def BtnModuleSettingsCB(self): modules = Modules() modules.show()
def bounding_boxes_images(args, DEFAULT_OID_DIR):
if not args.Dataset:
dataset_dir = os.path.join(DEFAULT_OID_DIR, 'Dataset')
csv_dir = os.path.join(DEFAULT_OID_DIR, 'csv_folder')
else:
dataset_dir = os.path.join(DEFAULT_OID_DIR, args.Dataset)
csv_dir = os.path.join(DEFAULT_OID_DIR, 'csv_folder')
name_file_class = 'class-descriptions-boxable.csv'
CLASSES_CSV = os.path.join(csv_dir, name_file_class)
if args.command == 'downloader':
logo(args.command)
if args.type_csv is None:
print(bc.FAIL + 'Missing type_csv argument.' + bc.ENDC)
exit(1)
if args.classes is None:
print(bc.FAIL + 'Missing classes argument.' + bc.ENDC)
exit(1)
if args.multiclasses is None:
args.multiclasses = 0
folder = ['train', 'validation', 'test']
file_list = [
'train-annotations-bbox.csv', 'validation-annotations-bbox.csv',
'test-annotations-bbox.csv'
]
if args.classes[0].endswith('.txt'):
with open(args.classes[0]) as f:
args.classes = f.readlines()
args.classes = [x.strip() for x in args.classes]
else:
args.classes = [arg.replace('_', ' ') for arg in args.classes]
if args.multiclasses == '0':
mkdirs(dataset_dir, csv_dir, args.classes, args.type_csv)
for classes in args.classes:
print(bc.INFO + 'Downloading {}.'.format(classes) + bc.ENDC)
class_name = classes
error_csv(name_file_class, csv_dir)
df_classes = pd.read_csv(CLASSES_CSV, header=None)
class_code = df_classes.loc[df_classes[1] ==
class_name].values[0][0]
if args.type_csv == 'train':
name_file = file_list[0]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[0], dataset_dir,
class_name, class_code)
else:
download(args,
df_val,
folder[0],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads))
elif args.type_csv == 'validation':
name_file = file_list[1]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[1], dataset_dir,
class_name, class_code)
else:
download(args,
df_val,
folder[1],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads))
elif args.type_csv == 'test':
name_file = file_list[2]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[2], dataset_dir,
class_name, class_code)
else:
download(args,
df_val,
folder[2],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads))
elif args.type_csv == 'all':
for i in range(3):
name_file = file_list[i]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[i], dataset_dir,
class_name, class_code)
else:
download(args,
df_val,
folder[i],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads))
else:
print(bc.ERROR + 'csv file not specified' + bc.ENDC)
exit(1)
elif args.multiclasses == '1':
class_list = args.classes
print(bc.INFO + 'Downloading {} together.'.format(class_list) +
bc.ENDC)
multiclass_name = ['_'.join(class_list)]
mkdirs(dataset_dir, csv_dir, multiclass_name, args.type_csv)
error_csv(name_file_class, csv_dir)
df_classes = pd.read_csv(CLASSES_CSV, header=None)
class_dict = {}
for class_name in class_list:
class_dict[class_name] = df_classes.loc[
df_classes[1] == class_name].values[0][0]
for class_name in class_list:
if args.type_csv == 'train':
name_file = file_list[0]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[0], dataset_dir,
class_name, class_dict[class_name],
class_list)
else:
download(args, df_val, folder[0], dataset_dir,
class_name, class_dict[class_name],
class_list, int(args.n_threads))
elif args.type_csv == 'validation':
name_file = file_list[1]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[1], dataset_dir,
class_name, class_dict[class_name],
class_list)
else:
download(args, df_val, folder[1], dataset_dir,
class_name, class_dict[class_name],
class_list, int(args.n_threads))
elif args.type_csv == 'test':
name_file = file_list[2]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[2], dataset_dir,
class_name, class_dict[class_name],
class_list)
else:
download(args, df_val, folder[2], dataset_dir,
class_name, class_dict[class_name],
class_list, int(args.n_threads))
elif args.type_csv == 'all':
for i in range(3):
name_file = file_list[i]
