[refactor] Init
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OleehyO
277
texteller/paddleocr/predict_det.py
Executable file
277
texteller/paddleocr/predict_det.py
Executable file
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# 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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import os
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os.environ["FLAGS_allocator_strategy"] = "auto_growth"
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import time
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import numpy as np
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from .DBPostProcess import DBPostProcess
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from .operators import DetResizeForTest, KeepKeys, NormalizeImage, ToCHWImage
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from .utility import create_predictor, get_logger
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def transform(data, ops=None):
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"""transform"""
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if ops is None:
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ops = []
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for op in ops:
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data = op(data)
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if data is None:
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return None
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return data
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logger = get_logger()
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class TextDetector(object):
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def __init__(self, args):
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self.args = args
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self.det_algorithm = args.det_algorithm
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self.use_onnx = args.use_onnx
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postprocess_params = {}
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assert self.det_algorithm == "DB"
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postprocess_params["name"] = "DBPostProcess"
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postprocess_params["thresh"] = args.det_db_thresh
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postprocess_params["box_thresh"] = args.det_db_box_thresh
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postprocess_params["max_candidates"] = 1000
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postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
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postprocess_params["use_dilation"] = args.use_dilation
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postprocess_params["score_mode"] = args.det_db_score_mode
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postprocess_params["box_type"] = args.det_box_type
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self.preprocess_op = [
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DetResizeForTest(
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limit_side_len=args.det_limit_side_len, limit_type=args.det_limit_type
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),
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NormalizeImage(
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std=[0.229, 0.224, 0.225],
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mean=[0.485, 0.456, 0.406],
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scale=1.0 / 255.0,
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order="hwc",
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),
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ToCHWImage(),
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KeepKeys(keep_keys=["image", "shape"]),
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]
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self.postprocess_op = DBPostProcess(**postprocess_params)
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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = create_predictor(args, "det", logger)
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assert self.use_onnx
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if self.use_onnx:
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img_h, img_w = self.input_tensor.shape[2:]
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if isinstance(img_h, str) or isinstance(img_w, str):
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pass
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elif img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
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self.preprocess_op[0] = DetResizeForTest(image_shape=[img_h, img_w])
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def order_points_clockwise(self, pts):
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rect = np.zeros((4, 2), dtype="float32")
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
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diff = np.diff(np.array(tmp), axis=1)
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rect[1] = tmp[np.argmin(diff)]
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rect[3] = tmp[np.argmax(diff)]
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return rect
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def clip_det_res(self, points, img_height, img_width):
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for pno in range(points.shape[0]):
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points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
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points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
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return points
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def filter_tag_det_res(self, dt_boxes, image_shape):
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img_height, img_width = image_shape[0:2]
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dt_boxes_new = []
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for box in dt_boxes:
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if type(box) is list:
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box = np.array(box)
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box = self.order_points_clockwise(box)
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box = self.clip_det_res(box, img_height, img_width)
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rect_width = int(np.linalg.norm(box[0] - box[1]))
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rect_height = int(np.linalg.norm(box[0] - box[3]))
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if rect_width <= 3 or rect_height <= 3:
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continue
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dt_boxes_new.append(box)
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dt_boxes = np.array(dt_boxes_new)
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return dt_boxes
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def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
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img_height, img_width = image_shape[0:2]
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dt_boxes_new = []
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for box in dt_boxes:
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if type(box) is list:
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box = np.array(box)
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box = self.clip_det_res(box, img_height, img_width)
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dt_boxes_new.append(box)
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dt_boxes = np.array(dt_boxes_new)
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return dt_boxes
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def predict(self, img):
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ori_im = img.copy()
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data = {"image": img}
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st = time.time()
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if self.args.benchmark:
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self.autolog.times.start()
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data = transform(data, self.preprocess_op)
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img, shape_list = data
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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shape_list = np.expand_dims(shape_list, axis=0)
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img = img.copy()
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if self.args.benchmark:
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self.autolog.times.stamp()
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = img
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outputs = self.predictor.run(self.output_tensors, input_dict)
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else:
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self.input_tensor.copy_from_cpu(img)
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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if self.args.benchmark:
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self.autolog.times.stamp()
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preds = {}
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if self.det_algorithm == "EAST":
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preds["f_geo"] = outputs[0]
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preds["f_score"] = outputs[1]
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elif self.det_algorithm == "SAST":
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preds["f_border"] = outputs[0]
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preds["f_score"] = outputs[1]
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preds["f_tco"] = outputs[2]
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preds["f_tvo"] = outputs[3]
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elif self.det_algorithm in ["DB", "PSE", "DB++"]:
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preds["maps"] = outputs[0]
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elif self.det_algorithm == "FCE":
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for i, output in enumerate(outputs):
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preds["level_{}".format(i)] = output
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elif self.det_algorithm == "CT":
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preds["maps"] = outputs[0]
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preds["score"] = outputs[1]
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else:
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raise NotImplementedError
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post_result = self.postprocess_op(preds, shape_list)
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dt_boxes = post_result[0]["points"]
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if self.args.det_box_type == "poly":
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dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
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else:
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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if self.args.benchmark:
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self.autolog.times.end(stamp=True)
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et = time.time()
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return dt_boxes, et - st
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def __call__(self, img):
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# For image like poster with one side much greater than the other side,
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# splitting recursively and processing with overlap to enhance performance.
