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ppstructure/layout/predict_layout.py

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+# 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 tools.infer.utility as utility
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read
+from ppstructure.utility import parse_args
+from picodet_postprocess import PicoDetPostProcess
+
+logger = get_logger()
+
+
+class LayoutPredictor(object):
+    def __init__(self, args):
+        pre_process_list = [
+            {"Resize": {"size": [800, 608]}},
+            {
+                "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"]}},
+        ]
+        postprocess_params = {
+            "name": "PicoDetPostProcess",
+            "layout_dict_path": args.layout_dict_path,
+            "score_threshold": args.layout_score_threshold,
+            "nms_threshold": args.layout_nms_threshold,
+        }
+
+        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, "layout", logger)
+        self.use_onnx = args.use_onnx
+
+    def __call__(self, img):
+        ori_im = img.copy()
+        data = {"image": img}
+        data = transform(data, self.preprocess_op)
+        img = data[0]
+
+        if img is None:
+            return None, 0
+
+        img = np.expand_dims(img, axis=0)
+        img = img.copy()
+
+        preds, elapse = 0, 1
+        starttime = time.time()
+
+        np_score_list, np_boxes_list = [], []
+        if self.use_onnx:
+            input_dict = {}
+            input_dict[self.input_tensor.name] = img
+            outputs = self.predictor.run(self.output_tensors, input_dict)
+            num_outs = int(len(outputs) / 2)
+            for out_idx in range(num_outs):
+                np_score_list.append(outputs[out_idx])
+                np_boxes_list.append(outputs[out_idx + num_outs])
+        else:
+            self.input_tensor.copy_from_cpu(img)
+            self.predictor.run()
+            output_names = self.predictor.get_output_names()
+            num_outs = int(len(output_names) / 2)
+            for out_idx in range(num_outs):
+                np_score_list.append(
+                    self.predictor.get_output_handle(
+                        output_names[out_idx]
+                    ).copy_to_cpu()
+                )
+                np_boxes_list.append(
+                    self.predictor.get_output_handle(
+                        output_names[out_idx + num_outs]
+                    ).copy_to_cpu()
+                )
+        preds = dict(boxes=np_score_list, boxes_num=np_boxes_list)
+
+        post_preds = self.postprocess_op(ori_im, img, preds)
+        elapse = time.time() - starttime
+        return post_preds, elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    layout_predictor = LayoutPredictor(args)
+    count = 0
+    total_time = 0
+
+    repeats = 50
+    for image_file in image_file_list:
+        img, flag, _ = check_and_read(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+
+        layout_res, elapse = layout_predictor(img)
+
+        logger.info("result: {}".format(layout_res))
+
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+
+
+if __name__ == "__main__":
+    main(parse_args())