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PaddleOCR
提交 1477b89a 编写于 作者: A andyjpaddle
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fix amp train for re

上级 03d38486
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tools/infer/predict_det_eval.py 已删除 100755 → 0
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# 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
__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 time
import sys
import tools.infer.utility as utility
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
import json
logger = get_logger()
class TextDetector(object):
def __init__(self, args):
self.args = args
self.det_algorithm = args.det_algorithm
self.use_onnx = args.use_onnx
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type,
}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
postprocess_params = {}
if self.det_algorithm == "DB":
postprocess_params['name'] = 'DBPostProcess'
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = args.use_dilation
postprocess_params["score_mode"] = args.det_db_score_mode
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST":
pre_process_list[0] = {
'DetResizeForTest': {
'resize_long': args.det_limit_side_len
}
}
postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
self.det_sast_polygon = args.det_sast_polygon
if self.det_sast_polygon:
postprocess_params["sample_pts_num"] = 6
postprocess_params["expand_scale"] = 1.2
postprocess_params["shrink_ratio_of_width"] = 0.2
else:
postprocess_params["sample_pts_num"] = 2
postprocess_params["expand_scale"] = 1.0
postprocess_params["shrink_ratio_of_width"] = 0.3
elif self.det_algorithm == "PSE":
postprocess_params['name'] = 'PSEPostProcess'
postprocess_params["thresh"] = args.det_pse_thresh
postprocess_params["box_thresh"] = args.det_pse_box_thresh
postprocess_params["min_area"] = args.det_pse_min_area
postprocess_params["box_type"] = args.det_pse_box_type
postprocess_params["scale"] = args.det_pse_scale
self.det_pse_box_type = args.det_pse_box_type
elif self.det_algorithm == "FCE":
pre_process_list[0] = {
'DetResizeForTest': {
'rescale_img': [1080, 736]
}
}
postprocess_params['name'] = 'FCEPostProcess'
postprocess_params["scales"] = args.scales
postprocess_params["alpha"] = args.alpha
postprocess_params["beta"] = args.beta
postprocess_params["fourier_degree"] = args.fourier_degree
postprocess_params["box_type"] = args.det_fce_box_type
else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0)
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
args, 'det', logger)
if self.use_onnx:
img_h, img_w = self.input_tensor.shape[2:]
if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
pre_process_list[0] = {
'DetResizeForTest': {
'image_shape': [img_h, img_w]
}
}
self.preprocess_op = create_operators(pre_process_list)
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="det",
model_precision=args.precision,
batch_size=1,
data_shape="dynamic",
save_path=None,
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=2,
logger=logger)
def order_points_clockwise(self, pts):
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
diff = np.diff(np.array(tmp), axis=1)
rect[1] = tmp[np.argmin(diff)]
rect[3] = tmp[np.argmax(diff)]
return rect
def clip_det_res(self, points, img_height, img_width):
for pno in range(points.shape[0]):
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
return points
def filter_tag_det_res(self, dt_boxes, image_shape):
img_height, img_width = image_shape[0:2]
dt_boxes_new = []
for box in dt_boxes:
box = self.order_points_clockwise(box)
box = self.clip_det_res(box, img_height, img_width)
rect_width = int(np.linalg.norm(box[0] - box[1]))
rect_height = int(np.linalg.norm(box[0] - box[3]))
if rect_width <= 3 or rect_height <= 3:
continue
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
img_height, img_width = image_shape[0:2]
dt_boxes_new = []
for box in dt_boxes:
box = self.clip_det_res(box, img_height, img_width)
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
def __call__(self, img):
ori_im = img.copy()
data = {'image': img}
st = time.time()
if self.args.benchmark:
self.autolog.times.start()
data = transform(data, self.preprocess_op)
img, shape_list = data
if img is None:
return None, 0
img = np.expand_dims(img, axis=0)
shape_list = np.expand_dims(shape_list, axis=0)
img = img.copy()
if self.args.benchmark:
self.autolog.times.stamp()
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = img
outputs = self.predictor.run(self.output_tensors, input_dict)
else:
self.input_tensor.copy_from_cpu(img)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.args.benchmark:
self.autolog.times.stamp()
preds = {}
if self.det_algorithm == "EAST":
preds['f_geo'] = outputs[0]
preds['f_score'] = outputs[1]
elif self.det_algorithm == 'SAST':
preds['f_border'] = outputs[0]
preds['f_score'] = outputs[1]
preds['f_tco'] = outputs[2]
preds['f_tvo'] = outputs[3]
elif self.det_algorithm in ['DB', 'PSE']:
preds['maps'] = outputs[0]
elif self.det_algorithm == 'FCE':
for i, output in enumerate(outputs):
preds['level_{}'.format(i)] = output
else:
raise NotImplementedError
#self.predictor.try_shrink_memory()
post_result = self.postprocess_op(preds, shape_list)
dt_boxes = post_result[0]['points']
if (self.det_algorithm == "SAST" and self.det_sast_polygon) or (
self.det_algorithm in ["PSE", "FCE"] and
self.postprocess_op.box_type == 'poly'):
dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
