Add PGNet

ppocr/postprocess/pg_postprocess.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+import numpy as np
+from .locality_aware_nms import nms_locality
+from ppocr.utils.e2e_utils.extract_textpoint import *
+from ppocr.utils.e2e_utils.ski_thin import *
+from ppocr.utils.e2e_utils.visual import *
+import paddle
+import cv2
+import time
+
+
+class PGPostProcess(object):
+    """
+    The post process for SAST.
+    """
+
+    def __init__(self,
+                 score_thresh=0.5,
+                 nms_thresh=0.2,
+                 sample_pts_num=2,
+                 shrink_ratio_of_width=0.3,
+                 expand_scale=1.0,
+                 tcl_map_thresh=0.5,
+                 **kwargs):
+        self.result_path = ""
+        self.valid_set = 'totaltext'
+        self.Lexicon_Table = [
+            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
+            'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
+            'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
+        ]
+        self.score_thresh = score_thresh
+        self.nms_thresh = nms_thresh
+        self.sample_pts_num = sample_pts_num
+        self.shrink_ratio_of_width = shrink_ratio_of_width
+        self.expand_scale = expand_scale
+        self.tcl_map_thresh = tcl_map_thresh
+
+        # c++ la-nms is faster, but only support python 3.5
+        self.is_python35 = False
+        if sys.version_info.major == 3 and sys.version_info.minor == 5:
+            self.is_python35 = True
+
+    def __call__(self, outs_dict, shape_list):
+        p_score, p_border, p_direction, p_char = outs_dict[:4]
+        p_score = p_score[0].numpy()
+        p_border = p_border[0].numpy()
+        p_direction = p_direction[0].numpy()
+        p_char = p_char[0].numpy()
+        src_h, src_w, ratio_h, ratio_w = shape_list[0]
+        if self.valid_set != 'totaltext':
+            is_curved = False
+        else:
+            is_curved = True
+        instance_yxs_list = generate_pivot_list(
+            p_score,
+            p_char,
+            p_direction,
+            score_thresh=self.score_thresh,
+            is_backbone=True,
+            is_curved=is_curved)
+        p_char = np.expand_dims(p_char, axis=0)
+        p_char = paddle.to_tensor(p_char)
+        char_seq_idx_set = []
+        for i in range(len(instance_yxs_list)):
+            gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
+            f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
+            featyre_seq = paddle.gather_nd(f_char_map, gather_info_lod)
+            featyre_seq = np.expand_dims(featyre_seq.numpy(), axis=0)
+            t = len(featyre_seq[0])
+            featyre_seq = paddle.to_tensor(featyre_seq)
+            l = np.array([[t]]).astype(np.int64)
+            length = paddle.to_tensor(l)
+            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
+                input=featyre_seq, blank=36, input_length=length)
+            seq_pred1 = seq_pred[0].numpy().tolist()[0]
+            seq_len = seq_pred[1].numpy()[0][0]
+            temp_t = []
+            for x in seq_pred1[:seq_len]:
+                temp_t.append(x)
+            char_seq_idx_set.append(temp_t)
+        seq_strs = []
+        for char_idx_set in char_seq_idx_set:
+            pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
+            seq_strs.append(pr_str)
+        poly_list = []
+        keep_str_list = []
+        all_point_list = []
+        all_point_pair_list = []
+        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+            if len(yx_center_line) == 1:
+                print('the length of tcl point is less than 2, repeat')
+                yx_center_line.append(yx_center_line[-1])
+
+            # expand corresponding offset for total-text.
+            offset_expand = 1.0
+            if self.valid_set == 'totaltext':
+                offset_expand = 1.2
+
+            point_pair_list = []
+            for batch_id, y, x in yx_center_line:
+                offset = p_border[:, y, x].reshape(2, 2)
+                if offset_expand != 1.0:
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
+                    offset_detal = offset / offset_length * expand_length
+                    offset = offset + offset_detal
+                ori_yx = np.array([y, x], dtype=np.float32)
+                point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
+                point_pair_list.append(point_pair)
+
+                # for visualization
+                all_point_list.append([
+                    int(round(x * 4.0 / ratio_w)),
+                    int(round(y * 4.0 / ratio_h))
+                ])
+                all_point_pair_list.append(point_pair.round().astype(np.int32)
+                                           .tolist())
+
+            # ndarry: (x, 2)
+            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
+            print('expand along width. {}'.format(detected_poly.shape))
+            detected_poly = expand_poly_along_width(
+                detected_poly, shrink_ratio_of_width=0.2)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
+
+            if len(keep_str) < 2:
+                print('--> too short, {}'.format(keep_str))
+                continue
+
+            keep_str_list.append(keep_str)
+            if self.valid_set == 'partvgg':
+                middle_point = len(detected_poly) // 2
+                detected_poly = detected_poly[
+                    [0, middle_point - 1, middle_point, -1], :]
+                poly_list.append(detected_poly)
+            elif self.valid_set == 'totaltext':
+                poly_list.append(detected_poly)
+            else:
+                print('--> Not supported format.')
+                exit(-1)
+        data = {
+            'points': poly_list,
+            'strs': keep_str_list,
+        }
+        # visualization
+        # if self.save_visualization:
+        #     visualize_e2e_result(im_fn, poly_list, keep_str_list, src_im)
+        #     visualize_point_result(im_fn, all_point_list, all_point_pair_list, src_im)
+
+        # save detected boxes
+        # txt_dir = (result_path[:-1] if result_path.endswith('/') else result_path) + '_txt_anno'
+        # if not os.path.exists(txt_dir):
+        #     os.makedirs(txt_dir)
+        # res_file = os.path.join(txt_dir, '{}.txt'.format(im_prefix))
+        # with open(res_file, 'w') as f:
+        #     for i_box, box in enumerate(poly_list):
+        #         seq_str = keep_str_list[i_box]
+        #         box = np.round(box).astype('int32')
+        #         box_str = ','.join(str(s) for s in (box.flatten().tolist()))
+        #         f.write('{}\t{}\r\n'.format(box_str, seq_str))
+        return data

ppocr/postprocess/sast_postprocess.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+
 __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
@@ -49,12 +50,12 @@ class SASTPostProcess(object):
         self.shrink_ratio_of_width = shrink_ratio_of_width
         self.expand_scale = expand_scale
         self.tcl_map_thresh = tcl_map_thresh
-        
+
         # c++ la-nms is faster, but only support python 3.5
         self.is_python35 = False
         if sys.version_info.major == 3 and sys.version_info.minor == 5:
             self.is_python35 = True
-            
+
     def point_pair2poly(self, point_pair_list):
         """
         Transfer vertical point_pairs into poly point in clockwise.
@@ -66,31 +67,42 @@ class SASTPostProcess(object):
             point_list[idx] = point_pair[0]
             point_list[point_num - 1 - idx] = point_pair[1]
         return np.array(point_list).reshape(-1, 2)
-    
-    def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.):
+
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
         """ 
         Generate shrink_quad_along_width.
         """
-        ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        ratio_pair = np.array(
+            [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
         p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
         p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
         return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
-    
+
     def expand_poly_along_width(self, poly, shrink_ratio_of_width=0.3):
         """
         expand poly along width.
         """
         point_num = poly.shape[0]
-        left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+        left_quad = np.array(
+            [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
         left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
-                    (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
-        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0)
-        right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1],
-                            poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32)
+                     (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio,
+                                                        1.0)
+        right_quad = np.array(
+            [
+                poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+                poly[point_num // 2], poly[point_num // 2 + 1]
+            ],
+            dtype=np.float32)
         right_ratio = 1.0 + \
-                    shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
-                    (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
-        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
+                      shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                      (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0,
+                                                         right_ratio)
         poly[0] = left_quad_expand[0]
         poly[-1] = left_quad_expand[-1]
         poly[point_num // 2 - 1] = right_quad_expand[1]
@@ -100,7 +112,7 @@ class SASTPostProcess(object):
     def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map):
         """Restore quad."""
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
-        xy_text = xy_text[:, ::-1] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
 
         # Sort the text boxes via the y axis
         xy_text = xy_text[np.argsort(xy_text[:, 1])]
@@ -112,7 +124,7 @@ class SASTPostProcess(object):
         point_num = int(tvo_map.shape[-1] / 2)
         assert point_num == 4
         tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :]
-        xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2)
+        xy_text_tile = np.tile(xy_text, (1, point_num))  # (n, point_num * 2)
         quads = xy_text_tile - tvo_map
 
         return scores, quads, xy_text
@@ -121,14 +133,12 @@ class SASTPostProcess(object):
         """
         compute area of a quad.
         """
-        edge = [
-            (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
-            (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
-            (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
-            (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
-        ]
+        edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
+                (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
+                (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
+                (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
         return np.sum(edge) / 2.
-        
+
     def nms(self, dets):
         if self.is_python35:
             import lanms
@@ -141,7 +151,7 @@ class SASTPostProcess(object):
         """
         Cluster pixels in tcl_map based on quads.
         """
-        instance_count = quads.shape[0] + 1 # contain background
+        instance_count = quads.shape[0] + 1  # contain background
         instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32)
         if instance_count == 1:
             return instance_count, instance_label_map
@@ -149,18 +159,19 @@ class SASTPostProcess(object):
         # predict text center
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
         n = xy_text.shape[0]
-        xy_text = xy_text[:, ::-1] # (n, 2)
-        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
+        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :]  # (n, 2)
         pred_tc = xy_text - tco
-        
+
         # get gt text center
         m = quads.shape[0]
-        gt_tc = np.mean(quads, axis=1) # (m, 2)
+        gt_tc = np.mean(quads, axis=1)  # (m, 2)
 
