From cbeebc6da73215a3014c51b6737c08b3dd7ed018 Mon Sep 17 00:00:00 2001
From: zhouweiqi <zhouweiqi@situdata.com>
Date: Wed, 29 Jun 2022 11:38:28 +0800
Subject: [PATCH] auth from done
---
.gitignore | 13 +++++++++++++
authorization_from/README.md | 18 ++++++++++++++++++
authorization_from/const.py | 38 ++++++++++++++++++++++++++++++++++++++
authorization_from/retriever.py | 130 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
classification/base_class.py | 17 +++++++++++++++++
classification/const.py | 6 ++++++
classification/main.py | 23 +++++++++++++++++++++++
classification/model.py | 188 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
classification/train.py | 0
9 files changed, 433 insertions(+), 0 deletions(-)
create mode 100644 authorization_from/README.md
create mode 100644 authorization_from/const.py
create mode 100644 authorization_from/retriever.py
create mode 100644 classification/base_class.py
create mode 100644 classification/const.py
create mode 100644 classification/main.py
create mode 100644 classification/model.py
delete mode 100644 classification/train.py
diff --git a/.gitignore b/.gitignore
index 485dee6..af8a18f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,14 @@
.idea
+
+# Byte-compiled / optimized / DLL files
+*.[oa]
+*~
+*.py[cod]
+*$py.class
+**/*.py[cod]
+
+#Hidden
+.*
+!.gitignore
+
+test.py
\ No newline at end of file
diff --git a/authorization_from/README.md b/authorization_from/README.md
new file mode 100644
index 0000000..a16489d
--- /dev/null
+++ b/authorization_from/README.md
@@ -0,0 +1,18 @@
+## Useage
+**F3个人授权书和企业授权书的信息提取**
+
+```python
+from retriever import Retriever
+import const
+
+# 个人授权书 {'姓名': 'xxx', '个人身份证件号码': 'xxx', '签字': '有'}
+r = Retriever(const.TARGET_FIELD_INDIVIDUALS)
+
+# 企业授权书 {'经销商名称': 'xx', '经销商代码-宝马中国': 'xx', '管理人员姓名-总经理': 'xx', '公司公章': '有', '法定代表人签章': '有'}
+# r = Retriever(const.TARGET_FIELD_COMPANIES)
+res = r.get_target_fields(go_res, signature_res)
+```
+
+
+
+
diff --git a/authorization_from/const.py b/authorization_from/const.py
new file mode 100644
index 0000000..3d86498
--- /dev/null
+++ b/authorization_from/const.py
@@ -0,0 +1,38 @@
+TARGET_FIELD_INDIVIDUALS = {
+ 'keys': {
+ '姓名': [('姓名', 'top1', {})],
+ '个人身份证件号码': [('个人身份证件号码', 'top1', {})],
+ },
+ 'value': {
+ '姓名': ('under', {'left_padding': 1, 'right_padding': 1}, ''),
+ '个人身份证件号码': ('under', {'left_padding': 0.5, 'right_padding': 0.5}, '')
+ },
+ 'signature': {
+ '签字': {'signature', }
+ }
+}
+
+TARGET_FIELD_COMPANIES = {
+ 'keys': {
+ '经销商名称': [
+ ('经销商名称', 'top1', {})
+ ],
+ '经销商代码-宝马中国': [
+ ('经销商代码', 'top1', {}),
+ ('宝马中国', 'right', {'top_padding': 1.5, 'bottom_padding': 0})
+ ],
+ '管理人员姓名-总经理': [
+ ('管理人员姓名', 'top1', {}),
+ ('总经理', 'right', {'top_padding': 1, 'bottom_padding': 0})
+ ],
+ },
+ 'value': {
+ '经销商名称': ('right', {'top_padding': 1, 'bottom_padding': 1}, ''),
+ '经销商代码-宝马中国': ('right', {'top_padding': 0.5, 'bottom_padding': 0.5}, ''),
+ '管理人员姓名-总经理': ('right', {'top_padding': 0.5, 'bottom_padding': 0.5}, '')
+ },
+ 'signature': {
+ '公司公章': {'circle', },
+ '法定代表人签章': {'signature', 'rectangle'}
+ }
+}
\ No newline at end of file
diff --git a/authorization_from/retriever.py b/authorization_from/retriever.py
new file mode 100644
index 0000000..43dc74c
--- /dev/null
+++ b/authorization_from/retriever.py
@@ -0,0 +1,130 @@
+class Retriever:
+
+ def __init__(self, target_fields):
+ self.keys_str = 'keys'
+ self.value_str = 'value'
+ self.signature_str = 'signature'
+ self.signature_have_str = '有'
+ self.signature_have_not_str = '无'
+ self.target_fields = target_fields
+ self.key_text_set = self.get_key_text_set(target_fields)
+
+ def get_key_text_set(self, target_fields):
+ key_text_set = set()
+ for key_text_list in target_fields[self.keys_str].values():
+ for key_text, _, _ in key_text_list:
+ key_text_set.add(key_text)
+ return key_text_set
+
+ @staticmethod
+ def key_top1(coordinates_list, key_coordinates):
+ coordinates_list.sort(key=lambda x: x[1])
+ return coordinates_list[0]
+
+ @staticmethod
+ def key_right(coordinates_list, key_coordinates, top_padding, bottom_padding):
+ if len(coordinates_list) == 1:
+ return coordinates_list[0]
+ height = key_coordinates[-1] - key_coordinates[1]
+ y_min = key_coordinates[1] - (top_padding * height)
+ y_max = key_coordinates[-1] + (bottom_padding * height)
+ x = key_coordinates[2]
+
+ x_min = None
+ key_coordinates = None
+ for x0, y0, x1, y1 in coordinates_list:
+ if y0 > y_min and y1 < y_max and x0 > x:
+ if x_min is None or x0 < x_min:
+ x_min = x0
+ key_coordinates = (x0, y0, x1, y1)
+ return key_coordinates
+
+ @staticmethod
+ def value_right(go_res, key_coordinates, top_padding, bottom_padding):
+ height = key_coordinates[-1] - key_coordinates[1]
+ y_min = key_coordinates[1] - (top_padding * height)
+ y_max = key_coordinates[-1] + (bottom_padding * height)
+ x = key_coordinates[2]
+
+ x_min = None
+ value = None
+ for (x0, y0, _, _, x1, y1, _, _), text in go_res.values():
+ if y0 > y_min and y1 < y_max and x0 > x:
+ if x_min is None or x0 < x_min:
+ x_min = x0
+ value = text
+ return value
+
+ @staticmethod
+ def value_under(go_res, key_coordinates, left_padding, right_padding):
+ width = key_coordinates[2] - key_coordinates[0]
+ x_min = key_coordinates[0] - (width * left_padding)
+ x_max = key_coordinates[2] + (width * right_padding)
+ y = key_coordinates[-1]
+
+ y_min = None
+ value = None
+ for (x0, y0, _, _, x1, y1, _, _), text in go_res.values():
+ if x0 > x_min and x1 < x_max and y0 > y:
+ if y_min is None or y0 < y_min:
+ y_min = y0
+ value = text
+ return value
+
+ def get_target_fields(self, go_res, signature_res_list):
+ # 搜索关键词
+ key_text_info = dict()
+ for (x0, y0, _, _, x1, y1, _, _), text in go_res.values():
+ if text in self.key_text_set:
+ key_text_info.setdefault(text, list()).append((x0, y0, x1, y1))
+
+ # 搜索关键词
+ key_coordinates_info = dict()
+ for field, key_text_list in self.target_fields[self.keys_str].items():
+ pre_key_coordinates = None
+ for key_text, direction, kwargs in key_text_list:
+ if key_text not in key_text_info:
+ break
+ key_coordinates = getattr(self, 'key_{0}'.format(direction))(
+ key_text_info[key_text],
+ pre_key_coordinates,
+ **kwargs)
+ if not isinstance(key_coordinates, tuple):
+ break
+ pre_key_coordinates = key_coordinates
+ else:
+ key_coordinates_info[field] = pre_key_coordinates
+
+ # 搜索字段值
+ res = dict()
+ for field, (direction, kwargs, default_value) in self.target_fields[self.value_str].items():
+ if not isinstance(key_coordinates_info.get(field), tuple):
+ res[field] = default_value
+ break
+ value = getattr(self, 'value_{0}'.format(direction))(
+ go_res,
+ key_coordinates_info[field],
+ **kwargs
+ )
+ if not isinstance(value, str):
+ res[field] = default_value
+ else:
+ res[field] = value
+
+ # 搜索签章
+ tmp_signature_count = dict()
+ for signature_dict in signature_res_list:
+ if signature_dict['label'] in tmp_signature_count:
+ tmp_signature_count[signature_dict['label']] += 1
+ else:
+ tmp_signature_count[signature_dict['label']] = 1
+ for field, signature_type_set in self.target_fields[self.signature_str].items():
+ for signature_type in signature_type_set:
+ if tmp_signature_count.get(signature_type, 0) > 0:
+ res[field] = self.signature_have_str
+ tmp_signature_count[signature_type] -= 1
+ break
+ else:
+ res[field] = self.signature_have_not_str
+
+ return res
diff --git a/classification/base_class.py b/classification/base_class.py
new file mode 100644
index 0000000..6eef066
--- /dev/null
+++ b/classification/base_class.py
@@ -0,0 +1,17 @@
+class BaseModel:
+ """
+ All Model classes should extend BaseModel.
