更新带中文算式验证码识别

chinese_detect/chd_interface.py

@@ -20,8 +20,8 @@ tf.compat.v1.disable_eager_execution()
 sess = tf.compat.v1.Session(graph=tf.Graph())
 
 package_dir = os.path.abspath(os.path.dirname(__file__))
-model_path = package_dir + "/models/char_yolo_loss_39.90.h5"
-anchors = get_anchors(package_dir + "/yolo_data/my_anchors.txt")
+model_path = package_dir + "/models/char_yolo_loss_16.99.h5"
+anchors = get_anchors(package_dir + "/yolo_data/my_anchors_new.txt")
 classes = get_classes(package_dir + "/yolo_data/my_classes.txt")
 colors = get_colors(len(classes))
 

chinese_detect/inference_yolo_char.py

@@ -8,12 +8,11 @@ import numpy as np
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 from chinese_detect.model_260 import tiny_yolo_body
 from chinese_detect.post_process import yolo_eval
-from utils import get_classes, get_colors, draw_boxes, get_anchors, pil_resize, np2pil
-
+from utils import get_classes, get_colors, draw_boxes, get_anchors, pil_resize, np2pil, limit_image_size
 
 package_dir = os.path.abspath(os.path.dirname(__file__))
-model_path = package_dir + "/models/char_yolo_loss_39.90.h5"
-anchors = get_anchors(package_dir + "/yolo_data/my_anchors.txt")
+model_path = package_dir + "/models/char_yolo_loss_16.99.h5"
+anchors = get_anchors(package_dir + "/yolo_data/my_anchors_new.txt")
 classes = get_classes(package_dir + "/yolo_data/my_classes.txt")
 colors = get_colors(len(classes))
 image_shape = (160, 256, 3)
@@ -30,7 +29,9 @@ def detect(image_np, model=None, sess=None, draw=False, is_tips=0):
                 model = get_tiny_inference_model(anchors, len(classes), weights_path=model_path)
 
     # create image input
+    image_np = limit_image_size(image_np)
     h, w = image_np.shape[:2]
+
     # print("h, w", h, w)
     best_h, best_w = image_shape[:2]
 
@@ -41,6 +42,8 @@ def detect(image_np, model=None, sess=None, draw=False, is_tips=0):
         image_resize[:tips_shape[0], :tips_shape[1], :] = image_tips[:, :, :]
     else:
         image_resize = pil_resize(image_np, best_h, best_w)
+        # cv2.imshow("image_resize", image_resize)
+        # cv2.waitKey(0)
     # image_pil = np2pil(image_resize)
     image_resize = np.array(image_resize, dtype='float32')
     image_resize = image_resize.astype('float32') / 255.

chinese_detect/yolo_data/my_anchors_new.txt

@@ -0,0 +1 @@
+15,15,  26,26,  37,37,  48,48,  59,59,  70,70

chinese_equation_denoise/ced_interface.py

@@ -0,0 +1,99 @@
+import base64
+import json
+import logging
+import os
+import sys
+import time
+import traceback
+from glob import glob
+import cv2
+import numpy as np
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+import tensorflow as tf
+from flask import Flask, request
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
+from chinese_equation_denoise.inference_equation_denoise import denoise
+from chinese_equation_denoise.model import u_net_denoise
+from utils import pil_resize, np2bytes, request_post, bytes2np, base64_decode, image_to_str, str_to_image
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+tf.compat.v1.disable_eager_execution()
+sess = tf.compat.v1.Session(graph=tf.Graph())
+
+package_dir = os.path.abspath(os.path.dirname(__file__))
+model_path = package_dir + "/models/denoise_loss_53.97.h5"
+image_shape = (32, 192, 1)
+
+# 接口配置
+app = Flask(__name__)
+
+
+@app.route('/ced', methods=['POST'])
+def ced():
+    start_time = time.time()
+    logging.info("into ced_interface ced")
+    try:
+        # 接收网络数据
+        if not request.form:
+            logging.info("ced no data!")
+            return json.dumps({"data": "", "success": 0})
+        data = request.form.get("data")
+        logging.info("ced_interface get data time" + str(time.time()-start_time))
+
+        # 加载模型
+        ced_model = globals().get("global_ced_model")
+        if ced_model is None:
+            print("=========== init ced model ===========")
+            ced_model = CedModels().get_model()
+            globals().update({"global_ced_model": ced_model})
+
+        # 数据转换
+        data = base64_decode(data)
+        image_np = bytes2np(data)
+        # 预测
+        image_result = denoise(image_np, ced_model, sess)
+        image_str = image_to_str(image_result)
+        return json.dumps({"data": image_str, "success": 1})
+    except:
+        traceback.print_exc()
+        return json.dumps({"data": "", "success": 0})
+    finally:
+        logging.info("ced interface finish time " + str(time.time()-start_time))
+
+
+class CedModels:
+    def __init__(self):
+        with sess.as_default():
+            with sess.graph.as_default():
+                self.model = u_net_denoise(input_shape=image_shape, class_num=image_shape[2])
+                self.model.load_weights(model_path)
+
+    def get_model(self):
+        return self.model
+
+
+def test_ced_model(from_remote=True):
+    paths = glob("D:/Project/captcha/data/test/FileInfo1021/19584571-511d-11ed-93ac-b4b5b67760ae_3.jpg")
+    for file_path in paths:
+        img_np = cv2.imread(file_path)
+        h, w = img_np.shape[:2]
+        file_bytes = np2bytes(img_np)
+        file_base64 = base64.b64encode(file_bytes)
+
+        if from_remote:
+            file_json = {"data": file_base64}
+            # _url = "http://192.168.2.102:17060/ced"
+            _url = "http://127.0.0.1:17060/ced"
+            result = json.loads(request_post(_url, file_json))
+            if result.get("success"):
+                img_new = str_to_image(result.get("data"))
+                cv2.imshow("img_np", img_np)
+                cv2.imshow("img_new", img_new)
+                cv2.waitKey(0)
+            else:
+                print("failed!")
+
+
+if __name__ == "__main__":
+    app.run(host='127.0.0.1', port=17060, debug=False)
+    # test_ced_model()

chinese_equation_denoise/inference_equation_denoise.py

@@ -0,0 +1,50 @@
+import os
+import re
+import sys
+from glob import glob
+import cv2
+import numpy as np
+import tensorflow as tf
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
+from model import u_net_denoise
+from utils import pil_resize
+
+package_dir = os.path.abspath(os.path.dirname(__file__))
+image_shape = (32, 192, 1)
+model_path = package_dir + "/models/e153-loss53.97-denoise.h5"
+
+
+def denoise(image_np, model=None, sess=None):
+    if sess is None:
+        sess = tf.compat.v1.Session(graph=tf.Graph())
+
+    if model is None:
+        with sess.as_default():
+            with sess.graph.as_default():
+                model = u_net_denoise(input_shape=image_shape, class_num=image_shape[2])
+                model.load_weights(model_path)
+
+    X = []
+    img = pil_resize(image_np, image_shape[0], image_shape[1])
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = np.expand_dims(img, axis=-1)
+    img = img / 255.
+
+    X.append(img)
+    X = np.array(X)
+    with sess.as_default():
+        with sess.graph.as_default():
+            pred = model.predict(X)
+    pred = np.uint8(pred[0]*255.)
+
+    # cv2.imshow("pred", pred)
+    # cv2.waitKey(0)
+    return pred
+
+
+if __name__ == "__main__":
+    # _path = "../data/test/char_9.jpg"
+    _paths = glob("../data/equation/*")
+    # _paths = glob("../data/test/FileInfo1021/*")
+    for _path in _paths:
+        denoise(cv2.imread(_path))