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MIN_BOUND_DISTANCE = 50
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dt_boxes = np.zeros((0, 4, 2), dtype=np.float32)
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elapse = 0
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if img.shape[0] / img.shape[1] > 2 and img.shape[0] > self.args.det_limit_side_len:
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start_h = 0
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end_h = 0
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while end_h <= img.shape[0]:
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end_h = start_h + img.shape[1] * 3 // 4
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subimg = img[start_h:end_h, :]
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if len(subimg) == 0:
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break
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sub_dt_boxes, sub_elapse = self.predict(subimg)
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offset = start_h
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# To prevent text blocks from being cut off, roll back a certain buffer area.
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if (
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len(sub_dt_boxes) == 0
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or img.shape[1] - max([x[-1][1] for x in sub_dt_boxes]) > MIN_BOUND_DISTANCE
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):
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start_h = end_h
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else:
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sorted_indices = np.argsort(sub_dt_boxes[:, 2, 1])
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sub_dt_boxes = sub_dt_boxes[sorted_indices]
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bottom_line = (
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0 if len(sub_dt_boxes) <= 1 else int(np.max(sub_dt_boxes[:-1, 2, 1]))
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)
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if bottom_line > 0:
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start_h += bottom_line
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sub_dt_boxes = sub_dt_boxes[sub_dt_boxes[:, 2, 1] <= bottom_line]
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else:
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start_h = end_h
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if len(sub_dt_boxes) > 0:
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if dt_boxes.shape[0] == 0:
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dt_boxes = sub_dt_boxes + np.array([0, offset], dtype=np.float32)
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else:
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dt_boxes = np.append(
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dt_boxes,
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sub_dt_boxes + np.array([0, offset], dtype=np.float32),
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axis=0,
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)
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elapse += sub_elapse
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elif img.shape[1] / img.shape[0] > 3 and img.shape[1] > self.args.det_limit_side_len * 3:
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start_w = 0
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end_w = 0
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while end_w <= img.shape[1]:
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end_w = start_w + img.shape[0] * 3 // 4
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subimg = img[:, start_w:end_w]
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if len(subimg) == 0:
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break
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sub_dt_boxes, sub_elapse = self.predict(subimg)
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offset = start_w
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if (
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len(sub_dt_boxes) == 0
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or img.shape[0] - max([x[-1][0] for x in sub_dt_boxes]) > MIN_BOUND_DISTANCE
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):
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start_w = end_w
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else:
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sorted_indices = np.argsort(sub_dt_boxes[:, 2, 0])
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sub_dt_boxes = sub_dt_boxes[sorted_indices]
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right_line = (
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0 if len(sub_dt_boxes) <= 1 else int(np.max(sub_dt_boxes[:-1, 1, 0]))
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)
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if right_line > 0:
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start_w += right_line
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sub_dt_boxes = sub_dt_boxes[sub_dt_boxes[:, 1, 0] <= right_line]
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else:
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start_w = end_w
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if len(sub_dt_boxes) > 0:
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if dt_boxes.shape[0] == 0:
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dt_boxes = sub_dt_boxes + np.array([offset, 0], dtype=np.float32)
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else:
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dt_boxes = np.append(
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dt_boxes,
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sub_dt_boxes + np.array([offset, 0], dtype=np.float32),
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axis=0,
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)
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elapse += sub_elapse
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else:
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dt_boxes, elapse = self.predict(img)
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return dt_boxes, elapse
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