实战 | 红酒瓶标签曲面展平+文字识别(附源码)

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重磅干货，第一时间送达 

【2】高斯滤波平滑 + 固定阈值二值化

def build_model(self, Config):          inputs = tf.keras.layers.Input((256,256,3))  s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)
  #Contraction path  c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)  c1 = tf.keras.layers.Dropout(Config['contraction_1_dropout'])(c1)  c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)  p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
  c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)  c2 = tf.keras.layers.Dropout(Config['contraction_2_dropout'])(c2)  c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)  p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
  c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)  c3 = tf.keras.layers.Dropout(Config['contraction_3_dropout'])(c3)  c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)  p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
  c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)  c4 = tf.keras.layers.Dropout(Config['contraction_4_dropout'])(c4)  c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)  p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
  c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)  c5 = tf.keras.layers.Dropout(Config['contraction_5_dropout'])(c5)  c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)
  #Expansive path   u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)  u6 = tf.keras.layers.concatenate([u6, c4])  c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)  c6 = tf.keras.layers.Dropout(Config['expansive_1_dropout'])(c6)  c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
  u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)  u7 = tf.keras.layers.concatenate([u7, c3])  c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)  c7 = tf.keras.layers.Dropout(Config['expansive_2_dropout'])(c7)  c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
  u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)  u8 = tf.keras.layers.concatenate([u8, c2])  c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)  c8 = tf.keras.layers.Dropout(Config['expansive_3_dropout'])(c8)  c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
  u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)  u9 = tf.keras.layers.concatenate([u9, c1], axis=3)  c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)  c9 = tf.keras.layers.Dropout(Config['expansive_4_dropout'])(c9)  c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
  outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)
  model = tf.keras.Model(inputs=[inputs], outputs=[outputs])  return model

# mask is the U-net output image# src is the source image# self is the parent class labelVision
mask = cv2.cvtColor(mask,cv2.COLOR_GRAY2RGB)mask=cv2.resize(mask,(src.shape[1],src.shape[0]))mask = np.round(mask) #binary transformr_src, r_mask = self.align_vertically(src, mask)

源码下载：

https://github.com/AntoninLeroy/wine_label_reader_toolkit

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