# 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 copy
import time
import os
from huggingface_hub import snapshot_download
from api.utils.file_utils import get_project_base_directory
from .operators import *
import numpy as np
import onnxruntime as ort
from .postprocess import build_post_process
from rag.settings import cron_logger
def transform(data, ops=None):
""" transform """
if ops is None:
ops = []
for op in ops:
data = op(data)
if data is None:
return None
return data
def create_operators(op_param_list, global_config=None):
"""
create operators based on the config
Args:
params(list): a dict list, used to create some operators
"""
assert isinstance(
op_param_list, list), ('operator config should be a list')
ops = []
for operator in op_param_list:
assert isinstance(operator,
dict) and len(operator) == 1, "yaml format error"
op_name = list(operator)[0]
param = {} if operator[op_name] is None else operator[op_name]
if global_config is not None:
param.update(global_config)
op = eval(op_name)(**param)
ops.append(op)
return ops
def load_model(model_dir, nm):
model_file_path = os.path.join(model_dir, nm + ".onnx")
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(
model_file_path))
options = ort.SessionOptions()
options.enable_cpu_mem_arena = False
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.intra_op_num_threads = 2
options.inter_op_num_threads = 2
if False and ort.get_device() == "GPU":
sess = ort.InferenceSession(
model_file_path,
options=options,
providers=['CUDAExecutionProvider'])
else:
sess = ort.InferenceSession(
model_file_path,
options=options,
providers=['CPUExecutionProvider'])
return sess, sess.get_inputs()[0]
class TextRecognizer(object):
def __init__(self, model_dir):
self.rec_image_shape = [int(v) for v in "3, 48, 320".split(",")]
self.rec_batch_num = 16
postprocess_params = {
'name': 'CTCLabelDecode',
"character_dict_path": os.path.join(model_dir, "ocr.res"),
"use_space_char": True
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor = load_model(model_dir, 'rec')
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
assert imgC == img.shape[2]
imgW = int((imgH * max_wh_ratio))
w = self.input_tensor.shape[3:][0]
if isinstance(w, str):
pass
elif 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))
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.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()
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[: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):
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()
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
for i in range(100000):
try:
outputs = self.predictor.run(None, input_dict)
break
except Exception as e:
if i >= 3:
raise e
time.sleep(5)
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]
return rec_res, time.time() - st
class TextDetector(object):
def __init__(self, model_dir):
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': 960,
'limit_type': "max",
}
}, {
'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 = {"name": "DBPostProcess", "thresh": 0.3, "box_thresh": 0.5, "max_candidates": 1000,
"unclip_ratio": 1.5, "use_dilation": False, "score_mode": "fast", "box_type": "quad"}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor = load_model(model_dir, 'det')
img_h, img_w = self.input_tensor.shape[2:]
if isinstance(img_h, str) or isinstance(img_w, str):
pass
elif 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)
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:
if isinstance(box, list):
box = np.array(box)
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:
if isinstance(box, list):
box = np.array(box)
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()
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()
input_dict = {}
input_dict[self.input_tensor.name] = img
for i in range(100000):
try:
outputs = self.predictor.run(None, input_dict)
break
except Exception as e:
if i >= 3:
raise e
time.sleep(5)
post_result = self.postprocess_op({"maps": outputs[0]}, shape_list)
dt_boxes = post_result[0]['points']
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
return dt_boxes, time.time() - st
class OCR(object):
def __init__(self, model_dir=None):
"""
If you have trouble downloading HuggingFace models, -_^ this might help!!
For Linux:
export HF_ENDPOINT=https://hf-mirror.com
For Windows:
Good luck
^_-
"""
if not model_dir:
try:
model_dir = os.path.join(
get_project_base_directory(),
"rag/res/deepdoc")
self.text_detector = TextDetector(model_dir)
self.text_recognizer = TextRecognizer(model_dir)
except Exception as e:
model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
local_dir_use_symlinks=False)
self.text_detector = TextDetector(model_dir)
self.text_recognizer = TextRecognizer(model_dir)
self.drop_score = 0.5
self.crop_image_res_index = 0
def get_rotate_crop_image(self, img, points):
'''
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - top
'''
assert len(points) == 4, "shape of points must be 4*2"
img_crop_width = int(
max(
np.linalg.norm(points[0] - points[1]),
np.linalg.norm(points[2] - points[3])))
img_crop_height = int(
max(
np.linalg.norm(points[0] - points[3]),
np.linalg.norm(points[1] - points[2])))
pts_std = np.float32([[0, 0], [img_crop_width, 0],
[img_crop_width, img_crop_height],
[0, img_crop_height]])
M = cv2.getPerspectiveTransform(points, pts_std)
dst_img = cv2.warpPerspective(
img,
M, (img_crop_width, img_crop_height),
borderMode=cv2.BORDER_REPLICATE,
flags=cv2.INTER_CUBIC)
dst_img_height, dst_img_width = dst_img.shape[0:2]
if dst_img_height * 1.0 / dst_img_width >= 1.5:
dst_img = np.rot90(dst_img)
return dst_img
def sorted_boxes(self, dt_boxes):
"""
Sort text boxes in order from top to bottom, left to right
args:
dt_boxes(array):detected text boxes with shape [4, 2]
return:
sorted boxes(array) with shape [4, 2]
"""
num_boxes = dt_boxes.shape[0]
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
_boxes = list(sorted_boxes)
for i in range(num_boxes - 1):
for j in range(i, -1, -1):
if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
(_boxes[j + 1][0][0] < _boxes[j][0][0]):
tmp = _boxes[j]
_boxes[j] = _boxes[j + 1]
_boxes[j + 1] = tmp
else:
break
return _boxes
def detect(self, img):
time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
if img is None:
return None, None, time_dict
start = time.time()
dt_boxes, elapse = self.text_detector(img)
time_dict['det'] = elapse
if dt_boxes is None:
end = time.time()
time_dict['all'] = end - start
return None, None, time_dict
else:
cron_logger.debug("dt_boxes num : {}, elapsed : {}".format(
len(dt_boxes), elapse))
return zip(self.sorted_boxes(dt_boxes), [
("", 0) for _ in range(len(dt_boxes))])
def recognize(self, ori_im, box):
img_crop = self.get_rotate_crop_image(ori_im, box)
rec_res, elapse = self.text_recognizer([img_crop])
text, score = rec_res[0]
if score < self.drop_score:
return ""
return text
def __call__(self, img, cls=True):
time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
if img is None:
return None, None, time_dict
start = time.time()
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
time_dict['det'] = elapse
if dt_boxes is None:
end = time.time()
time_dict['all'] = end - start
return None, None, time_dict
else:
cron_logger.debug("dt_boxes num : {}, elapsed : {}".format(
len(dt_boxes), elapse))
img_crop_list = []
dt_boxes = self.sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
img_crop_list.append(img_crop)
rec_res, elapse = self.text_recognizer(img_crop_list)
time_dict['rec'] = elapse
cron_logger.debug("rec_res num : {}, elapsed : {}".format(
len(rec_res), elapse))
filter_boxes, filter_rec_res = [], []
for box, rec_result in zip(dt_boxes, rec_res):
text, score = rec_result
if score >= self.drop_score:
filter_boxes.append(box)
filter_rec_res.append(rec_result)
end = time.time()
time_dict['all'] = end - start
# for bno in range(len(img_crop_list)):
# print(f"{bno}, {rec_res[bno]}")
return list(zip([a.tolist() for a in filter_boxes], filter_rec_res))