File size: 3,924 Bytes
2758e03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
# Definição da arquitetura da rede neural
class CaptchaCNN(nn.Module):
def __init__(self, input_dim, output_ndigits, output_vocab_size, dropout=(0.25, 0.5), dense_units=200, vocab=None):
super().__init__()
self.input_dim = input_dim
self.output_ndigits = output_ndigits
self.output_vocab_size = output_vocab_size
self.vocab = vocab
self.batchnorm0 = nn.BatchNorm2d(3)
self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
self.batchnorm1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.batchnorm2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 64, kernel_size=3)
self.batchnorm3 = nn.BatchNorm2d(64)
self.dropout1 = nn.Dropout(dropout[0])
self.dropout2 = nn.Dropout(dropout[1])
# Cálculo das dimensões após as camadas convolucionais
def calc_dim(x):
for _ in range(3):
x = (x - 2) // 2
return x
conv_h = calc_dim(input_dim[0])
conv_w = calc_dim(input_dim[1])
fc1_in_features = conv_h * conv_w * 64
self.fc1 = nn.Linear(fc1_in_features, dense_units)
self.batchnorm_dense = nn.BatchNorm1d(dense_units)
self.fc2 = nn.Linear(dense_units, output_vocab_size * output_ndigits)
def forward(self, x):
x = self.batchnorm0(x)
x = F.relu(self.batchnorm1(F.max_pool2d(self.conv1(x), 2)))
x = F.relu(self.batchnorm2(F.max_pool2d(self.conv2(x), 2)))
x = F.relu(self.batchnorm3(F.max_pool2d(self.conv3(x), 2)))
x = torch.flatten(x, start_dim=1)
x = self.dropout1(x)
x = F.relu(self.batchnorm_dense(self.fc1(x)))
x = self.dropout2(x)
x = self.fc2(x)
x = x.view(-1, self.output_ndigits, self.output_vocab_size)
return x
# Classe principal para carregar o modelo e fazer previsões
class TJMG:
def __init__(self, repo_id="julio/captcha", filename="captcha_model.safetensors"):
# Hiperparâmetros do modelo
self.input_dim = (40, 110)
self.output_vocab_size = 10
self.vocab = [str(x) for x in range(10)]
self.output_ndigits = 5
self.dropout = (0.25, 0.5)
self.dense_units = 200
# Baixar o modelo do Hugging Face
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Inicializar o modelo
self.model = CaptchaCNN(
input_dim=self.input_dim,
output_ndigits=self.output_ndigits,
output_vocab_size=self.output_vocab_size,
dropout=self.dropout,
dense_units=self.dense_units,
vocab=self.vocab
)
# Carregar os pesos do modelo
state_dict = load_file(model_path)
self.model.load_state_dict(state_dict)
self.model.eval()
# Transformação da imagem
self.transform = transforms.Compose([
transforms.Resize(self.input_dim),
transforms.ToTensor(),
])
def predict(self, image_path):
"""
Faz a previsão de um CAPTCHA.
Args:
image_path (str): Caminho da imagem do CAPTCHA.
Returns:
str: Texto previsto para o CAPTCHA.
"""
image = Image.open(image_path).convert('RGB')
image = self.transform(image).unsqueeze(0)
with torch.no_grad():
logits = self.model(image)
preds = torch.argmax(logits, dim=2).squeeze().tolist()
predicted_label = ''.join([self.vocab[i] for i in preds])
return predicted_label
|