113 lines
3.7 KiB
Python
Executable File
113 lines
3.7 KiB
Python
Executable File
# Copyright (c) 2021 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 numpy as np
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import cv2
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import paddle
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from paddle.nn import functional as F
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from ppocr.postprocess.pse_postprocess.pse import pse
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class PSEPostProcess(object):
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"""
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The post process for PSE.
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"""
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def __init__(self,
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thresh=0.5,
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box_thresh=0.85,
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min_area=16,
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box_type='box',
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scale=4,
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**kwargs):
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assert box_type in ['box', 'poly'], 'Only box and poly is supported'
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self.thresh = thresh
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self.box_thresh = box_thresh
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self.min_area = min_area
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self.box_type = box_type
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self.scale = scale
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def __call__(self, outs_dict, shape_list):
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pred = outs_dict['maps']
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if not isinstance(pred, paddle.Tensor):
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pred = paddle.to_tensor(pred)
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pred = F.interpolate(pred, scale_factor=4 // self.scale, mode='bilinear')
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score = F.sigmoid(pred[:, 0, :, :])
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kernels = (pred > self.thresh).astype('float32')
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text_mask = kernels[:, 0, :, :]
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kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask
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score = score.numpy()
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kernels = kernels.numpy().astype(np.uint8)
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boxes_batch = []
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for batch_index in range(pred.shape[0]):
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boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index])
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boxes_batch.append({'points': boxes, 'scores': scores})
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return boxes_batch
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def boxes_from_bitmap(self, score, kernels, shape):
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label = pse(kernels, self.min_area)
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return self.generate_box(score, label, shape)
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def generate_box(self, score, label, shape):
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src_h, src_w, ratio_h, ratio_w = shape
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label_num = np.max(label) + 1
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boxes = []
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scores = []
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for i in range(1, label_num):
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ind = label == i
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points = np.array(np.where(ind)).transpose((1, 0))[:, ::-1]
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if points.shape[0] < self.min_area:
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label[ind] = 0
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continue
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score_i = np.mean(score[ind])
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if score_i < self.box_thresh:
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label[ind] = 0
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continue
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if self.box_type == 'box':
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rect = cv2.minAreaRect(points)
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bbox = cv2.boxPoints(rect)
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elif self.box_type == 'poly':
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box_height = np.max(points[:, 1]) + 10
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box_width = np.max(points[:, 0]) + 10
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mask = np.zeros((box_height, box_width), np.uint8)
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mask[points[:, 1], points[:, 0]] = 255
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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bbox = np.squeeze(contours[0], 1)
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else:
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raise NotImplementedError
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bbox[:, 0] = np.clip(
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np.round(bbox[:, 0] / ratio_w), 0, src_w)
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bbox[:, 1] = np.clip(
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np.round(bbox[:, 1] / ratio_h), 0, src_h)
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boxes.append(bbox)
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scores.append(score_i)
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return boxes, scores
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