FORMAT_CONVERSION_MAXCOMPUTE/ocr/tools/infer/predict_system.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../..')))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
# print("sys.path", sys.path)
import cv2
import copy
import numpy as np
import time
from PIL import Image

os.environ['FLAGS_eager_delete_tensor_gb'] = '0'
import utility as utility
# import ocr.tools.infer.predict_rec as predict_rec
import ocr.tools.infer.predict_rec_pytorch as predict_rec  # pytorch rec model
# import ocr.tools.infer.predict_det as predict_det
import ocr.tools.infer.predict_det_pytorch as predict_det  # pytorch det model
import ocr.tools.infer.predict_cls as predict_cls
from ocr.ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ocr.ppocr.utils.logging import get_logger
from ocr.tools.infer.utility import draw_ocr_box_txt
from format_convert.utils import has_intersection, log
from format_convert import _global

logger = get_logger()


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)

    def get_rotate_crop_image(self, img, points):
        '''
        img_height, img_width = img.shape[0:2]
        left = int(np.min(points[:, 0]))
        right = int(np.max(points[:, 0]))
        top = int(np.min(points[:, 1]))
        bottom = int(np.max(points[:, 1]))
        img_crop = img[top:bottom, left:right, :].copy()
        points[:, 0] = points[:, 0] - left
        points[:, 1] = points[:, 1] - top
        '''
        # img_crop_width = int(
        #     max(
        #         np.linalg.norm(points[0] - points[1]),
        #         np.linalg.norm(points[2] - points[3])))
        # img_crop_height = int(
        #     max(
        #         np.linalg.norm(points[0] - points[3]),
        #         np.linalg.norm(points[1] - points[2])))
        # pts_std = np.float32([[0, 0], [img_crop_width, 0],
        #                       [img_crop_width, img_crop_height],
        #                       [0, img_crop_height]])
        # M = cv2.getPerspectiveTransform(points, pts_std)
        # dst_img = cv2.warpPerspective(
        #     img,
        #     M, (img_crop_width, img_crop_height),
        #     borderMode=cv2.BORDER_REPLICATE,
        #     flags=cv2.INTER_CUBIC)
        # print('dst_img.shape', dst_img.shape)
        #
        # print('points', points)
        w = abs(points[2][0] - points[0][0])
        h = abs(points[2][1] - points[0][1])
        dst_img = img[int(points[0][1]):int(points[0][1] + h), int(points[0][0]):int(points[0][0] + w), :]
        # print('dst_img.shape2', dst_img.shape)
        # cv2.imshow('dst_img', dst_img)
        # cv2.waitKey(0)
        # dst_img_height, dst_img_width = dst_img.shape[0:2]
        # # if dst_img_height * 1.0 / dst_img_width >= 1.5:
        # if dst_img_height * 1.0 / dst_img_width >= 2.0:
        #     dst_img = np.rot90(dst_img)
        return dst_img

    def print_draw_crop_rec_res(self, img_crop_list, rec_res):
        bbox_num = len(img_crop_list)
        for bno in range(bbox_num):
            cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
            logger.info(bno, rec_res[bno])

    def __call__(self, img):
        # print('into TextSystem __call__')
        # cv2.imshow('img',img)
        # cv2.waitKey(0)
        ori_im = img.copy()
        dt_boxes, elapse = self.text_detector(img)
        logger.info("dt_boxes num : {}, elapse : {}".format(
            len(dt_boxes), elapse))
        if dt_boxes is None:
            return [], []

        temp_list = []
        # print('dt_boxes', type(dt_boxes))
        # print('dt_boxes.shape', dt_boxes.shape)
        # 过滤一些比例离谱的box
        for b in dt_boxes:
            w = b[2][0] - b[0][0]
            h = b[2][1] - b[0][1]
            if h == 0 or w == 0 \
                    or h >= 10000 or w >= 10000 \
                    or w / h <= 0.5 or w / h >= 100:
                continue
            temp_list.append(b)

        if not temp_list:
            return [], []
        dt_boxes = np.array(temp_list)
        # print('dt_boxes.shape2', dt_boxes.shape)

        # show
        # for b in dt_boxes:
        #     p1 = [int(x) for x in b[0]]
        #     p2 = [int(x) for x in b[2]]
        #     cv2.rectangle(img, p1, p2, (0, 0, 255))
        # cv2.namedWindow('img', cv2.WINDOW_NORMAL)
        # cv2.imshow('img', img)
        # cv2.waitKey(0)

        # # 检测过多单字box，返回None
        # if len(dt_boxes) >= 150:
        #     short_box_cnt = 0
        #     long_box_cnt = 0
        #     for b in dt_boxes:
        #         w = b[2][0] - b[0][0]
        #         h = b[2][1] - b[0][1]
        #         if w / h < 1.3:
        #             short_box_cnt += 1
        #         if w / h >= 3:
        #             long_box_cnt += 1
        #         print('dt_boxes', w, h, round(w/h, 3))
        #     # print('short_box_cnt, len(dt_boxes)', short_box_cnt, len(dt_boxes))
        #     log('short_box_cnt, long_box_cnt, len(dt_boxes) ' + str([short_box_cnt, long_box_cnt, len(dt_boxes)]))
        #     if short_box_cnt >= 2/3 * len(dt_boxes) and long_box_cnt < 10:
        #         # print('short_box_cnt >= 2/3 * len(dt_boxes), return None')
        #         log('short_box_cnt >= 2/3 * len(dt_boxes), return None. ' + str([short_box_cnt, long_box_cnt, len(dt_boxes)]))
        #         return [], []

