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vis_vintext.py
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vis_vintext.py
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import os, sys
import torch
import numpy as np
from models.ests import build_ests
from util.slconfig import SLConfig
from util.visualizer import COCOVisualizer
from util import box_ops
from PIL import Image
import datasets.transforms as T
import pickle
dictionary = "aàáạảãâầấậẩẫăằắặẳẵAÀÁẠẢÃĂẰẮẶẲẴÂẦẤẬẨẪeèéẹẻẽêềếệểễEÈÉẸẺẼÊỀẾỆỂỄoòóọỏõôồốộổỗơờớợởỡOÒÓỌỎÕÔỒỐỘỔỖƠỜỚỢỞỠiìíịỉĩIÌÍỊỈĨuùúụủũưừứựửữƯỪỨỰỬỮUÙÚỤỦŨyỳýỵỷỹYỲÝỴỶỸ"
def make_groups():
groups = []
i = 0
while i < len(dictionary) - 5:
group = [c for c in dictionary[i : i + 6]]
i += 6
groups.append(group)
return groups
groups = make_groups()
TONES = ["", "ˋ", "ˊ", "﹒", "ˀ", "˜"]
SOURCES = ["ă", "â", "Ă", "Â", "ê", "Ê", "ô", "ơ", "Ô", "Ơ", "ư", "Ư", "Đ", "đ"]
TARGETS = ["aˇ", "aˆ", "Aˇ", "Aˆ", "eˆ", "Eˆ", "oˆ", "o˒", "Oˆ", "O˒", "u˒", "U˒", "D-", "d‑"]
def correct_tone_position(word):
word = word[:-1]
if len(word) < 2:
pass
first_ord_char = ""
second_order_char = ""
for char in word:
for group in groups:
if char in group:
second_order_char = first_ord_char
first_ord_char = group[0]
if word[-1] == first_ord_char and second_order_char != "":
pair_chars = ["qu", "Qu", "qU", "QU", "gi", "Gi", "gI", "GI"]
for pair in pair_chars:
if pair in word and second_order_char in ["u", "U", "i", "I"]:
return first_ord_char
return second_order_char
return first_ord_char
def decoder(recognition):
for char in TARGETS:
recognition = recognition.replace(char, SOURCES[TARGETS.index(char)])
if len(recognition) < 1:
return recognition
if recognition[-1] in TONES:
if len(recognition) < 2:
return recognition
replace_char = correct_tone_position(recognition)
tone = recognition[-1]
recognition = recognition[:-1]
for group in groups:
if replace_char in group:
recognition = recognition.replace(replace_char, group[TONES.index(tone)])
return recognition
CTLABELS = [' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', 'ˋ', 'ˊ', '﹒', 'ˀ', '˜', 'ˇ', 'ˆ', '˒', '‑']
def _decode_recognition(rec):
word = ''
rec = rec.tolist()
for c in rec:
if c>104:
continue
word += CTLABELS[c]
word = decoder(word)
return word
def build_model_main(args):
# we use register to maintain models from catdet6 on.
from models.registry import MODULE_BUILD_FUNCS
assert args.modelname in MODULE_BUILD_FUNCS._module_dict
build_func = MODULE_BUILD_FUNCS.get(args.modelname)
args.device = 'cuda'
model, criterion, postprocessors = build_func(args)
return model, criterion, postprocessors
model_config_path = "config/ESTS/ESTS_5scale_vintext_finetune.py" # change the path of the model config file
model_checkpoint_path = "vintext_checkpoint.pth" # change the path of the model checkpoint
args = SLConfig.fromfile(model_config_path)
model, criterion, postprocessors = build_model_main(args)
checkpoint = torch.load(model_checkpoint_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
model.eval()
model.cuda()
transform = T.Compose([
T.RandomResize([1000],max_size=1824),
T.ToTensor(),
T.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])]
)
image_dir = './datasets/vintext/test_image/'
dir = os.listdir(image_dir)
for idx, i in enumerate(dir):
image = Image.open(image_dir + i).convert('RGB')
image, _ = transform(image,None)
output = model(image[None].cuda())
output = postprocessors['bbox'](output, torch.Tensor([[1.0, 1.0]]))[0]
rec = [_decode_recognition(i) for i in output['rec']]
thershold = 0.3 # set a thershold
scores = output['scores']
labels = output['labels']
boxes = box_ops.box_xyxy_to_cxcywh(output['boxes'])
select_mask = scores > thershold
recs = []
for i,r in zip(select_mask,rec):
if i:
recs.append(r)
vslzr = COCOVisualizer()
# box_label = ['text' for item in rec[select_mask]]
pred_dict = {
'boxes': boxes[select_mask],
'size': torch.tensor([image.shape[1],image.shape[2]]),
'box_label': recs,
'image_id': idx,
'beziers': output['beziers'][select_mask],
}
vslzr.visualize(image, pred_dict, savedir='vis_fin_vin')