-        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
-        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
-        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
-        xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
+        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
+                               (1, m, 1))  # (n, m, 2)
+        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1))  # (n, m, 2)
+        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2)  # (n, m)
+        xy_text_assign = np.argmin(dist_mat, axis=1) + 1  # (n,)
 
         instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign
         return instance_count, instance_label_map
@@ -169,26 +180,47 @@ class SASTPostProcess(object):
         """
         Estimate sample points number.
         """
-        eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
-        ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
+        eh = (np.linalg.norm(quad[0] - quad[3]) +
+              np.linalg.norm(quad[1] - quad[2])) / 2.0
+        ew = (np.linalg.norm(quad[0] - quad[1]) +
+              np.linalg.norm(quad[2] - quad[3])) / 2.0
 
         dense_sample_pts_num = max(2, int(ew))
-        dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
-
-        dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
-        estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
+        dense_xy_center_line = xy_text[np.linspace(
+            0,
+            xy_text.shape[0] - 1,
+            dense_sample_pts_num,
+            endpoint=True,
+            dtype=np.float32).astype(np.int32)]
+
+        dense_xy_center_line_diff = dense_xy_center_line[
+            1:] - dense_xy_center_line[:-1]
+        estimate_arc_len = np.sum(
+            np.linalg.norm(
+                dense_xy_center_line_diff, axis=1))
 
         sample_pts_num = max(2, int(estimate_arc_len / eh))
         return sample_pts_num
 
-    def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, 
-                shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
+    def detect_sast(self,
+                    tcl_map,
+                    tvo_map,
+                    tbo_map,
+                    tco_map,
+                    ratio_w,
+                    ratio_h,
+                    src_w,
+                    src_h,
+                    shrink_ratio_of_width=0.3,
+                    tcl_map_thresh=0.5,
+                    offset_expand=1.0,
+                    out_strid=4.0):
         """
         first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
         """
         # restore quad
-        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
+        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
+                                                   tvo_map)
         dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
         dets = self.nms(dets)
         if dets.shape[0] == 0:
@@ -202,7 +234,8 @@ class SASTPostProcess(object):
 
         # instance segmentation
         # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
-        instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
+        instance_count, instance_label_map = self.cluster_by_quads_tco(
+            tcl_map, tcl_map_thresh, quads, tco_map)
 
         # restore single poly with tcl instance.
         poly_list = []
@@ -212,10 +245,10 @@ class SASTPostProcess(object):
             q_area = quad_areas[instance_idx - 1]
             if q_area < 5:
                 continue
-            
+
             #
-            len1 = float(np.linalg.norm(quad[0] -quad[1]))
-            len2 = float(np.linalg.norm(quad[1] -quad[2]))
+            len1 = float(np.linalg.norm(quad[0] - quad[1]))
+            len2 = float(np.linalg.norm(quad[1] - quad[2]))
             min_len = min(len1, len2)
             if min_len < 3:
                 continue
@@ -225,16 +258,18 @@ class SASTPostProcess(object):
                 continue
 
             # filter low confidence instance
-            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] 
+            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
             if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1:
-            # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
+                # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
                 continue
 
             # sort xy_text
-            left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
-                                        (quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
-            right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
-                                        (quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
+            left_center_pt = np.array(
+                [[(quad[0, 0] + quad[-1, 0]) / 2.0,
+                  (quad[0, 1] + quad[-1, 1]) / 2.0]])  # (1, 2)
+            right_center_pt = np.array(
+                [[(quad[1, 0] + quad[2, 0]) / 2.0,
+                  (quad[1, 1] + quad[2, 1]) / 2.0]])  # (1, 2)
             proj_unit_vec = (right_center_pt - left_center_pt) / \
                             (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
             proj_value = np.sum(xy_text * proj_unit_vec, axis=1)
@@ -245,33 +280,45 @@ class SASTPostProcess(object):
                 sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
             else:
                 sample_pts_num = self.sample_pts_num
-            xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+            xy_center_line = xy_text[np.linspace(
+                0,
+                xy_text.shape[0] - 1,
+                sample_pts_num,
+                endpoint=True,
+                dtype=np.float32).astype(np.int32)]
 
             point_pair_list = []
             for x, y in xy_center_line:
                 # get corresponding offset
                 offset = tbo_map[y, x, :].reshape(2, 2)
                 if offset_expand != 1.0:
-                    offset_length = np.linalg.norm(offset, axis=1, keepdims=True)
-                    expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0)
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
                     offset_detal = offset / offset_length * expand_length
-                    offset = offset + offset_detal                
-                # original point
+                    offset = offset + offset_detal
+                    # original point
                 ori_yx = np.array([y, x], dtype=np.float32)
-                point_pair = (ori_yx +  offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) 
+                point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
                 point_pair_list.append(point_pair)
 
             # ndarry: (x, 2), expand poly along width
             detected_poly = self.point_pair2poly(point_pair_list)
-            detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
-            detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
-            detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+            detected_poly = self.expand_poly_along_width(detected_poly,
+                                                         shrink_ratio_of_width)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
             poly_list.append(detected_poly)
 
         return poly_list
 
-    def __call__(self, outs_dict, shape_list):                
+    def __call__(self, outs_dict, shape_list):
         score_list = outs_dict['f_score']
         border_list = outs_dict['f_border']
         tvo_list = outs_dict['f_tvo']
@@ -281,20 +328,28 @@ class SASTPostProcess(object):
             border_list = border_list.numpy()
             tvo_list = tvo_list.numpy()
             tco_list = tco_list.numpy()
-                    
+
         img_num = len(shape_list)
         poly_lists = []
         for ino in range(img_num):
-            p_score = score_list[ino].transpose((1,2,0))
-            p_border = border_list[ino].transpose((1,2,0))
-            p_tvo = tvo_list[ino].transpose((1,2,0))
-            p_tco = tco_list[ino].transpose((1,2,0))
+            p_score = score_list[ino].transpose((1, 2, 0))
+            p_border = border_list[ino].transpose((1, 2, 0))
+            p_tvo = tvo_list[ino].transpose((1, 2, 0))
+            p_tco = tco_list[ino].transpose((1, 2, 0))
             src_h, src_w, ratio_h, ratio_w = shape_list[ino]
 
-            poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, 
-                                         shrink_ratio_of_width=self.shrink_ratio_of_width, 
-                                         tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
+            poly_list = self.detect_sast(
+                p_score,
+                p_tvo,
+                p_border,
+                p_tco,
+                ratio_w,
+                ratio_h,
+                src_w,
+                src_h,
+                shrink_ratio_of_width=self.shrink_ratio_of_width,
+                tcl_map_thresh=self.tcl_map_thresh,
+                offset_expand=self.expand_scale)
             poly_lists.append({'points': np.array(poly_list)})
 
