mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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356 lines
13 KiB
Python
Executable File
356 lines
13 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, '..'))
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import numpy as np
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from .locality_aware_nms import nms_locality
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import paddle
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import cv2
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import time
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class SASTPostProcess(object):
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"""
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The post process for SAST.
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"""
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def __init__(self,
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score_thresh=0.5,
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nms_thresh=0.2,
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sample_pts_num=2,
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shrink_ratio_of_width=0.3,
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expand_scale=1.0,
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tcl_map_thresh=0.5,
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**kwargs):
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self.score_thresh = score_thresh
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self.nms_thresh = nms_thresh
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self.sample_pts_num = sample_pts_num
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self.shrink_ratio_of_width = shrink_ratio_of_width
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self.expand_scale = expand_scale
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self.tcl_map_thresh = tcl_map_thresh
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# c++ la-nms is faster, but only support python 3.5
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self.is_python35 = False
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if sys.version_info.major == 3 and sys.version_info.minor == 5:
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self.is_python35 = True
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def point_pair2poly(self, point_pair_list):
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"""
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Transfer vertical point_pairs into poly point in clockwise.
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"""
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# constract poly
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point_num = len(point_pair_list) * 2
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point_list = [0] * point_num
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for idx, point_pair in enumerate(point_pair_list):
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point_list[idx] = point_pair[0]
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point_list[point_num - 1 - idx] = point_pair[1]
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return np.array(point_list).reshape(-1, 2)
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def shrink_quad_along_width(self,
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quad,
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begin_width_ratio=0.,
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end_width_ratio=1.):
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"""
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Generate shrink_quad_along_width.
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"""
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ratio_pair = np.array(
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[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
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p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
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p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
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return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
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def expand_poly_along_width(self, poly, shrink_ratio_of_width=0.3):
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"""
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expand poly along width.
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"""
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point_num = poly.shape[0]
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left_quad = np.array(
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[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
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left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
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(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
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left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio,
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1.0)
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right_quad = np.array(
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[
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poly[point_num // 2 - 2], poly[point_num // 2 - 1],
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poly[point_num // 2], poly[point_num // 2 + 1]
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],
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dtype=np.float32)
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right_ratio = 1.0 + \
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shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
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(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
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right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0,
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right_ratio)
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poly[0] = left_quad_expand[0]
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poly[-1] = left_quad_expand[-1]
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poly[point_num // 2 - 1] = right_quad_expand[1]
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poly[point_num // 2] = right_quad_expand[2]
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return poly
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def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map):
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"""Restore quad."""
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xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
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xy_text = xy_text[:, ::-1] # (n, 2)
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# Sort the text boxes via the y axis
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xy_text = xy_text[np.argsort(xy_text[:, 1])]
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scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
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scores = scores[:, np.newaxis]
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# Restore
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point_num = int(tvo_map.shape[-1] / 2)
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assert point_num == 4
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tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :]
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xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2)
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quads = xy_text_tile - tvo_map
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return scores, quads, xy_text
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def quad_area(self, quad):
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"""
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compute area of a quad.
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"""
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edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
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(quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
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(quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
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(quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
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return np.sum(edge) / 2.
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def nms(self, dets):
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if self.is_python35:
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import lanms
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dets = lanms.merge_quadrangle_n9(dets, self.nms_thresh)
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else:
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dets = nms_locality(dets, self.nms_thresh)
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return dets
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def cluster_by_quads_tco(self, tcl_map, tcl_map_thresh, quads, tco_map):
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"""
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Cluster pixels in tcl_map based on quads.
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"""
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instance_count = quads.shape[0] + 1 # contain background
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instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32)
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if instance_count == 1:
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return instance_count, instance_label_map
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# predict text center
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xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
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n = xy_text.shape[0]
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xy_text = xy_text[:, ::-1] # (n, 2)
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tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2)
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pred_tc = xy_text - tco
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# get gt text center
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m = quads.shape[0]
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gt_tc = np.mean(quads, axis=1) # (m, 2)
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pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
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(1, m, 1)) # (n, m, 2)
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gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
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dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
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xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
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instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign
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return instance_count, instance_label_map
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def estimate_sample_pts_num(self, quad, xy_text):
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"""
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Estimate sample points number.
