keras读取训练好的模型参数并把参数赋值给其它模型详解

inputs=Input(shape=(400,500,3))
X=Conv2D(32, (3, 3),name=“conv2d_1”)(inputs)
X=BatchNormalization(name=“batch_normalization_1”)(X)
X=Activation(‘relu',name=“activation_1”)(X)

from keras.models import load_model
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np
from keras.layers import Conv2D, MaxPooling2D,merge
from keras.layers import BatchNormalization,Activation
from keras.layers import Input, Dense
from PIL import Image
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten,Input
from keras.layers import Conv2D, MaxPooling2D,merge,AveragePooling2D,GlobalAveragePooling2D
from keras.layers import BatchNormalization,Activation
from sklearn.model_selection import train_test_split
from keras.applications.densenet import DenseNet169, DenseNet121
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_v3 import InceptionV3
from keras.optimizers import SGD
from keras import regularizers
from keras.models import Model
import tensorflow as tf
from PIL import Image
from keras.callbacks import TensorBoard
import os
import cv2
from keras import backend as K
from model import focal_loss
import keras.losses

#ReadMe 该代码是参考fast rcnn系列，先对整幅图像提取特征feature map，然后从原图对应位置上映射到feature map，并对feature map进行
# 切片，从而提取对应某个位置上的特征，并把该特征送进后面的识别网络进行分类识别。
keras.losses.focal_loss = focal_loss#这句代码是为了引入定义的loss
base_model=load_model('model_halcon_resenet.h5')
base_model.summary()