         return poly_lists
-

ppocr/utils/e2e_metric/Deteval.py

+from os import listdir
+import os, sys
+from scipy import io
+import numpy as np
+from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
+from tqdm import tqdm
+
+try:  # python2
+    range = xrange
+except Exception:
+    # python3
+    range = range
+"""
+Input format: y0,x0, ..... yn,xn. Each detection is separated by the end of line token ('\n')'
+"""
+
+# if len(sys.argv) != 4:
+#     print('\n usage: test.py pred_dir gt_dir savefile')
+#     sys.exit()
+
+
+def get_socre(gt_dict, pred_dict):
+    # allInputs = listdir(input_dir)
+    allInputs = 1
+
+    def input_reading_mod(pred_dict, input):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]['points']
+            text = pred_dict[i]['text']
+            # for i in range(len(points)):
+            point = ",".join(map(str, points.reshape(-1, )))
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dict, gt_id):
+        """This helper reads groundtruths from mat files"""
+        # gt_id = gt_id.split('.')[0]
+        gt = []
+        n = len(gt_dict)
+        for i in range(n):
+            points = gt_dict[i]['points'].tolist()
+            h = len(points)
+            text = gt_dict[i]['text']
+            xx = [
+                np.array(
+                    ['x:'], dtype='<U2'), 0, np.array(
+                        ['y:'], dtype='<U2'), 0, np.array(
+                            ['#'], dtype='<U1'), np.array(
+                                ['#'], dtype='<U1')
+            ]
+            t_x, t_y = [], []
+            for j in range(h):
+                t_x.append(points[j][0])
+                t_y.append(points[j][1])
+            xx[1] = np.array([t_x], dtype='int16')
+            xx[3] = np.array([t_y], dtype='int16')
+            if text != "":
+                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
+                xx[5] = np.array(['c'], dtype='<U1')
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            print
+            "liushanshan gt[1] = {}".format(gt[1])
+            print
+            "liushanshan gt[2] = {}".format(gt[2])
+            print
+            "liushanshan gt[3] = {}".format(gt[3])
+            print
+            "liushanshan gt[4] = {}".format(gt[4])
+            print
+            "liushanshan gt[5] = {}".format(gt[5])
+            if (gt[5] == '#') and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(',')]
+                    # detection = detection.split(',')
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        # print(area_of_intersection(det_x, det_y, gt_x, gt_y))
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(gt_x, gt_y)), 2)
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        tau = inter_area / det_area
+        """
+        # print "liushanshan det_x {}".format(det_x)
+        # print "liushanshan det_y {}".format(det_y)
+        # print "liushanshan area {}".format(area(det_x, det_y))
+        # print "liushanshan tau = {}".format(np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2))
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+    global_tp = 0
+    global_fp = 0
+    global_fn = 0
+    global_sigma = []
+    global_tau = []
+    tr = 0.7
+    tp = 0.6
+    fsc_k = 0.8
+    k = 2
+    global_pred_str = []
+    global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
+                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
+                and (input_id != 'Deteval_result_non_curved.txt'):
+            print(input_id)
+            detections = input_reading_mod(pred_dict, input_id)
+            # print "liushanshan detections = {}".format(detections)
+            groundtruths = gt_reading_mod(gt_dict, input_id)
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma.append(local_sigma_table)
+            global_tau.append(local_tau_table)
+            global_pred_str.append(local_pred_str)
+            global_gt_str.append(local_gt_str)
+            print
+            "liushanshan global_pred_str = {}".format(global_pred_str)
+            print
+            "liushanshan global_gt_str = {}".format(global_gt_str)
+
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(local_sigma_table, local_tau_table,
+                   local_accumulative_recall, local_accumulative_precision,
+                   global_accumulative_recall, global_accumulative_precision,
+                   gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr)
+            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
+                0].shape[0]
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp)
+            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
+                > tr)
+            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
+                0].shape[0]
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
+                tp)
+            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+                    (det_matching_num_qualified_sigma_candidates == 1) and (
+                    det_matching_num_qualified_tau_candidates == 1):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                print
+                "liushanshan one to one det_id = {}".format(matched_det_id)
+                print
+                "liushanshan one to one gt_id = {}".format(gt_id)
+                gt_str_cur = global_gt_str[idy][gt_id]
+                pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
+                    0]]
+                print
+                "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
+                print
+                "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
+                if pred_str_cur == gt_str_cur:
+                    hit_str_num += 1
+                else:
+                    if pred_str_cur.lower() == gt_str_cur.lower():
+                        hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def one_to_many(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where((local_tau_table[
+                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
+                num_qualified_tau_candidates = qualified_tau_candidates[
+                    0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
+                            and
+                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
+                         tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        print
+                        "liushanshan one to many det_id = {}".format(
+                            qualified_tau_candidates)
+                        print
+                        "liushanshan one to many gt_id = {}".format(gt_id)
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]]
+                        print
+                        "liushanshan one to many gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan one to many pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                        # recg end
+                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
+                      >= tr):
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan one to many det_id = {}".format(
+                        qualified_tau_candidates)
+                    print
+                    "liushanshan one to many gt_id = {}".format(gt_id)
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][
+                        qualified_tau_candidates[0].tolist()[0]]
+                    print
+                    "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
+                    print
+                    "liushanshan one to many pred_str_cur = {}".format(
+                        pred_str_cur)
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def many_to_one(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where((
+                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+                num_qualified_sigma_candidates = qualified_sigma_candidates[
+                    0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
+                         tp) and
+                        (local_sigma_table[qualified_sigma_candidates, det_id]
+                         >= tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        print
+                        "liushanshan many to one det_id = {}".format(det_id)
+                        print
+                        "liushanshan many to one gt_id = {}".format(
+                            qualified_sigma_candidates)
+                        pred_str_cur = global_pred_str[idy][det_id]
+                        gt_len = len(qualified_sigma_candidates[0])
+                        for idx in range(gt_len):
+                            ele_gt_id = qualified_sigma_candidates[0].tolist()[
+                                idx]
+                            if not global_gt_str[idy].has_key(ele_gt_id):
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            print
+                            "liushanshan many to one gt_str_cur = {}".format(
+                                gt_str_cur)
+                            print
+                            "liushanshan many to one pred_str_cur = {}".format(
+                                pred_str_cur)
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                break
+                        # recg end
+                elif (np.sum(local_tau_table[qualified_sigma_candidates,
+                                             det_id]) >= tp):
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan many to one det_id = {}".format(det_id)
+                    print
+                    "liushanshan many to one gt_id = {}".format(
+                        qualified_sigma_candidates)
+                    pred_str_cur = global_pred_str[idy][det_id]
+                    gt_len = len(qualified_sigma_candidates[0])
+                    for idx in range(gt_len):
+                        ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                        if not global_gt_str[idy].has_key(ele_gt_id):
+                            continue
+                        gt_str_cur = global_gt_str[idy][ele_gt_id]
+                        print
+                        "liushanshan many to one gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan many to one pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                            break
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                                break
+                            else:
+                                print
+                                'no match'
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    global_accumulative_precision = global_accumulative_precision + fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    single_data = {}
+    for idx in range(len(global_sigma)):
