def __init__(self, config_file):
self.config_file_path = config_file
# ----- CharNet configuration and initialization -----
cfg.merge_from_file(config_file)
cfg.freeze()
print(cfg)
self.charnet = CharNet()
self.charnet.load_state_dict(torch.load(cfg.WEIGHT))
self.charnet.eval()
if torch.cuda.is_available():
print("[*] Using cuda!")
self.charnet.cuda()
cv2.putText(img_word_ins, '{}'.format(word_ins.text),
(word_bbox[0], word_bbox[1]), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
return img_word_ins
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Test")
parser.add_argument("config_file", help="path to config file", type=str)
parser.add_argument("image_dir", type=str)
parser.add_argument("results_dir", type=str)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.freeze()
print(cfg)
charnet = CharNet()
charnet.load_state_dict(torch.load(cfg.WEIGHT))
charnet.eval()
charnet.cuda()
for im_name in tqdm(sorted(os.listdir(args.image_dir))):
#print("Processing {}...".format(im_name))
im_file = os.path.join(args.image_dir, im_name)
im_original = cv2.imread(im_file)
im, scale_w, scale_h, original_w, original_h = resize(
im_original, size=cfg.INPUT_SIZE)
return img_word_ins
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Test")
config_file='../configs/icdar2015_hourglass88.yaml'
image_dir='../../data/Cello_Vietnamese_TestSet'
results_dir='../../data/res_charnet'
# parser.add_argument("config_file", help="path to config file", type=str, default=config_file)
# parser.add_argument("image_dir", type=str, default=image_dir)
# parser.add_argument("results_dir", type=str, default=results_dir)
#
# args = parser.parse_args()
cfg.merge_from_file(config_file)
cfg.freeze()
print(cfg)
charnet = CharNet()
charnet.load_state_dict(torch.load(cfg.WEIGHT))
charnet.eval()
charnet.cuda()
for im_name in sorted(os.listdir(image_dir)):
print("Processing {}...".format(im_name))
im_file = os.path.join(image_dir, im_name)
im_original = cv2.imread(im_file)
im, scale_w, scale_h, original_w, original_h = resize(im_original, size=cfg.INPUT_SIZE)
with torch.no_grad():
from torch.autograd import Variable
import threading
from queue import Queue
import src.models.Models as Net
from models.multi_tasks import ELANetV3_modified_sigapore
from src.fun_makedecision import fun_detection_TrafficViolation
from src.model_inference_ObjectDetect_Elanet import detect
from src.model_inference_Segmentation import evaluateModel, evaluateModel_models
from src.fun_plotfunction import plot_ROI, plot_bbox_Violation, plot_bbox
from src.fun_modify_seg import fun_intergate_seg_LaneandRoad
from src.violation import save_violation, createFolder
from charnet.config import cfg
from charnet.modeling.model import CharNet
#################### 載入偵測車牌charnet的cfg&Model #######################
cfg.merge_from_file("./configs/icdar2015_hourglass88.yaml")
cfg.freeze()
charnet = CharNet()
charnet.load_state_dict(torch.load(cfg.WEIGHT))
charnet.eval()
#################### 載入偵測車牌charnet的cfg&Model #######################
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('GPU:', device)
# load segmentation model
filepath_model_seg_LaneLine = './models/ESPNet_Line_mytransfrom_full_256_512_weights_epoch_131.pth'
filepath_model_seg_road = './models/ESPNet_road_mytransfrom_full_256_512_weights_epoch_41.pth'
# Load Object Detection model
checkpoint_path = './models/od_NoPretrain/BEST_checkpoint.pth.tar'
video_path = '../../data/(Front)IPCamera_20200305095701_p000.avi'
