Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into table_pr

ppocr/postprocess/__init__.py

@@ -34,6 +34,7 @@ from .pg_postprocess import PGPostProcess
 from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess, DistillationSerPostProcess
 from .vqa_token_re_layoutlm_postprocess import VQAReTokenLayoutLMPostProcess, DistillationRePostProcess
 from .table_postprocess import TableMasterLabelDecode, TableLabelDecode
+from .picodet_postprocess import PicoDetPostProcess
 
 
 def build_post_process(config, global_config=None):
@@ -47,7 +48,7 @@ def build_post_process(config, global_config=None):
         'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode',
         'TableMasterLabelDecode', 'SPINLabelDecode',
         'DistillationSerPostProcess', 'DistillationRePostProcess',
-        'VLLabelDecode'
+        'VLLabelDecode', 'PicoDetPostProcess'
     ]
 
     if config['name'] == 'PSEPostProcess':

ppocr/postprocess/picodet_postprocess.py

+# Copyright (c) 2021 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 numpy as np
+from scipy.special import softmax
+
+
+def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
+    """
+    Args:
+        box_scores (N, 5): boxes in corner-form and probabilities.
+        iou_threshold: intersection over union threshold.
+        top_k: keep top_k results. If k <= 0, keep all the results.
+        candidate_size: only consider the candidates with the highest scores.
+    Returns:
+         picked: a list of indexes of the kept boxes
+    """
+    scores = box_scores[:, -1]
+    boxes = box_scores[:, :-1]
+    picked = []
+    indexes = np.argsort(scores)
+    indexes = indexes[-candidate_size:]
+    while len(indexes) > 0:
+        current = indexes[-1]
+        picked.append(current)
+        if 0 < top_k == len(picked) or len(indexes) == 1:
+            break
+        current_box = boxes[current, :]
+        indexes = indexes[:-1]
+        rest_boxes = boxes[indexes, :]
+        iou = iou_of(
+            rest_boxes,
+            np.expand_dims(
+                current_box, axis=0), )
+        indexes = indexes[iou <= iou_threshold]
+
+    return box_scores[picked, :]
+
+
+def iou_of(boxes0, boxes1, eps=1e-5):
+    """Return intersection-over-union (Jaccard index) of boxes.
+    Args:
+        boxes0 (N, 4): ground truth boxes.
+        boxes1 (N or 1, 4): predicted boxes.
+        eps: a small number to avoid 0 as denominator.
+    Returns:
+        iou (N): IoU values.
+    """
+    overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
+    overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
+
+    overlap_area = area_of(overlap_left_top, overlap_right_bottom)
+    area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
+    area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
+    return overlap_area / (area0 + area1 - overlap_area + eps)
+
+
+def area_of(left_top, right_bottom):
+    """Compute the areas of rectangles given two corners.
+    Args:
+        left_top (N, 2): left top corner.
+        right_bottom (N, 2): right bottom corner.
+    Returns:
+        area (N): return the area.
+    """
+    hw = np.clip(right_bottom - left_top, 0.0, None)
+    return hw[..., 0] * hw[..., 1]
+
+
+class PicoDetPostProcess(object):
+    """
+    Args:
+        input_shape (int): network input image size
+        ori_shape (int): ori image shape of before padding
+        scale_factor (float): scale factor of ori image
+        enable_mkldnn (bool): whether to open MKLDNN
+    """
+
+    def __init__(self,
+                 layout_dict_path,
+                 strides=[8, 16, 32, 64],
+                 score_threshold=0.4,
+                 nms_threshold=0.5,
+                 nms_top_k=1000,
+                 keep_top_k=100):
+        self.labels = self.load_layout_dict(layout_dict_path)
+        self.strides = strides
+        self.score_threshold = score_threshold
+        self.nms_threshold = nms_threshold
+        self.nms_top_k = nms_top_k
+        self.keep_top_k = keep_top_k
+
+    def load_layout_dict(self, layout_dict_path):
+        with open(layout_dict_path, 'r', encoding='utf-8') as fp:
+            labels = fp.readlines()
+        return [label.strip('\n') for label in labels]
+
+    def warp_boxes(self, boxes, ori_shape):
+        """Apply transform to boxes
+        """
+        width, height = ori_shape[1], ori_shape[0]
+        n = len(boxes)
+        if n:
+            # warp points
+            xy = np.ones((n * 4, 3))
+            xy[:, :2] = boxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(
+                n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+            # xy = xy @ M.T  # transform
+            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
+            # create new boxes
+            x = xy[:, [0, 2, 4, 6]]
+            y = xy[:, [1, 3, 5, 7]]
+            xy = np.concatenate(
+                (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+            # clip boxes
+            xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
+            xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
+            return xy.astype(np.float32)
+        else:
+            return boxes
+
+    def img_info(self, ori_img, img):
+        origin_shape = ori_img.shape
+        resize_shape = img.shape
+        im_scale_y = resize_shape[2] / float(origin_shape[0])
+        im_scale_x = resize_shape[3] / float(origin_shape[1])
+        scale_factor = np.array([im_scale_y, im_scale_x], dtype=np.float32)
+        img_shape = np.array(img.shape[2:], dtype=np.float32)
+
+        input_shape = np.array(img).astype('float32').shape[2:]
+        ori_shape = np.array((img_shape, )).astype('float32')
+        scale_factor = np.array((scale_factor, )).astype('float32')
+        return ori_shape, input_shape, scale_factor
+
+    def __call__(self, ori_img, img, preds):
+        scores, raw_boxes = preds['boxes'], preds['boxes_num']
+        batch_size = raw_boxes[0].shape[0]
+        reg_max = int(raw_boxes[0].shape[-1] / 4 - 1)
