# 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 logging
import os
from copy import deepcopy
import onnxruntime as ort
from huggingface_hub import snapshot_download
from api.utils.file_utils import get_project_base_directory
from .operators import *
class Recognizer(object):
def __init__(self, label_list, task_name, 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:
model_dir = os.path.join(
get_project_base_directory(),
"rag/res/deepdoc")
model_file_path = os.path.join(model_dir, task_name + ".onnx")
if not os.path.exists(model_file_path):
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)
model_file_path = os.path.join(model_dir, task_name + ".onnx")
else:
model_file_path = os.path.join(model_dir, task_name + ".onnx")
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(
model_file_path))
if False and ort.get_device() == "GPU":
options = ort.SessionOptions()
options.enable_cpu_mem_arena = False
self.ort_sess = ort.InferenceSession(model_file_path, options=options, providers=[('CUDAExecutionProvider')])
else:
self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
self.input_names = [node.name for node in self.ort_sess.get_inputs()]
self.output_names = [node.name for node in self.ort_sess.get_outputs()]
self.input_shape = self.ort_sess.get_inputs()[0].shape[2:4]
self.label_list = label_list
@staticmethod
def sort_Y_firstly(arr, threashold):
# sort using y1 first and then x1
arr = sorted(arr, key=lambda r: (r["top"], r["x0"]))
for i in range(len(arr) - 1):
for j in range(i, -1, -1):
# restore the order using th
if abs(arr[j + 1]["top"] - arr[j]["top"]) < threashold \
and arr[j + 1]["x0"] < arr[j]["x0"]:
tmp = deepcopy(arr[j])
arr[j] = deepcopy(arr[j + 1])
arr[j + 1] = deepcopy(tmp)
return arr
@staticmethod
def sort_X_firstly(arr, threashold, copy=True):
# sort using y1 first and then x1
arr = sorted(arr, key=lambda r: (r["x0"], r["top"]))
for i in range(len(arr) - 1):
for j in range(i, -1, -1):
# restore the order using th
if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \
and arr[j + 1]["top"] < arr[j]["top"]:
tmp = deepcopy(arr[j]) if copy else arr[j]
arr[j] = deepcopy(arr[j + 1]) if copy else arr[j + 1]
arr[j + 1] = deepcopy(tmp) if copy else tmp
return arr
@staticmethod
def sort_C_firstly(arr, thr=0):
# sort using y1 first and then x1
# sorted(arr, key=lambda r: (r["x0"], r["top"]))
arr = Recognizer.sort_X_firstly(arr, thr)
for i in range(len(arr) - 1):
for j in range(i, -1, -1):
# restore the order using th
if "C" not in arr[j] or "C" not in arr[j + 1]:
continue
if arr[j + 1]["C"] < arr[j]["C"] \
or (
arr[j + 1]["C"] == arr[j]["C"]
and arr[j + 1]["top"] < arr[j]["top"]
):
tmp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = tmp
return arr
return sorted(arr, key=lambda r: (r.get("C", r["x0"]), r["top"]))
@staticmethod
def sort_R_firstly(arr, thr=0):
# sort using y1 first and then x1
# sorted(arr, key=lambda r: (r["top"], r["x0"]))
arr = Recognizer.sort_Y_firstly(arr, thr)
for i in range(len(arr) - 1):
for j in range(i, -1, -1):
if "R" not in arr[j] or "R" not in arr[j + 1]:
continue
if arr[j + 1]["R"] < arr[j]["R"] \
or (
arr[j + 1]["R"] == arr[j]["R"]
and arr[j + 1]["x0"] < arr[j]["x0"]
):
tmp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = tmp
return arr
@staticmethod
def overlapped_area(a, b, ratio=True):
tp, btm, x0, x1 = a["top"], a["bottom"], a["x0"], a["x1"]
if b["x0"] > x1 or b["x1"] < x0:
return 0
if b["bottom"] < tp or b["top"] > btm:
return 0
x0_ = max(b["x0"], x0)
x1_ = min(b["x1"], x1)
assert x0_ <= x1_, "Fuckedup! T:{},B:{},X0:{},X1:{} ==> {}".format(
tp, btm, x0, x1, b)
tp_ = max(b["top"], tp)
btm_ = min(b["bottom"], btm)
assert tp_ <= btm_, "Fuckedup! T:{},B:{},X0:{},X1:{} => {}".format(
tp, btm, x0, x1, b)
ov = (btm_ - tp_) * (x1_ - x0_) if x1 - \
x0 != 0 and btm - tp != 0 else 0
if ov > 0 and ratio:
ov /= (x1 - x0) * (btm - tp)
return ov