df_val = TTV(csv_dir, name_file)
if not args.n_threads:
download(args, df_val, folder[i], dataset_dir,
class_name, class_dict[class_name],
class_list)
else:
download(args, df_val, folder[i], dataset_dir,
class_name, class_dict[class_name],
class_list, int(args.n_threads))
elif args.command == 'visualizer':
logo(args.command)
flag = 0
while (True):
if flag == 0:
print(
"Which folder do you want to visualize (train, test, validation)? <exit>"
)
image_dir = input("> ")
flag = 1
if image_dir == 'exit':
exit(1)
class_image_dir = os.path.join(dataset_dir, image_dir)
print("Which class? <exit>")
show_classes(os.listdir(class_image_dir))
class_name = input("> ")
if class_name == 'exit':
exit(1)
download_dir = os.path.join(dataset_dir, image_dir, class_name)
label_dir = os.path.join(dataset_dir, image_dir, class_name,
'Label')
if not os.path.isdir(download_dir):
print("[ERROR] Images folder not found")
exit(1)
if not os.path.isdir(label_dir):
print("[ERROR] Labels folder not found")
exit(1)
index = 0
print(
dedent("""
--------------------------------------------------------
INFO:
- Press 'd' to select next image
- Press 'a' to select previous image
- Press 'e' to select a new class
- Press 'w' to select a new folder
- Press 'q' to exit
You can resize the window if it's not optimal
--------------------------------------------------------
"""))
show(class_name, download_dir, label_dir,
len(os.listdir(download_dir)) - 1, index)
while True:
progression_bar(len(os.listdir(download_dir)) - 1, index + 1)
k = cv2.waitKey(0) & 0xFF
if k == ord('d'):
cv2.destroyAllWindows()
if index < (len(os.listdir(download_dir)) - 2):
index += 1
show(class_name, download_dir, label_dir,
len(os.listdir(download_dir)) - 1, index)
elif k == ord('a'):
cv2.destroyAllWindows()
if index > 0:
index -= 1
show(class_name, download_dir, label_dir,
len(os.listdir(download_dir)) - 1, index)
elif k == ord('e'):
cv2.destroyAllWindows()
break
elif k == ord('w'):
flag = 0
cv2.destroyAllWindows()
break
elif k == ord('q'):
cv2.destroyAllWindows()
exit(1)
break
print("[ERROR] Labels folder not found")
exit(1)
index = 0
print(dedent("""
--------------------------------------------------------
INFO:
- Press 'd' to select next image
- Press 'a' to select previous image
- Press 'w' to proceed at the previous menu
- You can resize the window if it's not optimal
--------------------------------------------------------
"""))
show(class_name, download_dir, label_dir, index)
while True:
progression_bar(len(os.listdir(download_dir))-1, index+1)
k = cv2.waitKey(0) & 0xFF
if k == ord('d'):
cv2.destroyAllWindows()
if index < len(os.listdir(download_dir)) - 2:
index += 1
show(class_name, download_dir, label_dir, index)
elif k == ord('a'):
cv2.destroyAllWindows()
if index > 0:
import modules
X = 10
Y = 50
WIDTH = 1190
HEIGHT = 720
while True:
original_screen = modules.capture(x=X, y=Y, width=WIDTH, height=HEIGHT)
processed_screen = modules.process(original_screen)
if not modules.show(processed_screen):
break
index = 0
print(
dedent("""
--------------------------------------------------------
INFO:
- Press 'd' to select next image
- Press 'a' to select previous image
- Press 'e' to select a new class
- Press 'w' to select a new folder
- Press 'q' to exit
You can resize the window if it's not optimal
--------------------------------------------------------
"""))
show(class_name, download_dir, label_dir,
len(os.listdir(download_dir)) - 1, index)
while True:
progression_bar(len(os.listdir(download_dir)) - 1, index + 1)
k = cv2.waitKey(0) & 0xFF
if k == ord('d'):
cv2.destroyAllWindows()
if index < len(os.listdir(download_dir)) - 1:
index += 1
show(class_name, download_dir, label_dir,
len(os.listdir(download_dir)) - 1, index)
elif k == ord('a'):
cv2.destroyAllWindows()
index = 0
print(dedent("""
--------------------------------------------------------
INFO:
- Press 'd' to select next image
- Press 'a' to select previous image
- Press 'e' to select a new class
