app.py

import os
import gradio as gr
import omegaconf
import torch
import numpy

import easyocr
from PIL import Image

from vietocr.model.transformerocr import VietOCR
from vietocr.model.vocab import Vocab
from vietocr.translate import translate, process_input

reader = easyocr.Reader(['vi'])

examples_data = os.listdir('examples')
examples_data = [os.path.join('examples', line.split('\t')[0]) for line in examples_data]

config = omegaconf.OmegaConf.load("vgg-seq2seq.yaml")
config = omegaconf.OmegaConf.to_container(config, resolve=True)

vocab = Vocab(config['vocab'])
model = VietOCR(len(vocab),
        config['backbone'],
        config['cnn'], 
        config['transformer'],
        config['seq_modeling'])
model.load_state_dict(torch.load('train_old.pth', map_location=torch.device('cpu')))
def viet_ocr_predict(inp):
    img = process_input(inp, config['dataset']['image_height'], 
                    config['dataset']['image_min_width'], config['dataset']['image_max_width'])
    out = translate(img, model)[0].tolist()
    out = vocab.decode(out)
    return out
def predict(filepath):
    bounds = reader.readtext(filepath)
    im = Image.open(filepath)
    inp = numpy.asarray(im)

    #inp = cv2.imread(filepath)

    width, height, _ = inp.shape
    if width>height:
        height, width, _ = inp.shape

    texts=''
    for (bbox, text, prob) in bounds:
        (tl, tr, br, bl) = bbox
        tl = (int(tl[0]), int(tl[1]))
        tr = (int(tr[0]), int(tr[1]))
        br = (int(br[0]), int(br[1]))
        bl = (int(bl[0]), int(bl[1]))

        min_x = min(tl[0], tr[0], br[0], bl[0])
        min_x = max(0, min_x)
        max_x = max(tl[0], tr[0], br[0], bl[0])
        max_x = min(width-1, max_x)
        min_y = min(tl[1], tr[1], br[1], bl[1])
        min_y = max(0, min_y)
        max_y = max(tl[1], tr[1], br[1], bl[1])
        max_y = min(height-1, max_y)
        # crop the region of interest (ROI)
        try:
            cropped_image = inp[min_y:max_y,min_x:max_x,:] # crop the image
            cropped_image = Image.fromarray(cropped_image)
            out = viet_ocr_predict(cropped_image)
        except:
            out = text
        print(out)
        texts = texts + '\t' + out
    
    return texts

gr.Interface(fn=predict,
             title='Vietnamese Handwriting Recognition',
             inputs=gr.Image(type='filepath'),
             outputs=gr.Text(),
             #examples=examples_data,
).launch()