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import streamlit as st
from PIL import Image
import tensorflow as tf
import numpy as np
from keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import os
# Load custom CTC Layer if necessary
class CTCLayer(tf.keras.layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = tf.keras.backend.ctc_batch_cost
def call(self, y_true, y_pred, input_length, label_length):
# Compute the training-time loss value and add it
# to the layer using `self.add_loss()`.
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# On test time, just return the computed loss
return loss
# Load the trained model with a custom CTC layer if needed
@st.cache_resource
def load_model():
model_path = "model_ocr.h5" # Update with the correct model file path
model = tf.keras.models.load_model(model_path, custom_objects={"CTCLayer": CTCLayer})
return model
model = load_model()
# Menambahkan definisi img_width dan img_height
img_width, img_height = 200, 50 # Ganti sesuai dimensi input gambar yang digunakan oleh model Anda
# Definisikan max_length (misalnya panjang label maksimal)
max_length = 50 # Ganti sesuai dengan panjang label teks maksimal yang diinginkan
# Function to preprocess the image
def prepare_image(img):
# Resize gambar sesuai dengan ukuran yang diharapkan oleh model
img = img.resize((img_width, img_height)) # Resize to (200, 50)
# Konversi gambar ke array
img_array = img_to_array(img)
# Tambahkan dimensi untuk batch (menjadi 1, 50, 200) dan reshape ke bentuk (1, 50, 200, 1)
img_array = np.expand_dims(img_array, axis=0) # Tambahkan dimensi untuk batch
img_array = np.transpose(img_array, (0, 2, 1, 3)) # Mengubah urutan dimensi menjadi (1, 200, 50, 1)
# Menyusun input_length dan label_length untuk model OCR
input_length = np.ones((img_array.shape[0], 1)) * (img_width // 4) # Sesuaikan dengan input panjang
label_length = np.ones((img_array.shape[0], 1)) * max_length # Example label length
# Menambahkan input dummy untuk label (jika perlu untuk prediksi)
dummy_label = np.zeros((img_array.shape[0], max_length)) # Input dummy jika model mengharapkan label input
# Melakukan prediksi
preds = model.predict([img_array, input_length, label_length, dummy_label]) # Berikan 4 input
pred_texts = decode_batch_predictions(preds)
return pred_texts, preds
def decode_batch_predictions(pred):
# This function should convert the predictions (logits) to text
# Modify this function based on your specific character map
pred_texts = []
for i in range(pred.shape[0]):
pred_text = ''.join([characters[int(c)] for c in pred[i] if c != -1]) # Map to characters
pred_texts.append(pred_text)
return pred_texts
def run():
st.title("OCR Model Deployment")
# Upload image
img_file = st.file_uploader("Choose an Image", type=["jpg", "png"])
if img_file is not None:
img = Image.open(img_file).convert('L') # Convert to grayscale if needed
st.image(img, use_column_width=True)
# Save the uploaded image
upload_dir = './upload_images/'
os.makedirs(upload_dir, exist_ok=True)
save_image_path = os.path.join(upload_dir, img_file.name)
with open(save_image_path, "wb") as f:
f.write(img_file.getbuffer())
# Process the image and make prediction
pred_texts = prepare_image(img)
# Show predicted text
st.success(f"**Predicted Text: {pred_texts[0]}**")
if __name__ == "__main__":
run()
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