app.py

# -*- encoding: utf-8 -*-
import math
import random
import time
from pathlib import Path

import cv2
import gradio as gr
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from rapidocr_onnxruntime import RapidOCR


def draw_ocr_box_txt(image, boxes, txts, font_path,
                     scores=None, text_score=0.5):
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new('RGB', (w, h), (255, 255, 255))

    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and float(scores[idx]) < text_score:
            continue

        color = (random.randint(0, 255),
                 random.randint(0, 255),
                 random.randint(0, 255))

        box = [tuple(v) for v in box]
        draw_left.polygon(box, fill=color)
        draw_right.polygon([box[0][0], box[0][1],
                            box[1][0], box[1][1],
                            box[2][0], box[2][1],
                            box[3][0], box[3][1]],
                           outline=color)

        box_height = math.sqrt((box[0][0] - box[3][0])**2
                               + (box[0][1] - box[3][1])**2)

        box_width = math.sqrt((box[0][0] - box[1][0])**2
                              + (box[0][1] - box[1][1])**2)

        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
            font = ImageFont.truetype(font_path, font_size,
                                      encoding="utf-8")
            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
                draw_right.text((box[0][0] + 3, cur_y), c,
                                fill=(0, 0, 0), font=font)
                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
            draw_right.text([box[0][0], box[0][1]], txt,
                            fill=(0, 0, 0), font=font)

    img_left = Image.blend(image, img_left, 0.5)
    img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
    img_show.paste(img_left, (0, 0, w, h))
    img_show.paste(img_right, (w, 0, w * 2, h))
    return np.array(img_show)


def visualize(image_path, boxes, txts, scores,
              font_path="./FZYTK.TTF"):
    image = Image.open(image_path)

    draw_img = draw_ocr_box_txt(image, boxes,
                                txts, font_path,
                                scores,
                                text_score=0.5)

    draw_img_save = Path("./inference_results/")
    if not draw_img_save.exists():
        draw_img_save.mkdir(parents=True, exist_ok=True)

    time_stamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
    image_save = str(draw_img_save / f'{time_stamp}_{Path(image_path).name}')
    cv2.imwrite(image_save, draw_img[:, :, ::-1])
    return image_save


def inference(img_path, box_thresh=0.5, unclip_ratio=1.6, text_score=0.5,
              text_det=None, text_rec=None):
    det_model_path = str(Path('models') / 'text_det' / text_det)
    rec_model_path = str(Path('models') / 'text_rec' / text_rec)
    if 'v2' in rec_model_path:
        rec_image_shape = [3, 32, 320]
    else:
        rec_image_shape = [3, 48, 320]

    print('Init Class')
    s = time.time()
    rapid_ocr = RapidOCR(det_model_path=det_model_path,
                         rec_model_path=rec_model_path,
                         rec_img_shape=rec_image_shape)
    print(det_model_path, rec_model_path, rec_image_shape)
    elapse = time.time() - s
    print(elapse)

    img = cv2.imread(img_path)
    ocr_result, _ = rapid_ocr(img, box_thresh=box_thresh,
                              unclip_ratio=unclip_ratio,
                              text_score=text_score)
    if not ocr_result:
        return img_path, '未识别到有效文本'

    dt_boxes, rec_res, scores = list(zip(*ocr_result))
    img_save_path = visualize(img_path, dt_boxes, rec_res, scores)
    output_text = [f'{one_rec} {float(score):.4f}'
                   for one_rec, score in zip(rec_res, scores)]
    return img_save_path, output_text


examples = [['images/1.jpg'], ['images/ch_en_num.jpg']]

with gr.Blocks(title='RapidOCR') as demo:
    gr.Markdown("""
        <h1><center><a href="https://github.com/RapidAI/RapidOCR" target="_blank">Rapid⚡OCR</a></center></h1>

        ### Docs: [Docs](https://rapidocr.rtfd.io/)
        ### Parameters docs: [link](https://github.com/RapidAI/RapidOCR/tree/main/python#configyaml%E4%B8%AD%E5%B8%B8%E7%94%A8%E5%8F%82%E6%95%B0%E4%BB%8B%E7%BB%8D)
        - **box_thresh**: 检测到的框是文本的概率，值越大，框中是文本的概率就越大。存在漏检时，调低该值。取值范围：[0, 1.0]
        - **unclip_ratio**: 控制文本检测框的大小，值越大，检测框整体越大。在出现框截断文字的情况，调大该值。取值范围：[1.5, 2.0]
        - **text_score**: 文本识别结果是正确的置信度，值越大，显示出的识别结果更准确。存在漏检时，调低该值。取值范围：[0, 1.0]
        ### 运行环境：
        Python: 3.8 | onnxruntime: 1.14.1 | rapidocr_onnxruntime: 1.2.5""")
    gr.Markdown('**超参数调节**')
    with gr.Row():
        box_thresh = gr.Slider(minimum=0, maximum=1.0, value=0.5,
                               label='box_thresh', step=0.1,
                               interactive=True,
                               info='[0, 1.0]')
        unclip_ratio = gr.Slider(minimum=1.5, maximum=2.0, value=1.6,
                                 label='unclip_ratio', step=0.1,
                                 interactive=True,
                                 info='[1.5, 2.0]')
        text_score = gr.Slider(minimum=0, maximum=1.0, value=0.5,
                               label='text_score', step=0.1,
                               interactive=True,
                               info='[0, 1.0]')

    gr.Markdown('**模型选择**')
    with gr.Row():
        text_det = gr.Dropdown(['ch_PP-OCRv3_det_infer.onnx',
                                'ch_PP-OCRv2_det_infer.onnx',
                                'ch_ppocr_server_v2.0_det_infer.onnx'],
                               label='选择文本检测模型',
                               value='ch_PP-OCRv3_det_infer.onnx',
                               interactive=True)

        text_rec = gr.Dropdown(['ch_PP-OCRv3_rec_infer.onnx',
                                'ch_PP-OCRv2_rec_infer.onnx',
                                'ch_ppocr_server_v2.0_rec_infer.onnx'],
                               label='选择文本识别模型',
                               value='ch_PP-OCRv3_rec_infer.onnx',
                               interactive=True)

    with gr.Row():
        input_img = gr.Image(type='filepath', label='Input')
        out_img = gr.Image(type='filepath', label='Output')
    out_txt = gr.outputs.Textbox(type='text', label='RecText')
    button = gr.Button('Submit')
    button.click(fn=inference,
                 inputs=[input_img, box_thresh, unclip_ratio, text_score,
                         text_det, text_rec],
                 outputs=[out_img, out_txt])
    gr.Examples(examples=examples,
                inputs=[input_img, box_thresh, unclip_ratio, text_score,
                        text_det, text_rec],
                outputs=[out_img, out_txt], fn=inference)
demo.launch(debug=True, enable_queue=True)