+        # print(allInputs[idx])
+        local_sigma_table = global_sigma[idx]
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+                                                    local_accumulative_recall, local_accumulative_precision,
+                                                    global_accumulative_recall, global_accumulative_precision,
+                                                    gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+
+        # fid = open(fid_path, 'a+')
+        try:
+            local_precision = local_accumulative_precision / num_det
+        except ZeroDivisionError:
+            local_precision = 0
+
+        try:
+            local_recall = local_accumulative_recall / num_gt
+        except ZeroDivisionError:
+            local_recall = 0
+
+        try:
+            local_f_score = 2 * local_precision * local_recall / (
+                local_precision + local_recall)
+        except ZeroDivisionError:
+            local_f_score = 0
+
+        # temp = ('%s: Recall=%.4f, Precision=%.4f, f_score=%.4f\n' % (
+        #     allInputs[idx], local_recall, local_precision, local_f_score))
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    single_data["recall"] = local_recall
+    single_data['precision'] = local_precision
+    single_data['f_score'] = local_f_score
+    return single_data
+
+
+def combine_results(all_data):
+    tr = 0.7
+    tp = 0.6
+    fsc_k = 0.8
+    k = 2
+    global_sigma = []
+    global_tau = []
+    global_pred_str = []
+    global_gt_str = []
+    for data in all_data:
+        global_sigma.append(data['sigma'][0])
+        global_tau.append(data['global_tau'][0])
+        global_pred_str.append(data['global_pred_str'][0])
+        global_gt_str.append(data['global_gt_str'][0])
+
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(local_sigma_table, local_tau_table,
+                   local_accumulative_recall, local_accumulative_precision,
+                   global_accumulative_recall, global_accumulative_precision,
+                   gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr)
+            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
+                0].shape[0]
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp)
+            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
+                > tr)
+            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
+                0].shape[0]
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
+                tp)
+            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+                    (det_matching_num_qualified_sigma_candidates == 1) and (
+                    det_matching_num_qualified_tau_candidates == 1):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                print
+                "liushanshan one to one det_id = {}".format(matched_det_id)
+                print
+                "liushanshan one to one gt_id = {}".format(gt_id)
+                gt_str_cur = global_gt_str[idy][gt_id]
+                pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
+                    0]]
+                print
+                "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
+                print
+                "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
+                if pred_str_cur == gt_str_cur:
+                    hit_str_num += 1
+                else:
+                    if pred_str_cur.lower() == gt_str_cur.lower():
+                        hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def one_to_many(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where((local_tau_table[
+                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
+                num_qualified_tau_candidates = qualified_tau_candidates[
+                    0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
+                            and
+                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
+                         tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        print
+                        "liushanshan one to many det_id = {}".format(
+                            qualified_tau_candidates)
+                        print
+                        "liushanshan one to many gt_id = {}".format(gt_id)
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]]
+                        print
+                        "liushanshan one to many gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan one to many pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                        # recg end
+                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
+                      >= tr):
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan one to many det_id = {}".format(
+                        qualified_tau_candidates)
+                    print
+                    "liushanshan one to many gt_id = {}".format(gt_id)
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][
+                        qualified_tau_candidates[0].tolist()[0]]
+                    print
+                    "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
+                    print
+                    "liushanshan one to many pred_str_cur = {}".format(
+                        pred_str_cur)
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def many_to_one(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where((
+                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+                num_qualified_sigma_candidates = qualified_sigma_candidates[
+                    0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
+                         tp) and
+                        (local_sigma_table[qualified_sigma_candidates, det_id]
+                         >= tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        print
+                        "liushanshan many to one det_id = {}".format(det_id)
+                        print
+                        "liushanshan many to one gt_id = {}".format(
+                            qualified_sigma_candidates)
+                        pred_str_cur = global_pred_str[idy][det_id]
+                        gt_len = len(qualified_sigma_candidates[0])
+                        for idx in range(gt_len):
+                            ele_gt_id = qualified_sigma_candidates[0].tolist()[
+                                idx]
+                            if ele_gt_id not in global_gt_str[idy]:
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            print
+                            "liushanshan many to one gt_str_cur = {}".format(
+                                gt_str_cur)
+                            print
+                            "liushanshan many to one pred_str_cur = {}".format(
+                                pred_str_cur)
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                break
+                        # recg end
+                elif (np.sum(local_tau_table[qualified_sigma_candidates,
+                                             det_id]) >= tp):
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan many to one det_id = {}".format(det_id)
+                    print
+                    "liushanshan many to one gt_id = {}".format(
+                        qualified_sigma_candidates)
+                    pred_str_cur = global_pred_str[idy][det_id]
+                    gt_len = len(qualified_sigma_candidates[0])
+                    for idx in range(gt_len):
+                        ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                        if not global_gt_str[idy].has_key(ele_gt_id):
+                            continue
+                        gt_str_cur = global_gt_str[idy][ele_gt_id]
+                        print
+                        "liushanshan many to one gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan many to one pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                            break
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                                break
+                            else:
+                                print
+                                'no match'
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    global_accumulative_precision = global_accumulative_precision + fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    for idx in range(len(global_sigma)):
+        local_sigma_table = np.array(global_sigma[idx])
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+                                                    local_accumulative_recall, local_accumulative_precision,
+                                                    global_accumulative_recall, global_accumulative_precision,
+                                                    gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+    try:
+        recall = global_accumulative_recall / total_num_gt
+    except ZeroDivisionError:
+        recall = 0
+
+    try:
+        precision = global_accumulative_precision / total_num_det
+    except ZeroDivisionError:
+        precision = 0
+
+    try:
+        f_score = 2 * precision * recall / (precision + recall)
+    except ZeroDivisionError:
+        f_score = 0
+
+    try:
+        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+    except ZeroDivisionError:
+        seqerr = 1
+
+    try:
+        recall_e2e = float(hit_str_count) / total_num_gt
+    except ZeroDivisionError:
+        recall_e2e = 0
+
+    try:
+        precision_e2e = float(hit_str_count) / total_num_det
+    except ZeroDivisionError:
+        precision_e2e = 0
+
+    try:
+        f_score_e2e = 2 * precision_e2e * recall_e2e / (
+            precision_e2e + recall_e2e)
+    except ZeroDivisionError:
+        f_score_e2e = 0
+
+    final = {
+        'total_num_gt': total_num_gt,
+        'total_num_det': total_num_det,
+        'global_accumulative_recall': global_accumulative_recall,
+        'hit_str_count': hit_str_count,
+        'recall': recall,
+        'precision': precision,
+        'f_score': f_score,
+        'seqerr': seqerr,
+        'recall_e2e': recall_e2e,
+        'precision_e2e': precision_e2e,
+        'f_score_e2e': f_score_e2e
+    }
+    return final
+
+
+# a = [1526, 642, 1565, 629, 1579, 627, 1593, 625, 1607, 623, 1620, 622, 1634, 620, 1659, 620, 1654, 681, 1631, 680, 1618,
+#      681, 1606, 681, 1594, 681, 1584, 682, 1573, 685, 1542, 694]
+# gt_dict = [{'points': np.array(a).reshape(-1, 2), 'text': 'MILK'}]
+# pred_dict = [{'points': np.array(a), 'text': 'ccc'},
+#              {'points': np.array(a), 'text': 'ccf'}]
+# result = []
+# for i in range(2):
+#     result.append(get_socre(gt_dict, pred_dict))
+# print(111)
+# a = combine_results(result)
+# print(a)