chinese_equation_denoise/loss.py

@@ -0,0 +1,113 @@
+import tensorflow as tf
+import keras.backend as K
+
+
+def ctc_lambda_func(args):
+    """
+        定义ctc损失函数
+        参数：y_pred:预测值,labels:标签，input_length:lstm tiemstep,label_length:标签长度
+    """
+    y_pred, labels, input_length, label_length = args
+    # return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
+    return my_ctc_batch_cost(labels, y_pred, input_length, label_length, mode=1)
+
+
+def my_ctc_batch_cost(y_true, y_pred, input_length, label_length, mode=0):
+    """Runs CTC loss algorithm on each batch element.
+
+    Args:
+        y_true: tensor `(samples, max_string_length)`
+            containing the truth labels.
+        y_pred: tensor `(samples, time_steps, num_categories)`
+            containing the prediction, or output of the softmax.
+        input_length: tensor `(samples, 1)` containing the sequence length for
+            each batch item in `y_pred`.
+        label_length: tensor `(samples, 1)` containing the sequence length for
+            each batch item in `y_true`.
+
+    Returns:
+        Tensor with shape (samples,1) containing the
+            CTC loss of each element.
+    """
+    label_length = tf.cast(
+        tf.squeeze(label_length, axis=-1), tf.int32)
+    input_length = tf.cast(
+        tf.squeeze(input_length, axis=-1), tf.int32)
+    sparse_labels = tf.cast(
+        K.ctc_label_dense_to_sparse(y_true, label_length), tf.int32)
+
+    y_pred = tf.math.log(tf.compat.v1.transpose(y_pred, perm=[1, 0, 2]) + K.epsilon())
+
+    loss = tf.compat.v1.nn.ctc_loss(inputs=y_pred,
+                                    labels=sparse_labels,
+                                    sequence_length=input_length,
+                                    preprocess_collapse_repeated=False,
+                                    ctc_merge_repeated=False)
+    loss = tf.expand_dims(loss, 1)
+
+    if mode == 1:
+        loss = focal_ctc(sparse_labels, y_pred, input_length, loss)
+
+    return loss
+
+
+def focal_ctc(targets, logits, seq_len, ctc_loss, alpha=0.5, gamma=2.0):
+    # FOCAL LOSS
+    # This function computes Focal Loss
+    # Inputs: alpha, gamma, targets, logits, seq_len
+    # Default Values: alpha=0.5 and gamma=2.0
+    # Output: loss
+
+    # ctc_loss = tf.compat.v1.nn.ctc_loss(labels=targets, inputs=logits, sequence_length=seq_len, time_major=True)
+    p = tf.exp(-ctc_loss)
+    # ((alpha)*((1-p)**gamma)*(ctc_loss))
+    focal_ctc_loss = tf.multiply(tf.multiply(alpha, tf.pow((1-p), gamma)), ctc_loss)
+    loss = tf.reduce_mean(focal_ctc_loss)
+
+    return loss
+
+
+def perceptual_loss(gamma=2., alpha=.25):
+    from model import Vgg19
+    def perceptual_loss_fixed(y_true, y_pred):
+        if globals().get("vgg") is None:
+            vgg = Vgg19("./vgg19.npy")
+            globals().update({"vgg": vgg})
+            print("init vgg19 success!")
+        else:
+            vgg = globals().get("vgg")
+
+        # mask_1 = tf.where(y_true[:, :, :, 0] >= 0.75, 1, 0)
+        # mask_2 = tf.where(y_true[:, :, :, 1] >= 0.75, 1, 0)
+        # mask_3 = tf.where(y_true[:, :, :, 2] >= 0.75, 1, 0)
+        # mask_white = tf.expand_dims(mask_1 * mask_2 * mask_3, -1)
+        # mask_white = tf.concat([mask_white, mask_white, mask_white], -1)
+        # y_true_mask = tf.where(mask_white == 1, 1., y_true)
+        # y_pred_mask = tf.where(mask_white == 1, 1., y_pred)
+
+        # print("y_pred.shape", y_pred.shape)
+        y_pred = tf.concat([y_pred, y_pred, y_pred], -1)
+        y_true = tf.concat([y_true, y_true, y_true], -1)
+
+        vgg.build(y_true)
+        vgg_true_1 = vgg.conv1_1
+        vgg_true_2 = vgg.conv2_1
+        vgg_true_3 = vgg.conv3_1
+        vgg_true_4 = vgg.conv4_1
+        vgg_true_5 = vgg.conv5_1
+
+        vgg.build(y_pred)
+        vgg_pred_1 = vgg.conv1_1
+        vgg_pred_2 = vgg.conv2_1
+        vgg_pred_3 = vgg.conv3_1
+        vgg_pred_4 = vgg.conv4_1
+        vgg_pred_5 = vgg.conv5_1
+
+        loss_0 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(y_true, y_pred)
+        loss_1 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(vgg_true_1, vgg_pred_1)
+        loss_2 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(vgg_true_2, vgg_pred_2)
+        loss_3 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(vgg_true_3, vgg_pred_3)
+        loss_4 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(vgg_true_4, vgg_pred_4)
+        loss_5 = l2_focal_loss(threshold=0.2, ratio=1000, reverse=True)(vgg_true_5, vgg_pred_5)
+        return (loss_0+loss_1+loss_2+loss_3+loss_4+loss_5) / 6
+    return perceptual_loss_fixed

chinese_equation_denoise/model.py

@@ -0,0 +1,284 @@
+from keras import Input, Model
+import keras.backend as K
+import tensorflow as tf
+import numpy as np
+from keras.layers import Conv2D, BatchNormalization, LeakyReLU, MaxPooling2D, UpSampling2D, concatenate, Lambda, \
+    Bidirectional, GRU, Dense
+
+from chinese_equation_denoise.loss import ctc_lambda_func
+
+
+def crnn_ctc_equation(input_shape=(32, 192, 3), class_num=32, is_train=True):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    down3 = Conv2D(64, (4, 4), use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+
+    sq = Lambda(lambda x: K.squeeze(x, axis=1))(down3)
+
+    x = Bidirectional(GRU(32, return_sequences=True))(sq)
+    x = Bidirectional(GRU(32, return_sequences=True))(x)
+
+    x = Dense(class_num, activation='softmax')(x)
+
+    if not is_train:
+        model = Model(inputs=_input, outputs=x)
+
+    else:
+        labels = Input(name='the_labels', shape=[None], dtype='float32')
+        input_length = Input(name='input_length', shape=[1], dtype='int64')
+        label_length = Input(name='label_length', shape=[1], dtype='int64')
+        loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])
+        model = Model(inputs=[_input, labels, input_length, label_length], outputs=loss_out)
+        model.summary()
+    return model
+
+
+def crnn_ctc_equation_large(input_shape=(32, 192, 3), class_num=32, is_train=True):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    down3 = Conv2D(128, (4, 4), use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+
+    sq = Lambda(lambda x: K.squeeze(x, axis=1))(down3)
+
+    x = Bidirectional(GRU(64, return_sequences=True))(sq)
+    x = Bidirectional(GRU(64, return_sequences=True))(x)
+
+    x = Dense(class_num, activation='softmax')(x)
+
+    if not is_train:
+        model = Model(inputs=_input, outputs=x)
+
+    else:
+        labels = Input(name='the_labels', shape=[None], dtype='float32')
+        input_length = Input(name='input_length', shape=[1], dtype='int64')
+        label_length = Input(name='label_length', shape=[1], dtype='int64')
+        loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])
+        model = Model(inputs=[_input, labels, input_length, label_length], outputs=loss_out)
+        model.summary()
+    return model
+
+
+def u_net_denoise(input_shape=(32, 192, 3), class_num=3):
+    inputs = Input(shape=input_shape)
+    use_bias = False
+
+    # 128
+    down1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    # 64
+    down2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    # 32
+    down3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(down3)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
+
+    # 16
+    center = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down3_pool)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+    center = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(center)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+
+    # 32
+    up3 = UpSampling2D((2, 2))(center)
+    up3 = concatenate([down3, up3], axis=3)
+    up3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+    up3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+
+    # 64
+    up2 = UpSampling2D((2, 2))(up3)
+    up2 = concatenate([down2, up2], axis=3)
+    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+    up2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+
+    # 128
+    up1 = UpSampling2D((2, 2))(up2)
+    up1 = K.concatenate([down1, up1], axis=3)
+    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+    up1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+
+    classify = Conv2D(class_num, (1, 1), activation='sigmoid')(up1)
+    # classify = Dense(cls_num, activation="softmax")(up1)
+    model = Model(inputs=inputs, outputs=classify)
+    # model.summary()
+    return model
+
+
+def ctc_decode(image, model):
+    x = model.output
+    input_length = Input(batch_shape=[None], dtype='int32')
+    ctc_decode = K.ctc_decode(x, input_length=input_length * K.shape(x)[1], greedy=False, beam_width=6)
+    decode = K.function([model.input, input_length], [ctc_decode[0][0]])
+    out = decode([image, np.ones(image.shape[0])])[0][0]
+    return out
+
+
+class Vgg19:
+    def __init__(self, vgg19_npy_path=None):
+        if vgg19_npy_path is None:
+            print("there is no vgg_16_npy!")
+            raise
+
+        self.data_dict = np.load(vgg19_npy_path, encoding='latin1', allow_pickle=True).item()
+
+    def build(self, bgr):
+        """
+        load variable from npy to build the VGG
+        :param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
+        """
+        bgr = bgr * 255.0
+        # bgr = bgr - np.array(VGG_MEAN).reshape((1, 1, 1, 3))
+
+        self.conv1_1 = self.conv_layer(bgr, "conv1_1")
+        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
+        self.pool1 = self.max_pool(self.conv1_2, 'pool1')
+
+        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
+        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
+        self.pool2 = self.max_pool(self.conv2_2, 'pool2')
+
+        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
+        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
+        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
+        self.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4")
+        self.pool3 = self.max_pool(self.conv3_4, 'pool3')
+
+        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
+        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
+        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
+        self.conv4_4 = self.conv_layer(self.conv4_3, "conv4_4")
+        self.pool4 = self.max_pool(self.conv4_4, 'pool4')
+
+        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
+        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
+        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
+        self.conv5_4 = self.conv_layer(self.conv5_3, "conv5_4")
+        self.pool5 = self.max_pool(self.conv5_4, 'pool5')
+
+    def avg_pool(self, bottom, name):
+        return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def max_pool(self, bottom, name):
+        return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def conv_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            filt = self.get_conv_filter(name)
+
+            conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
+
+            conv_biases = self.get_bias(name)
+            bias = tf.nn.bias_add(conv, conv_biases)
+
+            relu = tf.nn.relu(bias)
+            return relu
+
+    def fc_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            shape = bottom.get_shape().as_list()
+            dim = 1
+            for d in shape[1:]:
+                dim *= d
+            x = tf.reshape(bottom, [-1, dim])
+
+            weights = self.get_fc_weight(name)
+            biases = self.get_bias(name)
+
+            # Fully connected layer. Note that the '+' operation automatically
+            # broadcasts the biases.
+            fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
+
+            return fc
+
+    def get_conv_filter(self, name):
+        return tf.constant(self.data_dict[name][0], name="filter")
+
+    def get_bias(self, name):
+        return tf.constant(self.data_dict[name][1], name="biases")
+
+    def get_fc_weight(self, name):
+        return tf.constant(self.data_dict[name][0], name="weights")

