predict_det.py

import os
import sys
import paddle

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'

import cv2
import numpy as np
import time
import sys
from scipy.spatial import distance as dist

import tools.infer.utility as utility
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
import json
logger = get_logger()


class TextDetector(object):
    def __init__(self, args):
        self.args = args
        self.det_algorithm = args.det_algorithm
        self.use_onnx = args.use_onnx
        pre_process_list = [{
            'DetResizeForSingle': None
        }, {
            'NormalizeImage': {
                'std': [0.229, 0.224, 0.225],
                'mean': [0.485, 0.456, 0.406],
                'scale': '1./255.',
                'order': 'hwc'
            }
        }, {
            'ToCHWImage': None
        }, {
            'KeepKeys': {
                'keep_keys': ['image', 'shape']
            }
        }]
        postprocess_params = {}
        if self.det_algorithm == "DB":
            postprocess_params['name'] = 'DBPostProcess'
            postprocess_params["thresh"] = args.det_db_thresh
            postprocess_params["box_thresh"] = args.det_db_box_thresh
            postprocess_params["max_candidates"] = 1000
            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
            postprocess_params["use_dilation"] = args.use_dilation
            postprocess_params["score_mode"] = args.det_db_score_mode
        else:
            logger.info("not support det_algorithm:{}".format(self.det_algorithm))
            sys.exit(0)

        self.preprocess_op = create_operators(pre_process_list)
        self.postprocess_op = build_post_process(postprocess_params)
        self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
            args, 'det', logger)

    def order_points_clockwise(self, pts):
        rect = np.zeros((4, 2), dtype="float32")
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]
        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
        diff = np.diff(np.array(tmp), axis=1)
        rect[1] = tmp[np.argmin(diff)]
        rect[3] = tmp[np.argmax(diff)]
        return rect

    def clip_det_res(self, points, img_height, img_width):
        for pno in range(points.shape[0]):
            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
        return points

    def filter_tag_det_res(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.order_points_clockwise(box)
            box = self.clip_det_res(box, img_height, img_width)
            rect_width = int(np.linalg.norm(box[0] - box[1]))
            rect_height = int(np.linalg.norm(box[0] - box[3]))
            if rect_width <= 3 or rect_height <= 3:
                continue
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.clip_det_res(box, img_height, img_width)
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def __call__(self, img):
        ori_im = img.copy()
        data = {'image': img}

        st = time.time()

        if self.args.benchmark:
            self.autolog.times.start()

        data = transform(data, self.preprocess_op)
        img, shape_list = data
        if img is None:
            return None, 0
        img = np.expand_dims(img, axis=0)
        shape_list = np.expand_dims(shape_list, axis=0)
        # print(img.shape)
        img = img.copy()

        self.input_tensor.copy_from_cpu(img)
        self.predictor.run()
        paddle.device.cuda.synchronize()
        outputs = []
        for output_tensor in self.output_tensors:
            output = output_tensor.copy_to_cpu()
            outputs.append(output)
        if self.args.benchmark:
            self.autolog.times.stamp()

        preds = {}
        if self.det_algorithm in ['DB', 'PSE']:
            preds['maps'] = outputs[0]
        else:
            raise NotImplementedError

        post_result = self.postprocess_op(preds, shape_list)
        dt_boxes = post_result[0]['points']
        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)

        if self.args.benchmark:
            self.autolog.times.end(stamp=True)
        et = time.time()
        return dt_boxes, et - st


if __name__ == "__main__":
    args = utility.parse_args()
    image_file_list = get_image_file_list(args.image_dir)
    text_detector = TextDetector(args)
    count = 0
    total_time = 0
    draw_img_save = "./inference_results"

    if args.warmup:
        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
        for i in range(2):
            res = text_detector(img)

    if not os.path.exists(draw_img_save):
        os.makedirs(draw_img_save)
    save_results = []
    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
        st = time.time()
        dt_boxes, _ = text_detector(img)
        elapse = time.time() - st
        if count > 0:
            total_time += elapse
        count += 1
        save_pred = os.path.basename(image_file) + "\t" + str(
            json.dumps([x.tolist() for x in dt_boxes])) + "\n"
        save_results.append(save_pred)
        logger.info(save_pred)
        logger.info("The predict time of {}: {}".format(image_file, elapse))
        src_im = utility.draw_text_det_res(dt_boxes, image_file)
        img_name_pure = os.path.split(image_file)[-1]
        img_path = os.path.join(draw_img_save,
                                "det_res_{}".format(img_name_pure))
        cv2.imwrite(img_path, src_im)
        logger.info("The visualized image saved in {}".format(img_path))

    with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
        f.writelines(save_results)
        f.close()
    if args.benchmark:
        text_detector.autolog.report()