def parse_args():
# Command line args
parser = argparse.ArgumentParser()
parser.add_argument("--config_file", type=str, default=ROOT + "config/config.yaml",
help="path to config file")
parser.add_argument("--verify_mask", type=MyBool, default=False,
help="Whether write masked templates to file for user to verify if mask is correct or not")
parser.add_argument("--augment_imgs", type=MyBool, default=False,
help="Whether do image augment and create many new images")
parser.add_argument("--setup_train_test_txt", type=MyBool, default=False,
help="Setup train.txt and valid.txt for yolo. Copy validation images to a new folder.")
parser.add_argument("--setup_yolo", type=MyBool, default=False,
help="Setup yolo.cfg, yolo.data, ")
parser.add_argument("--create_bash_for_yolo", type=MyBool, default=False,
help="Create two bash scripts for trainning yolo and doing inference: s2_train.sh & s3_inference.sh")
args_from_command_line = parser.parse_args()
# Args from configuration file
args_from_file = read_all_args(args_from_command_line.config_file)
# Combine the two
args = args_from_command_line
args.__dict__.update(args_from_file.__dict__)
return args
def set_args():
args = cf.SimpleNamespace()
configs = read_all_args(ROOT + "config/config.yaml")
# 1. Path to model definition file
# e.g.: "data/custom1_generated/yolo.cfg"
args.f_yolo_config = configs.f_yolo_config
# 2. Path to data config file
# e.g.: "data/custom1_generated/yolo.data"
args.f_yolo_data = configs.f_yolo_data
# 3. Other training settings
training_args = set_training_args()
args.__dict__.update(training_args.__dict__)
# 4. Folder to save model
save_model_to = "checkpoints/" + cf.get_readable_time(no_blank=True) + "/"
cf.create_folder(save_model_to)
args.save_model_to = save_model_to
cf.write_dict(save_model_to + "configs.yaml", args.__dict__)
# return
print("\nArgs:\n", args)
return args
def __init__(self, config_path, weights_path):
args = read_all_args(config_path)
args_inference = cf.dict2class(args.yolo_inference)
self.model = create_model(weights_path, args.f_yolo_config, args_inference.img_size)
self.classes = load_classes(args.f_yolo_classes) # Extracts class labels from file
self.plotter = Yolo_Detection_Plotter_CV2(classes=self.classes, if_show=False)
self.args, self.args_inference = args, args_inference
def parse_args():
# Command line args
parser = argparse.ArgumentParser()
parser.add_argument(
"--config_file",
type=str,
default="config/config.yaml",
help="path to config file. e.g.: ROOT/config/config.yaml")
parser.add_argument(
"--verify_mask",
type=MyBool,
default=False,
help=
"Whether write masked template images to file for user to verify if mask is correct or not"
)
parser.add_argument(
"--augment_imgs",
type=MyBool,
default=False,
help="Whether do image augment and create many new images")
parser.add_argument(
"--setup_yolo",
type=MyBool,
default=False,
help=
"Setup config files for yolo: yolo.cfg, yolo.data, train.txt, valid.txt"
)
parser.add_argument(
"--create_bash_for_yolo",
type=MyBool,
default=False,
help=
"Create two bash scripts for trainning yolo and doing inference: s2_train.sh & s3_inference.sh"
)
args = parser.parse_args()
# Config file
args_file = read_all_args(args.config_file)
# Update
args.__dict__.update(args_file.__dict__)
return args
if 1: # Set path
import sys, os
ROOT = os.path.dirname(
os.path.abspath(__file__)) + "/../../" # root of the project
sys.path.append(ROOT)
import cv2
from config.config import read_all_args
args = read_all_args(config_file="config/config.yaml")
def read_list(filename):
with open(filename, 'r') as f:
lines = [line.rstrip() for line in f]
return lines
# Get input images list
file_eval_images = args.f_yolo_valid
fnames = read_list(file_eval_images)
# Get output folder
folder_data_eval = args.f_data_eval
if not os.path.exists(folder_data_eval): os.makedirs(folder_data_eval)
print(f"Writing images to {folder_data_eval}")
# Copy images
for i, name in enumerate(fnames):
print(f"{i}/{len(fnames)}: {name}")
I = cv2.imread(name, cv2.IMREAD_UNCHANGED)
cv2.imwrite(folder_data_eval + "/" + name.split('/')[-1], I)