chinese_equation_recognize/cer_interface.py

@@ -0,0 +1,100 @@
+import base64
+import json
+import logging
+import os
+import sys
+import time
+import traceback
+from glob import glob
+import cv2
+import numpy as np
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+import tensorflow as tf
+from flask import Flask, request
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
+from chinese_equation_recognize.inference_equation import recognize
+from chinese_equation_recognize.model import crnn_ctc_equation_large, crnn_ctc_equation_loss
+from utils import pil_resize, np2bytes, request_post, bytes2np, base64_decode, image_to_str, str_to_image
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+tf.compat.v1.disable_eager_execution()
+sess = tf.compat.v1.Session(graph=tf.Graph())
+
+package_dir = os.path.abspath(os.path.dirname(__file__))
+model_path = package_dir + "/models/e55-loss0.14-equation.h5"
+image_shape = (32, 192, 1)
+
+# 接口配置
+app = Flask(__name__)
+
+
+@app.route('/cer', methods=['POST'])
+def cer():
+    start_time = time.time()
+    logging.info("into cer_interface cer")
+    try:
+        # 接收网络数据
+        if not request.form:
+            logging.info("cer no data!")
+            return json.dumps({"data": "", "success": 0})
+        data = request.form.get("data")
+        logging.info("cer_interface get data time" + str(time.time()-start_time))
+
+        # 加载模型
+        cer_model = globals().get("global_cer_model")
+        if cer_model is None:
+            print("=========== init cer model ===========")
+            cer_model = CerModels().get_model()
+            globals().update({"global_cer_model": cer_model})
+
+        # 数据转换
+        data = base64_decode(data)
+        image_np = bytes2np(data)
+        # 预测
+        result = recognize(image_np, cer_model, sess)
+        if result is None:
+            return json.dumps({"data": "", "success": 0})
+        return json.dumps({"data": result, "success": 1})
+    except:
+        traceback.print_exc()
+        return json.dumps({"data": "", "success": 0})
+    finally:
+        logging.info("cer interface finish time " + str(time.time()-start_time))
+
+
+class CerModels:
+    def __init__(self):
+        with sess.as_default():
+            with sess.graph.as_default():
+                self.model = crnn_ctc_equation_loss(input_shape=image_shape, class_num=35+2, is_train=False)
+                self.model.load_weights(model_path)
+
+    def get_model(self):
+        return self.model
+
+
+def test_cer_model(from_remote=True):
+    paths = glob("D:/Project/captcha/data/test/FileInfo1021/1d419189-5116-11ed-851c-b4b5b67760ae_7.jpg")
+    for file_path in paths:
+        img_np = cv2.imread(file_path)
+        h, w = img_np.shape[:2]
+        file_bytes = np2bytes(img_np)
+        file_base64 = base64.b64encode(file_bytes)
+
+        if from_remote:
+            file_json = {"data": file_base64}
+            # _url = "http://192.168.2.102:17061/cer"
+            _url = "http://127.0.0.1:17061/cer"
+            result = json.loads(request_post(_url, file_json))
+            if result.get("success"):
+                result = int(result.get("data"))
+                cv2.imshow("img_np", img_np)
+                print("equation result", result)
+                cv2.waitKey(0)
+            else:
+                print("failed!")
+
+
+if __name__ == "__main__":
+    # app.run(host='127.0.0.1', port=17061, debug=False)
+    test_cer_model()

chinese_equation_recognize/equation.txt

@@ -0,0 +1,35 @@
+1
+2
+3
+4
+5
+6
+7
+8
+9
+0
+一
+二
+三
+四
+五
+六
+七
+八
+九
+零
+加
+减
+乘
+除
++
+-
+*
+×
+/
+÷
+=
+?
+上
+去
+以

chinese_equation_recognize/inference_equation.py

@@ -0,0 +1,132 @@
+import os
+import re
+import sys
+from glob import glob
+import cv2
+import numpy as np
+import tensorflow as tf
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
+from model import crnn_ctc_equation, ctc_decode, crnn_ctc_equation_large
+from utils import pil_resize, add_contrast
+
+package_dir = os.path.abspath(os.path.dirname(__file__))
+image_shape = (32, 192, 1)
+model_path = package_dir + "/models/e55-loss0.14-equation.h5"
+
+with open(package_dir + "/equation.txt", 'r') as f:
+    char_list = f.readlines()
+char_str = "".join(char_list)
+char_str = re.sub("\n", "", char_str)
+
+
+def recognize(image_np, model=None, sess=None):
+    if sess is None:
+        sess = tf.compat.v1.Session(graph=tf.Graph())
+
+    if model is None:
+        with sess.as_default():
+            with sess.graph.as_default():
+                model = crnn_ctc_equation_large(input_shape=image_shape, class_num=35+2, is_train=False)
+                model.load_weights(model_path)
+
+    img = image_np
+    img = pil_resize(img, image_shape[0], image_shape[1])
+    if img.shape[2] == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = np.expand_dims(img, axis=-1)
+    img = add_contrast(img)
+    # cv2.imshow("contrast", img)
+
+    X = []
+    img = img / 255.
+    X.append(img)
+    X = np.array(X)
+    with sess.as_default():
+        with sess.graph.as_default():
+            pred = ctc_decode(X, model)
+
+    result_list = []
+    for index in pred:
+        index = int(index-1)
+        if index < 0:
+            continue
+        result_list.append(char_str[index])
+        # print(char_str[index])
+    # cv2.waitKey(0)
+    print("cer result", result_list)
+    return calculate(result_list)
+
+
+def calculate(_list):
+    char_dict = {
+        "1": 1,
+        "2": 2,
+        "3": 3,
+        "4": 4,
+        "5": 5,
+        "6": 6,
+        "7": 7,
+        "8": 8,
+        "9": 9,
+        "0": 0,
+        "一": 1,
+        "二": 2,
+        "三": 3,
+        "四": 4,
+        "五": 5,
+        "六": 6,
+        "七": 7,
+        "八": 8,
+        "九": 9,
+        "零": 0,
+        "加": "加",
+        "减": "减",
+        "乘": "乘",
+        "除": "除",
+        "+": "加",
+        "-": "减",
+        "*": "乘",
+        "×": "乘",
+        "/": "除",
+        "÷": "除",
+        "=": "",
+        "?": "",
+        "上": "",
+        "去": "",
+        "以": "",
+    }
+    equation_str = ""
+    for c in _list:
+        equation_str += str(char_dict.get(c))
+    op = re.findall("加|减|乘|除", equation_str)
+    op = list(set(op))
+    if len(op) != 1:
+        return None
+    nums = re.split("加|减|乘|除", equation_str)
+    if len(nums) != 2:
+        return None
+    try:
+        num1 = int(nums[0])
+        num2 = int(nums[1])
+    except:
+        print("非数字！")
+        return None
+
+    op = op[0]
+    if op == "加":
+        result = num1 + num2
+    elif op == '减':
+        result = num1 - num2
+    elif op == '乘':
+        result = num1 * num2
+    elif op == '除':
+        result = int(num1 / max(num2, 1))
+    return result
+
+
+if __name__ == "__main__":
+    # _path = "../data/test/char_9.jpg"
+    # _path = "../data/equation/38376_减_1_问_加_4_除.jpg"
+    _paths = glob("../data/test/FileInfo1021/*")
+    for _path in _paths:
+        recognize(_path)

