def class_image_enhance( imageDir ):
#特征读取一个一个读取，运行速度相对慢些，但是占用电脑资源更少
#读取原始图像路径， image_path_dict记录原始图像路径，如 ‘a.jpg', '/root/kenneth/a.jpg'格式
src_image_path = os.path.join( imageDir, 'src_image_path.path')
if not os.path.exists(src_image_path):
print('ErrorMessage:', src_image_path, ' is not exisit!')
return -1
image_path_dict = rw_feature.read_dict(src_image_path)
#获取原始图像特征路径， feature_path_dict记录图像特征路径， 如 ’a.jpg'：‘/root/kenneth/a.surf'
src_image_feature_path = os.path.join( imageDir, 'src_image_feature_path.path')
if not os.path.exists(src_image_feature_path):
print('ErrorMessage:', src_image_feature_path, ' is not exisit!')
return -1
feature_path_dict = rw_feature.read_dict(src_image_feature_path)
#查看image_class_floder 文件夹是否存在，如果不存在创建一个
image_class_floder = os.path.join( imageDir, 'image_class_folder' )
if not os.path.exists( image_class_floder ):
os.mkdir( image_class_floder )
# 获取索引特征字典，如果存在读取，如果不存在创建一个dict，用于后面存储特征索引，
iif_save_path = os.path.join( imageDir, 'index_image_feature.path' )
if os.path.exists(iif_save_path):
index_image_feature_path_dict = rw_feature.read_dict(iif_save_path)
else:
index_image_feature_path_dict = dict()
#获取所以特征的key，即所以特征的图像名称list
key_list = feature_path_dict.keys()
svm_clf = ana_svm()
# res = svm_clf.predict( [[5.5, 1.7]] )
#将图像分类、并拷贝一份到image_class_floder文件夹中，其中的子文件夹是以first_key的名称命名，即用来比对的图像名称
while key_list:
first_key = key_list.pop(0)
_, first_kp, first_des = read_des_feature_by_path(feature_path_dict[first_key])
cp_flag = 0 #复制标记，当标记为1时候证明已经复制，如果循环结束还是0， 则证明索引目录中没有符合的文件夹，需要新创建文件夹
for ind_key in index_image_feature_path_dict.keys():
_, one_kp, one_des = read_des_feature_by_path(feature_path_dict[ind_key])
ransanc_gmatch, gmatch = match(first_kp, first_des.astype(np.float32),one_kp, one_des.astype(np.float32))
if svm_clf.predict([[float( len(ransanc_gmatch)), float( len(gmatch))]]):
img_name, _ = os.path.splitext(ind_key)
cfd_folder_name = os.path.join( image_class_floder, img_name )
shutil.copyfile( image_path_dict[first_key], os.path.join(cfd_folder_name, first_key) )
cp_flag = 1
break
if not cp_flag:
img_name, _ = os.path.splitext(first_key)
cfd_folder_name = os.path.join( image_class_floder, img_name )
os.mkdir( cfd_folder_name )
shutil.copyfile( image_path_dict[first_key], os.path.join(cfd_folder_name, first_key) )
index_image_feature_path_dict[first_key] = feature_path_dict[first_key]
#将索引特征路径dict保存
rw_feature.save_dict(index_image_feature_path_dict, iif_save_path)
def save_image_path(imageDir, save_path):
#imageDir : src image path
#save_path : the proto file save path
if not os.path.exists(imageDir):
print('ErrorMessage:', imageDir, ' is not exisit!')
return -1
path_dict = get_path_dict( imageDir )
rw_feature.save_dict( path_dict, save_path )
def save_feature(proto_path, save_root_path):
# save feature proto and feature path proto
#proto_path : image path dict, proto file
# save_root_path : save root folder, the subfolder will be create
if not os.path.exists(proto_path):
print('ErrorMessage:', proto_path, ' is not exisit!!!')
return -1
if not os.path.isdir(save_root_path):
os.makedirs(save_root_path)
image_feature_folder = os.path.join(save_root_path, 'image_feature_folder')
if os.path.isdir(image_feature_folder):
print('Warnning:', image_feature_folder,
' is exisit, please change a new path')
else:
os.makedirs(image_feature_folder)
image_path_dict = image_path.read_image_path(proto_path)
surf_detector = create_detector()
image_feature_path_file = os.path.join(save_root_path,
'src_image_feature_path.path')
if not os.path.exists(image_feature_path_file):
image_feature_path_dict = dict()
else:
image_feature_path_dict = rw_feature.read_dict(image_feature_path_file)
image_idx = int(config.get('MetaData', 'image_idx')) #从中断处加载,继续处理
save_interval = int(config.get('MetaData', 'save_interval'))
for k, img_path_key in enumerate(image_path_dict.keys()[image_idx:]):
try:
image_idx += 1
img_path = image_path_dict[img_path_key]
img = cv2.imread(img_path)
img = cv2.resize(img, (360, 360))
except:
print('wrong read!')