@staticmethod
def layouts_cleanup(boxes, layouts, far=2, thr=0.7):
def notOverlapped(a, b):
return any([a["x1"] < b["x0"],
a["x0"] > b["x1"],
a["bottom"] < b["top"],
a["top"] > b["bottom"]])
i = 0
while i + 1 < len(layouts):
j = i + 1
while j < min(i + far, len(layouts)) \
and (layouts[i].get("type", "") != layouts[j].get("type", "")
or notOverlapped(layouts[i], layouts[j])):
j += 1
if j >= min(i + far, len(layouts)):
i += 1
continue
if Recognizer.overlapped_area(layouts[i], layouts[j]) < thr \
and Recognizer.overlapped_area(layouts[j], layouts[i]) < thr:
i += 1
continue
if layouts[i].get("score") and layouts[j].get("score"):
if layouts[i]["score"] > layouts[j]["score"]:
layouts.pop(j)
else:
layouts.pop(i)
continue
area_i, area_i_1 = 0, 0
for b in boxes:
if not notOverlapped(b, layouts[i]):
area_i += Recognizer.overlapped_area(b, layouts[i], False)
if not notOverlapped(b, layouts[j]):
area_i_1 += Recognizer.overlapped_area(b, layouts[j], False)
if area_i > area_i_1:
layouts.pop(j)
else:
layouts.pop(i)
return layouts
def create_inputs(self, imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
im_shape = []
scale_factor = []
if len(imgs) == 1:
inputs['image'] = np.array((imgs[0],)).astype('float32')
inputs['im_shape'] = np.array(
(im_info[0]['im_shape'],)).astype('float32')
inputs['scale_factor'] = np.array(
(im_info[0]['scale_factor'],)).astype('float32')
return inputs
for e in im_info:
im_shape.append(np.array((e['im_shape'],)).astype('float32'))
scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
inputs['im_shape'] = np.concatenate(im_shape, axis=0)
inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
max_shape_h = max([e[0] for e in imgs_shape])
max_shape_w = max([e[1] for e in imgs_shape])
padding_imgs = []
for img in imgs:
im_c, im_h, im_w = img.shape[:]
padding_im = np.zeros(
(im_c, max_shape_h, max_shape_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = img
padding_imgs.append(padding_im)
inputs['image'] = np.stack(padding_imgs, axis=0)
return inputs
@staticmethod
def find_overlapped(box, boxes_sorted_by_y, naive=False):
if not boxes_sorted_by_y:
return
bxs = boxes_sorted_by_y
s, e, ii = 0, len(bxs), 0
while s < e and not naive:
ii = (e + s) // 2
pv = bxs[ii]
if box["bottom"] < pv["top"]:
e = ii
continue
if box["top"] > pv["bottom"]:
s = ii + 1
continue
break
while s < ii:
if box["top"] > bxs[s]["bottom"]:
s += 1
break
while e - 1 > ii:
if box["bottom"] < bxs[e - 1]["top"]:
e -= 1
break
max_overlaped_i, max_overlaped = None, 0
for i in range(s, e):
ov = Recognizer.overlapped_area(bxs[i], box)
if ov <= max_overlaped:
continue
max_overlaped_i = i
max_overlaped = ov
return max_overlaped_i
@staticmethod
def find_horizontally_tightest_fit(box, boxes):
if not boxes:
return
min_dis, min_i = 1000000, None
for i,b in enumerate(boxes):
if box.get("layoutno", "0") != b.get("layoutno", "0"): continue
dis = min(abs(box["x0"] - b["x0"]), abs(box["x1"] - b["x1"]), abs(box["x0"]+box["x1"] - b["x1"] - b["x0"])/2)
if dis < min_dis:
min_i = i
min_dis = dis
return min_i
@staticmethod
def find_overlapped_with_threashold(box, boxes, thr=0.3):
if not boxes:
return
max_overlapped_i, max_overlapped, _max_overlapped = None, thr, 0
s, e = 0, len(boxes)
for i in range(s, e):
ov = Recognizer.overlapped_area(box, boxes[i])
_ov = Recognizer.overlapped_area(boxes[i], box)
if (ov, _ov) < (max_overlapped, _max_overlapped):
continue
max_overlapped_i = i
max_overlapped = ov
_max_overlapped = _ov
return max_overlapped_i
def preprocess(self, image_list):
inputs = []
if "scale_factor" in self.input_names:
preprocess_ops = []
for op_info in [
{'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
{'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
{'type': 'Permute'},
{'stride': 32, 'type': 'PadStride'}
]:
new_op_info = op_info.copy()
op_type = new_op_info.pop('type')
preprocess_ops.append(eval(op_type)(**new_op_info))