chinese_equation_recognize/loss.py

@@ -0,0 +1,154 @@
+import tensorflow as tf
+import keras.backend as K
+
+
+def ctc_lambda_func(args):
+    """
+        定义ctc损失函数
+        参数：y_pred:预测值,labels:标签，input_length:lstm tiemstep,label_length:标签长度
+    """
+    y_pred, labels, input_length, label_length = args
+    # return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
+    return my_ctc_batch_cost(labels, y_pred, input_length, label_length, mode=1)
+
+
+def my_ctc_batch_cost(y_true, y_pred, input_length, label_length, mode=0):
+    """Runs CTC loss algorithm on each batch element.
+
+    Args:
+        y_true: tensor `(samples, max_string_length)`
+            containing the truth labels.
+        y_pred: tensor `(samples, time_steps, num_categories)`
+            containing the prediction, or output of the softmax.
+        input_length: tensor `(samples, 1)` containing the sequence length for
+            each batch item in `y_pred`.
+        label_length: tensor `(samples, 1)` containing the sequence length for
+            each batch item in `y_true`.
+
+    Returns:
+        Tensor with shape (samples,1) containing the
+            CTC loss of each element.
+    """
+    label_length = tf.cast(
+        tf.squeeze(label_length, axis=-1), tf.int32)
+    input_length = tf.cast(
+        tf.squeeze(input_length, axis=-1), tf.int32)
+    sparse_labels = tf.cast(
+        K.ctc_label_dense_to_sparse(y_true, label_length), tf.int32)
+
+    y_pred = tf.math.log(tf.compat.v1.transpose(y_pred, perm=[1, 0, 2]) + K.epsilon())
+
+    loss = tf.compat.v1.nn.ctc_loss(inputs=y_pred,
+                                    labels=sparse_labels,
+                                    sequence_length=input_length,
+                                    preprocess_collapse_repeated=False,
+                                    ctc_merge_repeated=False)
+    loss = tf.expand_dims(loss, 1)
+
+    if mode == 1:
+        loss = focal_ctc(sparse_labels, y_pred, input_length, loss)
+
+    return loss
+
+
+def focal_ctc(targets, logits, seq_len, ctc_loss, alpha=0.5, gamma=2.0):
+    # FOCAL LOSS
+    # This function computes Focal Loss
+    # Inputs: alpha, gamma, targets, logits, seq_len
+    # Default Values: alpha=0.5 and gamma=2.0
+    # Output: loss
+
+    # ctc_loss = tf.compat.v1.nn.ctc_loss(labels=targets, inputs=logits, sequence_length=seq_len, time_major=True)
+    p = tf.exp(-ctc_loss)
+    # ((alpha)*((1-p)**gamma)*(ctc_loss))
+    focal_ctc_loss = tf.multiply(tf.multiply(alpha, tf.pow((1-p), gamma)), ctc_loss)
+    loss = tf.reduce_mean(focal_ctc_loss)
+
+    return loss
+
+
+def ctc_decode_mse_loss2(args):
+    batch_size = 32
+    num_classes = 35+2
+    time_step = 21
+    label_len = 8
+
+    # [32, 21, 37]
+    y_pred, labels, input_length, label_length = args
+    # [32, 21]
+    y_max = tf.argmax(y_pred, axis=-1, name='raw_prediction', output_type=tf.int32)
+
+    # [32, 8]
+    labels = tf.cast(labels, tf.int32)
+    # [batch, step]
+    new_label = tf.zeros((batch_size, time_step), dtype=tf.int32)
+
+    @tf.function
+    def body(_i, _label):
+
+        new_sample = tf.zeros((1, time_step), dtype=tf.int32)
+        for j in range(0, label_len):
+            # if tf.greater(0, y_max[_i, j]):
+            find_flag = False
+            for k in range(0, time_step):
+                # 循环y_pred，找对应labels，会漏掉
+                # if k < K.shape(labels)[1] and tf.equal(y_max[_i, j], labels[_i, k]):
+                #     # tf.print("equal", y_max[_i, j], labels[_i, k])
+                #     if j == 0:
+                #         new_sample = tf.concat([labels[_i:_i+1, k:k+1], new_sample[:, j+1:]], axis=-1)
+                #     elif j >= time_step-1:
+                #         new_sample = tf.concat([new_sample[:, :j], labels[_i:_i+1, k:k+1]], axis=-1)
+                #     else:
+                #         new_sample = tf.concat([new_sample[:, :j], labels[_i:_i+1, k:k+1], new_sample[:, j+1:]], axis=-1)
+
+                # 循环labels，找对应y_pred，漏掉的找个0位置覆盖
+                # tf.print("labels", labels[_i], last_k, j, labels[_i].shape, new_sample.shape)
+                if tf.equal(y_max[_i, k], labels[_i, j]) and tf.not_equal(y_max[_i, k], 0):
+                    find_flag = True
+                    if k == 0:
+                        new_sample = tf.concat([labels[_i:_i+1, j:j+1], new_sample[:, k+1:]], axis=-1)
+                    elif k >= time_step-1:
+                        new_sample = tf.concat([new_sample[:, :k], labels[_i:_i+1, j:j+1]], axis=-1)
+                    else:
+                        new_sample = tf.concat([new_sample[:, :k], labels[_i:_i+1, j:j+1], new_sample[:, k+1:]], axis=-1)
+                    # tf.print("new_sample", new_sample, last_k, j, K.shape(labels[_i]), K.shape(new_sample))
+            if not find_flag and tf.not_equal(labels[_i, j], 0):
+                find_flag2 = False
+                for k in range(0, time_step):
+                    if not find_flag2 and tf.equal(y_max[_i, k], 0) and tf.equal(new_sample[0, k], 0):
+                        find_flag2 = True
+                        if k == 0:
+                            new_sample = tf.concat([labels[_i:_i+1, j:j+1], new_sample[:, k+1:]], axis=-1)
+                        elif k >= time_step-1:
+                            new_sample = tf.concat([new_sample[:, :k], labels[_i:_i+1, j:j+1]], axis=-1)
+                        else:
+                            new_sample = tf.concat([new_sample[:, :k], labels[_i:_i+1, j:j+1], new_sample[:, k+1:]], axis=-1)
+                    # tf.print("new_sample", new_sample, labels[_i, j], find_flag, find_flag2, summarize=100)
+            # tf.print("new_sample", new_sample, summarize=100)
+        # tf.print("equal", new_sample, labels[_i], summarize=100)
+
+        if _i == 0:
+            _label = tf.concat([new_sample[:, :], _label[_i+1:, :]], axis=0)
+        elif _i >= time_step-1:
+            _label = tf.concat([_label[:_i, :], new_sample[:, :]], axis=0)
+        else:
+            _label = tf.concat([_label[:_i, :], new_sample[:, :], _label[_i+1:, :]], axis=0)
+        _i = tf.add(_i, 1)
+        return _i, _label
+
+    def cond(_i, _label):
+        return tf.less(_i, K.shape(labels)[0])
+
+    i = tf.constant(1, dtype=tf.int32)
+    _, new_label = tf.while_loop(cond, body, [i, new_label],
+                                 shape_invariants=[i.get_shape(), tf.TensorShape([None, None])])
+    new_label = tf.one_hot(new_label, depth=num_classes, axis=1, dtype=tf.float32)
+    new_label = tf.compat.v1.transpose(new_label, perm=[0, 2, 1])
+
+    # print("y_pred", y_pred.shape)
+    # print("new_label", new_label.shape)
+
+    loss = tf.reduce_mean(tf.abs((new_label-y_pred)), axis=-1)
+    loss = tf.reduce_mean(loss*1, axis=-1)
+    loss = tf.expand_dims(loss, -1)
+    return loss