continue
kp, des = detect(surf_detector, img)
if not kp:
continue
_, tmpfilename = os.path.split(img_path)
filename, _ = os.path.splitext(tmpfilename)
one_image_feature_path = os.path.join(image_feature_folder,
filename + '.surf')
rw_feature.save_feature(tmpfilename, kp, des, one_image_feature_path)
image_feature_path_dict[img_path_key] = one_image_feature_path
if (not k % save_interval) or image_idx == len(image_path_dict.keys()):
rw_feature.save_dict(image_feature_path_dict,
image_feature_path_file)
config.set('MetaData', 'image_idx', str(image_idx - 1))
with open('config.ini', 'wb') as configfile:
config.write(configfile)
def class_image_slow(imageDir):
#特征读取一个一个读取，运行速度相对慢些，但是占用电脑资源更少
#读取原始图像路径， image_path_dict记录原始图像路径，如 ‘a.jpg', '/root/kenneth/a.jpg'格式
src_image_path = os.path.join(imageDir, 'src_image_path.path')
if not os.path.exists(src_image_path):
print('ErrorMessage:', src_image_path,
'the image path protofile is not exisit!')
return -1
image_path_dict = rw_feature.read_dict(src_image_path)
#获取原始图像特征路径， feature_path_dict记录图像特征路径， 如 ’a.jpg'：‘/root/kenneth/a.surf'
src_image_feature_path = os.path.join(imageDir,
'src_image_feature_path.path')
if not os.path.exists(src_image_feature_path):
print('ErrorMessage:', src_image_feature_path,
'the image feature save path protofile is not exisit!')
return -1
feature_path_dict = rw_feature.read_dict(src_image_feature_path)
#查看image_class_floder 文件夹是否存在，如果不存在创建一个
image_class_floder = os.path.join(imageDir, 'image_class_folder')
if not os.path.exists(image_class_floder):
os.mkdir(image_class_floder)
# 获取索引特征字典，如果存在读取，如果不存在创建一个dict，用于后面存储特征索引，
iif_save_path = os.path.join(imageDir, 'index_image_feature.path')
index_image_feature_path_dict = dict()
#从特征文件夹中获取所以的kp和des数据，并以字典方式保存，key是图像名称
# kp_dict, des_dict = read_des_feature(feature_path_dict)
key_list = list()
key_list_path = config.get('MetaData', 'key_list_path')
break_from_flag = int(config.get('MetaData', 'break_from_flag'))
if break_from_flag:
index_image_feature_path_dict = rw_feature.read_dict(iif_save_path)
with open(key_list_path, 'r') as f:
temp_key = f.readline().strip()
while temp_key:
key_list.append(temp_key)
temp_key = f.readline().strip()
break_from_flag = 0
else:
#获取所以特征的key，即所以特征的图像名称list
key_list = feature_path_dict.keys()
#测试
if test_flag:
print(key_list)
data_ana = [] #测试时候使用
svm_clf = ana_svm()
# res = svm_clf.predict( [[5.5, 1.7]] )
#将图像分类、并拷贝一份到image_class_floder文件夹中，其中的子文件夹是以first_key的名称命名，即用来比对的图像名称
while key_list:
first_key = key_list.pop(0)
_, first_kp, first_des = read_des_feature_by_path(
feature_path_dict[first_key])
# first_kp = kp_dict[first_key]
first_des = first_des.astype(np.float32)
#将索引特征路径加入index_image_feature.path中
index_image_feature_path_dict[first_key] = feature_path_dict[first_key]
#创建分类图像文件夹，以第一图像名称命名文件夹，并将第一图像拷贝到该文件夹中，如果已经存在则报错
img_name, _ = os.path.splitext(first_key)
cfd_folder_name = os.path.join(image_class_floder, img_name)
if not os.path.exists(cfd_folder_name):
os.mkdir(cfd_folder_name)
shutil.copyfile(image_path_dict[first_key],
os.path.join(cfd_folder_name, first_key))
else:
print('warnning:', cfd_folder_name, 'is already exisit!')
for i, key in enumerate(key_list):
_, one_kp, one_des = read_des_feature_by_path(
feature_path_dict[key])
ransanc_gmatch, gmatch = match(first_kp, first_des, one_kp,
one_des.astype(np.float32))
# if len(ransanc_gmatch) > 30 and len(ransanc_gmatch)/float(len(gmatch)) > 0.4:
if svm_clf.predict(
[[float(len(ransanc_gmatch)),
float(len(gmatch))]]):
shutil.copyfile(image_path_dict[key],
os.path.join(cfd_folder_name, key))
#测试
if test_flag:
data_ana.append([
len(ransanc_gmatch),
len(gmatch),
len(ransanc_gmatch) / float(len(gmatch)), first_key,
key_list[i], 1
])
key_list[i] = 0
else:
#测试
if test_flag:
data_ana.append([
len(ransanc_gmatch),
len(gmatch),
len(ransanc_gmatch) / float(len(gmatch)), first_key,
key_list[i], 0
])
key_list = [elem for elem in key_list if elem is not 0]
rw_feature.save_dict(index_image_feature_path_dict, iif_save_path)
with open(key_list_path, 'w') as f:
for one_key in key_list:
f.writelines(one_key + '\n')
#测试时候保存数据，用于分析
if test_flag:
with open('data_ana.txt', 'w') as f:
for one_data in data_ana:
txt = ''
for elem in one_data:
if isinstance(elem, int) or isinstance(elem, float):
elem = str(elem)
txt += elem + '\t'
f.writelines(txt + '\n')