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rename vision, add layour and tsr recognizer (#70)
* rename vision, add layour and tsr recognizer * trivial fixing
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KevinHuSh
327
deepdoc/vision/recognizer.py
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327
deepdoc/vision/recognizer.py
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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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#
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import os
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from copy import deepcopy
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import onnxruntime as ort
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from huggingface_hub import snapshot_download
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from . import seeit
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from .operators import *
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from rag.settings import cron_logger
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class Recognizer(object):
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def __init__(self, label_list, task_name, model_dir=None):
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"""
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If you have trouble downloading HuggingFace models, -_^ this might help!!
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For Linux:
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export HF_ENDPOINT=https://hf-mirror.com
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For Windows:
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Good luck
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^_-
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"""
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if not model_dir:
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model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
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model_file_path = os.path.join(model_dir, task_name + ".onnx")
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if not os.path.exists(model_file_path):
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raise ValueError("not find model file path {}".format(
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model_file_path))
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if ort.get_device() == "GPU":
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self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
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else:
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self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
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self.label_list = label_list
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@staticmethod
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def sort_Y_firstly(arr, threashold):
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# sort using y1 first and then x1
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arr = sorted(arr, key=lambda r: (r["top"], r["x0"]))
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for i in range(len(arr) - 1):
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for j in range(i, -1, -1):
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# restore the order using th
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if abs(arr[j + 1]["top"] - arr[j]["top"]) < threashold \
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and arr[j + 1]["x0"] < arr[j]["x0"]:
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tmp = deepcopy(arr[j])
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arr[j] = deepcopy(arr[j + 1])
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arr[j + 1] = deepcopy(tmp)
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return arr
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@staticmethod
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def sort_X_firstly(arr, threashold, copy=True):
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# sort using y1 first and then x1
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arr = sorted(arr, key=lambda r: (r["x0"], r["top"]))
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for i in range(len(arr) - 1):
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for j in range(i, -1, -1):
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# restore the order using th
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if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \
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and arr[j + 1]["top"] < arr[j]["top"]:
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tmp = deepcopy(arr[j]) if copy else arr[j]
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arr[j] = deepcopy(arr[j + 1]) if copy else arr[j + 1]
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arr[j + 1] = deepcopy(tmp) if copy else tmp
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return arr
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@staticmethod
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def sort_C_firstly(arr, thr=0):
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# sort using y1 first and then x1
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# sorted(arr, key=lambda r: (r["x0"], r["top"]))
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arr = Recognizer.sort_X_firstly(arr, thr)
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for i in range(len(arr) - 1):
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for j in range(i, -1, -1):
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# restore the order using th
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if "C" not in arr[j] or "C" not in arr[j + 1]:
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continue
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if arr[j + 1]["C"] < arr[j]["C"] \
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or (
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arr[j + 1]["C"] == arr[j]["C"]
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and arr[j + 1]["top"] < arr[j]["top"]
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):
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tmp = arr[j]
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arr[j] = arr[j + 1]
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arr[j + 1] = tmp
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return arr
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return sorted(arr, key=lambda r: (r.get("C", r["x0"]), r["top"]))
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@staticmethod
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def sort_R_firstly(arr, thr=0):
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# sort using y1 first and then x1
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# sorted(arr, key=lambda r: (r["top"], r["x0"]))
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arr = Recognizer.sort_Y_firstly(arr, thr)
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for i in range(len(arr) - 1):
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for j in range(i, -1, -1):
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if "R" not in arr[j] or "R" not in arr[j + 1]:
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continue
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if arr[j + 1]["R"] < arr[j]["R"] \
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or (
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arr[j + 1]["R"] == arr[j]["R"]
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and arr[j + 1]["x0"] < arr[j]["x0"]
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):
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tmp = arr[j]
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arr[j] = arr[j + 1]
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arr[j + 1] = tmp
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return arr
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@staticmethod
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def overlapped_area(a, b, ratio=True):
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tp, btm, x0, x1 = a["top"], a["bottom"], a["x0"], a["x1"]
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if b["x0"] > x1 or b["x1"] < x0:
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return 0
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if b["bottom"] < tp or b["top"] > btm:
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return 0
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x0_ = max(b["x0"], x0)
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x1_ = min(b["x1"], x1)
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assert x0_ <= x1_, "Fuckedup! T:{},B:{},X0:{},X1:{} ==> {}".format(
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tp, btm, x0, x1, b)
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tp_ = max(b["top"], tp)
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btm_ = min(b["bottom"], btm)
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assert tp_ <= btm_, "Fuckedup! T:{},B:{},X0:{},X1:{} => {}".format(
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tp, btm, x0, x1, b)
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ov = (btm_ - tp_) * (x1_ - x0_) if x1 - \
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x0 != 0 and btm - tp != 0 else 0
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if ov > 0 and ratio:
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ov /= (x1 - x0) * (btm - tp)
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return ov
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@staticmethod
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def layouts_cleanup(boxes, layouts, far=2, thr=0.7):
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def notOverlapped(a, b):
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return any([a["x1"] < b["x0"],
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a["x0"] > b["x1"],
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a["bottom"] < b["top"],
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a["top"] > b["bottom"]])
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i = 0
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while i + 1 < len(layouts):
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j = i + 1
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while j < min(i + far, len(layouts)) \
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and (layouts[i].get("type", "") != layouts[j].get("type", "")
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or notOverlapped(layouts[i], layouts[j])):
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j += 1
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if j >= min(i + far, len(layouts)):
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i += 1
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continue
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if Recognizer.overlapped_area(layouts[i], layouts[j]) < thr \
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and Recognizer.overlapped_area(layouts[j], layouts[i]) < thr:
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i += 1
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continue
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if layouts[i].get("score") and layouts[j].get("score"):
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if layouts[i]["score"] > layouts[j]["score"]:
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layouts.pop(j)
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else:
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layouts.pop(i)
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continue
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area_i, area_i_1 = 0, 0
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for b in boxes:
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if not notOverlapped(b, layouts[i]):
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area_i += Recognizer.overlapped_area(b, layouts[i], False)
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if not notOverlapped(b, layouts[j]):
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area_i_1 += Recognizer.overlapped_area(b, layouts[j], False)
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if area_i > area_i_1:
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layouts.pop(j)
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else:
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layouts.pop(i)
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return layouts
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def create_inputs(self, imgs, im_info):
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"""generate input for different model type
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Args:
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imgs (list(numpy)): list of images (np.ndarray)
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im_info (list(dict)): list of image info
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Returns:
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inputs (dict): input of model
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"""
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inputs = {}
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im_shape = []
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scale_factor = []
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if len(imgs) == 1:
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inputs['image'] = np.array((imgs[0],)).astype('float32')
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inputs['im_shape'] = np.array(
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(im_info[0]['im_shape'],)).astype('float32')
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inputs['scale_factor'] = np.array(
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(im_info[0]['scale_factor'],)).astype('float32')
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return inputs
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for e in im_info:
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im_shape.append(np.array((e['im_shape'],)).astype('float32'))
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scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
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inputs['im_shape'] = np.concatenate(im_shape, axis=0)
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inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
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imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
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max_shape_h = max([e[0] for e in imgs_shape])
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max_shape_w = max([e[1] for e in imgs_shape])
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padding_imgs = []
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for img in imgs:
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im_c, im_h, im_w = img.shape[:]
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padding_im = np.zeros(
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(im_c, max_shape_h, max_shape_w), dtype=np.float32)
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padding_im[:, :im_h, :im_w] = img
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padding_imgs.append(padding_im)
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inputs['image'] = np.stack(padding_imgs, axis=0)
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return inputs
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@staticmethod
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def find_overlapped(box, boxes_sorted_by_y, naive=False):
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if not boxes_sorted_by_y:
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return
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bxs = boxes_sorted_by_y
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s, e, ii = 0, len(bxs), 0
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while s < e and not naive:
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ii = (e + s) // 2
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pv = bxs[ii]
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if box["bottom"] < pv["top"]:
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e = ii
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continue
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if box["top"] > pv["bottom"]:
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s = ii + 1
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continue
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break
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while s < ii:
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if box["top"] > bxs[s]["bottom"]:
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s += 1
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break
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while e - 1 > ii:
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if box["bottom"] < bxs[e - 1]["top"]:
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e -= 1
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break
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max_overlaped_i, max_overlaped = None, 0
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for i in range(s, e):
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ov = Recognizer.overlapped_area(bxs[i], box)
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if ov <= max_overlaped:
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continue
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max_overlaped_i = i
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max_overlaped = ov
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return max_overlaped_i
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@staticmethod
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def find_overlapped_with_threashold(box, boxes, thr=0.3):
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if not boxes:
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return
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max_overlaped_i, max_overlaped, _max_overlaped = None, thr, 0
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s, e = 0, len(boxes)
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for i in range(s, e):
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ov = Recognizer.overlapped_area(box, boxes[i])
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_ov = Recognizer.overlapped_area(boxes[i], box)
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if (ov, _ov) < (max_overlaped, _max_overlaped):
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continue
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max_overlaped_i = i
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max_overlaped = ov
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_max_overlaped = _ov
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return max_overlaped_i
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def preprocess(self, image_list):
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preprocess_ops = []
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for op_info in [
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{'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
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{'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
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{'type': 'Permute'},
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{'stride': 32, 'type': 'PadStride'}
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]:
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new_op_info = op_info.copy()
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op_type = new_op_info.pop('type')
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preprocess_ops.append(eval(op_type)(**new_op_info))
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inputs = []
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for im_path in image_list:
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im, im_info = preprocess(im_path, preprocess_ops)
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inputs.append({"image": np.array((im,)).astype('float32'), "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
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return inputs
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def __call__(self, image_list, thr=0.7, batch_size=16):
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res = []
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imgs = []
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for i in range(len(image_list)):
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if not isinstance(image_list[i], np.ndarray):
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imgs.append(np.array(image_list[i]))
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else: imgs.append(image_list[i])
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batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
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for i in range(batch_loop_cnt):
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start_index = i * batch_size
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end_index = min((i + 1) * batch_size, len(imgs))
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batch_image_list = imgs[start_index:end_index]
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inputs = self.preprocess(batch_image_list)
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for ins in inputs:
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bb = []
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for b in self.ort_sess.run(None, ins)[0]:
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clsid, bbox, score = int(b[0]), b[2:], b[1]
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if score < thr:
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continue
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if clsid >= len(self.label_list):
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cron_logger.warning(f"bad category id")
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continue
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bb.append({
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"type": self.label_list[clsid].lower(),
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"bbox": [float(t) for t in bbox.tolist()],
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"score": float(score)
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})
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res.append(bb)
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#seeit.save_results(image_list, res, self.label_list, threshold=thr)
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return res
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