init

backend/tools/infer/predict_e2e.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 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
+
+logger = get_logger()
+
+
+class TextE2E(object):
+    def __init__(self, args):
+        self.args = args
+        self.e2e_algorithm = args.e2e_algorithm
+        self.use_onnx = args.use_onnx
+        pre_process_list = [{
+            'E2EResizeForTest': {}
+        }, {
+            '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.e2e_algorithm == "PGNet":
+            pre_process_list[0] = {
+                'E2EResizeForTest': {
+                    'max_side_len': args.e2e_limit_side_len,
+                    'valid_set': 'totaltext'
+                }
+            }
+            postprocess_params['name'] = 'PGPostProcess'
+            postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
+            postprocess_params["character_dict_path"] = args.e2e_char_dict_path
+            postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
+            postprocess_params["mode"] = args.e2e_pgnet_mode
+        else:
+            logger.info("unknown e2e_algorithm:{}".format(self.e2e_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, _ = utility.create_predictor(
+            args, 'e2e', logger)  # paddle.jit.load(args.det_model_dir)
+        # self.predictor.eval()
+
+    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_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}
+        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()
+        starttime = time.time()
+
+        if self.use_onnx:
+            input_dict = {}
+            input_dict[self.input_tensor.name] = img
+            outputs = self.predictor.run(self.output_tensors, input_dict)
+            preds = {}
+            preds['f_border'] = outputs[0]
+            preds['f_char'] = outputs[1]
+            preds['f_direction'] = outputs[2]
+            preds['f_score'] = outputs[3]
+        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)
+
+            preds = {}
+            if self.e2e_algorithm == 'PGNet':
+                preds['f_border'] = outputs[0]
+                preds['f_char'] = outputs[1]
+                preds['f_direction'] = outputs[2]
+                preds['f_score'] = outputs[3]
+            else:
+                raise NotImplementedError
+        post_result = self.postprocess_op(preds, shape_list)
+        points, strs = post_result['points'], post_result['texts']
+        dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
+        elapse = time.time() - starttime
+        return dt_boxes, strs, elapse
+
+
+if __name__ == "__main__":
+    args = utility.parse_args()
+    image_file_list = get_image_file_list(args.image_dir)
+    text_detector = TextE2E(args)
+    count = 0
+    total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
+    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
+        points, strs, elapse = text_detector(img)
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        src_im = utility.draw_e2e_res(points, strs, image_file)
+        img_name_pure = os.path.split(image_file)[-1]
+        img_path = os.path.join(draw_img_save,
+                                "e2e_res_{}".format(img_name_pure))
+        cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
+    if count > 1:
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))