# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
from PIL import Image
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import numpy as np
import math
import time
import traceback
import paddle
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read
logger = get_logger()
class TextRecognizer(object):
def __init__(self, args):
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
postprocess_params = {
'name': 'CTCLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
if self.rec_algorithm == "SRN":
postprocess_params = {
'name': 'SRNLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
'name': 'AttnLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'NRTR':
postprocess_params = {
'name': 'NRTRLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "SAR":
postprocess_params = {
'name': 'SARLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "VisionLAN":
postprocess_params = {
'name': 'VLLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'ViTSTR':
postprocess_params = {
'name': 'ViTSTRLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'ABINet':
postprocess_params = {
'name': 'ABINetLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "SPIN":
postprocess_params = {
'name': 'SPINLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RobustScanner":
postprocess_params = {
'name': 'SARLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
"rm_symbol": True
}
elif self.rec_algorithm == 'RFL':
postprocess_params = {
'name': 'RFLLabelDecode',
"character_dict_path": None,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "PREN":
postprocess_params = {'name': 'PRENLabelDecode'}
elif self.rec_algorithm == "CAN":
self.inverse = args.rec_image_inverse
postprocess_params = {
'name': 'CANLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger)
self.benchmark = args.benchmark
self.use_onnx = args.use_onnx
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="rec",
model_precision=args.precision,
batch_size=args.rec_batch_num,
data_shape="dynamic",
save_path=None, #args.save_log_path,
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=0,
logger=logger)
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR':
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# return padding_im
image_pil = Image.fromarray(np.uint8(img))
if self.rec_algorithm == 'ViTSTR':
img = image_pil.resize([imgW, imgH], Image.BICUBIC)
else:
img = image_pil.resize([imgW, imgH], Image.ANTIALIAS)
img = np.array(img)
norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1))
if self.rec_algorithm == 'ViTSTR':
norm_img = norm_img.astype(np.float32) / 255.
else:
norm_img = norm_img.astype(np.float32) / 128. - 1.
return norm_img
elif self.rec_algorithm == 'RFL':
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
resized_image = resized_image.astype('float32')
resized_image = resized_image / 255
resized_image = resized_image[np.newaxis, :]
resized_image -= 0.5
resized_image /= 0.5
return resized_image
assert imgC == img.shape[2]
imgW = int((imgH * max_wh_ratio))
if self.use_onnx:
w = self.input_tensor.shape[3:][0]
if w is not None and w > 0:
imgW = w
h, w = img.shape[:2]
ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW:
resized_w = imgW
else:
resized_w = int(math.ceil(imgH * ratio))
if self.rec_algorithm == 'RARE':
if resized_w > self.rec_image_shape[2]:
resized_w = self.rec_image_shape[2]
imgW = self.rec_image_shape[2]
resized_image = cv2.resize(img, (resized_w, imgH))
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
padding_im[:, :, 0:resized_w] = resized_image
return padding_im
def resize_norm_img_vl(self, img, image_shape):
imgC, imgH, imgW = image_shape
img = img[:, :, ::-1] # bgr2rgb
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
return resized_image
def resize_norm_img_srn(self, img, image_shape):
imgC, imgH, imgW = image_shape
img_black = np.zeros((imgH, imgW))
im_hei = img.shape[0]
im_wid = img.shape[1]
if im_wid <= im_hei * 1:
img_new = cv2.resize(img, (imgH * 1, imgH))
elif im_wid <= im_hei * 2:
img_new = cv2.resize(img, (imgH * 2, imgH))
elif im_wid <= im_hei * 3:
img_new = cv2.resize(img, (imgH * 3, imgH))
else:
img_new = cv2.resize(img, (imgW, imgH))
img_np = np.asarray(img_new)
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
img_black[:, 0:img_np.shape[1]] = img_np
img_black = img_black[:, :, np.newaxis]
row, col, c = img_black.shape
c = 1
return np.reshape(img_black, (c, row, col)).astype(np.float32)
def srn_other_inputs(self, image_shape, num_heads, max_text_length):
imgC, imgH, imgW = image_shape
feature_dim = int((imgH / 8) * (imgW / 8))
encoder_word_pos = np.array(range(0, feature_dim)).reshape(
(feature_dim, 1)).astype('int64')
gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
(max_text_length, 1)).astype('int64')
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
[-1, 1, max_text_length, max_text_length])
gsrm_slf_attn_bias1 = np.tile(
gsrm_slf_attn_bias1,
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
[-1, 1, max_text_length, max_text_length])
gsrm_slf_attn_bias2 = np.tile(
gsrm_slf_attn_bias2,
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
encoder_word_pos = encoder_word_pos[np.newaxis, :]
gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
return [
encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2
]
def process_image_srn(self, img, image_shape, num_heads, max_text_length):
norm_img = self.resize_norm_img_srn(img, image_shape)
norm_img = norm_img[np.newaxis, :]
[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
self.srn_other_inputs(image_shape, num_heads, max_text_length)
gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
encoder_word_pos = encoder_word_pos.astype(np.int64)
gsrm_word_pos = gsrm_word_pos.astype(np.int64)
return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2)
def resize_norm_img_sar(self, img, image_shape,
width_downsample_ratio=0.25):
imgC, imgH, imgW_min, imgW_max = image_shape
h = img.shape[0]
w = img.shape[1]