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"""
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eh = (np.linalg.norm(quad[0] - quad[3]) +
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np.linalg.norm(quad[1] - quad[2])) / 2.0
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ew = (np.linalg.norm(quad[0] - quad[1]) +
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np.linalg.norm(quad[2] - quad[3])) / 2.0
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dense_sample_pts_num = max(2, int(ew))
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dense_xy_center_line = xy_text[np.linspace(
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0,
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xy_text.shape[0] - 1,
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dense_sample_pts_num,
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endpoint=True,
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dtype=np.float32).astype(np.int32)]
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dense_xy_center_line_diff = dense_xy_center_line[
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1:] - dense_xy_center_line[:-1]
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estimate_arc_len = np.sum(
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np.linalg.norm(
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dense_xy_center_line_diff, axis=1))
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sample_pts_num = max(2, int(estimate_arc_len / eh))
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return sample_pts_num
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def detect_sast(self,
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tcl_map,
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tvo_map,
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tbo_map,
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tco_map,
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ratio_w,
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ratio_h,
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src_w,
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src_h,
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shrink_ratio_of_width=0.3,
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tcl_map_thresh=0.5,
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offset_expand=1.0,
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out_strid=4.0):
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"""
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first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
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"""
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# restore quad
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scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
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tvo_map)
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dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
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dets = self.nms(dets)
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if dets.shape[0] == 0:
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return []
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quads = dets[:, :-1].reshape(-1, 4, 2)
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# Compute quad area
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quad_areas = []
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for quad in quads:
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quad_areas.append(-self.quad_area(quad))
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# instance segmentation
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# instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
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instance_count, instance_label_map = self.cluster_by_quads_tco(
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tcl_map, tcl_map_thresh, quads, tco_map)
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# restore single poly with tcl instance.
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poly_list = []
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for instance_idx in range(1, instance_count):
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xy_text = np.argwhere(instance_label_map == instance_idx)[:, ::-1]
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quad = quads[instance_idx - 1]
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q_area = quad_areas[instance_idx - 1]
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if q_area < 5:
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continue
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#
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len1 = float(np.linalg.norm(quad[0] - quad[1]))
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len2 = float(np.linalg.norm(quad[1] - quad[2]))
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min_len = min(len1, len2)
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if min_len < 3:
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continue
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# filter small CC
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if xy_text.shape[0] <= 0:
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continue
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# filter low confidence instance
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xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
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if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1:
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# if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
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continue
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# sort xy_text
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left_center_pt = np.array(
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[[(quad[0, 0] + quad[-1, 0]) / 2.0,
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(quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
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right_center_pt = np.array(
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[[(quad[1, 0] + quad[2, 0]) / 2.0,
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(quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
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proj_unit_vec = (right_center_pt - left_center_pt) / \
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(np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
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proj_value = np.sum(xy_text * proj_unit_vec, axis=1)
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xy_text = xy_text[np.argsort(proj_value)]
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# Sample pts in tcl map
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if self.sample_pts_num == 0:
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sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
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else:
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sample_pts_num = self.sample_pts_num
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xy_center_line = xy_text[np.linspace(
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0,
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xy_text.shape[0] - 1,
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sample_pts_num,
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endpoint=True,
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dtype=np.float32).astype(np.int32)]
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point_pair_list = []
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for x, y in xy_center_line:
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# get corresponding offset
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offset = tbo_map[y, x, :].reshape(2, 2)
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if offset_expand != 1.0:
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offset_length = np.linalg.norm(
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offset, axis=1, keepdims=True)
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expand_length = np.clip(
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offset_length * (offset_expand - 1),
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a_min=0.5,
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a_max=3.0)
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offset_detal = offset / offset_length * expand_length
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offset = offset + offset_detal
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# original point
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ori_yx = np.array([y, x], dtype=np.float32)
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point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
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[ratio_w, ratio_h]).reshape(-1, 2)
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point_pair_list.append(point_pair)
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# ndarry: (x, 2), expand poly along width
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detected_poly = self.point_pair2poly(point_pair_list)
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detected_poly = self.expand_poly_along_width(detected_poly,
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shrink_ratio_of_width)
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detected_poly[:, 0] = np.clip(
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detected_poly[:, 0], a_min=0, a_max=src_w)
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detected_poly[:, 1] = np.clip(
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detected_poly[:, 1], a_min=0, a_max=src_h)
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poly_list.append(detected_poly)
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return poly_list
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def __call__(self, outs_dict, shape_list):
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score_list = outs_dict['f_score']
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border_list = outs_dict['f_border']
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tvo_list = outs_dict['f_tvo']
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tco_list = outs_dict['f_tco']
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if isinstance(score_list, paddle.Tensor):
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score_list = score_list.numpy()
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border_list = border_list.numpy()
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tvo_list = tvo_list.numpy()
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tco_list = tco_list.numpy()
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img_num = len(shape_list)
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poly_lists = []
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for ino in range(img_num):
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p_score = score_list[ino].transpose((1, 2, 0))
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p_border = border_list[ino].transpose((1, 2, 0))
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p_tvo = tvo_list[ino].transpose((1, 2, 0))
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p_tco = tco_list[ino].transpose((1, 2, 0))
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src_h, src_w, ratio_h, ratio_w = shape_list[ino]
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poly_list = self.detect_sast(
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p_score,
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p_tvo,
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p_border,
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p_tco,
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ratio_w,
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ratio_h,
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src_w,
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src_h,
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shrink_ratio_of_width=self.shrink_ratio_of_width,
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tcl_map_thresh=self.tcl_map_thresh,
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offset_expand=self.expand_scale)
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poly_lists.append({'points': np.array(poly_list)})
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return poly_lists
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