chinese_equation_recognize/model.py

@@ -0,0 +1,342 @@
+from keras import Input, Model
+import keras.backend as K
+import tensorflow as tf
+import numpy as np
+from keras.layers import Conv2D, BatchNormalization, LeakyReLU, MaxPooling2D, UpSampling2D, concatenate, Lambda, \
+    Bidirectional, GRU, Dense, Dropout, Add
+
+from chinese_equation_denoise.loss import ctc_lambda_func
+from chinese_equation_recognize.loss import ctc_decode_mse_loss2
+
+
+def crnn_ctc_equation(input_shape=(32, 192, 3), class_num=32, is_train=True):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    down3 = Conv2D(64, (4, 4), use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+
+    sq = Lambda(lambda x: K.squeeze(x, axis=1))(down3)
+
+    x = Bidirectional(GRU(32, return_sequences=True))(sq)
+    x = Bidirectional(GRU(32, return_sequences=True))(x)
+
+    x = Dense(class_num, activation='softmax')(x)
+
+    if not is_train:
+        model = Model(inputs=_input, outputs=x)
+
+    else:
+        labels = Input(name='the_labels', shape=[None], dtype='float32')
+        input_length = Input(name='input_length', shape=[1], dtype='int64')
+        label_length = Input(name='label_length', shape=[1], dtype='int64')
+        loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])
+        model = Model(inputs=[_input, labels, input_length, label_length], outputs=loss_out)
+        model.summary()
+    return model
+
+
+def crnn_ctc_equation_large(input_shape=(32, 192, 3), class_num=32, is_train=True):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    down3 = Conv2D(128, (4, 4), use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+
+    sq = Lambda(lambda x: K.squeeze(x, axis=1))(down3)
+
+    x = Bidirectional(GRU(64, return_sequences=True))(sq)
+    x = Bidirectional(GRU(64, return_sequences=True))(x)
+
+    x = Dense(class_num, activation='softmax')(x)
+
+    if not is_train:
+        model = Model(inputs=_input, outputs=x)
+
+    else:
+        labels = Input(name='the_labels', shape=[None], dtype='float32')
+        input_length = Input(name='input_length', shape=[1], dtype='int64')
+        label_length = Input(name='label_length', shape=[1], dtype='int64')
+        loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])
+        model = Model(inputs=[_input, labels, input_length, label_length], outputs=loss_out)
+        model.summary()
+    return model
+
+
+def crnn_ctc_equation_loss(input_shape=(32, 192, 3), class_num=32, is_train=True):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    down3 = Conv2D(128, (4, 4), use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+
+    sq = Lambda(lambda x: K.squeeze(x, axis=1))(down3)
+
+    x = Bidirectional(GRU(64, return_sequences=True))(sq)
+    x = Bidirectional(GRU(64, return_sequences=True))(x)
+
+    x = Dense(64, activation="relu")(x)
+    x = Dropout(rate=0.2)(x)
+    x = Dense(class_num, activation='softmax')(x)
+
+    if not is_train:
+        model = Model(inputs=_input, outputs=x)
+
+    else:
+        labels = Input(name='the_labels', shape=[None], dtype='float32')
+        input_length = Input(name='input_length', shape=[1], dtype='int64')
+        label_length = Input(name='label_length', shape=[1], dtype='int64')
+        loss_out_1 = Lambda(ctc_lambda_func, output_shape=(1,), )([x, labels, input_length, label_length])
+        loss_out_2 = Lambda(ctc_decode_mse_loss2, output_shape=(1, ))([x, labels, input_length, label_length])
+        # loss_out_2 = CtcDecodeMseLoss(name='ctc')([x, labels, input_length, label_length])
+        loss_out = Add(name='ctc')([loss_out_1, loss_out_2])
+        model = Model(inputs=[_input, labels, input_length, label_length], outputs=loss_out)
+        model.summary(130)
+    return model
+
+
+def u_net_denoise(input_shape=(32, 192, 3), class_num=3):
+    inputs = Input(shape=input_shape)
+    use_bias = False
+
+    # 128
+    down1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    # 64
+    down2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    # 32
+    down3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(down3)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
+
+    # 16
+    center = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down3_pool)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+    center = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(center)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+
+    # 32
+    up3 = UpSampling2D((2, 2))(center)
+    up3 = concatenate([down3, up3], axis=3)
+    up3 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+    up3 = Conv2D(64, (1, 1), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+
+    # 64
+    up2 = UpSampling2D((2, 2))(up3)
+    up2 = concatenate([down2, up2], axis=3)
+    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+    up2 = Conv2D(32, (1, 1), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+
+    # 128
+    up1 = UpSampling2D((2, 2))(up2)
+    up1 = K.concatenate([down1, up1], axis=3)
+    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+    up1 = Conv2D(16, (1, 1), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+
+    classify = Conv2D(class_num, (1, 1), activation='sigmoid')(up1)
+    # classify = Dense(cls_num, activation="softmax")(up1)
+    model = Model(inputs=inputs, outputs=classify)
+    # model.summary()
+    return model
+
+
+def ctc_decode(image, model):
+    x = model.output
+    input_length = Input(batch_shape=[None], dtype='int32')
+    ctc_decode = K.ctc_decode(x, input_length=input_length * K.shape(x)[1], greedy=False, beam_width=6)
+    decode = K.function([model.input, input_length], [ctc_decode[0][0]])
+    out = decode([image, np.ones(image.shape[0])])[0][0]
+    return out
+
+
+class Vgg19:
+    def __init__(self, vgg19_npy_path=None):
+        if vgg19_npy_path is None:
+            print("there is no vgg_16_npy!")
+            raise
+
+        self.data_dict = np.load(vgg19_npy_path, encoding='latin1', allow_pickle=True).item()
+
+    def build(self, bgr):
+        """
+        load variable from npy to build the VGG
+        :param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
+        """
+        bgr = bgr * 255.0
+        # bgr = bgr - np.array(VGG_MEAN).reshape((1, 1, 1, 3))
+
+        self.conv1_1 = self.conv_layer(bgr, "conv1_1")
+        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
+        self.pool1 = self.max_pool(self.conv1_2, 'pool1')
+
+        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
+        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
+        self.pool2 = self.max_pool(self.conv2_2, 'pool2')
+
+        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
+        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
+        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
+        self.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4")
+        self.pool3 = self.max_pool(self.conv3_4, 'pool3')
+
+        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
+        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
+        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
+        self.conv4_4 = self.conv_layer(self.conv4_3, "conv4_4")
+        self.pool4 = self.max_pool(self.conv4_4, 'pool4')
+
+        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
+        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
+        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
+        self.conv5_4 = self.conv_layer(self.conv5_3, "conv5_4")
+        self.pool5 = self.max_pool(self.conv5_4, 'pool5')
+
+    def avg_pool(self, bottom, name):
+        return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def max_pool(self, bottom, name):
+        return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def conv_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            filt = self.get_conv_filter(name)
+
+            conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
+
+            conv_biases = self.get_bias(name)
+            bias = tf.nn.bias_add(conv, conv_biases)
+
+            relu = tf.nn.relu(bias)
+            return relu
+
+    def fc_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            shape = bottom.get_shape().as_list()
+            dim = 1
+            for d in shape[1:]:
+                dim *= d
+            x = tf.reshape(bottom, [-1, dim])
+
+            weights = self.get_fc_weight(name)
+            biases = self.get_bias(name)
+
+            # Fully connected layer. Note that the '+' operation automatically
+            # broadcasts the biases.
+            fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
+
+            return fc
+
+    def get_conv_filter(self, name):
+        return tf.constant(self.data_dict[name][0], name="filter")
+
+    def get_bias(self, name):
+        return tf.constant(self.data_dict[name][1], name="biases")
+
+    def get_fc_weight(self, name):
+        return tf.constant(self.data_dict[name][0], name="weights")