ppocr/utils/e2e_metric/polygon_fast.py

+import numpy as np
+from shapely.geometry import Polygon
+#import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

ppocr/utils/e2e_metric/tttt.py

+from os import listdir
+import os, sys
+from scipy import io
+import numpy as np
+from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
+from tqdm import tqdm
+
+try:  # python2
+    range = xrange
+except Exception:
+    # python3
+    range = range
+"""
+Input format: y0,x0, ..... yn,xn. Each detection is separated by the end of line token ('\n')'
+"""
+
+# if len(sys.argv) != 4:
+#     print('\n usage: test.py pred_dir gt_dir savefile')
+#     sys.exit()
+global_tp = 0
+global_fp = 0
+global_fn = 0
+
+tr = 0.7
+tp = 0.6
+fsc_k = 0.8
+k = 2
+
+
+def get_socre(gt_dict, pred_dict):
+    # allInputs = listdir(input_dir)
+    allInputs = 1
+    global_pred_str = []
+    global_gt_str = []
+    global_sigma = []
+    global_tau = []
+
+    def input_reading_mod(pred_dict, input):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]['points']
+            text = pred_dict[i]['text']
+            # for i in range(len(points)):
+            point = ",".join(map(str, points.reshape(-1, )))
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dict, gt_id):
+        """This helper reads groundtruths from mat files"""
+        # gt_id = gt_id.split('.')[0]
+        gt = []
+        n = len(gt_dict)
+        for i in range(n):
+            points = gt_dict[i]['points'].tolist()
+            h = len(points)
+            text = gt_dict[i]['text']
+            xx = [
+                np.array(
+                    ['x:'], dtype='<U2'), 0, np.array(
+                        ['y:'], dtype='<U2'), 0, np.array(
+                            ['#'], dtype='<U1'), np.array(
+                                ['#'], dtype='<U1')
+            ]
+            t_x, t_y = [], []
+            for j in range(h):
+                t_x.append(points[j][0])
+                t_y.append(points[j][1])
+            xx[1] = np.array([t_x], dtype='int16')
+            xx[3] = np.array([t_y], dtype='int16')
+            if text != "":
+                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
+                xx[5] = np.array(['c'], dtype='<U1')
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            print
+            "liushanshan gt[1] = {}".format(gt[1])
+            print
+            "liushanshan gt[2] = {}".format(gt[2])
+            print
+            "liushanshan gt[3] = {}".format(gt[3])
+            print
+            "liushanshan gt[4] = {}".format(gt[4])
+            print
+            "liushanshan gt[5] = {}".format(gt[5])
+            if (gt[5] == '#') and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(',')]
+                    # detection = detection.split(',')
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        # print(area_of_intersection(det_x, det_y, gt_x, gt_y))
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(gt_x, gt_y)), 2)
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        tau = inter_area / det_area
+        """
+        # print "liushanshan det_x {}".format(det_x)
+        # print "liushanshan det_y {}".format(det_y)
+        # print "liushanshan area {}".format(area(det_x, det_y))
+        # print "liushanshan tau = {}".format(np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2))
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+
+    ###############################################################################
+    single_data = {}
+    for input_id in range(allInputs):
+
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
+                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
+                and (input_id != 'Deteval_result_non_curved.txt'):
+            print(input_id)
+            detections = input_reading_mod(pred_dict, input_id)
+            # print "liushanshan detections = {}".format(detections)
+            groundtruths = gt_reading_mod(gt_dict, input_id)
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma.append(local_sigma_table)
+            global_tau.append(local_tau_table)
+            global_pred_str.append(local_pred_str)
+            global_gt_str.append(local_gt_str)
+            print
+            "liushanshan global_pred_str = {}".format(global_pred_str)
+            print
+            "liushanshan global_gt_str = {}".format(global_gt_str)
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    return single_data
+
+
+def combine_results(all_data):
+    global_sigma, global_tau, global_pred_str, global_gt_str = [], [], [], []
+    for data in all_data:
+        global_sigma.append(data['sigma'])
+        global_tau.append(data['global_tau'])
+        global_pred_str.append(data['global_pred_str'])
+        global_gt_str.append(data['global_gt_str'])
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(local_sigma_table, local_tau_table,
+                   local_accumulative_recall, local_accumulative_precision,
+                   global_accumulative_recall, global_accumulative_precision,
+                   gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr)
+            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
+                0].shape[0]
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp)
+            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
+                > tr)
+            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
+                0].shape[0]
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
+                tp)
+            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+                    (det_matching_num_qualified_sigma_candidates == 1) and (
+                    det_matching_num_qualified_tau_candidates == 1):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                print
+                "liushanshan one to one det_id = {}".format(matched_det_id)
+                print
+                "liushanshan one to one gt_id = {}".format(gt_id)
+                gt_str_cur = global_gt_str[idy][gt_id]
+                pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
+                    0]]
+                print
+                "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
+                print
+                "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
+                if pred_str_cur == gt_str_cur:
+                    hit_str_num += 1
+                else:
+                    if pred_str_cur.lower() == gt_str_cur.lower():
+                        hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def one_to_many(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where((local_tau_table[
+                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
+                num_qualified_tau_candidates = qualified_tau_candidates[
+                    0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
+                            and
+                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
+                         tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        print
+                        "liushanshan one to many det_id = {}".format(
+                            qualified_tau_candidates)
+                        print
+                        "liushanshan one to many gt_id = {}".format(gt_id)
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]]
+                        print
+                        "liushanshan one to many gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan one to many pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                        # recg end
+                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
+                      >= tr):
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan one to many det_id = {}".format(
+                        qualified_tau_candidates)
+                    print
+                    "liushanshan one to many gt_id = {}".format(gt_id)
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][
+                        qualified_tau_candidates[0].tolist()[0]]
+                    print
+                    "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
+                    print
+                    "liushanshan one to many pred_str_cur = {}".format(
+                        pred_str_cur)
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def many_to_one(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where((
+                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+                num_qualified_sigma_candidates = qualified_sigma_candidates[
+                    0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
+                         tp) and
+                        (local_sigma_table[qualified_sigma_candidates, det_id]
+                         >= tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        print
+                        "liushanshan many to one det_id = {}".format(det_id)
+                        print
+                        "liushanshan many to one gt_id = {}".format(
+                            qualified_sigma_candidates)
+                        pred_str_cur = global_pred_str[idy][det_id]
+                        gt_len = len(qualified_sigma_candidates[0])
+                        for idx in range(gt_len):
+                            ele_gt_id = qualified_sigma_candidates[0].tolist()[
+                                idx]
+                            if not global_gt_str[idy].has_key(ele_gt_id):
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            print
+                            "liushanshan many to one gt_str_cur = {}".format(
+                                gt_str_cur)
+                            print
+                            "liushanshan many to one pred_str_cur = {}".format(
+                                pred_str_cur)
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                break
+                        # recg end
+                elif (np.sum(local_tau_table[qualified_sigma_candidates,
+                                             det_id]) >= tp):
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    print
+                    "liushanshan many to one det_id = {}".format(det_id)
+                    print
+                    "liushanshan many to one gt_id = {}".format(
+                        qualified_sigma_candidates)
+                    pred_str_cur = global_pred_str[idy][det_id]
+                    gt_len = len(qualified_sigma_candidates[0])
+                    for idx in range(gt_len):
+                        ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                        if not global_gt_str[idy].has_key(ele_gt_id):
+                            continue
+                        gt_str_cur = global_gt_str[idy][ele_gt_id]
+                        print
+                        "liushanshan many to one gt_str_cur = {}".format(
+                            gt_str_cur)
+                        print
+                        "liushanshan many to one pred_str_cur = {}".format(
+                            pred_str_cur)
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                            break
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                                break
+                            else:
+                                print
+                                'no match'
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    global_accumulative_precision = global_accumulative_precision + fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    for idx in range(len(global_sigma)):
+        # print(allInputs[idx])
+        local_sigma_table = np.array(global_sigma[idx])
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+                                                    local_accumulative_recall, local_accumulative_precision,
+                                                    global_accumulative_recall, global_accumulative_precision,
+                                                    gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+
+    try:
+        recall = global_accumulative_recall / total_num_gt
+    except ZeroDivisionError:
+        recall = 0
+
+    try:
+        precision = global_accumulative_precision / total_num_det
+    except ZeroDivisionError:
+        precision = 0
+
+    try:
+        f_score = 2 * precision * recall / (precision + recall)
+    except ZeroDivisionError:
+        f_score = 0
+
+    try:
+        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+    except ZeroDivisionError:
+        seqerr = 1
+
+    try:
+        recall_e2e = float(hit_str_count) / total_num_gt
+    except ZeroDivisionError:
+        recall_e2e = 0
+
+    try:
+        precision_e2e = float(hit_str_count) / total_num_det
+    except ZeroDivisionError:
+        precision_e2e = 0
+
+    try:
+        f_score_e2e = 2 * precision_e2e * recall_e2e / (
+            precision_e2e + recall_e2e)
+    except ZeroDivisionError:
+        f_score_e2e = 0
+
+    final = {
+        'total_num_gt': total_num_gt,
+        'total_num_det': total_num_det,
+        'global_accumulative_recall': global_accumulative_recall,
+        'hit_str_count': hit_str_count,
+        'recall': recall,
+        'precision': precision,
+        'f_score': f_score,
+        'seqerr': seqerr,
+        'recall_e2e': recall_e2e,
+        'precision_e2e': precision_e2e,
+        'f_score_e2e': f_score_e2e
+    }
+    return final
+
+
+# def combine_results(all_data):
+#     tr = 0.7
+#     tp = 0.6
+#     fsc_k = 0.8
+#     k = 2
+#     global_sigma = []
+#     global_tau = []
+#     global_pred_str = []
+#     global_gt_str = []
+#     for data in all_data:
+#         global_sigma.append(data['sigma'])
+#         global_tau.append(data['global_tau'])
+#         global_pred_str.append(data['global_pred_str'])
+#         global_gt_str.append(data['global_gt_str'])
+#
+#     global_accumulative_recall = 0
+#     global_accumulative_precision = 0
+#     total_num_gt = 0
+#     total_num_det = 0
+#     hit_str_count = 0
+#     hit_count = 0
+#
+#     def one_to_one(local_sigma_table, local_tau_table, local_accumulative_recall,
+#                    local_accumulative_precision, global_accumulative_recall, global_accumulative_precision,
+#                    gt_flag, det_flag, idy):
+#         hit_str_num = 0
+#         for gt_id in range(num_gt):
+#             gt_matching_qualified_sigma_candidates = np.where(local_sigma_table[gt_id, :] > tr)
+#             gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[0].shape[0]
+#             gt_matching_qualified_tau_candidates = np.where(local_tau_table[gt_id, :] > tp)
+#             gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[0].shape[0]
+#
+#             det_matching_qualified_sigma_candidates = np.where(
+#                 local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]] > tr)
+#             det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[0].shape[0]
+#             det_matching_qualified_tau_candidates = np.where(
+#                 local_tau_table[:, gt_matching_qualified_tau_candidates[0]] > tp)