- Press 'w' to select a new folder
- Press 'q' to exit
You can resize the window if it's not optimal
--------------------------------------------------------
"""))
show(class_name, download_dir, label_dir,len(os.listdir(download_dir))-1, index)
while True:
progression_bar(len(os.listdir(download_dir))-1, index+1)
k = cv2.waitKey(0) & 0xFF
if k == ord('d'):
cv2.destroyAllWindows()
if index < len(os.listdir(download_dir)) - 1:
index += 1
show(class_name, download_dir, label_dir,len(os.listdir(download_dir))-1, index)
elif k == ord('a'):
cv2.destroyAllWindows()
if index > 0:
def bounding_boxes_images(args, DEFAULT_OID_DIR):
if not args.Dataset:
dataset_dir = os.path.join(DEFAULT_OID_DIR, 'Dataset')
csv_dir = os.path.join(DEFAULT_OID_DIR, 'csv_folder')
else:
dataset_dir = os.path.join(DEFAULT_OID_DIR, args.Dataset)
csv_dir = os.path.join(DEFAULT_OID_DIR, 'csv_folder')
name_file_class = 'class-descriptions-boxable.csv'
CLASSES_CSV = os.path.join(csv_dir, name_file_class)
if args.command == 'downloader':
save_args_conf(args, dataset_dir)
logo(args.command)
if args.type_csv is None:
print(bc.FAIL + 'Missing type_csv argument.' + bc.ENDC)
exit(1)
if args.classes is None:
print(bc.FAIL + 'Missing classes argument.' + bc.ENDC)
exit(1)
if args.multiclasses is None:
args.multiclasses = 0
folder = ['train', 'validation', 'test']
file_list = [
'train-annotations-bbox.csv', 'validation-annotations-bbox.csv',
'test-annotations-bbox.csv'
]
if args.classes[0].endswith('.txt'):
with open(args.classes[0]) as f:
args.classes = f.readlines()
args.classes = [x.strip() for x in args.classes]
else:
args.classes = [arg.replace('_', ' ') for arg in args.classes]
if args.multiclasses == '0':
mkdirs(dataset_dir, csv_dir, args.classes, args.type_csv)
for classes in args.classes:
print(bc.INFO + 'Downloading {}.'.format(classes) + bc.ENDC)
class_name = classes
error_csv(name_file_class, csv_dir, args.yes)
df_classes = pd.read_csv(CLASSES_CSV, header=None)
try:
class_code = df_classes.loc[df_classes[1].str.lower(
) == class_name.lower()].values[0][0]
except:
print(
bc.FAIL + " '" + class_name +
"', please check spelling ! or that the class is already exist in the dataset"
+ bc.ENDC)
exit(1)
if args.type_csv == 'train':
name_file = file_list[0]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[0],
dataset_dir,
class_name,
class_code,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[0],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'validation':
name_file = file_list[1]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[1],
dataset_dir,
class_name,
class_code,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[1],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'test':
name_file = file_list[2]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[2],
dataset_dir,
class_name,
class_code,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[2],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'all':
for i in range(3):
name_file = file_list[i]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[i],
dataset_dir,
class_name,
class_code,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[i],
dataset_dir,
class_name,
class_code,
threads=int(args.n_threads),
class_list_for_yolo=args.classes)
else:
print(bc.FAIL + 'csv file not specified' + bc.ENDC)
exit(1)
elif args.multiclasses == '1':
class_list = args.classes
print(bc.INFO + 'Downloading {} together.'.format(class_list) +
bc.ENDC)
multiclass_name = ['_'.join(class_list)]
mkdirs(dataset_dir, csv_dir, multiclass_name, args.type_csv)
error_csv(name_file_class, csv_dir, args.yes)
df_classes = pd.read_csv(CLASSES_CSV, header=None)
class_dict = {}
for class_name in class_list:
try:
class_dict[class_name] = df_classes.loc[
df_classes[1].str.lower() ==
class_name.lower()].values[0][0]
except:
print(
bc.FAIL + " '" + class_name +
"', please check spelling ! or that the class is already exist in the dataset"
+ bc.ENDC)
exit(1)
for class_name in class_list:
if args.type_csv == 'train':