chinese_recognize/chinese_6270.txt

@@ -0,0 +1,6270 @@
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+其
+骑
+刈
+全
+消
+昏
+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
+要
+月
+田
+节
+陂
+鄙
+捌
+备
+拳
+伺
+眼
+网
+盎
+大
+傍
+心
+东
+愉
+汇
+蹿
+科
+每
+业
+里
+航
+晏
+字
+平
+录
+先
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+为
+含
+肥
+酉
+相
+鄱
+七
+编
+猥
+锛
+日
+镀
+蒂
+掰
+倒
+辆
+栾
+栗
+综
+涩
+州
+雌
+滑
+馀
+了
+机
+块
+司
+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
+国
+绥
+獠
+报
+开
+民
+蜇
+何
+分
+凇
+长
+讥
+藏
+掏
+施
+羽
+中
+讲
+派
+嘟
+人
+提
+浼
+间
+世
+而
+古
+多
+倪
+唇
+饯
+控
+庚
+首
+赛
+蜓
+味
+断
+制
+觉
+技
+替
+艰
+溢
+潮
+夕
+钺
+外
+摘
+枋
+动
+双
+单
+啮
+户
+枇
+确
+锦
+曜
+杜
+或
+能
+效
+霜
+盒
+然
+侗
+电
+晁
+放
+步
+鹃
+新
+杖
+蜂
+吒
+濂
+瞬
+评
+总
+隍
+对
+独
+合
+也
+是
+府
+青
+天
+诲
+墙
+组
+滴
+级
+邀
+帘
+示
+已
+时
+骸
+仄
+泅
+和
+遨
+店
+雇
+疫
+持
+巍
+踮
+境
+只
+亨
+目
+鉴
+崤
+闲
+体
+泄
+杂
+作
+般
+轰
+化
+解
+迂
+诿
+蛭
+璀
+腾
+告
+版
+服
+省
+师
+小
+规
+程
+线
+海
+办
+引
+二
+桧
+牌
+砺
+洄
+裴
+修
+图
+痫
+胡
+许
+犊
+事
+郛
+基
+柴
+呼
+食
+研
+奶
+律
+蛋
+因
+葆
+察
+戏
+褒
+戒
+再
+李
+骁
+工
+貂
+油
+鹅
+章
+啄
+休
+场
+给
+睡
+纷
+豆
+器
+捎
+说
+敏
+学
+会
+浒
+设
+诊
+格
+廓
+查
+来
+霓
+室
+溆
+诡
+寥
+焕
+舜
+柒
+狐
+回
+戟
+砾
+厄
+实
+翩
+尿
+五
+入
+径
+惭
+喹
+股
+宇
+篝
+美
+期
+云
+九
+祺
+扮
+靠
+锝
+槌
+系
+企
+酰
+阊
+暂
+蚕
+忻
+豁
+本
+羹
+执
+条
+钦
+獒
+限
+进
+季
+楦
+于
+芘
+玖
+铋
+茯
+未
+答
+粘
+括
+样
+精
+欠
+矢
+甥
+帷
+嵩
+扣
+令
+仔
+风
+皈
+行
+支
+部
+蓉
+刮
+站
+蜡
+救
+钊
+汗
+松
+嫌
+成
+可
+鹤
+院
+从
+交
+政
+怕
+活
+调
+球
+局
+验
+髌
+第
+韫
+谗
+串
+到
+圆
+年
+米
+友
+忿
+检
+区
+看
+自
+敢
+刃
+个
+兹
+弄
+流
+留
+同
+没
+齿
+星
+聆
+轼
+湖
+什
+三
+建
+蛔
+儿
+椋
+汕
+震
+颧
+鲤
+跟
+力
+情
+璺
+铨
+陪
+务
+指
+族
+训
+滦
+鄣
+濮
+扒
+商
+箱
+十
+召
+慷
+辗
+所
+莞
+管
+护
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chinese_recognize/chr_interface.py

@@ -12,17 +12,17 @@ os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 import tensorflow as tf
 from flask import Flask, request
-from chinese_recognize.model import cnn_net
+from chinese_recognize.model import cnn_net, cnn_net_tiny, cnn_net_small
 from chinese_recognize.inference_char import recognize
-from utils import pil_resize, np2bytes, request_post, bytes2np, base64_decode
+from utils import pil_resize, np2bytes, request_post, bytes2np, base64_decode, str_to_image
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 tf.compat.v1.disable_eager_execution()
 sess = tf.compat.v1.Session(graph=tf.Graph())
 
 package_dir = os.path.abspath(os.path.dirname(__file__))
-image_shape = (40, 40, 3)
-model_path = package_dir + "/models/char_f1_0.93.h5"
+image_shape = (40, 40, 1)
+model_path = package_dir + "/models/char_acc_0.89.h5"
 
 # 接口配置
 app = Flask(__name__)
@@ -51,8 +51,9 @@ def _chr():
         str_list = json.loads(data)
         image_np_list = []
         for _str in str_list:
-            b64 = _str.encode("utf-8")
-            image_np = bytes2np(base64_decode(b64))
+            image_np = str_to_image(_str)
+            # b64 = _str.encode("utf-8")
+            # image_np = bytes2np(base64_decode(b64))
             image_np_list.append(image_np)
 
         # 预测
@@ -69,7 +70,7 @@ class ChrModels:
     def __init__(self):
         with sess.as_default():
             with sess.graph.as_default():
-                self.model = cnn_net(input_shape=image_shape)
+                self.model = cnn_net_small(input_shape=image_shape)
                 self.model.load_weights(model_path)
 
     def get_model(self):
@@ -77,10 +78,10 @@ class ChrModels:
 
 
 def test_chr_model(from_remote=True):
-    paths = glob("D:/Project/captcha/data/test/phrase_2.jpg")
+    paths = glob("D:/Project/captcha/data/test/char_9.jpg")
     str_list = []
     for file_path in paths:
-        img_np = cv2.imread(file_path)[67:117, 35:83, :]
+        img_np = cv2.imread(file_path)
         cv2.imshow("img_np", img_np)
         cv2.waitKey(0)
         h, w = img_np.shape[:2]
@@ -88,10 +89,9 @@ def test_chr_model(from_remote=True):
         file_base64 = base64.b64encode(file_bytes)
         file_str = file_base64.decode("utf-8")
         str_list.append(file_str)
-
     if from_remote:
         file_json = {"data": json.dumps(str_list)}
-        _url = "http://127.0.0.1:17055/chr"
+        _url = "http://192.168.2.103:17057/chr"
         result = json.loads(request_post(_url, file_json))
         if result.get("success"):
             char_list = result.get("data")

chinese_recognize/inference_char.py

@@ -1,17 +1,20 @@
 import os
+import re
 import sys
 import tensorflow as tf
 import cv2
 import numpy as np
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-from chinese_recognize.model import cnn_net
+from chinese_recognize.model import cnn_net, cnn_net_tiny, cnn_net_small
 from utils import pil_resize
 
 package_dir = os.path.abspath(os.path.dirname(__file__))
-image_shape = (40, 40, 3)
-model_path = package_dir + "/models/char_f1_0.93.h5"
-with open(package_dir + "/chinese_5710.txt") as f:
-    char_str = f.read()
+image_shape = (40, 40, 1)
+model_path = package_dir + "/models/char_acc_0.89.h5"
+with open(package_dir + "/chinese_6270.txt") as f:
+    char_list = f.readlines()
+char_str = "".join(char_list)
+char_str = re.sub("\n", "", char_str)
 
 
 def recognize(image_np_list, model=None, sess=None):
@@ -21,7 +24,7 @@ def recognize(image_np_list, model=None, sess=None):
     if model is None:
         with sess.as_default():
             with sess.graph.as_default():
-                model = cnn_net(input_shape=image_shape)
+                model = cnn_net_small(input_shape=image_shape)
                 model.load_weights(model_path)
 
     if len(image_np_list) > 30:
@@ -31,6 +34,10 @@ def recognize(image_np_list, model=None, sess=None):
     X = np.zeros((len(image_np_list), image_shape[0], image_shape[1], image_shape[2]))
     for i in range(len(image_np_list)):
         img = pil_resize(image_np_list[i], image_shape[0], image_shape[1])
+        # cv2.imshow("char img", img)
+        # cv2.waitKey(0)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = np.expand_dims(img, axis=-1)
         img = img / 255.
         X[i] = img
 
@@ -57,6 +64,8 @@ def recognize_no_sess(image_np_list, model=None):
     X = np.zeros((len(image_np_list), image_shape[0], image_shape[1], image_shape[2]))
     for i in range(len(image_np_list)):
         img = pil_resize(image_np_list[i], image_shape[0], image_shape[1])
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = np.expand_dims(img, axis=-1)
         img = img / 255.
         X[i] = img
 
@@ -70,5 +79,5 @@ def recognize_no_sess(image_np_list, model=None):
 
 
 if __name__ == "__main__":
-    _path = "D:/Project/captcha/data/test/char_6.jpg"
+    _path = "D:/Project/captcha/data/test/char_4.jpg"
     print(recognize([cv2.imread(_path)]))

chinese_recognize/model.py

@@ -1,6 +1,6 @@
 from keras import Input, Model
-from keras.layers import Dense, Conv2D, Reshape,  BatchNormalization, \
-     LeakyReLU, MaxPooling2D, Dropout
+from keras.layers import Dense, Conv2D, Reshape, BatchNormalization, \
+    LeakyReLU, MaxPooling2D, Dropout, GlobalAveragePooling2D
 
 
 def cnn_net(input_shape, output_shape=5710):
@@ -48,3 +48,98 @@ def cnn_net(input_shape, output_shape=5710):
     return model
 