        img_crop_list = []
        dt_boxes = sorted_boxes(dt_boxes)
        for bno in range(len(dt_boxes)):
            tmp_box = copy.deepcopy(dt_boxes[bno])
            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
            img_crop_list.append(img_crop)
        # print('system len(img_crop_list)', len(img_crop_list))
        # for img in img_crop_list:
        #     if img.shape[1] / img.shape[0] <= 0.5:
        # print('system img.shape[1] / img.shape[0] <= 0.5', img.shape)

        if self.use_angle_cls:
            img_crop_list, angle_list, elapse = self.text_classifier(
                img_crop_list)
            logger.info("cls num  : {}, elapse : {}".format(
                len(img_crop_list), elapse))

        rec_res, elapse = self.text_recognizer(img_crop_list)
        logger.info("rec_res num  : {}, elapse : {}".format(
            len(rec_res), elapse))
        # self.print_draw_crop_rec_res(img_crop_list, rec_res)
        filter_boxes, filter_rec_res = [], []

        # dt_boxes 上下重合检测框修正
        # t1 = time.time()
        dt_boxes = boxex_points_fixup(dt_boxes)
        # print("boxex_points_fixup cost:",time.time()-t1)

        for box, rec_reuslt in zip(dt_boxes, rec_res):
            text, score = rec_reuslt
            if score >= self.drop_score:
                filter_boxes.append(box)
                filter_rec_res.append(rec_reuslt)
        return filter_boxes, filter_rec_res


def boxex_points_fixup(dt_boxes):
    # 检查框全部转换为矩形
    # for i in range(len(dt_boxes)):
    #     box1 = dt_boxes[i]
    #     x_list = [box1[0][0],box1[1][0],box1[2][0],box1[3][0]]
    #     y_list = [box1[0][1],box1[1][1],box1[2][1],box1[3][1]]
    #     x_max = max(x_list)
    #     x_min = min(x_list)
    #     y_max = max(y_list)
    #     y_min = min(y_list)
    #     dt_boxes[i] = np.array([[x_min,y_min],[x_max,y_min],[x_max,y_max],[x_min,y_max]])

    for i in range(len(dt_boxes)):
        box1 = dt_boxes[i]
        box1_point3 = box1[2]
        box1_point4 = box1[3]  # 四边形底边的两点坐标
        bottom_line = (min(box1_point3[0], box1_point4[0]), max(box1_point3[0], box1_point4[0]))
        bottom_line_len = abs(bottom_line[1] - bottom_line[0])

        for j in range(i + 1, len(dt_boxes)):
            box2 = dt_boxes[j]
            box2_point1 = box2[0]
            box2_point2 = box2[1]  # 四边形顶边的两点坐标
            top_line = (min(box2_point1[0], box2_point2[0]), max(box2_point1[0], box2_point2[0]))
            top_line_len = abs(top_line[1] - top_line[0])
            if has_intersection(box1, box2):  # 四边形框是否有交集
                if not (min(top_line) >= max(bottom_line) or min(bottom_line) >= max(top_line)):  # x轴方向上有交集
                    # 求重合部分y中间值
                    mid_y = ((box2_point1[1] + box2_point2[1]) / 2 + (box1_point3[1] + box1_point4[1]) / 2) // 2
                    if not mid_y:
                        continue
                    max_line_len = max(bottom_line_len, top_line_len)
                    cross_line_len = bottom_line_len + top_line_len - \
                                     (max(bottom_line[1], bottom_line[0], top_line[1], top_line[0]) - min(
                                         bottom_line[1], bottom_line[0], top_line[1], top_line[0]))
                    # print(cross_line_len,max_line_len,cross_line_len/max_line_len)
                    if cross_line_len / max_line_len >= 0.55:  # 重合比例
                        box1[2] = [box1_point3[0], mid_y]
                        box1[3] = [box1_point4[0], mid_y]
                        box2[0] = [box2_point1[0], mid_y]
                        box2[1] = [box2_point2[0], mid_y]
                        break

    return dt_boxes


def sorted_boxes(dt_boxes):
    """
    Sort text boxes in order from top to bottom, left to right
    args:
        dt_boxes(array):detected text boxes with shape [4, 2]
    return:
        sorted boxes(array) with shape [4, 2]
    """
    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):
        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
            tmp = _boxes[i]
            _boxes[i] = _boxes[i + 1]
            _boxes[i + 1] = tmp
    return _boxes


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    text_sys = TextSystem(args)
    is_visualize = True
    font_path = args.vis_font_path
    drop_score = args.drop_score
    for image_file in image_file_list:
        img, flag = check_and_read_gif(image_file)
        if not flag:
            img = cv2.imread(image_file)
        if img is None:
            logger.info("error in loading image:{}".format(image_file))
            continue
        starttime = time.time()
        dt_boxes, rec_res = text_sys(img)
        elapse = time.time() - starttime
        logger.info("Predict time of %s: %.3fs" % (image_file, elapse))

        for text, score in rec_res:
            logger.info("{}, {:.3f}".format(text, score))

        if is_visualize:
            image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            boxes = dt_boxes
            txts = [rec_res[i][0] for i in range(len(rec_res))]
            scores = [rec_res[i][1] for i in range(len(rec_res))]

            draw_img = draw_ocr_box_txt(
                image,
                boxes,
                txts,
                scores,
                drop_score=drop_score,
                font_path=font_path)
            draw_img_save = "./inference_results/"
            if not os.path.exists(draw_img_save):
                os.makedirs(draw_img_save)
            cv2.imwrite(
                os.path.join(draw_img_save, os.path.basename(image_file)),
                draw_img[:, :, ::-1])
            logger.info("The visualized image saved in {}".format(
                os.path.join(draw_img_save, os.path.basename(image_file))))


if __name__ == "__main__":
    main(utility.parse_args())

    pass