),
("second_field", ctypes.c_int)]
#First Class
Class1 = Class_Example()
len_fv1 = len(fv1)
fv1_int = map(float, fv1)
c_first1 = (ctypes.c_double * len_fv1)(*fv1_int)
Class1.first_field = ctypes.cast(c_first1, ctypes.POINTER(ctypes.c_double))
Class1.second_field = ctypes.c_int(len_fv1)
p_Class1 = ctypes.pointer(Class1)
#Second Class
Class2 = Class_Example()
len_fv2 = len(fv2)
fv2_int = map(float, fv2)
c_first2 = (ctypes.c_double * len_fv2)(*fv2_int)
Class2.first_field = ctypes.cast(c_first2, ctypes.POINTER(ctypes.c_double))
Class2.second_field = ctypes.c_int(len_fv2)
p_Class2 = ctypes.pointer(Class2)
#Parameters of the Distance function
plugin.Distance.restype = ctypes.c_double
#-------------------------------------------------------------------------
#Execution
distance = plugin.Distance(p_Class1, p_Class2)
return distance