PaddleOcr_v4/tools/infer/predict_system_1.py

import os
import sys
import subprocess
import cv2
import copy
import numpy as np
from PIL import Image
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.predict_cls as predict_cls
from ppocr.utils.utility import get_image_file_list, check_and_read
from tools.infer.utility import (
    get_rotate_crop_image,
    get_minarea_rect_crop,
    slice_generator,
    merge_fragmented,
)


class TextSystem(object):
    def __init__(self, args):
        self.text_detector = predict_det.TextDetector(args)
        self.text_recognizer = predict_rec.TextRecognizer(args)
        self.use_angle_cls = args.use_angle_cls
        self.drop_score = args.drop_score
        if self.use_angle_cls:
            self.text_classifier = predict_cls.TextClassifier(args)

        self.args = args
        self.crop_image_res_index = 0

    def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
        os.makedirs(output_dir, exist_ok=True)
        bbox_num = len(img_crop_list)
        for bno in range(bbox_num):
            cv2.imwrite(
                os.path.join(
                    output_dir, f"mg_crop_{bno + self.crop_image_res_index}.jpg"
                ),
                img_crop_list[bno],
            )
        self.crop_image_res_index += bbox_num

    def __call__(self, img, cls=True, slice={}):
        if img is None:
            return None, None, {}

        ori_im = img.copy()
        if slice:
            slice_gen = slice_generator(
                img,
                horizontal_stride=slice["horizontal_stride"],
                vertical_stride=slice["vertical_stride"],
            )
            elapsed = []
            dt_slice_boxes = []
            for slice_crop, v_start, h_start in slice_gen:
                dt_boxes, elapse = self.text_detector(slice_crop, use_slice=True)
                if dt_boxes.size:
                    dt_boxes[:, :, 0] += h_start
                    dt_boxes[:, :, 1] += v_start
                    dt_slice_boxes.append(dt_boxes)
                    elapsed.append(elapse)
            dt_boxes = np.concatenate(dt_slice_boxes)

            dt_boxes = merge_fragmented(
                boxes=dt_boxes,
                x_threshold=slice["merge_x_thres"],
                y_threshold=slice["merge_y_thres"],
            )
            elapse = sum(elapsed)
        else:
            dt_boxes, elapse = self.text_detector(img)

        if dt_boxes is None:
            return None, None, {}

        img_crop_list = []

        dt_boxes = sorted_boxes(dt_boxes)

        for bno in range(len(dt_boxes)):
            tmp_box = copy.deepcopy(dt_boxes[bno])
            if self.args.det_box_type == "quad":
                img_crop = get_rotate_crop_image(ori_im, tmp_box)
            else:
                img_crop = get_minarea_rect_crop(ori_im, tmp_box)
            img_crop_list.append(img_crop)
        if self.use_angle_cls and cls:
            img_crop_list, angle_list, elapse = self.text_classifier(img_crop_list)
        if len(img_crop_list) > 1000:
            pass

        rec_res, elapse = self.text_recognizer(img_crop_list)
        filter_boxes, filter_rec_res = [], []
        for box, rec_result in zip(dt_boxes, rec_res):
            text, score = rec_result[0], rec_result[1]
            if score >= self.drop_score:
                filter_boxes.append(box)
                filter_rec_res.append(rec_result)
        return filter_boxes, filter_rec_res, {}


def sorted_boxes(dt_boxes):
    num_boxes = dt_boxes.shape[0]
    sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
    _boxes = list(sorted_boxes)

    for i in range(num_boxes - 1):
        for j in range(i, -1, -1):
            if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and (
                    _boxes[j + 1][0][0] < _boxes[j][0][0]
            ):
                tmp = _boxes[j]
                _boxes[j] = _boxes[j + 1]
                _boxes[j + 1] = tmp
            else:
                break
    return _boxes


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    image_file_list = image_file_list[args.process_id:: args.total_process_num]
    text_sys = TextSystem(args)

    # Warm-up (optional)
    if args.warmup:
        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
        for i in range(10):
            text_sys(img)

    for idx, image_file in enumerate(image_file_list):
        img, flag_gif, flag_pdf = check_and_read(image_file)
        if not flag_gif and not flag_pdf:
            img = cv2.imread(image_file)
        if not flag_pdf:
            if img is None:
                continue
            imgs = [img]
        else:
            page_num = args.page_num
            if page_num > len(img) or page_num == 0:
                page_num = len(img)
            imgs = img[:page_num]

        for index, img in enumerate(imgs):
            dt_boxes, rec_res, _ = text_sys(img)

            # Output the recognized text
            for text, _ in rec_res:
                print(f"{text}")


if __name__ == "__main__":
    args = utility.parse_args()
    if args.use_mp:
        p_list = []
        total_process_num = args.total_process_num
        for process_id in range(total_process_num):
            cmd = (
                    [sys.executable, "-u"]
                    + sys.argv
                    + ["--process_id={}".format(process_id), "--use_mp={}".format(False)]
            )
            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
            p_list.append(p)
        for p in p_list:
            p.wait()
    else:
        main(args)