def OnFdTextChanged(root, pgbar, pglabel, btn, entry, ft_label):
fd_name = entry.get()
file_io = FileIO()
ft_existed = False
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
for path in pic_dataset_paths:
if path == fd_name:
ft_existed = True
break
if ft_existed:
pgbar["value"] = 100
pglabel.configure(text="100.00%")
btn["text"] = "重新提取特征"
if getattr(sys, 'frozen', False):
application_path = os.path.dirname(sys.executable)
elif __file__:
application_path = os.path.dirname(__file__)
ft_name = "feature_" + hashlib.md5(fd_name.encode()).hexdigest() + ".h5"
ft_label["text"] = os.path.abspath(os.path.join(application_path, ft_name))
else:
pgbar["value"] = 0
pglabel.configure(text="0.00%")
btn["text"] = "提取特征"
ft_label["text"] = "当前文件夹没有特征被提取"
root.update()
def ShowOriginalImage(event):
img_no = int(str(event.widget).split(".")[-1])
file_io = FileIO()
imlist = file_io.load_obj("imlist")
ipath = imlist[img_no]
img = mpimg.imread(ipath)
imgplot = plt.imshow(img)
plt.show()
def GetFoldernameFromSelector(root, e):
root.update()
filename = askdirectory()
if filename != '':
file_io = FileIO()
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
ft_existed = 0
for path in pic_dataset_paths:
if path == filename:
break
ft_existed += 1
if ft_existed >= len(pic_dataset_paths):
pic_dataset_paths.append(filename)
else:
tmp = pic_dataset_paths[ft_existed]
pic_dataset_paths[ft_existed] = pic_dataset_paths[-1]
pic_dataset_paths[-1] = tmp
else:
pic_dataset_paths = [filename]
e["values"] = pic_dataset_paths
e.current(len(pic_dataset_paths) - 1)
root.update()
def GenerateFeatureDatabase(fd_entry, ft_label, pgbar, pglabel, root):
ft_name = "feature"
fd_name = fd_entry.get()
root.update()
if os.path.exists(fd_name) and os.path.isdir(fd_name):
pass
else:
messagebox.showerror(title="文件夹读取错误", message="无法读取文件或文件夹不存在")
return
file_handler = FileHandler()
# img_list = file_handler.get_imlist(fd_name)
img_list = [os.path.join(dp, f) for dp, dn, filenames in os.walk(fd_name) for f in filenames if os.path.splitext(f)[1] == '.jpg' or os.path.splitext(f)[1] == '.jpeg']
# print(img_list)
print("--------------------------------------------------")
print(" feature extraction starts")
print("--------------------------------------------------")
feats = []
names = []
model = VGGNet()
ite_no = 0
ite_to = len(img_list)
error_occured = False
if getattr(sys, 'frozen', False):
application_path = os.path.dirname(sys.executable)
elif __file__:
application_path = os.path.dirname(__file__)
for i, img_path in enumerate(img_list):
try:
ite_no += 1
norm_feat = model.extract_feat(img_path)
img_name = img_path
feats.append(norm_feat)
names.append(img_name.encode('utf-8'))
print("extracting feature from image No. %d , %d images in total" %((i+1), len(img_list)))
pgbar['value'] = (100 * ite_no) / ite_to
print(pgbar['value'])
label_content="{0:0.2f}%".format(pgbar['value'])
pglabel.configure(text=label_content)
root.update()
except Exception as e:
error_occured = True
logger_fname = os.path.join(application_path, 'error.log')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
with open(logger_fname, "a+", encoding='utf-8') as f:
f.write('[Error] ' + dt_string + "\n")
f.write('[Trace Back] ' + str(e) + "\n")
f.write('[Detail] ' + img_path + "\n")
feats = np.array(feats)
print("--------------------------------------------------")
print(" writing feature extraction results ...")
print("--------------------------------------------------")
if error_occured:
messagebox.showerror(title="提取特征时发生错误", message="提取特征时发生错误，程序跳过了错误文件完成特征提取，请将error.log文件，以及后台截图发给开发者。")
output = ft_name + "_" + hashlib.md5(fd_name.encode()).hexdigest() + ".h5"
output = os.path.join(application_path, output)
h5f = h5py.File(output, 'w')
h5f.create_dataset('dataset_1', data = feats)
h5f.create_dataset('dataset_2', data = np.string_(names))
h5f.close()
ft_label.configure(text=os.path.abspath(output))
# Load array from file and update the file path array
file_io = FileIO()
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
ft_existed = 0
for path in pic_dataset_paths:
if path == fd_name:
break
ft_existed += 1
if ft_existed >= len(pic_dataset_paths):
pic_dataset_paths.append(fd_name)
else:
tmp = pic_dataset_paths[ft_existed]
pic_dataset_paths[ft_existed] = pic_dataset_paths[-1]
pic_dataset_paths[-1] = tmp
print(pic_dataset_paths)
else:
pic_dataset_paths = [fd_name]
file_io.save_obj(pic_dataset_paths,"save")
fd_entry["values"] = pic_dataset_paths
fd_entry.current(len(pic_dataset_paths) - 1)
feature_text = "暂时没有特征被提取"
label_feature_name = Label(root, text=feature_text)
label_feature_name.grid(row=1, column=1, sticky=W)
# Setup progress bar for feature extraction
pgbar = ttk.Progressbar(root, orient=HORIZONTAL, length=550, mode='determinate')
pgbar.grid(row=2, column=1, sticky=W)
l_pg = Label(root, text="0.00%")
l_pg.grid(row=2, column=1, sticky=E)
# Create database and extract features
Label(root, text="图片库路径：").grid(row=0, column=0, sticky=W)
file_io = FileIO()
dbFolder_text = [""]
if file_io.check_obj('save'):
dbFolder_text = file_io.load_obj('save')
e_dbFolder = ttk.Combobox(root, values=dbFolder_text, width=80)
e_dbFolder.grid(row=0, column=1, sticky=W)
Button(root, text="浏览文件夹", command=lambda: GetFoldernameFromSelector(root, e_dbFolder)).grid(row=0, column=1, sticky=E)
ft_extract_btn = Button(root, text="提取特征", command=lambda: GenerateFeatureDatabase(e_dbFolder, label_feature_name, pgbar, l_pg, root))
ft_extract_btn.grid(row=1, column=1, sticky=E)
e_var = StringVar()
e_var.trace("w", lambda a, b, c: OnFdTextChanged(root, pgbar, l_pg, ft_extract_btn, e_dbFolder, label_feature_name))
e_dbFolder["textvar"] = e_var
# Setup the maximum number of similar images
Label(root, text="最大图片显示数量：").grid(row=4, column=0, sticky=W)
e_maxRsltNo = Entry(root, width=20)
e_maxRsltNo.grid(row=4, column=1, sticky=W)
e_maxRsltNo.insert(0, "3")