predict_det_eval.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.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

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 = [{
            'DetResizeForTest': {
                'limit_side_len': args.det_limit_side_len,
                'limit_type': args.det_limit_type,
            }
        }, {
            '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
        elif self.det_algorithm == "EAST":
            postprocess_params['name'] = 'EASTPostProcess'
            postprocess_params["score_thresh"] = args.det_east_score_thresh
            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
        elif self.det_algorithm == "SAST":
            pre_process_list[0] = {
                'DetResizeForTest': {
                    'resize_long': args.det_limit_side_len
                }
            }
            postprocess_params['name'] = 'SASTPostProcess'
            postprocess_params["score_thresh"] = args.det_sast_score_thresh
            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
            self.det_sast_polygon = args.det_sast_polygon
            if self.det_sast_polygon:
                postprocess_params["sample_pts_num"] = 6
                postprocess_params["expand_scale"] = 1.2
                postprocess_params["shrink_ratio_of_width"] = 0.2
            else:
                postprocess_params["sample_pts_num"] = 2
                postprocess_params["expand_scale"] = 1.0
                postprocess_params["shrink_ratio_of_width"] = 0.3
        elif self.det_algorithm == "PSE":
            postprocess_params['name'] = 'PSEPostProcess'
            postprocess_params["thresh"] = args.det_pse_thresh
            postprocess_params["box_thresh"] = args.det_pse_box_thresh
            postprocess_params["min_area"] = args.det_pse_min_area
            postprocess_params["box_type"] = args.det_pse_box_type
            postprocess_params["scale"] = args.det_pse_scale
            self.det_pse_box_type = args.det_pse_box_type
        elif self.det_algorithm == "FCE":
            pre_process_list[0] = {
                'DetResizeForTest': {
                    'rescale_img': [1080, 736]
                }
            }
            postprocess_params['name'] = 'FCEPostProcess'
            postprocess_params["scales"] = args.scales
            postprocess_params["alpha"] = args.alpha
            postprocess_params["beta"] = args.beta
            postprocess_params["fourier_degree"] = args.fourier_degree
            postprocess_params["box_type"] = args.det_fce_box_type
        else:
            logger.info("unknown 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)

        if self.use_onnx:
            img_h, img_w = self.input_tensor.shape[2:]
            if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
                pre_process_list[0] = {
                    'DetResizeForTest': {
                        'image_shape': [img_h, img_w]
                    }
                }
        self.preprocess_op = create_operators(pre_process_list)

        if args.benchmark:
            import auto_log
            pid = os.getpid()
            gpu_id = utility.get_infer_gpuid()
            self.autolog = auto_log.AutoLogger(
                model_name="det",
                model_precision=args.precision,
                batch_size=1,
                data_shape="dynamic",
                save_path=None,
                inference_config=self.config,
                pids=pid,
                process_name=None,
                gpu_ids=gpu_id if args.use_gpu else None,
                time_keys=[
                    'preprocess_time', 'inference_time', 'postprocess_time'
                ],
                warmup=2,
                logger=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)
        img = img.copy()

        if self.args.benchmark:
            self.autolog.times.stamp()
        if self.use_onnx:
            input_dict = {}
            input_dict[self.input_tensor.name] = img
            outputs = self.predictor.run(self.output_tensors, input_dict)
        else:
            self.input_tensor.copy_from_cpu(img)
            self.predictor.run()
            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 == "EAST":
            preds['f_geo'] = outputs[0]
            preds['f_score'] = outputs[1]
        elif self.det_algorithm == 'SAST':
            preds['f_border'] = outputs[0]
            preds['f_score'] = outputs[1]
            preds['f_tco'] = outputs[2]
            preds['f_tvo'] = outputs[3]
        elif self.det_algorithm in ['DB', 'PSE']:
            preds['maps'] = outputs[0]
        elif self.det_algorithm == 'FCE':
            for i, output in enumerate(outputs):
                preds['level_{}'.format(i)] = output
        else:
            raise NotImplementedError

        #self.predictor.try_shrink_memory()
        post_result = self.postprocess_op(preds, shape_list)
        dt_boxes = post_result[0]['points']
        if (self.det_algorithm == "SAST" and self.det_sast_polygon) or (
                self.det_algorithm in ["PSE", "FCE"] and
                self.postprocess_op.box_type == 'poly'):
            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
        else:
            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__":
    from ppocr.metrics.eval_det_iou import DetectionIoUEvaluator
    evaluator = DetectionIoUEvaluator()
    args = utility.parse_args()

    # image_file_list = get_image_file_list(args.image_dir)
    def _check_image_file(path):
        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
        return any([path.lower().endswith(e) for e in img_end])

    def get_image_file_list_from_txt(img_file):
        imgs_lists = []
        label_lists = []
        if img_file is None or not os.path.exists(img_file):
            raise Exception("not found any img file in {}".format(img_file))

        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
        root_dir = img_file.split('/')[0]
        with open(img_file, 'r') as f:
            lines = f.readlines()
            for line in lines:
                line = line.replace('\n', '').split('\t')
                file_path, label = line[0], line[1]
                file_path = os.path.join(root_dir, file_path)
                if os.path.isfile(file_path) and _check_image_file(file_path):
                    imgs_lists.append(file_path)
                    label_lists.append(label)

        if len(imgs_lists) == 0:
            raise Exception("not found any img file in {}".format(img_file))
        return imgs_lists, label_lists

    image_file_list, label_list = get_image_file_list_from_txt(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 = []
    results = []
    for idx in range(len(image_file_list)):
        image_file = image_file_list[idx]
        label = json.loads(label_list[idx])
        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)

        # for eval
        gt_info_list = []
        det_info_list = []
        for dt_box in dt_boxes:
            det_info = {
                'points': np.array(
                    dt_box, dtype=np.float32),
                'text': ''
            }
            det_info_list.append(det_info)
        for lab in label:
            gt_info = {
                'points': np.array(
                    lab['points'], dtype=np.float32),
                'text': '',
                'ignore': False
            }
            gt_info_list.append(gt_info)
        result = evaluator.evaluate_image(gt_info_list, det_info_list)
        results.append(result)

    metrics = evaluator.combine_results(results)
    print('predict det eval on ', args.image_dir)
    print('metrics: ', metrics)

# 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()