valid_ratio = 1.0
# make sure new_width is an integral multiple of width_divisor.
width_divisor = int(1 / width_downsample_ratio)
# resize
ratio = w / float(h)
resize_w = math.ceil(imgH * ratio)
if resize_w % width_divisor != 0:
resize_w = round(resize_w / width_divisor) * width_divisor
if imgW_min is not None:
resize_w = max(imgW_min, resize_w)
if imgW_max is not None:
valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
resize_w = min(imgW_max, resize_w)
resized_image = cv2.resize(img, (resize_w, imgH))
resized_image = resized_image.astype('float32')
# norm
if image_shape[0] == 1:
resized_image = resized_image / 255
resized_image = resized_image[np.newaxis, :]
else:
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
resize_shape = resized_image.shape
padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
padding_im[:, :, 0:resize_w] = resized_image
pad_shape = padding_im.shape
return padding_im, resize_shape, pad_shape, valid_ratio
def resize_norm_img_spin(self, img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# return padding_im
img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
img = np.array(img, np.float32)
img = np.expand_dims(img, -1)
img = img.transpose((2, 0, 1))
mean = [127.5]
std = [127.5]
mean = np.array(mean, dtype=np.float32)
std = np.array(std, dtype=np.float32)
mean = np.float32(mean.reshape(1, -1))
stdinv = 1 / np.float32(std.reshape(1, -1))
img -= mean
img *= stdinv
return img
def resize_norm_img_svtr(self, img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
return resized_image
def resize_norm_img_abinet(self, img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image / 255.
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
resized_image = (
resized_image - mean[None, None, ...]) / std[None, None, ...]
resized_image = resized_image.transpose((2, 0, 1))
resized_image = resized_image.astype('float32')
return resized_image
def norm_img_can(self, img, image_shape):
img = cv2.cvtColor(
img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
if self.inverse:
img = 255 - img
if self.rec_image_shape[0] == 1:
h, w = img.shape
_, imgH, imgW = self.rec_image_shape
if h < imgH or w < imgW:
padding_h = max(imgH - h, 0)
padding_w = max(imgW - w, 0)
img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
'constant',
constant_values=(255))
img = img_padded
img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
img = img.astype('float32')
return img
def __call__(self, img_list):
img_num = len(img_list)
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[1] / float(img.shape[0]))
# Sorting can speed up the recognition process
indices = np.argsort(np.array(width_list))
rec_res = [['', 0.0]] * img_num
batch_num = self.rec_batch_num
st = time.time()
if self.benchmark:
self.autolog.times.start()
for beg_img_no in range(0, img_num, batch_num):
end_img_no = min(img_num, beg_img_no + batch_num)
norm_img_batch = []
if self.rec_algorithm == "SRN":
encoder_word_pos_list = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
if self.rec_algorithm == "SAR":
valid_ratios = []
imgC, imgH, imgW = self.rec_image_shape[:3]
max_wh_ratio = imgW / imgH
# max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
if self.rec_algorithm == "SAR":
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
img_list[indices[ino]], self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
valid_ratio = np.expand_dims(valid_ratio, axis=0)
valid_ratios.append(valid_ratio)
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "SRN":
norm_img = self.process_image_srn(
img_list[indices[ino]], self.rec_image_shape, 8, 25)
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
gsrm_slf_attn_bias2_list.append(norm_img[4])
norm_img_batch.append(norm_img[0])
elif self.rec_algorithm == "SVTR":
norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],
self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
elif self.rec_algorithm in ["VisionLAN", "PREN"]:
norm_img = self.resize_norm_img_vl(img_list[indices[ino]],
self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
elif self.rec_algorithm == 'SPIN':
norm_img = self.resize_norm_img_spin(img_list[indices[ino]])
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "ABINet":