+#             det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[0].shape[0]
+#
+#             if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+#                     (det_matching_num_qualified_sigma_candidates == 1) and (
+#                     det_matching_num_qualified_tau_candidates == 1):
+#                 global_accumulative_recall = global_accumulative_recall + 1.0
+#                 global_accumulative_precision = global_accumulative_precision + 1.0
+#                 local_accumulative_recall = local_accumulative_recall + 1.0
+#                 local_accumulative_precision = local_accumulative_precision + 1.0
+#
+#                 gt_flag[0, gt_id] = 1
+#                 matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+#                 # recg start
+#                 print
+#                 "liushanshan one to one det_id = {}".format(matched_det_id)
+#                 print
+#                 "liushanshan one to one gt_id = {}".format(gt_id)
+#                 gt_str_cur = global_gt_str[idy][gt_id]
+#                 pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[0]]
+#                 print
+#                 "liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
+#                 print
+#                 "liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
+#                 if pred_str_cur == gt_str_cur:
+#                     hit_str_num += 1
+#                 else:
+#                     if pred_str_cur.lower() == gt_str_cur.lower():
+#                         hit_str_num += 1
+#                 # recg end
+#                 det_flag[0, matched_det_id] = 1
+#         return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+#
+#     def one_to_many(local_sigma_table, local_tau_table, local_accumulative_recall,
+#                     local_accumulative_precision, global_accumulative_recall, global_accumulative_precision,
+#                     gt_flag, det_flag, idy):
+#         hit_str_num = 0
+#         for gt_id in range(num_gt):
+#             # skip the following if the groundtruth was matched
+#             if gt_flag[0, gt_id] > 0:
+#                 continue
+#
+#             non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+#             num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+#
+#             if num_non_zero_in_sigma >= k:
+#                 ####search for all detections that overlaps with this groundtruth
+#                 qualified_tau_candidates = np.where((local_tau_table[gt_id, :] >= tp) & (det_flag[0, :] == 0))
+#                 num_qualified_tau_candidates = qualified_tau_candidates[0].shape[0]
+#
+#                 if num_qualified_tau_candidates == 1:
+#                     if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp) and (
+#                             local_sigma_table[gt_id, qualified_tau_candidates] >= tr)):
+#                         # became an one-to-one case
+#                         global_accumulative_recall = global_accumulative_recall + 1.0
+#                         global_accumulative_precision = global_accumulative_precision + 1.0
+#                         local_accumulative_recall = local_accumulative_recall + 1.0
+#                         local_accumulative_precision = local_accumulative_precision + 1.0
+#
+#                         gt_flag[0, gt_id] = 1
+#                         det_flag[0, qualified_tau_candidates] = 1
+#                         # recg start
+#                         print
+#                         "liushanshan one to many det_id = {}".format(qualified_tau_candidates)
+#                         print
+#                         "liushanshan one to many gt_id = {}".format(gt_id)
+#                         gt_str_cur = global_gt_str[idy][gt_id]
+#                         pred_str_cur = global_pred_str[idy][qualified_tau_candidates[0].tolist()[0]]
+#                         print
+#                         "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
+#                         print
+#                         "liushanshan one to many pred_str_cur = {}".format(pred_str_cur)
+#                         if pred_str_cur == gt_str_cur:
+#                             hit_str_num += 1
+#                         else:
+#                             if pred_str_cur.lower() == gt_str_cur.lower():
+#                                 hit_str_num += 1
+#                         # recg end
+#                 elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates]) >= tr):
+#                     gt_flag[0, gt_id] = 1
+#                     det_flag[0, qualified_tau_candidates] = 1
+#                     # recg start
+#                     print
+#                     "liushanshan one to many det_id = {}".format(qualified_tau_candidates)
+#                     print
+#                     "liushanshan one to many gt_id = {}".format(gt_id)
+#                     gt_str_cur = global_gt_str[idy][gt_id]
+#                     pred_str_cur = global_pred_str[idy][qualified_tau_candidates[0].tolist()[0]]
+#                     print
+#                     "liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
+#                     print
+#                     "liushanshan one to many pred_str_cur = {}".format(pred_str_cur)
+#                     if pred_str_cur == gt_str_cur:
+#                         hit_str_num += 1
+#                     else:
+#                         if pred_str_cur.lower() == gt_str_cur.lower():
+#                             hit_str_num += 1
+#                     # recg end
+#
+#                     global_accumulative_recall = global_accumulative_recall + fsc_k
+#                     global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+#
+#                     local_accumulative_recall = local_accumulative_recall + fsc_k
+#                     local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+#
+#         return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+#
+#     def many_to_one(local_sigma_table, local_tau_table, local_accumulative_recall,
+#                     local_accumulative_precision, global_accumulative_recall, global_accumulative_precision,
+#                     gt_flag, det_flag, idy):
+#         hit_str_num = 0
+#         for det_id in range(num_det):
+#             # skip the following if the detection was matched
+#             if det_flag[0, det_id] > 0:
+#                 continue
+#
+#             non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+#             num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+#
+#             if num_non_zero_in_tau >= k:
+#                 ####search for all detections that overlaps with this groundtruth
+#                 qualified_sigma_candidates = np.where((local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+#                 num_qualified_sigma_candidates = qualified_sigma_candidates[0].shape[0]
+#
+#                 if num_qualified_sigma_candidates == 1:
+#                     if ((local_tau_table[qualified_sigma_candidates, det_id] >= tp) and (
+#                             local_sigma_table[qualified_sigma_candidates, det_id] >= tr)):
+#                         # became an one-to-one case
+#                         global_accumulative_recall = global_accumulative_recall + 1.0
+#                         global_accumulative_precision = global_accumulative_precision + 1.0
+#                         local_accumulative_recall = local_accumulative_recall + 1.0
+#                         local_accumulative_precision = local_accumulative_precision + 1.0
+#
+#                         gt_flag[0, qualified_sigma_candidates] = 1
+#                         det_flag[0, det_id] = 1
+#                         # recg start
+#                         print
+#                         "liushanshan many to one det_id = {}".format(det_id)
+#                         print
+#                         "liushanshan many to one gt_id = {}".format(qualified_sigma_candidates)
+#                         pred_str_cur = global_pred_str[idy][det_id]
+#                         gt_len = len(qualified_sigma_candidates[0])
+#                         for idx in range(gt_len):
+#                             ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+#                             if ele_gt_id not in global_gt_str[idy]:
+#                                 continue
+#                             gt_str_cur = global_gt_str[idy][ele_gt_id]
+#                             print
+#                             "liushanshan many to one gt_str_cur = {}".format(gt_str_cur)
+#                             print
+#                             "liushanshan many to one pred_str_cur = {}".format(pred_str_cur)
+#                             if pred_str_cur == gt_str_cur:
+#                                 hit_str_num += 1
+#                                 break
+#                             else:
+#                                 if pred_str_cur.lower() == gt_str_cur.lower():
+#                                     hit_str_num += 1
+#                                 break
+#                         # recg end
+#                 elif (np.sum(local_tau_table[qualified_sigma_candidates, det_id]) >= tp):
+#                     det_flag[0, det_id] = 1
+#                     gt_flag[0, qualified_sigma_candidates] = 1
+#                     # recg start
+#                     print
+#                     "liushanshan many to one det_id = {}".format(det_id)
+#                     print
+#                     "liushanshan many to one gt_id = {}".format(qualified_sigma_candidates)
+#                     pred_str_cur = global_pred_str[idy][det_id]
+#                     gt_len = len(qualified_sigma_candidates[0])
+#                     for idx in range(gt_len):
+#                         ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+#                         if not global_gt_str[idy].has_key(ele_gt_id):
+#                             continue
+#                         gt_str_cur = global_gt_str[idy][ele_gt_id]
+#                         print
+#                         "liushanshan many to one gt_str_cur = {}".format(gt_str_cur)
+#                         print
+#                         "liushanshan many to one pred_str_cur = {}".format(pred_str_cur)
+#                         if pred_str_cur == gt_str_cur:
+#                             hit_str_num += 1
+#                             break
+#                         else:
+#                             if pred_str_cur.lower() == gt_str_cur.lower():
+#                                 hit_str_num += 1
+#                                 break
+#                             else:
+#                                 print
+#                                 'no match'
+#                     # recg end
+#
+#                     global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+#                     global_accumulative_precision = global_accumulative_precision + fsc_k
+#
+#                     local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+#                     local_accumulative_precision = local_accumulative_precision + fsc_k
+#         return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+#
+#     for idx in range(len(global_sigma)):
+#         local_sigma_table = np.array(global_sigma[idx])
+#         local_tau_table = np.array(global_tau[idx])
+#
+#         num_gt = local_sigma_table.shape[0]
+#         num_det = local_sigma_table.shape[1]
+#
+#         total_num_gt = total_num_gt + num_gt
+#         total_num_det = total_num_det + num_det
+#
+#         local_accumulative_recall = 0
+#         local_accumulative_precision = 0
+#         gt_flag = np.zeros((1, num_gt))
+#         det_flag = np.zeros((1, num_det))
+#
+#         #######first check for one-to-one case##########
+#         local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+#         gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+#                                                     local_accumulative_recall, local_accumulative_precision,
+#                                                     global_accumulative_recall, global_accumulative_precision,
+#                                                     gt_flag, det_flag, idx)
+#
+#         hit_str_count += hit_str_num
+#         #######then check for one-to-many case##########
+#         local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+#         gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+#                                                      local_accumulative_recall, local_accumulative_precision,
+#                                                      global_accumulative_recall, global_accumulative_precision,
+#                                                      gt_flag, det_flag, idx)
+#         hit_str_count += hit_str_num
+#         #######then check for many-to-one case##########
+#         local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+#         gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+#                                                      local_accumulative_recall, local_accumulative_precision,
+#                                                      global_accumulative_recall, global_accumulative_precision,
+#                                                      gt_flag, det_flag, idx)
+#     try:
+#         recall = global_accumulative_recall / total_num_gt
+#     except ZeroDivisionError:
+#         recall = 0
+#
+#     try:
+#         precision = global_accumulative_precision / total_num_det
+#     except ZeroDivisionError:
+#         precision = 0
+#
+#     try:
+#         f_score = 2 * precision * recall / (precision + recall)
+#     except ZeroDivisionError:
+#         f_score = 0
+#
+#     try:
+#         seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+#     except ZeroDivisionError:
+#         seqerr = 1
+#
+#     try:
+#         recall_e2e = float(hit_str_count) / total_num_gt
+#     except ZeroDivisionError:
+#         recall_e2e = 0
+#
+#     try:
+#         precision_e2e = float(hit_str_count) / total_num_det
+#     except ZeroDivisionError:
+#         precision_e2e = 0
+#
+#     try:
+#         f_score_e2e = 2 * precision_e2e * recall_e2e / (precision_e2e + recall_e2e)
+#     except ZeroDivisionError:
+#         f_score_e2e = 0
+#
+#     final = {
+#         'total_num_gt': total_num_gt,
+#         'total_num_det': total_num_det,
+#         'global_accumulative_recall': global_accumulative_recall,
+#         'hit_str_count': hit_str_count,
+#         'recall': recall,
+#         'precision': precision,
+#         'f_score': f_score,
+#         'seqerr': seqerr,
+#         'recall_e2e': recall_e2e,
+#         'precision_e2e': precision_e2e,
+#         'f_score_e2e': f_score_e2e
+#     }
+#     return final
+
+a = [
+    1526, 642, 1565, 629, 1579, 627, 1593, 625, 1607, 623, 1620, 622, 1634, 620,
+    1659, 620, 1654, 681, 1631, 680, 1618, 681, 1606, 681, 1594, 681, 1584, 682,
+    1573, 685, 1542, 694
+]
+gt_dict = [{'points': np.array(a).reshape(-1, 2), 'text': 'MILK'}]
+pred_dict = [{
+    'points': np.array(a),
+    'text': 'ccc'
+}, {
+    'points': np.array(a),
+    'text': 'ccf'
+}]
+result = []
+result.append(get_socre(gt_dict, gt_dict))
+a = combine_results(result)
+print(a)