if self.args.benchmark:
self.autolog.times.end(stamp=True)
et = time.time()
return dt_boxes, et - st
if __name__ == "__main__":
from ppocr.metrics.eval_det_iou import DetectionIoUEvaluator
evaluator = DetectionIoUEvaluator()
args = utility.parse_args()
# image_file_list = get_image_file_list(args.image_dir)
def _check_image_file(path):
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
return any([path.lower().endswith(e) for e in img_end])
def get_image_file_list_from_txt(img_file):
imgs_lists = []
label_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
root_dir = img_file.split('/')[0]
with open(img_file, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.replace('\n', '').split('\t')
file_path, label = line[0], line[1]
file_path = os.path.join(root_dir, file_path)
if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
label_lists.append(label)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))
return imgs_lists, label_lists
image_file_list, label_list = get_image_file_list_from_txt(args.image_dir)
text_detector = TextDetector(args)
count = 0
total_time = 0
draw_img_save = "./inference_results"
if args.warmup:
img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
for i in range(2):
res = text_detector(img)
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
save_results = []
results = []
for idx in range(len(image_file_list)):
image_file = image_file_list[idx]
label = json.loads(label_list[idx])
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
st = time.time()
dt_boxes, _ = text_detector(img)
elapse = time.time() - st
if count > 0:
total_time += elapse
count += 1
save_pred = os.path.basename(image_file) + "\t" + str(
json.dumps([x.tolist() for x in dt_boxes])) + "\n"
save_results.append(save_pred)
# for eval
gt_info_list = []
det_info_list = []
for dt_box in dt_boxes:
det_info = {
'points': np.array(
dt_box, dtype=np.float32),
'text': ''
}
det_info_list.append(det_info)
for lab in label:
gt_info = {
'points': np.array(
lab['points'], dtype=np.float32),
'text': '',
'ignore': False
}
gt_info_list.append(gt_info)
result = evaluator.evaluate_image(gt_info_list, det_info_list)
results.append(result)
metrics = evaluator.combine_results(results)
print('predict det eval on ', args.image_dir)
print('metrics: ', metrics)
# logger.info(save_pred)
# logger.info("The predict time of {}: {}".format(image_file, elapse))
# src_im = utility.draw_text_det_res(dt_boxes, image_file)
# img_name_pure = os.path.split(image_file)[-1]
# img_path = os.path.join(draw_img_save,
# "det_res_{}".format(img_name_pure))
# cv2.imwrite(img_path, src_im)
# logger.info("The visualized image saved in {}".format(img_path))
# with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
# f.writelines(save_results)
# f.close()
# if args.benchmark:
# text_detector.autolog.report()
tools/infer/predict_rec_eval.py 已删除 100755 → 0
浏览文件 @ 03d38486
# 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_gif
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 == '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
}
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
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_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_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 __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 = []
imgC, imgH, imgW = self.rec_image_shape
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 = []
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 = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
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 == "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)
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,
valid_ratios,
]
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]
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)
def _check_image_file(path):
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
return any([path.lower().endswith(e) for e in img_end])
def get_image_file_list_from_txt(img_file):
imgs_lists = []
label_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
root_dir = img_file.split('/')[0]
with open(img_file, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.replace('\n', '').split('\t')
file_path, label = line[0], line[1]
file_path = os.path.join(root_dir, file_path)
if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
label_lists.append(label)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))
return imgs_lists, label_lists
image_file_list, label_list = get_image_file_list_from_txt(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_gif(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()
correct_num = 0
for ino in range(len(img_list)):
pred = rec_res[ino][0]
gt = label_list[ino]
if pred == gt:
correct_num += 1
acc = correct_num * 1.0 / len(img_list)
print('predict rec eval on ', args.image_dir)
print('acc: ', acc)
# for debug bad case
bad_case_lines = []
for ino in range(len(img_list)):
pred = rec_res[ino][0]
gt = label_list[ino]
if pred != gt and len(gt) <= 25:
bad_case = valid_image_file_list[
ino] + '\t' + 'pred:' + pred + '\t' + 'gt:' + gt + '\n'
bad_case_lines.append(bad_case)
with open('bad_case_hwdb2.txt', 'a+') as f:
f.writelines(bad_case_lines)
# end debug case
if args.benchmark:
text_recognizer.autolog.report()
if __name__ == "__main__":
main(utility.parse_args())
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