+        out_boxes_num = []
+        out_boxes_list = []
+        results = []
+        ori_shape, input_shape, scale_factor = self.img_info(ori_img, img)
+
+        for batch_id in range(batch_size):
+            # generate centers
+            decode_boxes = []
+            select_scores = []
+            for stride, box_distribute, score in zip(self.strides, raw_boxes,
+                                                     scores):
+                box_distribute = box_distribute[batch_id]
+                score = score[batch_id]
+                # centers
+                fm_h = input_shape[0] / stride
+                fm_w = input_shape[1] / stride
+                h_range = np.arange(fm_h)
+                w_range = np.arange(fm_w)
+                ww, hh = np.meshgrid(w_range, h_range)
+                ct_row = (hh.flatten() + 0.5) * stride
+                ct_col = (ww.flatten() + 0.5) * stride
+                center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1)
+
+                # box distribution to distance
+                reg_range = np.arange(reg_max + 1)
+                box_distance = box_distribute.reshape((-1, reg_max + 1))
+                box_distance = softmax(box_distance, axis=1)
+                box_distance = box_distance * np.expand_dims(reg_range, axis=0)
+                box_distance = np.sum(box_distance, axis=1).reshape((-1, 4))
+                box_distance = box_distance * stride
+
+                # top K candidate
+                topk_idx = np.argsort(score.max(axis=1))[::-1]
+                topk_idx = topk_idx[:self.nms_top_k]
+                center = center[topk_idx]
+                score = score[topk_idx]
+                box_distance = box_distance[topk_idx]
+
+                # decode box
+                decode_box = center + [-1, -1, 1, 1] * box_distance
+
+                select_scores.append(score)
+                decode_boxes.append(decode_box)
+
+            # nms
+            bboxes = np.concatenate(decode_boxes, axis=0)
+            confidences = np.concatenate(select_scores, axis=0)
+            picked_box_probs = []
+            picked_labels = []
+            for class_index in range(0, confidences.shape[1]):
+                probs = confidences[:, class_index]
+                mask = probs > self.score_threshold
+                probs = probs[mask]
+                if probs.shape[0] == 0:
+                    continue
+                subset_boxes = bboxes[mask, :]
+                box_probs = np.concatenate(
+                    [subset_boxes, probs.reshape(-1, 1)], axis=1)
+                box_probs = hard_nms(
+                    box_probs,
+                    iou_threshold=self.nms_threshold,
+                    top_k=self.keep_top_k, )
+                picked_box_probs.append(box_probs)
+                picked_labels.extend([class_index] * box_probs.shape[0])
+
+            if len(picked_box_probs) == 0:
+                out_boxes_list.append(np.empty((0, 4)))
+                out_boxes_num.append(0)
+
+            else:
+                picked_box_probs = np.concatenate(picked_box_probs)
+
+                # resize output boxes
+                picked_box_probs[:, :4] = self.warp_boxes(
+                    picked_box_probs[:, :4], ori_shape[batch_id])
+                im_scale = np.concatenate([
+                    scale_factor[batch_id][::-1], scale_factor[batch_id][::-1]
+                ])
+                picked_box_probs[:, :4] /= im_scale
+                # clas score box
+                out_boxes_list.append(
+                    np.concatenate(
+                        [
+                            np.expand_dims(
+                                np.array(picked_labels),
+                                axis=-1), np.expand_dims(
+                                    picked_box_probs[:, 4], axis=-1),
+                            picked_box_probs[:, :4]
+                        ],
+                        axis=1))
+                out_boxes_num.append(len(picked_labels))
+
+        out_boxes_list = np.concatenate(out_boxes_list, axis=0)
+        out_boxes_num = np.asarray(out_boxes_num).astype(np.int32)
+
+        for dt in out_boxes_list:
+            clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
+            label = self.labels[clsid]
+            result = {'bbox': bbox, 'label': label}
+            results.append(result)
+        return results

ppstructure/layout/predict_layout.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 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_gif
+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)
+
+    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()
+
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
+
+        np_score_list, np_boxes_list = [], []
+        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_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
+
+        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())

ppstructure/utility.py

@@ -34,15 +34,18 @@ def init_args():
         type=str,
         default="../ppocr/utils/dict/table_structure_dict.txt")
     # params for layout
+    parser.add_argument("--layout_model_dir", type=str)
     parser.add_argument(
-        "--layout_path_model",
+        "--layout_dict_path",
         type=str,
-        default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
+        default="../ppocr/utils/dict/layout_pubalynet_dict.txt")
     parser.add_argument(
-        "--layout_label_map",
-        type=ast.literal_eval,
-        default=None,
-        help='label map according to ppstructure/layout/README_ch.md')
+        "--layout_score_threshold",
+        type=float,
+        default=0.5,
+        help="Threshold of score.")
+    parser.add_argument(
+        "--layout_nms_threshold", type=float, default=0.5, help="Threshold of nms.")
     # params for vqa
     parser.add_argument("--vqa_algorithm", type=str, default='LayoutXLM')
     parser.add_argument("--ser_model_dir", type=str)