add fast postprocess

doc/doc_ch/pgnet.md

@@ -146,7 +146,7 @@ python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img=
 ```
 
 ### 预测推理
-#### (1).四边形文本检测模型（ICDAR2015）  
+#### (1). 四边形文本检测模型（ICDAR2015）  
 首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，以英文数据集训练的模型为例[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar) ，可以使用如下命令进行转换：
 ```
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
@@ -160,7 +160,7 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 
 ![](../imgs_results/e2e_res_img_10_pgnet.jpg)
 
-#### (2).弯曲文本检测模型（Total-Text）
+#### (2). 弯曲文本检测模型（Total-Text）
 对于弯曲文本样例
 
 **PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
@@ -170,3 +170,8 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
 
 ![](../imgs_results/e2e_res_img623_pgnet.jpg)
+
+#### (3). 精度与FPS
+|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|
+| --- | --- | --- | --- | --- | --- | --- |
+|87.03|82.48|84.69|61.71|58.43|60.03|62.61|

doc/doc_en/pgnet_en.md

@@ -173,3 +173,7 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
 
 ![](../imgs_results/e2e_res_img623_pgnet.jpg)
+#### (3). Metric and FPS
+|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|
+| --- | --- | --- | --- | --- | --- | --- |
+|87.03|82.48|84.69|61.71|58.43|60.03|62.61|

ppocr/utils/e2e_utils/extract_textpoint_fast.py

@@ -21,7 +21,7 @@ import math
 
 import numpy as np
 from itertools import groupby
-from cv2.ximgproc import thinning as thin
+from skimage.morphology._skeletonize import thin
 
 
 def get_dict(character_dict_path):
@@ -362,11 +362,10 @@ def generate_pivot_list_fast(p_score,
     """
     p_score = p_score[0]
     f_direction = f_direction.transpose(1, 2, 0)
-    ret, p_tcl_map = cv2.threshold(p_score, score_thresh, 255,
-                                   cv2.THRESH_BINARY)
-    skeleton_map = thin(p_tcl_map.astype('uint8'))
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map.astype(np.uint8))
     instance_count, instance_label_map = cv2.connectedComponents(
-        skeleton_map, connectivity=8)
+        skeleton_map.astype(np.uint8), connectivity=8)
 
     # get TCL Instance
     all_pos_yxs = []

ppocr/utils/e2e_utils/extract_textpoint_slow.py

@@ -21,7 +21,7 @@ import math
 
 import numpy as np
 from itertools import groupby
-from cv2.ximgproc import thinning as thin
+from skimage.morphology._skeletonize import thin
 
 
 def get_dict(character_dict_path):
@@ -35,6 +35,64 @@ def get_dict(character_dict_path):
     return dict_character
 
 
+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())
+
+    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 softmax(logits):
     """
     logits: N x d

ppocr/utils/e2e_utils/pgnet_pp_utils.py

@@ -16,9 +16,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import paddle
+import os
+import sys
 
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
 from extract_textpoint_slow import *
-from extract_textpoint_fast import *
+from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
 
 
 class PGNet_PostProcess(object):