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import gradio as gr
import numpy as np
import cv2
import tensorflow as tf
import keras
from keras import layers, models
model = model = tf.keras.models.load_model('model/ocr_model.h5')
def preprocessImage(img, shape):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (shape))
img = (img/255).astype(np.float32)
img = img.T
img = np.expand_dims(img, axis=-1)
return img
label2char ={0: ' ',
1: "'",
2: '-',
3: 'A',
4: 'B',
5: 'C',
6: 'D',
7: 'E',
8: 'F',
9: 'G',
10: 'H',
11: 'I',
12: 'J',
13: 'K',
14: 'L',
15: 'M',
16: 'N',
17: 'O',
18: 'P',
19: 'Q',
20: 'R',
21: 'S',
22: 'T',
23: 'U',
24: 'V',
25: 'W',
26: 'X',
27: 'Y',
28: 'Z',
29: '`'}
def getStringFromEncode(lst :list):
return ''.join([label2char[i] if i in label2char else '' for i in lst])
def decode_batch_predictions(pred):
pred = pred[:, :-2] # first two layers of ctc garbage
input_len = np.ones(pred.shape[0])*pred.shape[1]
results = keras.backend.ctc_decode(pred,
input_length=input_len,
greedy=True)[0][0]
output_text = []
for res in results.numpy():
outstr = getStringFromEncode(res)
output_text.append(outstr)
# return final text results
return output_text
def predict(img):
img = preprocessImage(img, (256,64))
img = np.expand_dims(img, axis=0) # 1 image in batch
preds = model.predict(img)
pred_texts = decode_batch_predictions(preds)
return pred_texts[0]
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |