app.py

import os

os.system('cd fairseq;'
          'pip install --use-feature=in-tree-build ./; cd ..')

os.system('cd ezocr;'
          'pip install .; cd ..')

import torch
import numpy as np
from fairseq import utils, tasks
from fairseq import checkpoint_utils
from utils.eval_utils import eval_step
from data.mm_data.ocr_dataset import ocr_resize
from tasks.mm_tasks.ocr import OcrTask
from PIL import Image, ImageDraw
from torchvision import transforms
from typing import List, Tuple
import cv2
from easyocrlite import ReaderLite
import gradio as gr


# Register refcoco task
tasks.register_task('ocr', OcrTask)

os.system('wget https://shuangqing-multimodal.oss-cn-zhangjiakou.aliyuncs.com/ocr_general_clean.pt; '
          'mkdir -p checkpoints; mv ocr_general_clean.pt checkpoints/ocr.pt')

# turn on cuda if GPU is available
use_cuda = torch.cuda.is_available()
# use fp16 only when GPU is available
use_fp16 = False

mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]

Rect = Tuple[int, int, int, int]
FourPoint = Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int], Tuple[int, int]]


def four_point_transform(image: np.ndarray, rect: FourPoint) -> np.ndarray:
    (tl, tr, br, bl) = rect

    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))

    # compute the height of the new image, which will be the
    # maximum distance between the top-right and bottom-right
    # y-coordinates or the top-left and bottom-left y-coordinates
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    dst = np.array(
        [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
        dtype="float32",
    )

    # compute the perspective transform matrix and then apply it
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

    return warped


def get_images(image_path: str, reader: ReaderLite, **kwargs):
    results = reader.process(image_path, **kwargs)
    return results


def draw_boxes(image, bounds, color='red', width=2):
    draw = ImageDraw.Draw(image)
    for bound in bounds:
        p0, p1, p2, p3 = bound
        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
    return image


def encode_text(task, text, length=None, append_bos=False, append_eos=False):
    bos_item = torch.LongTensor([task.src_dict.bos()])
    eos_item = torch.LongTensor([task.src_dict.eos()])
    pad_idx = task.src_dict.pad()

    s = task.tgt_dict.encode_line(
        line=task.bpe.encode(text),
        add_if_not_exist=False,
        append_eos=False
    ).long()
    if length is not None:
        s = s[:length]
    if append_bos:
        s = torch.cat([bos_item, s])
    if append_eos:
        s = torch.cat([s, eos_item])
    return s


def patch_resize_transform(patch_image_size=480, is_document=False):
    _patch_resize_transform = transforms.Compose(
        [
            lambda image: ocr_resize(
                image, patch_image_size, is_document=is_document
            ),
            transforms.ToTensor(),
            transforms.Normalize(mean=mean, std=std),
        ]
    )
    
    return _patch_resize_transform


reader = ReaderLite()
overrides={"eval_cider": False, "beam": 8, "max_len_b": 128, "patch_image_size": 480,
           "orig_patch_image_size": 224, "no_repeat_ngram_size": 0, "seed": 7}
models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
    utils.split_paths('checkpoints/ocr.pt'),
    arg_overrides=overrides
)

# Move models to GPU
for model in models:
    model.eval()
    if use_fp16:
        model.half()
    if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
        model.cuda()
    model.prepare_for_inference_(cfg)

# Initialize generator
generator = task.build_generator(models, cfg.generation)

bos_item = torch.LongTensor([task.src_dict.bos()])
eos_item = torch.LongTensor([task.src_dict.eos()])
pad_idx = task.src_dict.pad()


# Construct input for caption task
def construct_sample(task, image: Image, patch_image_size=480):
    patch_image = patch_resize_transform(patch_image_size)(image).unsqueeze(0)
    patch_mask = torch.tensor([True])
    src_text = encode_text(task, "图片上的文字是什么?", append_bos=True, append_eos=True).unsqueeze(0)
    src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text])
    sample = {
        "id":np.array(['42']),
        "net_input": {
            "src_tokens": src_text,
            "src_lengths": src_length,
            "patch_images": patch_image,
            "patch_masks": patch_mask,
        },
        "target": None
    }
    return sample


# Function to turn FP32 to FP16
def apply_half(t):
    if t.dtype is torch.float32:
        return t.to(dtype=torch.half)
    return t


def ocr(img):
    out_img = img
    results = get_images(img, reader)
    box_list, image_list = zip(*results)
    draw_boxes(out_img, box_list)

    ocr_result = []
    for box, image in zip(box_list, image_list):
        image = Image.fromarray(image)
        sample = construct_sample(task, image, cfg.task.patch_image_size)
        sample = utils.move_to_cuda(sample) if use_cuda else sample
        sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample

        with torch.no_grad():
            result, scores = eval_step(task, generator, models, sample)
        ocr_result.append(result[0]['ocr'].replace(' ', ''))

    result = '\n'.join(ocr_result)

    return out_img, result


title = "OFA-OCR"
description = "Gradio Demo for OFA-OCR. Upload your own image or click any one of the examples, and click " \
              "\"Submit\" and then wait for the generated OCR result.  "
article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
          "Repo</a></p> "
examples = [['lihe.png'], ['chinese.jpg'], ['paibian.jpeg'], ['shupai.png'], ['zuowen.jpg']]
io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='pil'),
                  outputs=[gr.outputs.Image(type='pil'), gr.outputs.Textbox(label="OCR result")],
                  title=title, description=description, article=article, examples=examples,
                  allow_flagging=False, allow_screenshot=False)
io.launch(cache_examples=True)