name_file = file_list[0]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[0],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[0],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'validation':
name_file = file_list[1]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[1],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[1],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'test':
name_file = file_list[2]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[2],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[2],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.type_csv == 'all':
for i in range(3):
name_file = file_list[i]
df_val = TTV(csv_dir, name_file, args.yes)
if not args.n_threads:
download(args,
df_val,
folder[i],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
class_list_for_yolo=args.classes)
else:
download(args,
df_val,
folder[i],
dataset_dir,
class_name,
class_dict[class_name],
class_list,
int(args.n_threads),
class_list_for_yolo=args.classes)
elif args.command == 'visualizer':
try:
print(bc.INFO + ' try to laod dataset configurations at ' +
os.path.join(dataset_dir, 'config.json') + bc.ENDC)
with open(os.path.join(dataset_dir, 'config.json'), 'r') as f:
config_dict = json.load(f)
args.classes = config_dict['classes']
args.multiclasses = config_dict['multiclasses']
args.yoloLabelStyle = config_dict['multiclasses']
except Exception as e:
print(e)
print(bc.FAIL + ' cannot load Dataset configurations at ' +
os.path.join(dataset_dir, 'config.json') + bc.ENDC)
print(
bc.INFO +
' you have to specifiy the dataset config by ur self if you faced any errors later'
+ bc.ENDC)
logo(args.command)
flag = 0
while (True):
if flag == 0:
print(
"Which folder do you want to visualize (train, test, validation)? <exit>"
)
image_dir = input("> ")
flag = 1
if image_dir == 'exit':
exit(1)
class_image_dir = os.path.join(dataset_dir, image_dir)
print("Which class? <exit>")
show_classes(os.listdir(class_image_dir))
class_name = input("> ")
if class_name == 'exit':
exit(1)
if args.multiclasses == '0':
download_dir = os.path.join(dataset_dir, image_dir, class_name,
'images')
label_dir = os.path.join(dataset_dir, image_dir, class_name,
'labels')
else:
download_dir = os.path.join(dataset_dir, image_dir, class_name)
label_dir = os.path.join(dataset_dir, image_dir, class_name)
if not os.path.isdir(download_dir):
print("[ERROR] Images folder not found")
print(
"[INFO] If u downloaded your images in multiclasses format, then you have to specify it here also "
)
exit(1)
if not os.path.isdir(label_dir):
print("[ERROR] Labels folder not found")
print(
"[INFO] If u downloaded your images in multiclasses format, then you have to specify it here also "
)
exit(1)
index = 0
images_files = glob(os.path.join(download_dir, '*.jpg'))
images_files += glob(os.path.join(download_dir, '*.JPG'))
images_files += glob(os.path.join(download_dir, '*.jpeg'))
images_files += glob(os.path.join(download_dir, '*.JPEG'))
images_files += glob(os.path.join(download_dir, '*.png'))
images_files += glob(os.path.join(download_dir, '*.PNG'))
print(
dedent("""
--------------------------------------------------------
INFO:
- Press 'd' to select next image
- Press 'a' to select previous image
- Press 'e' to select a new class
- Press 'w' to select a new folder
- Press 'q' to exit
You can resize the window if it's not optimal
--------------------------------------------------------
"""))
classes_list = None
if args.yoloLabelStyle:
classes_list = args.classes
index = show(class_name, download_dir, label_dir, images_files,
index, args)
while True:
progression_bar(len(images_files), index + 1)
k = cv2.waitKey(0) & 0xFF
if k == ord('d'):
index += 1
elif k == ord('a'):
index -= 1
elif k == ord('e'):
cv2.destroyAllWindows()
break
elif k == ord('w'):
flag = 0
cv2.destroyAllWindows()
break
elif k == ord('q'):
cv2.destroyAllWindows()
exit(1)
break
cv2.destroyAllWindows()
index = show(class_name, download_dir, label_dir, images_files,
index, args)