+ """
+
+ def load_model(self):
+ """
+ Defining the network structure and return
+ """
+ raise NotImplementedError(".load() must be overridden.")
+
+ def train(self, dataset_dir, epoch, batch_size, ckpt_path, train_dir_name='train', validate_dir_name='test'):
+ """
+ Model training process
+ """
+ raise NotImplementedError(".train() must be overridden.")
+
diff --git a/classification/const.py b/classification/const.py
new file mode 100644
index 0000000..cbcefe6
--- /dev/null
+++ b/classification/const.py
@@ -0,0 +1,6 @@
+CLASS_OTHER_CN = '其他'
+
+CLASS_OTHER_FIRST = True
+
+CLASS_CN_LIST = [CLASS_OTHER_CN, '身份证', '营业执照', '经销商授权书', '个人授权书']
+
diff --git a/classification/main.py b/classification/main.py
new file mode 100644
index 0000000..b899c34
--- /dev/null
+++ b/classification/main.py
@@ -0,0 +1,23 @@
+import os
+from datetime import datetime
+from model import F3Classification
+import const
+
+
+if __name__ == '__main__':
+ base_dir = os.path.dirname(os.path.abspath(__file__))
+
+ m = F3Classification(
+ class_name_list=const.CLASS_CN_LIST,
+ class_other_first=const.CLASS_OTHER_FIRST
+ )
+
+ # m.test()
+
+ dataset_dir = '/home/zwq/data/data_224'
+ ckpt_path = os.path.join(base_dir, 'ckpt_{0}.h5'.format(datetime.now().strftime('%Y-%m-%d_%H:%M:%S')))
+ epoch = 100
+ batch_size = 128
+
+ m.train(dataset_dir, epoch, batch_size, ckpt_path, train_dir_name='train', validate_dir_name='test')
+
diff --git a/classification/model.py b/classification/model.py
new file mode 100644
index 0000000..b0070ff
--- /dev/null
+++ b/classification/model.py
@@ -0,0 +1,188 @@
+import os
+import random
+import tensorflow as tf
+import tensorflow_addons as tfa
+from keras.applications.mobilenet_v2 import MobileNetV2
+from keras import layers, models, optimizers, losses, metrics, callbacks, applications
+import matplotlib.pyplot as plt
+
+from base_class import BaseModel
+
+
+@tf.function
+def random_rgb_2_bgr(image, label):
+ if random.random() > 0.5:
+ return image, label
+ image = image[:, :, ::-1]
+ return image, label
+
+
+@tf.function
+def random_grayscale_expand(image, label):
+ if random.random() > 0.1:
+ return image, label
+ image = tf.image.rgb_to_grayscale(image)
+ image = tf.image.grayscale_to_rgb(image)
+ return image, label
+
+
+@tf.function
+def load_image(image_path, label):
+ image = tf.io.read_file(image_path)
+ image = tf.image.decode_image(image, channels=3)
+ return image, label
+
+
+@tf.function
+def preprocess_input(image, label):
+ image = tf.image.resize(image, [224, 224])
+ image = applications.mobilenet_v2.preprocess_input(image)
+ return image, label
+
+
+class F3Classification(BaseModel):
+
+ def __init__(self, class_name_list, class_other_first, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.class_count = len(class_name_list) if not class_other_first else len(class_name_list) - 1
+ self.class_label_map = self.get_class_label_map(class_name_list, class_other_first)
+
+ @staticmethod
+ def get_class_label_map(class_name_list, class_other_first=False):
+ return {cn_name: idx - 1 if class_other_first else idx for idx, cn_name in enumerate(class_name_list)}
+
+ def get_image_label_list(self, dataset_dir):
+ image_path_list = []
+ label_list = []
+ for class_name in os.listdir(dataset_dir):
+ class_dir_path = os.path.join(dataset_dir, class_name)
+ if not os.path.isdir(class_dir_path):
+ continue
+ if class_name not in self.class_label_map:
+ continue
+ label = self.class_label_map[class_name]
+ for file_name in os.listdir(class_dir_path):
+ # TODO image check
+ file_path = os.path.join(class_dir_path, file_name)
+ image_path_list.append(file_path)
+ label_list.append(tf.one_hot(label, depth=self.class_count))
+ return image_path_list, label_list
+
+ def load_dataset(self, dataset_dir, name, batch_size=128, augmentation_methods=[]):