norm_img = self.resize_norm_img_abinet(
img_list[indices[ino]], self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "RobustScanner":
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
img_list[indices[ino]],
self.rec_image_shape,
width_downsample_ratio=0.25)
norm_img = norm_img[np.newaxis, :]
valid_ratio = np.expand_dims(valid_ratio, axis=0)
valid_ratios = []
valid_ratios.append(valid_ratio)
norm_img_batch.append(norm_img)
word_positions_list = []
word_positions = np.array(range(0, 40)).astype('int64')
word_positions = np.expand_dims(word_positions, axis=0)
word_positions_list.append(word_positions)
elif self.rec_algorithm == "CAN":
norm_img = self.norm_img_can(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
norm_image_mask = np.ones(norm_img.shape, dtype='float32')
word_label = np.ones([1, 36], dtype='int64')
norm_img_mask_batch = []
word_label_list = []
norm_img_mask_batch.append(norm_image_mask)
word_label_list.append(word_label)
else:
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
if self.benchmark:
self.autolog.times.stamp()
if self.rec_algorithm == "SRN":
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
gsrm_slf_attn_bias1_list = np.concatenate(
gsrm_slf_attn_bias1_list)
gsrm_slf_attn_bias2_list = np.concatenate(
gsrm_slf_attn_bias2_list)
inputs = [
norm_img_batch,
encoder_word_pos_list,
gsrm_word_pos_list,
gsrm_slf_attn_bias1_list,
gsrm_slf_attn_bias2_list,
]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = {"predict": outputs[2]}
else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(
input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = {"predict": outputs[2]}
elif self.rec_algorithm == "SAR":
valid_ratios = np.concatenate(valid_ratios)
inputs = [
norm_img_batch,
np.array(
[valid_ratios], dtype=np.float32),
]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = outputs[0]
else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(
input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs[0]
elif self.rec_algorithm == "RobustScanner":
valid_ratios = np.concatenate(valid_ratios)
word_positions_list = np.concatenate(word_positions_list)
inputs = [norm_img_batch, valid_ratios, word_positions_list]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = outputs[0]
else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(
input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs[0]
elif self.rec_algorithm == "CAN":
norm_img_mask_batch = np.concatenate(norm_img_mask_batch)
word_label_list = np.concatenate(word_label_list)
inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = outputs
else:
input_names = self.predictor.get_input_names()
input_tensor = []
for i in range(len(input_names)):
input_tensor_i = self.predictor.get_input_handle(
input_names[i])
input_tensor_i.copy_from_cpu(inputs[i])
input_tensor.append(input_tensor_i)
self.input_tensor = input_tensor
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs
else:
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors,
input_dict)
preds = outputs[0]
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
if len(outputs) != 1:
preds = outputs
else:
preds = outputs[0]
rec_result = self.postprocess_op(preds)
for rno in range(len(rec_result)):
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
if self.benchmark:
self.autolog.times.end(stamp=True)
return rec_res, time.time() - st
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_recognizer = TextRecognizer(args)
valid_image_file_list = []
img_list = []
logger.info(
"In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
"if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
)
# warmup 2 times
if args.warmup:
img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
for i in range(2):
res = text_recognizer([img] * int(args.rec_batch_num))
for image_file in image_file_list:
img, flag, _ = check_and_read(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
valid_image_file_list.append(image_file)
img_list.append(img)
try:
rec_res, _ = text_recognizer(img_list)
except Exception as E:
logger.info(traceback.format_exc())
logger.info(E)
exit()
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
rec_res[ino]))
if args.benchmark:
text_recognizer.autolog.report()
if __name__ == "__main__":
main(utility.parse_args())