ppocr/utils/e2e_utils/extract_textpoint.py

+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import cv2
+import time
+import math
+
+import numpy as np
+from itertools import groupby
+from ppocr.utils.e2e_utils.ski_thin import thin
+
+
+def softmax(logits):
+    """
+    logits: N x d
+    """
+    max_value = np.max(logits, axis=1, keepdims=True)
+    exp = np.exp(logits - max_value)
+    exp_sum = np.sum(exp, axis=1, keepdims=True)
+    dist = exp / exp_sum
+    return dist
+
+
+def get_keep_pos_idxs(labels, remove_blank=None):
+    """
+    Remove duplicate and get pos idxs of keep items.
+    The value of keep_blank should be [None, 95].
+    """
+    duplicate_len_list = []
+    keep_pos_idx_list = []
+    keep_char_idx_list = []
+    for k, v_ in groupby(labels):
+        current_len = len(list(v_))
+        if k != remove_blank:
+            current_idx = int(sum(duplicate_len_list) + current_len // 2)
+            keep_pos_idx_list.append(current_idx)
+            keep_char_idx_list.append(k)
+        duplicate_len_list.append(current_len)
+    return keep_char_idx_list, keep_pos_idx_list
+
+
+def remove_blank(labels, blank=0):
+    new_labels = [x for x in labels if x != blank]
+    return new_labels
+
+
+def insert_blank(labels, blank=0):
+    new_labels = [blank]
+    for l in labels:
+        new_labels += [l, blank]
+    return new_labels
+
+
+def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
+    """
+    CTC greedy (best path) decoder.
+    """
+    raw_str = np.argmax(np.array(probs_seq), axis=1)
+    remove_blank_in_pos = None if keep_blank_in_idxs else blank
+    dedup_str, keep_idx_list = get_keep_pos_idxs(
+        raw_str, remove_blank=remove_blank_in_pos)
+    dst_str = remove_blank(dedup_str, blank=blank)
+    return dst_str, keep_idx_list
+
+
+def instance_ctc_greedy_decoder(gather_info,
+                                logits_map,
+                                keep_blank_in_idxs=True):
+    """
+    gather_info: [[x, y], [x, y] ...]
+    logits_map: H x W X (n_chars + 1)
+    """
+    _, _, C = logits_map.shape
+    ys, xs = zip(*gather_info)
+    logits_seq = logits_map[list(ys), list(xs)]  # n x 96
+    probs_seq = softmax(logits_seq)
+    dst_str, keep_idx_list = ctc_greedy_decoder(
+        probs_seq, blank=C - 1, keep_blank_in_idxs=keep_blank_in_idxs)
+    keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
+    return dst_str, keep_gather_list
+
+
+def ctc_decoder_for_image(gather_info_list, logits_map,
+                          keep_blank_in_idxs=True):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_results = []
+    for gather_info in gather_info_list:
+        res = instance_ctc_greedy_decoder(
+            gather_info, logits_map, keep_blank_in_idxs=keep_blank_in_idxs)
+        decoder_results.append(res)
+    return decoder_results
+
+
+def sort_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point, np.array(sorted_direction)
+
+
+def add_id(pos_list, image_id=0):
+    """
+    Add id for gather feature, for inference.
+    """
+    new_list = []
+    for item in pos_list:
+        new_list.append((image_id, item[0], item[1]))
+    return new_list
+
+
+def sort_and_expand_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    left_list = []
+    right_list = []
+    for i in range(append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            left_list.append((ly, lx))
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            right_list.append((ry, rx))
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def generate_pivot_list_curved(p_score,
+                               p_char_maps,
+                               f_direction,
+                               score_thresh=0.5,
+                               is_expand=True,
+                               is_backbone=False,
+                               image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+
+            if is_expand:
+                pos_list_sorted = sort_and_expand_with_direction_v2(
+                    pos_list, f_direction, p_tcl_map)
+            else:
+                pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list_horizontal(p_score,
+                                   p_char_maps,
+                                   f_direction,
+                                   score_thresh=0.5,
+                                   is_backbone=False,
+                                   image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map_bi = (p_score > score_thresh) * 1.0
+    instance_count, instance_label_map = cv2.connectedComponents(
+        p_tcl_map_bi.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 5:
+                continue
+
+            # add rule here
+            main_direction = extract_main_direction(pos_list,
+                                                    f_direction)  # y x
+            reference_directin = np.array([0, 1]).reshape([-1, 2])  # y x
+            is_h_angle = abs(np.sum(
+                main_direction * reference_directin)) < math.cos(math.pi / 180 *
+                                                                 70)
+
+            point_yxs = np.array(pos_list)
+            max_y, max_x = np.max(point_yxs, axis=0)
+            min_y, min_x = np.min(point_yxs, axis=0)
+            is_h_len = (max_y - min_y) < 1.5 * (max_x - min_x)
+
+            pos_list_final = []
+            if is_h_len:
+                xs = np.unique(xs)
+                for x in xs:
+                    ys = instance_label_map[:, x].copy().reshape((-1, ))
+                    y = int(np.where(ys == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+            else:
+                ys = np.unique(ys)
+                for y in ys:
+                    xs = instance_label_map[y, :].copy().reshape((-1, ))
+                    x = int(np.where(xs == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+
+            pos_list_sorted, _ = sort_with_direction(pos_list_final,
+                                                     f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list(p_score,
+                        p_char_maps,
+                        f_direction,
+                        score_thresh=0.5,
+                        is_backbone=False,
+                        is_curved=True,
+                        image_id=0):
+    """
+    Warp all the function together.
+    """
+    if is_curved:
+        return generate_pivot_list_curved(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_expand=True,
+            is_backbone=is_backbone,
+            image_id=image_id)
+    else:
+        return generate_pivot_list_horizontal(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_backbone=is_backbone,
+            image_id=image_id)
+
+
+# for refine module
+def extract_main_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    pos_list = np.array(pos_list)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    average_direction = average_direction / (
+        np.linalg.norm(average_direction) + 1e-6)
+    return average_direction
+
+
+def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
+    """
+    pos_list_full = np.array(pos_list).reshape(-1, 3)
+    pos_list = pos_list_full[:, 1:]
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+    return sorted_list
+
+
+def sort_by_direction_with_image_id(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list_full, point_direction):
+        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
+        pos_list = pos_list_full[:, 1:]
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 3)
+    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point
+
+
+def generate_pivot_list_tt_inference(p_score,
+                                     p_char_maps,
+                                     f_direction,
+                                     score_thresh=0.5,
+                                     is_backbone=False,
+                                     is_curved=True,
+                                     image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
+            # pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
+            pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
+            all_pos_yxs.append(pos_list_sorted_with_id)
+    return all_pos_yxs
+
+
+if __name__ == '__main__':
+    np.random.seed(0)
+    import time
+
+    logits_map = np.random.random([10, 20, 33])
+    # a list of [x, y]
+    instance_gather_info_1 = [(2, 3), (2, 4), (3, 5)]
+    instance_gather_info_2 = [(15, 6), (15, 7), (18, 8)]
+    instance_gather_info_3 = [(8, 8), (8, 8), (8, 8)]
+    gather_info_list = [
+        instance_gather_info_1, instance_gather_info_2, instance_gather_info_3
+    ]
+
+    time0 = time.time()
+    res = ctc_decoder_for_image(
+        gather_info_list, logits_map, keep_blank_in_idxs=True)
+    print(res)
+    print('cost {}'.format(time.time() - time0))
+    print('--' * 20)

ppocr/utils/e2e_utils/ski_thin.py

+"""
+Algorithms for computing the skeleton of a binary image
+"""
+
+import numpy as np
+from scipy import ndimage as ndi
+
+G123_LUT = np.array(
+    [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
+        0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
+        1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
+        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0
+    ],
+    dtype=np.bool)
+
+G123P_LUT = np.array(
+    [
+        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
+        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    ],
+    dtype=np.bool)
+
+
+def thin(image, max_iter=None):
+    """
+    Perform morphological thinning of a binary image.
+    Parameters
+    ----------
+    image : binary (M, N) ndarray
+        The image to be thinned.
+    max_iter : int, number of iterations, optional
+        Regardless of the value of this parameter, the thinned image
+        is returned immediately if an iteration produces no change.
+        If this parameter is specified it thus sets an upper bound on
+        the number of iterations performed.
+    Returns
+    -------
+    out : ndarray of bool
+        Thinned image.
+    See also
+    --------
+    skeletonize, medial_axis
+    Notes
+    -----
+    This algorithm [1]_ works by making multiple passes over the image,
+    removing pixels matching a set of criteria designed to thin
+    connected regions while preserving eight-connected components and
+    2 x 2 squares [2]_. In each of the two sub-iterations the algorithm
+    correlates the intermediate skeleton image with a neighborhood mask,
+    then looks up each neighborhood in a lookup table indicating whether
+    the central pixel should be deleted in that sub-iteration.
+    References
+    ----------
+    .. [1] Z. Guo and R. W. Hall, "Parallel thinning with
+           two-subiteration algorithms," Comm. ACM, vol. 32, no. 3,
+           pp. 359-373, 1989. :DOI:`10.1145/62065.62074`
+    .. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning
+           Methodologies-A Comprehensive Survey," IEEE Transactions on
+           Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
+           p. 879, 1992. :DOI:`10.1109/34.161346`
+    Examples
+    --------
+    >>> square = np.zeros((7, 7), dtype=np.uint8)
+    >>> square[1:-1, 2:-2] = 1
+    >>> square[0, 1] =  1
+    >>> square
+    array([[0, 1, 0, 0, 0, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> skel = thin(square)
+    >>> skel.astype(np.uint8)
+    array([[0, 1, 0, 0, 0, 0, 0],
+           [0, 0, 1, 0, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    """
+    # convert image to uint8 with values in {0, 1}
+    skel = np.asanyarray(image, dtype=bool).astype(np.uint8)
+
+    # neighborhood mask
+    mask = np.array([[8, 4, 2], [16, 0, 1], [32, 64, 128]], dtype=np.uint8)
+
+    # iterate until convergence, up to the iteration limit
+    max_iter = max_iter or np.inf
+    n_iter = 0
+    n_pts_old, n_pts_new = np.inf, np.sum(skel)
+    while n_pts_old != n_pts_new and n_iter < max_iter:
+        n_pts_old = n_pts_new
+
+        # perform the two "subiterations" described in the paper
+        for lut in [G123_LUT, G123P_LUT]:
+            # correlate image with neighborhood mask
+            N = ndi.correlate(skel, mask, mode='constant')
+            # take deletion decision from this subiteration's LUT
+            D = np.take(lut, N)
+            # perform deletion
+            skel[D] = 0
+
+        n_pts_new = np.sum(skel)  # count points after thinning
+        n_iter += 1
+
+    return skel.astype(np.bool)