+ image_and_label_list = self.get_image_label_list(dataset_dir)
+ tensor_slice_dataset = tf.data.Dataset.from_tensor_slices(image_and_label_list, name=name)
+ tensor_slice_dataset.shuffle(len(image_and_label_list[0]), reshuffle_each_iteration=True)
+ tensor_slice_dataset.map(load_image,
+ num_parallel_calls=tf.data.AUTOTUNE,
+ deterministic=False)
+ for augmentation_method in augmentation_methods:
+ tensor_slice_dataset.map(getattr(self, augmentation_method),
+ num_parallel_calls=tf.data.AUTOTUNE,
+ deterministic=False)
+ tensor_slice_dataset.map(preprocess_input,
+ num_parallel_calls=tf.data.AUTOTUNE,
+ deterministic=False)
+ parallel_batch_dataset = tensor_slice_dataset.batch(
+ batch_size=batch_size,
+ drop_remainder=True,
+ num_parallel_calls=tf.data.AUTOTUNE,
+ deterministic=False,
+ name=name,
+ ).prefetch(tf.data.AUTOTUNE)
+ return parallel_batch_dataset
+
+ def load_model(self):
+ base_model = MobileNetV2(
+ input_shape=(224, 224, 3),
+ alpha=0.35,
+ include_top=False,
+ weights='imagenet',
+ pooling='avg',
+ )
+ x = base_model.output
+ x = layers.Dropout(0.5)(x)
+ x = layers.Dense(256, activation='sigmoid', name='dense')(x)
+ x = layers.Dropout(0.5)(x)
+ x = layers.Dense(self.class_count, activation='sigmoid', name='output')(x)
+ model = models.Model(inputs=base_model.input, outputs=x)
+
+ freeze = True
+ for layer in model.layers:
+ layer.trainable = not freeze
+ if freeze and layer.name == 'block_16_project_BN':
+ freeze = False
+ return model
+
+ def train(self, dataset_dir, epoch, batch_size, ckpt_path, train_dir_name='train', validate_dir_name='test'):
+ # model = self.load_model()
+ # model.summary()
+ #
+ # model.compile(
+ # optimizer=optimizers.Adam(learning_rate=3e-4),
+ # loss=tfa.losses.SigmoidFocalCrossEntropy(),
+ # metrics=['accuracy', ],
+ #
+ # loss_weights=None,
+ # weighted_metrics=None,
+ # run_eagerly=None,
+ # steps_per_execution=None,
+ # jit_compile=None,
+ # )
+
+ train_dataset = self.load_dataset(
+ dataset_dir=os.path.join(dataset_dir, train_dir_name),
+ name=train_dir_name,
+ batch_size=batch_size,
+ augmentation_methods=[],
+ # augmentation_methods=['random_rgb_2_bgr', 'random_grayscale_expand'],
+ )
+ validate_dataset = self.load_dataset(
+ dataset_dir=os.path.join(dataset_dir, validate_dir_name),
+ name=validate_dir_name,
+ batch_size=batch_size,
+ augmentation_methods=[]
+ )
+
+ # ckpt_callback = callbacks.ModelCheckpoint(ckpt_path, save_best_only=True)
+ #
+ # history = model.fit(
+ # train_dataset,
+ # epochs=epoch,
+ # validation_data=validate_dataset,
+ # callbacks=[ckpt_callback, ],
+ # )
+ #
+ # acc = history.history['accuracy']
+ # val_acc = history.history['val_accuracy']
+ #
+ # loss = history.history['loss']
+ # val_loss = history.history['val_loss']
+ #
+ # plt.figure(figsize=(8, 8))
+ # plt.subplot(2, 1, 1)
+ # plt.plot(acc, label='Training Accuracy')
+ # plt.plot(val_acc, label='Validation Accuracy')
+ # plt.legend(loc='lower right')
+ # plt.ylabel('Accuracy')
+ # plt.ylim([min(plt.ylim()), 1])
+ # plt.title('Training and Validation Accuracy')
+ #
+ # plt.subplot(2, 1, 2)
+ # plt.plot(loss, label='Training Loss')
+ # plt.plot(val_loss, label='Validation Loss')
+ # plt.legend(loc='upper right')
+ # plt.ylabel('Cross Entropy')
+ # plt.ylim([0, 1.0])
+ # plt.title('Training and Validation Loss')
+ # plt.xlabel('epoch')
+ # plt.show()
+
+ def test(self):
+ print(self.class_label_map)
+ print(self.class_count)
+ # path = '/home/zwq/data/data_224/train/银行卡/bc_1.jpg'
+ # label = 5
+ # image, label = self.load_image(path, label)
+ # print(image.shape)
+ # image, label = self.preprocess_input(image, label)
+ # print(image.shape)
diff --git a/classification/train.py b/classification/train.py
deleted file mode 100644
index e69de29..0000000
--- a/classification/train.py
+++ /dev/null
--
libgit2 0.24.0