[refactor] Init
This commit is contained in:
OleehyO
379
texteller/paddleocr/predict_rec.py
Executable file
379
texteller/paddleocr/predict_rec.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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import sys
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from PIL import Image
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
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os.environ["FLAGS_allocator_strategy"] = "auto_growth"
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import cv2
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import numpy as np
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import math
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import time
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import utility
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from utility import get_logger
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from CTCLabelDecode import CTCLabelDecode
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logger = get_logger()
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class TextRecognizer(object):
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def __init__(self, args):
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_batch_num = args.rec_batch_num
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self.rec_algorithm = args.rec_algorithm
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self.postprocess_op = CTCLabelDecode(
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character_dict_path=args.rec_char_dict_path, use_space_char=args.use_space_char
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)
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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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) = utility.create_predictor(args, "rec", logger)
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self.benchmark = args.benchmark
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self.use_onnx = args.use_onnx
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self.return_word_box = args.return_word_box
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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if self.rec_algorithm == "NRTR" or self.rec_algorithm == "ViTSTR":
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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image_pil = Image.fromarray(np.uint8(img))
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if self.rec_algorithm == "ViTSTR":
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img = image_pil.resize([imgW, imgH], Image.BICUBIC)
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else:
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img = image_pil.resize([imgW, imgH], Image.Resampling.LANCZOS)
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img = np.array(img)
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norm_img = np.expand_dims(img, -1)
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norm_img = norm_img.transpose((2, 0, 1))
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if self.rec_algorithm == "ViTSTR":
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norm_img = norm_img.astype(np.float32) / 255.0
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else:
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norm_img = norm_img.astype(np.float32) / 128.0 - 1.0
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return norm_img
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elif self.rec_algorithm == "RFL":
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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resized_image -= 0.5
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resized_image /= 0.5
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return resized_image
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assert imgC == img.shape[2]
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imgW = int((imgH * max_wh_ratio))
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if self.use_onnx:
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w = self.input_tensor.shape[3:][0]
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if isinstance(w, str):
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pass
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elif w is not None and w > 0:
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imgW = w
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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if self.rec_algorithm == "RARE":
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if resized_w > self.rec_image_shape[2]:
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resized_w = self.rec_image_shape[2]
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imgW = self.rec_image_shape[2]
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_vl(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img = img[:, :, ::-1] # bgr2rgb
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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return resized_image
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def resize_norm_img_srn(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img_black = np.zeros((imgH, imgW))
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im_hei = img.shape[0]
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im_wid = img.shape[1]
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if im_wid <= im_hei * 1:
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img_new = cv2.resize(img, (imgH * 1, imgH))
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elif im_wid <= im_hei * 2:
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img_new = cv2.resize(img, (imgH * 2, imgH))
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elif im_wid <= im_hei * 3:
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img_new = cv2.resize(img, (imgH * 3, imgH))
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else:
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img_new = cv2.resize(img, (imgW, imgH))
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img_np = np.asarray(img_new)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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img_black[:, 0 : img_np.shape[1]] = img_np
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img_black = img_black[:, :, np.newaxis]
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row, col, c = img_black.shape
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c = 1
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(self, image_shape, num_heads, max_text_length):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64")
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gsrm_word_pos = (
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np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype("int64")
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)
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[-1, 1, max_text_length, max_text_length]
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)
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gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype(
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"float32"
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) * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[-1, 1, max_text_length, max_text_length]
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)
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gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype(
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"float32"
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) * [-1e9]
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encoder_word_pos = encoder_word_pos[np.newaxis, :]
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gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
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return [
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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]
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def process_image_srn(self, img, image_shape, num_heads, max_text_length):
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norm_img = self.resize_norm_img_srn(img, image_shape)
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norm_img = norm_img[np.newaxis, :]
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[
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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] = self.srn_other_inputs(image_shape, num_heads, max_text_length)
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gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
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gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
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encoder_word_pos = encoder_word_pos.astype(np.int64)
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gsrm_word_pos = gsrm_word_pos.astype(np.int64)
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return (
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norm_img,
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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)
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def resize_norm_img_sar(self, img, image_shape, width_downsample_ratio=0.25):
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imgC, imgH, imgW_min, imgW_max = image_shape
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h = img.shape[0]
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w = img.shape[1]
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valid_ratio = 1.0
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# make sure new_width is an integral multiple of width_divisor.
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width_divisor = int(1 / width_downsample_ratio)
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# resize
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ratio = w / float(h)
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resize_w = math.ceil(imgH * ratio)
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if resize_w % width_divisor != 0:
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resize_w = round(resize_w / width_divisor) * width_divisor
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if imgW_min is not None:
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resize_w = max(imgW_min, resize_w)
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if imgW_max is not None:
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valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
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resize_w = min(imgW_max, resize_w)
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resized_image = cv2.resize(img, (resize_w, imgH))
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resized_image = resized_image.astype("float32")
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# norm
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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resize_shape = resized_image.shape
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padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
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padding_im[:, :, 0:resize_w] = resized_image
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pad_shape = padding_im.shape
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return padding_im, resize_shape, pad_shape, valid_ratio
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def resize_norm_img_spin(self, img):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
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img = np.array(img, np.float32)
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img = np.expand_dims(img, -1)
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img = img.transpose((2, 0, 1))
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mean = [127.5]
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std = [127.5]
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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mean = np.float32(mean.reshape(1, -1))
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stdinv = 1 / np.float32(std.reshape(1, -1))
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img -= mean
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img *= stdinv
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return img
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def resize_norm_img_svtr(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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return resized_image
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def resize_norm_img_cppd_padding(
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self, img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR
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):
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imgC, imgH, imgW = image_shape
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h = img.shape[0]
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w = img.shape[1]
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if not padding:
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation)
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resized_w = imgW
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else:
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype("float32")
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_abinet(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image / 255.0
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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resized_image = (resized_image - mean[None, None, ...]) / std[None, None, ...]
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resized_image = resized_image.transpose((2, 0, 1))
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resized_image = resized_image.astype("float32")
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return resized_image
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def norm_img_can(self, img, image_shape):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
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if self.inverse:
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img = 255 - img
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if self.rec_image_shape[0] == 1:
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h, w = img.shape
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_, imgH, imgW = self.rec_image_shape
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if h < imgH or w < imgW:
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padding_h = max(imgH - h, 0)
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padding_w = max(imgW - w, 0)
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img_padded = np.pad(
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img,
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((0, padding_h), (0, padding_w)),
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"constant",
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constant_values=(255),
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)
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img = img_padded
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img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
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img = img.astype("float32")
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return img
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def __call__(self, img_list):
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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rec_res = [["", 0.0]] * img_num
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batch_num = self.rec_batch_num
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st = time.time()
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if self.benchmark:
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self.autolog.times.start()
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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imgC, imgH, imgW = self.rec_image_shape[:3]
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max_wh_ratio = imgW / imgH
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wh_ratio_list = []
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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wh_ratio_list.append(wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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if self.benchmark:
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self.autolog.times.stamp()
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assert self.use_onnx
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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outputs = self.predictor.run(self.output_tensors, input_dict)
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preds = outputs[0]
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rec_result = self.postprocess_op(
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preds,
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return_word_box=self.return_word_box,
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wh_ratio_list=wh_ratio_list,
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max_wh_ratio=max_wh_ratio,
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)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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if self.benchmark:
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self.autolog.times.end(stamp=True)
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return rec_res, time.time() - st
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