for im_path in image_list:
im, im_info = preprocess(im_path, preprocess_ops)
inputs.append({"image": np.array((im,)).astype('float32'),
"scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
else:
hh, ww = self.input_shape
for img in image_list:
h, w = img.shape[:2]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(np.array(img).astype('float32'), (ww, hh))
# Scale input pixel values to 0 to 1
img /= 255.0
img = img.transpose(2, 0, 1)
img = img[np.newaxis, :, :, :].astype(np.float32)
inputs.append({self.input_names[0]: img, "scale_factor": [w/ww, h/hh]})
return inputs
def postprocess(self, boxes, inputs, thr):
if "scale_factor" in self.input_names:
bb = []
for b in boxes:
clsid, bbox, score = int(b[0]), b[2:], b[1]
if score < thr:
continue
if clsid >= len(self.label_list):
continue
bb.append({
"type": self.label_list[clsid].lower(),
"bbox": [float(t) for t in bbox.tolist()],
"score": float(score)
})
return bb
def xywh2xyxy(x):
# [x, y, w, h] to [x1, y1, x2, y2]
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
return y
def compute_iou(box, boxes):
# Compute xmin, ymin, xmax, ymax for both boxes
xmin = np.maximum(box[0], boxes[:, 0])
ymin = np.maximum(box[1], boxes[:, 1])
xmax = np.minimum(box[2], boxes[:, 2])
ymax = np.minimum(box[3], boxes[:, 3])
# Compute intersection area
intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
# Compute union area
box_area = (box[2] - box[0]) * (box[3] - box[1])
boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
union_area = box_area + boxes_area - intersection_area
# Compute IoU
iou = intersection_area / union_area
return iou
def iou_filter(boxes, scores, iou_threshold):
sorted_indices = np.argsort(scores)[::-1]
keep_boxes = []
while sorted_indices.size > 0:
# Pick the last box
box_id = sorted_indices[0]
keep_boxes.append(box_id)
# Compute IoU of the picked box with the rest
ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
# Remove boxes with IoU over the threshold
keep_indices = np.where(ious < iou_threshold)[0]
# print(keep_indices.shape, sorted_indices.shape)
sorted_indices = sorted_indices[keep_indices + 1]
return keep_boxes
boxes = np.squeeze(boxes).T
# Filter out object confidence scores below threshold
scores = np.max(boxes[:, 4:], axis=1)
boxes = boxes[scores > thr, :]
scores = scores[scores > thr]
if len(boxes) == 0: return []
# Get the class with the highest confidence
class_ids = np.argmax(boxes[:, 4:], axis=1)
boxes = boxes[:, :4]
input_shape = np.array([inputs["scale_factor"][0], inputs["scale_factor"][1], inputs["scale_factor"][0], inputs["scale_factor"][1]])
boxes = np.multiply(boxes, input_shape, dtype=np.float32)
boxes = xywh2xyxy(boxes)
unique_class_ids = np.unique(class_ids)
indices = []
for class_id in unique_class_ids:
class_indices = np.where(class_ids == class_id)[0]
class_boxes = boxes[class_indices, :]
class_scores = scores[class_indices]
class_keep_boxes = iou_filter(class_boxes, class_scores, 0.2)
indices.extend(class_indices[class_keep_boxes])
return [{
"type": self.label_list[class_ids[i]].lower(),
"bbox": [float(t) for t in boxes[i].tolist()],
"score": float(scores[i])
} for i in indices]
def __call__(self, image_list, thr=0.7, batch_size=16):
res = []
imgs = []
for i in range(len(image_list)):
if not isinstance(image_list[i], np.ndarray):
imgs.append(np.array(image_list[i]))
else: imgs.append(image_list[i])
batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
for i in range(batch_loop_cnt):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, len(imgs))
batch_image_list = imgs[start_index:end_index]
inputs = self.preprocess(batch_image_list)
logging.debug("preprocess")
for ins in inputs:
bb = self.postprocess(self.ort_sess.run(None, {k:v for k,v in ins.items() if k in self.input_names})[0], ins, thr)
res.append(bb)
#seeit.save_results(image_list, res, self.label_list, threshold=thr)
return res