 
+def cnn_net_tiny(input_shape, output_shape=6270):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    conv = Conv2D(64, (3, 3))(down2_pool)
+    bn = BatchNormalization()(conv)
+    rl = LeakyReLU(alpha=0.1)(bn)
+    conv = Conv2D(64, (3, 3))(rl)
+    bn = BatchNormalization()(conv)
+    rl = LeakyReLU(alpha=0.1)(bn)
+
+    conv = Conv2D(output_shape, (1, 1), activation='softmax')(rl)
+    pool = GlobalAveragePooling2D()(conv)
+    x = Reshape((output_shape,))(pool)
+
+
+    #
+    # dense = Dense(16, activation="relu")(rl)
+    # drop = Dropout(0.2)(dense)
+    # dense = Dense(output_shape, activation="softmax")(drop)
+    # drop = Dropout(0.2)(dense)
+    # x = Reshape((output_shape,))(drop)
+
+    model = Model(_input, x)
+    model.summary()
+    return model
+
+
+def cnn_net_small(input_shape, output_shape=6270):
+    _input = Input(input_shape)
+
+    use_bias = False
+    down0 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(_input)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+
+    conv = Conv2D(128, (3, 3))(down2_pool)
+    bn = BatchNormalization()(conv)
+    rl = LeakyReLU(alpha=0.1)(bn)
+    conv = Conv2D(128, (3, 3))(rl)
+    bn = BatchNormalization()(conv)
+    rl = LeakyReLU(alpha=0.1)(bn)
+
+    conv = Conv2D(output_shape, (1, 1), activation='softmax')(rl)
+    pool = GlobalAveragePooling2D()(conv)
+    x = Reshape((output_shape,))(pool)
+
+    model = Model(_input, x)
+    model.summary()
+    return model
+

interface/captcha_interface.py

@@ -1,17 +1,19 @@
+# encoding:utf-8
 import base64
 import json
 import logging
 import os
+import random
 import sys
 import time
 import traceback
 from glob import glob
 import numpy as np
 import cv2
-
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 from flask import Flask, request
-from utils import request_post, bytes2np, np2bytes
+from utils import request_post, bytes2np, np2bytes, base64_decode, image_to_str, str_to_image
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 
@@ -21,9 +23,20 @@ chd_url = "http://127.0.0.1:17056/chd"
 chr_url = "http://127.0.0.1:17057/chr"
 cho_url = "http://127.0.0.1:17058/cho"
 pzd_url = "http://127.0.0.1:17059/pzd"
+ced_url = "http://127.0.0.1:17060/ced"
+cer_url = "http://127.0.0.1:17061/cer"
 captcha_url = "http://127.0.0.1:17054/captcha"
 
 
+#     验证码类型：
+#     1:  拖拽还原拼图
+#     2:  拖拽还原图片
+#     3:  点击中文(带提示文字图片)
+#     4:  点击中文(按语序)
+#     5:  点击中文(带提示文字)
+#     6:  计算算式(带中文)
+
+
 # 接口配置
 app = Flask(__name__)
 
@@ -39,20 +52,28 @@ def captcha():
             return json.dumps({"success": False, "cost": time.time()-start_time})
         base64_data = request.form.get("base64pic")
         base64_data2 = request.form.get("base64pic2")
+        tip_char = request.form.get("char")
         code = int(request.form.get("code"))
+        if tip_char:
+            logging.info("tip_char " + str(tip_char))
         logging.info("code " + str(code))
         logging.info("captcha_interface get data time" + str(time.time()-start_time))
 
-        if base64_data2 is None:
-            result = get_captcha_result([base64_data], code)
-        else:
+        if base64_data2 is not None:
             result = get_captcha_result([base64_data, base64_data2], code)
+        elif tip_char is not None:
+            result = get_captcha_result([base64_data, tip_char], code)
+        else:
+            result = get_captcha_result([base64_data], code)
 
         if result is None:
+            logging.info("success False")
             return json.dumps({"success": False, "cost": time.time()-start_time})
+        logging.info("success True")
         return json.dumps({"predict": result, "success": True, "cost": time.time()-start_time})
     except:
         traceback.print_exc()
+        logging.info("success False")
         return json.dumps({"success": False, "cost": time.time()-start_time})
     finally:
         logging.info("captcha interface finish time " + str(time.time()-start_time))
@@ -63,9 +84,10 @@ def get_captcha_result(base64_list, code):
     验证码类型：
     1:  拖拽还原拼图
     2:  拖拽还原图片
-    3:  点击中文(带提示)
+    3:  点击中文(带提示文字图片)
     4:  点击中文(按语序)
-    5:
+    5:  点击中文(带提示文字)
+    6:  计算算式(带中文)
 
     :param base64_list: base64格式图片列表
     :param code: 验证码类型
@@ -93,6 +115,8 @@ def get_captcha_result(base64_list, code):
         else:
             return None
         logging.info("code " + str(code) + "  chd2  " + "predict " + str(box_list_tips))
+        if len(box_list_tips) == 0:
+            return None
         # recognize tips
         result = json.loads(request_post(chr_url, prepare_data([base64_list[1], box_list_tips], "chr")))
         if result.get("success"):
@@ -110,6 +134,9 @@ def get_captcha_result(base64_list, code):
         else:
             return None
         logging.info("code " + str(code) + "  chd  " + "predict " + str(box_list))
+        if len(box_list) == 0:
+            return None
+
         # recognize
         result = json.loads(request_post(chr_url, prepare_data([base64_list[0], box_list], "chr")))
         if result.get("success"):
@@ -135,6 +162,8 @@ def get_captcha_result(base64_list, code):
         else:
             return None
         logging.info("code " + str(code) + "  chd  " + "predict " + str(box_list))
+        if len(box_list) == 0:
+            return None
 
         # recognize
         result = json.loads(request_post(chr_url, prepare_data([base64_list[0], box_list], "chr")))
@@ -158,11 +187,69 @@ def get_captcha_result(base64_list, code):
         predict = [_dict.get(x) for x in ordered_char_list]
         return predict
 
+    elif code == 5:
+        # detect
+        result = json.loads(request_post(chd_url, prepare_data(base64_list[0], "chd")))
+        if result.get("success"):
+            box_list = result.get("data")
+        else:
+            return None
+        logging.info("code " + str(code) + "  chd  " + "predict " + str(box_list))
+        if len(box_list) == 0:
+            return None
+
+        # recognize
+        result = json.loads(request_post(chr_url, prepare_data([base64_list[0], box_list], "chr")))
+        if result.get("success"):
+            char_list = result.get("data")
+        else:
+            return None
+        logging.info("code " + str(code) + "  chr  " + "predict " + str(char_list))
+        if len(char_list) != len(box_list):
+            return None
+
+        tip_char = base64_list[1]
+        _dict = {char_list[i]: box_list[i] for i in range(len(box_list))}
+        predict = [_dict.get(x) for x in tip_char]
+
+        # guess
+        if predict.count(None) == 1:
+            guess_list = []
+            for c in char_list:
+                if c not in tip_char:
+                    guess_list.append(_dict.get(c))
+            # print("guess_list", guess_list)
+            predict[predict.index(None)] = random.choice(guess_list)
+            return predict
+
+        if None in predict:
+            return None
+        else:
+            return predict
+
+    elif code == 6:
+        # denoise
+        result = json.loads(request_post(ced_url, prepare_data(base64_list[0], "ced")))
+        if result.get("success"):
+            image_np = str_to_image(result.get("data"))
+        else:
+            return None
+        logging.info("code " + str(code) + "  ced  " + "predict success")
+
+        # recognize
+        result = json.loads(request_post(cer_url, prepare_data([image_np], "cer")))
+        if result.get("success"):
+            equation_result = result.get("data")
+        else:
+            return None
+        logging.info("code " + str(code) + "  cer  " + "predict " + str(equation_result))
+        return equation_result
+
     return None
 
 
 def prepare_data(data, _type):
-    if _type in ['pzd', 'bdr', 'chd']:
+    if _type in ['pzd', 'bdr', 'chd', 'ced']:
         return {"data": data}
 
     elif _type in ['chd2']:
@@ -171,26 +258,34 @@ def prepare_data(data, _type):
     elif _type in ['chr']:
         image_base64 = data[0]
         box_list = data[1]
-        image_bytes = base64.b64decode(image_base64)
+        image_bytes = base64_decode(image_base64)
         image_np = bytes2np(image_bytes)
         str_list = []
         for box in box_list:
-            file_bytes = np2bytes(image_np[box[1]:box[3], box[0]:box[2], :])
-            file_base64 = base64.b64encode(file_bytes)
-            file_str = file_base64.decode("utf-8")
+            file_str = image_to_str(image_np[box[1]:box[3], box[0]:box[2], :])
+            # file_bytes = np2bytes(image_np[box[1]:box[3], box[0]:box[2], :])
+            # file_base64 = base64.b64encode(file_bytes)
+            # file_str = file_base64.decode("utf-8")
             str_list.append(file_str)
         return {"data": json.dumps(str_list)}
 
