Esempio n. 1
0
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()
Esempio n. 2
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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()
Esempio n. 3
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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)
Esempio n. 4
0
Label(root, text="提取特征存储文件名称:").grid(row=1, column=0, sticky=W)
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")