ppocr/utils/e2e_utils/visual.py

+import os
+import numpy as np
+import cv2
+import time
+
+
+def visualize_e2e_result(im_fn, poly_list, seq_strs, src_im):
+    """
+    """
+    result_path = './out'
+    im_basename = os.path.basename(im_fn)
+    im_prefix = im_basename[:im_basename.rfind('.')]
+    vis_det_img = src_im.copy()
+    valid_set = 'partvgg'
+    gt_dir = "/Users/hongyongjie/Downloads/part_vgg_synth/train"
+    text_path = os.path.join(gt_dir, im_prefix + '.txt')
+    fid = open(text_path, 'r')
+    lines = [line.strip() for line in fid.readlines()]
+    for line in lines:
+        if valid_set == 'partvgg':
+            tokens = line.strip().split('\t')[0].split(',')
+            # tokens = line.strip().split(',')
+            coords = tokens[:]
+            coords = list(map(float, coords))
+            gt_poly = np.array(coords).reshape(1, 4, 2)
+        elif valid_set == 'totaltext':
+            tokens = line.strip().split('\t')[0].split(',')
+            coords = tokens[:]
+            coords_len = len(coords) / 2
+            coords = list(map(float, coords))
+            gt_poly = np.array(coords).reshape(1, coords_len, 2)
+        cv2.polylines(
+            vis_det_img,
+            np.array(gt_poly).astype(np.int32),
+            isClosed=True,
+            color=(255, 0, 0),
+            thickness=2)
+
+    for detected_poly, recognized_str in zip(poly_list, seq_strs):
+        cv2.polylines(
+            vis_det_img,
+            np.array(detected_poly[np.newaxis, ...]).astype(np.int32),
+            isClosed=True,
+            color=(0, 0, 255),
+            thickness=2)
+        cv2.putText(
+            vis_det_img,
+            recognized_str,
+            org=(int(detected_poly[0, 0]), int(detected_poly[0, 1])),
+            fontFace=cv2.FONT_HERSHEY_COMPLEX,
+            fontScale=0.7,
+            color=(0, 255, 0),
+            thickness=1)
+
+    if not os.path.exists(result_path):
+        os.makedirs(result_path)
+    cv2.imwrite("{}/{}_detection.jpg".format(result_path, im_prefix),
+                vis_det_img)
+
+
+def visualization_output(src_image,
+                         f_tcl,
+                         f_chars,
+                         output_dir,
+                         image_prefix=None):
+    """
+    """
+    # restore BGR image, CHW -> HWC
+    im_mean = [0.485, 0.456, 0.406]
+    im_std = [0.229, 0.224, 0.225]
+
+    im_mean = np.array(im_mean).reshape((3, 1, 1))
+    im_std = np.array(im_std).reshape((3, 1, 1))
+    src_image *= im_std
+    src_image += im_mean
+    src_image = src_image.transpose([1, 2, 0])
+    src_image = src_image[:, :, ::-1] * 255  # BGR -> RGB
+    H, W, _ = src_image.shape
+
+    file_prefix = image_prefix if image_prefix is not None else str(
+        int(time.time() * 1000))
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # visualization f_tcl
+    tcl_file_name = os.path.join(output_dir, file_prefix + '_0_tcl.jpg')
+    vis_tcl_img = src_image.copy()
+    f_tcl_resized = cv2.resize(f_tcl, dsize=(W, H))
+    vis_tcl_img[:, :, 1] = f_tcl_resized * 255
+    cv2.imwrite(tcl_file_name, vis_tcl_img)
+
+    # visualization char maps
+    vis_char_img = src_image.copy()
+    # CHW -> HWC
+    char_file_name = os.path.join(output_dir, file_prefix + '_1_chars.jpg')
+    f_chars = np.argmax(f_chars, axis=2)[:, :, np.newaxis].astype('float32')
+    f_chars[f_chars < 95] = 1.0
+    f_chars[f_chars == 95] = 0.0
+    f_chars_resized = cv2.resize(f_chars, dsize=(W, H))
+    vis_char_img[:, :, 1] = f_chars_resized * 255
+    cv2.imwrite(char_file_name, vis_char_img)
+
+
+def visualize_point_result(im_fn, point_list, point_pair_list, src_im, gt_dir,
+                           result_path):
+    """
+    """
+    im_basename = os.path.basename(im_fn)
+    im_prefix = im_basename[:im_basename.rfind('.')]
+    vis_det_img = src_im.copy()
+
+    # draw gt bbox on the image.
+    text_path = os.path.join(gt_dir, im_prefix + '.txt')
+    fid = open(text_path, 'r')
+    lines = [line.strip() for line in fid.readlines()]
+    for line in lines:
+        tokens = line.strip().split('\t')
+        coords = tokens[0].split(',')
+        coords_len = len(coords)
+        coords = list(map(float, coords))
+        gt_poly = np.array(coords).reshape(1, coords_len / 2, 2)
+        cv2.polylines(
+            vis_det_img,
+            np.array(gt_poly).astype(np.int32),
+            isClosed=True,
+            color=(255, 255, 255),
+            thickness=1)
+
+    for point, point_pair in zip(point_list, point_pair_list):
+        cv2.line(
+            vis_det_img,
+            tuple(point_pair[0]),
+            tuple(point_pair[1]), (0, 255, 255),
+            thickness=1)
+        cv2.circle(vis_det_img, tuple(point), 2, (0, 0, 255))
+        cv2.circle(vis_det_img, tuple(point_pair[0]), 2, (255, 0, 0))
+        cv2.circle(vis_det_img, tuple(point_pair[1]), 2, (0, 255, 0))
+
+    if not os.path.exists(result_path):
+        os.makedirs(result_path)
+    cv2.imwrite("{}/{}_border_points.jpg".format(result_path, im_prefix),
+                vis_det_img)
+
+
+def resize_image(im, max_side_len=512):
+    """
+    resize image to a size multiple of max_stride which is required by the network
+    :param im: the resized image
+    :param max_side_len: limit of max image size to avoid out of memory in gpu
+    :return: the resized image and the resize ratio
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    # Fix the longer side
+    if resize_h > resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_min(im, max_side_len=512):
+    """
+    """
+    print('--> Using resize_image_min')
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    # Fix the longer side
+    if resize_h < resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_for_totaltext(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+    ratio = 1.25
+    if h * ratio > max_side_len:
+        ratio = float(max_side_len) / resize_h
+        # Fix the longer side
+        # if resize_h > resize_w:
+        #    ratio = float(max_side_len) / resize_h
+        # else:
+        #    ratio = float(max_side_len) / resize_w
+    ###
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    # constract poly
+    point_num = len(point_pair_list) * 2
+    point_list = [0] * point_num
+    for idx, point_pair in enumerate(point_pair_list):
+        point_list[idx] = point_pair[0]
+        point_list[point_num - 1 - idx] = point_pair[1]
+    return np.array(point_list).reshape(-1, 2), pair_info
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    """
+    Generate shrink_quad_along_width.
+    """
+    ratio_pair = np.array(
+        [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+    p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+    p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+    return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+
+def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
+    """
+    expand poly along width.
+    """
+    point_num = poly.shape[0]
+    left_quad = np.array(
+        [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+    left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                 (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+    left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
+    right_quad = np.array(
+        [
+            poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+            poly[point_num // 2], poly[point_num // 2 + 1]
+        ],
+        dtype=np.float32)
+    right_ratio = 1.0 + \
+                  shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                  (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+    right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
+    poly[0] = left_quad_expand[0]
+    poly[-1] = left_quad_expand[-1]
+    poly[point_num // 2 - 1] = right_quad_expand[1]
+    poly[point_num // 2] = right_quad_expand[2]
+    return poly
+
+
+def norm2(x, axis=None):
+    if axis:
+        return np.sqrt(np.sum(x**2, axis=axis))
+    return np.sqrt(np.sum(x**2))
+
+
+def cos(p1, p2):
+    return (p1 * p2).sum() / (norm2(p1) * norm2(p2))
+
+
+def generate_direction_info(image_fn,
+                            H,
+                            W,
+                            ratio_h,
+                            ratio_w,
+                            max_length=640,
+                            out_scale=4,
+                            gt_dir=None):
+    """
+    """
+    im_basename = os.path.basename(image_fn)
+    im_prefix = im_basename[:im_basename.rfind('.')]
+    instance_direction_map = np.zeros(shape=[H // out_scale, W // out_scale, 3])
+
+    if gt_dir is None:
+        gt_dir = '/home/vis/huangzuming/data/SYNTH_DATA/part_vgg_synth_icdar/processed/val/poly'
+
+    # get gt label map
+    text_path = os.path.join(gt_dir, im_prefix + '.txt')
+    fid = open(text_path, 'r')
+    lines = [line.strip() for line in fid.readlines()]
+    for label_idx, line in enumerate(lines, start=1):
+        coords, txt = line.strip().split('\t')
+        if txt == '###':
+            continue
+        tokens = coords.strip().split(',')
+        coords = list(map(float, tokens))
+        poly = np.array(coords).reshape(4, 2) * np.array(
+            [ratio_w, ratio_h]).reshape(1, 2) / out_scale
+        mid_idx = poly.shape[0] // 2
+        direct_vector = (
+            (poly[mid_idx] + poly[mid_idx - 1]) - (poly[0] + poly[-1])) / 2.0
+
+        direct_vector /= len(txt)
+        # l2_distance = norm2(direct_vector)
+        # avg_char_distance = l2_distance / len(txt)
+        avg_char_distance = 1.0
+
+        direct_label = (direct_vector[0], direct_vector[1], avg_char_distance)
+        cv2.fillPoly(instance_direction_map,
+                     poly.round().astype(np.int32)[np.newaxis, :, :],
+                     direct_label)
+    instance_direction_map = instance_direction_map.transpose([2, 0, 1])
+    return instance_direction_map[:2, ...]

tools/infer_e2e.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def draw_e2e_res(dt_boxes, strs, config, img, img_name):
+    if len(dt_boxes) > 0:
+        src_im = img
+        for box, str in zip(dt_boxes, strs):
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+            cv2.putText(src_im, str, org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+                    fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.7, color=(0, 255, 0), thickness=1)
+        save_det_path = os.path.dirname(config['Global'][
+            'save_res_path']) + "/e2e_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The e2e Image saved in {}".format(save_path))
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+
+    init_model(config, model, logger)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'])
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image', 'shape']
+        transforms.append(op)
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, shape_list)
+            points, strs = post_result['points'], post_result['strs']
+            # write resule
+            dt_boxes_json = []
+            for poly, str in zip(points, strs):
+                tmp_json = {"transcription": str}
+                tmp_json['points'] = poly.tolist()
+                dt_boxes_json.append(tmp_json)
+            otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
+            fout.write(otstr.encode())
+            src_img = cv2.imread(file)
+            draw_e2e_res(points, strs, config, src_img, file)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()
\ No newline at end of file

tools/program.py

@@ -44,6 +44,7 @@ class ArgsParser(ArgumentParser):
 
     def parse_args(self, argv=None):
         args = super(ArgsParser, self).parse_args(argv)
+        args.config = '/Users/hongyongjie/project/PaddleOCR/configs/e2e/e2e_r50_vd_pg.yml'
         assert args.config is not None, \
             "Please specify --config=configure_file_path."
         args.opt = self._parse_opt(args.opt)
@@ -374,7 +375,8 @@ def preprocess(is_train=False):
 
     alg = config['Architecture']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
+        'CLS', 'PG'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'