     elif _type in ['cho']:
         return {"data": json.dumps(data)}
 
+    elif _type in ['cer']:
+        data = image_to_str(data[0])
+        return {"data": json.dumps(data)}
+
     else:
         raise
 
 
 def test_interface(from_remote=True):
-    captcha_url = "http://192.168.2.103:17054/captcha"
-    paths = glob("../dev/click_captcha/data/test/yolo_19.jpg")
+    # captcha_url = "http://192.168.2.103:17054/captcha"
+    captcha_url = "http://127.0.0.1:17054/captcha"
+    # paths = glob("D:/Project/captcha/data/test/yolo_15.jpg")
+    # paths = glob("D:/Project/captcha/data/test/FileInfo1021/19584571-511d-11ed-93ac-b4b5b67760ae_3.jpg")
+    paths = glob("yolo_15.jpg")
     for file_path in paths:
         img_np = cv2.imread(file_path)
         file_bytes = np2bytes(img_np)
@@ -242,6 +337,25 @@ def test_interface(from_remote=True):
                     cv2.waitKey(0)
                 else:
                     print("failed!")
+            elif code in [5]:
+                tip_char = "蠢贱榨"
+                file_json = {"base64pic": file_base64, "char": tip_char, "code": code}
+                result = json.loads(request_post(captcha_url, file_json))
+                print("result", result)
+                if result.get("success"):
+                    out_boxes = result.get("predict")
+                    print("out_boxes", out_boxes)
+                    for box in out_boxes:
+                        cv2.rectangle(img_np, (box[0], box[1]), (box[2], box[3]), (0, 0, 255))
+                    cv2.imshow("img_np", img_np)
+                    cv2.waitKey(0)
+            elif code in [6]:
+                file_json = {"base64pic": file_base64, "code": code}
+                result = json.loads(request_post(captcha_url, file_json))
+                print("result", result)
+                if result.get("success"):
+                    equation_result = result.get("predict")
+                    print(equation_result)
 
 
 if __name__ == "__main__":

interface/start.sh

@@ -2,4 +2,6 @@ nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17054 --
 nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17056 --chdir /data/python/fangjiasheng/VerificationCode/chinese_detect/ chd_interface:app &
 nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17057 --chdir /data/python/fangjiasheng/VerificationCode/chinese_recognize/ chr_interface:app &
 nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17058 --chdir /data/python/fangjiasheng/VerificationCode/chinese_order/ cho_interface:app &
-nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17059 --chdir /data/python/fangjiasheng/VerificationCode/puzzle_detect/ pzd_interface:app &
+nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17059 --chdir /data/python/fangjiasheng/VerificationCode/puzzle_detect/ pzd_interface:app &
+nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17060 --chdir /data/python/fangjiasheng/VerificationCode/chinese_equation_denoise/ ced_interface:app &
+nohup /home/user/anaconda3/envs/tf2/bin/gunicorn -w 2 -t 300 -b 0.0.0.0:17061 --chdir /data/python/fangjiasheng/VerificationCode/chinese_equation_recognize/ cer_interface:app &

interface/stop.sh

@@ -2,4 +2,6 @@ kill -9 $(lsof -i:17054|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
 kill -9 $(lsof -i:17056|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
 kill -9 $(lsof -i:17057|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
 kill -9 $(lsof -i:17058|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
-kill -9 $(lsof -i:17059|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
+kill -9 $(lsof -i:17059|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
+kill -9 $(lsof -i:17060|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')
+kill -9 $(lsof -i:17061|sed -n '2,$p'|awk '{print $2}'|tr '\n' ' ')

interface/test.py

@@ -7,18 +7,18 @@ from PIL import Image
 
 from utils import base64_decode
 
-with open(r"C:\Users\Administrator\Desktop\base64_2.txt", "r") as f:
-    _str = f.read()
-
-_str, _str_old = base64_decode(_str, is_test=True)
-print(len(_str_old)%4)
-
-with open(r"C:\Users\Administrator\Desktop\base64_wrong.txt", "w") as f:
-    f.write(_str_old)
-
-image_np = cv2.imdecode(np.frombuffer(_str, np.uint8), cv2.IMREAD_COLOR)
-cv2.imshow("i", image_np)
-cv2.waitKey(0)
+# with open(r"C:\Users\Administrator\Desktop\base64_2.txt", "r") as f:
+#     _str = f.read()
+#
+# _str, _str_old = base64_decode(_str, is_test=True)
+# print(len(_str_old)%4)
+#
+# with open(r"C:\Users\Administrator\Desktop\base64_wrong.txt", "w") as f:
+#     f.write(_str_old)
+#
+# image_np = cv2.imdecode(np.frombuffer(_str, np.uint8), cv2.IMREAD_COLOR)
+# cv2.imshow("i", image_np)
+# cv2.waitKey(0)
 
 
 
@@ -33,4 +33,8 @@ cv2.waitKey(0)
 # cv2.imshow("i", image_np)
 # cv2.waitKey(0)
 # with open(r"C:\Users\Administrator\Desktop\2.jpg", "wb") as f:
-#     f.write(_str)
+#     f.write(_str)
+
+
+_l = [None, 1, None]
+print(_l.count(None))

puzzle_detect/inference_yolo_puzzle.py

@@ -48,8 +48,8 @@ def detect(image_np, model=None, sess=None, draw=False):
     with sess.as_default():
         with sess.graph.as_default():
             out_boxes, out_scores, out_classes = model.predict([image_resize, need_shape], steps=1)
-    print("out_boxes", out_boxes)
-    print("out_scores", out_scores)
+    # print("out_boxes", out_boxes)
+    # print("out_scores", out_scores)
     out_boxes = out_boxes.astype(np.int32)
     out_classes = out_classes.astype(np.int32)
 

utils.py

@@ -279,10 +279,92 @@ def bytes2np(_b):
 
 
 def base64_decode(b64, is_test=False):
+    if type(b64) == bytes:
+        return base64.b64decode(b64)
     # b64 = re.sub(r"\r\n", "\n", b64)
     missing_padding = 4 - len(b64) % 4
     if missing_padding:
         b64 += '=' * missing_padding
     if is_test:
         return base64.b64decode(b64), b64
-    return base64.b64decode(b64)
+    return base64.b64decode(b64)
+
+
+def limit_image_size(image_np, threshold=2000):
+    h, w = image_np.shape[:2]
+    if h <= threshold and w <= threshold:
+        return image_np
+    scale = threshold / max(h, w)
+    h = int(h * scale)
+    w = int(w * scale)
+    image_np = pil_resize(image_np, h, w)
+    return image_np
+
+
+def get_best_predict_size(image_np, times=32):
+    sizes = []
+    for i in range(1, 100):
+        if i*times <= 1300:
+            sizes.append(i*times)
+    sizes.sort(key=lambda x: x, reverse=True)
+
+    min_len = 10000
+    best_height = sizes[0]
+    for height in sizes:
+        if abs(image_np.shape[0] - height) < min_len:
+            min_len = abs(image_np.shape[0] - height)
+            best_height = height
+
+    min_len = 10000
+    best_width = sizes[0]
+    for width in sizes:
+        if abs(image_np.shape[1] - width) < min_len:
+            min_len = abs(image_np.shape[1] - width)
+            best_width = width
+
+    return best_height, best_width
+
+
+def add_contrast(image_np):
+    # cv2.imshow("image_np", image_np)
+    img = image_np.astype(np.float32)
+    bri_mean = np.mean(img)
+
+    # a = np.arange(5, 16, 5) / 10
+    # b = np.arange(-30, 31, 30)
+    #
+    # a_len = len(a)
+    # b_len = len(b)
+    # print(a_len, b_len)
+    #
+    # for i in range(a_len):
+    #     for j in range(b_len):
+    #         aa = a[i]
+    #         bb = b[j]
+    #         img_a = aa * (img-bri_mean) + bb + bri_mean
+    #         print(i, j, aa, bb)
+    #         img_a = np.clip(img_a, 0, 255).astype(np.uint8)
+    #         cv2.imshow("img_a", img_a)
+    #         cv2.waitKey(0)
+
+    aa = 3
+    bb = -50
+
+    img_a = aa * (img-bri_mean) + bb + bri_mean
+    img_a = np.clip(img_a, 0, 255).astype(np.uint8)
+    # cv2.imshow("img_a", img_a)
+    # cv2.waitKey(0)
+    return img_a
+
+
+def image_to_str(image_np):
+    file_bytes = np2bytes(image_np)
+    file_base64 = base64.b64encode(file_bytes)
+    file_str = file_base64.decode("utf-8")
+    return file_str
+
+
+def str_to_image(_str):
+    b64 = _str.encode("utf-8")
+    image_np = bytes2np(base64_decode(b64))
+    return image_np