inputs=Input(shape=(400,500,3))
X=Conv2D(32, (3, 3),name="conv2d_1")(inputs)
X=BatchNormalization(name="batch_normalization_1")(X)
X=Activation('relu',name="activation_1")(X)
#第一个残差模块
X_1=Conv2D(32, (3, 3),padding='same',name="conv2d_2")(X)
X_1=BatchNormalization(name="batch_normalization_2")(X_1)
X_1= Activation('relu',name="activation_2")(X_1)
X_1 = Conv2D(32, (3, 3),padding='same',name="conv2d_3")(X_1)
X_1 = BatchNormalization(name="batch_normalization_3")(X_1)
merge_data = merge([X_1, X], mode='sum',name="merge_1")
X = Activation('relu',name="activation_3")(merge_data)
#第一个残差模块结束
X=MaxPooling2D(pool_size=(2, 2),strides=(2,2),name="max_pooling2d_1")(X)
X=Conv2D(64, (3, 3),kernel_regularizer=regularizers.l2(0.01),name="conv2d_4")(X)
X=BatchNormalization(name="batch_normalization_4")(X)
X=Activation('relu',name="activation_4")(X)
#第二个残差模块
X_2=Conv2D(64, (3, 3),padding='same',name="conv2d_5")(X)
X_2=BatchNormalization(name="batch_normalization_5")(X_2)
X_2= Activation('relu',name="activation_5")(X_2)
X_2 = Conv2D(64, (3, 3),padding='same',name="conv2d_6")(X_2)
X_2 = BatchNormalization(name="batch_normalization_6")(X_2)
merge_data = merge([X_2, X], mode='sum',name="merge_2")
X = Activation('relu',name="activation_6")(merge_data)
#第二个残差模块结束
X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),name="max_pooling2d_2")(X)
X=Conv2D(64, (3, 3),name="conv2d_7")(X)
X=BatchNormalization(name="batch_normalization_7")(X)
X=Activation('relu',name="activation_7")(X)
X=MaxPooling2D(pool_size=(2, 2),strides=(2,2),name="max_pooling2d_3")(X)
#第三个残差模块开始
X_3=Conv2D(64, (3, 3),padding='same',name="conv2d_8")(X)
X_3=BatchNormalization(name="batch_normalization_8")(X_3)
X_3= Activation('relu',name="activation_8")(X_3)
X_3 = Conv2D(64, (3, 3),padding='same',name="conv2d_9")(X_3)
X_3 = BatchNormalization(name="batch_normalization_9")(X_3)
merge_data = merge([X_3, X], mode='sum',name="merge_3")
X = Activation('relu',name="activation_9")(merge_data)
#第三个残差模块结束
X=Conv2D(32, (3, 3),kernel_regularizer=regularizers.l2(0.01),name="conv2d_10")(X)
X=BatchNormalization(name="batch_normalization_10")(X)
X=Activation('relu',name="activation_10")(X)
#第四个残差模块开始
X_4=Conv2D(32, (3, 3),padding='same',name="conv2d_11")(X)
X_4=BatchNormalization(name="batch_normalization_11")(X_4)
X_4= Activation('relu',name="activation_11")(X_4)
X_4 = Conv2D(32, (3, 3),padding='same',name="conv2d_12")(X_4)
X_4 = BatchNormalization(name="batch_normalization_12")(X_4)
merge_data = merge([X_4, X], mode='sum',name="merge_4")
X = Activation('relu',name="activation_12")(merge_data)
#第四个残差模块结束
X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),name="max_pooling2d_4")(X)
X = Conv2D(64, (3, 3),name="conv2d_13")(X)
X = BatchNormalization(name="batch_normalization_13")(X)
X = Activation('relu',name="activation_13")(X)
#第五个残差模块开始
X_5=Conv2D(64, (3, 3),padding='same',name="conv2d_14")(X)
X_5=BatchNormalization(name="batch_normalization_14")(X_5)
X_5= Activation('relu',name="activation_14")(X_5)
X_5 = Conv2D(64, (3, 3),padding='same',name="conv2d_15")(X_5)
X_5 = BatchNormalization(name="batch_normalization_15")(X_5)
merge_data = merge([X_5, X], mode='sum',name="merge_5")
X = Activation('relu',name="activation_15")(merge_data)
#第五个残差模块结束
model=Model(inputs=inputs, outputs=X)
model.load_weights('model_halcon_resenet.h5', by_name=True)
#读取指定图像数据
image_dir='C:/Users/18301/Desktop/blister/new/blister_mixed_11.png'
img = image.load_img(image_dir, target_size=(400, 500))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
#利用第一个模型预测出特征数据，并对特征数据进行切片
feature_map=model.predict(x)
T=np.array(feature_map)
f_1=T[:,16:21,0:10,:]
print(f_1.shape)
print(feature_map.shape)
#第一个模型没有问题
#定义第二个模型
inputs_sec=Input(shape=(1,5,10,64))
X_= Flatten(name="flatten_1")(inputs_sec)
X_ = Dense(256, activation='relu',name="dense_1")(X_)
X_ = Dropout(0.5,name="dropout_1")(X_)
predictiOns= Dense(6, activation='softmax',name="dense_2")(X_)
model_sec=Model(inputs=inputs_sec, outputs=predictions)
model_sec.load_weights('model_halcon_resenet.h5', by_name=True)
#第二个模型定义结束
model_sec.summary()
#开始对整幅图像进行切片，并记录坐标位置
pic=cv2.imread(image_dir)
cor_list=[]
name_list=['blank','green_blank','red_blank','yellow','yellow_balnk','yellow_blue']
fOnt= cv2.FONT_HERSHEY_SIMPLEX
for i in range(3):
 for j in range(5):
 if(i==2):
  cut_feature = T[:, 4 * j:4 * j + 5, 17:27, :]
  data = np.expand_dims(cut_feature, axis=0)
  result = model_sec.predict(data)
  print(result)
  result_data=result[0].tolist()
  #如果置信度过低，则舍弃
  # if(max(result_data)<=0.7):
  # continue
  index_num = result_data.index(max(result_data))
  name=name_list[index_num]
  cor_list = [i * 160 + 6, j * 80] # 每个切片数据，映射到原图上，检测框对应的左上角坐标
  x=cor_list[0]
  y=cor_list[1]
  cv2.rectangle(pic, (160 * i + 6, 80 * j), ((i + 1) * 160 + 6, 80 * (j+ 1)), (0, 255, 0), 2)
  cv2.putText(pic, name, (x + 40, y + 40), font, 0.5, (0, 0, 255), 1)
 else:
  cut_feature = T[:, 4 * j:4 * j + 5, 9 * i:9 * i + 10, :]
  data = np.expand_dims(cut_feature, axis=0)
  result = model_sec.predict(data)
  print(result)
  result_data = result[0].tolist()
  #如果置信度过低，则舍弃
  # if (max(result_data) <= 0.7):
  # continue
  index_num = result_data.index(max(result_data))
  name = name_list[index_num]
  cor_list = [i * 160 + 6, j * 80] # 每个切片数据，映射到原图上，检测框对应的左上角坐标
  x = cor_list[0]
  y = cor_list[1]
  cv2.rectangle(pic, (160 * i + 6, 80 * j), ((i + 1) * 160 + 6, 80 * (j + 1)), (0, 255, 0), 2)
  cv2.putText(pic, name, (x + 40, y + 40), font, 0.5, (0, 0, 255), 1)

cv2.imshow('pic',pic)
cv2.waitKey(0)
cv2.destroyAllWindows()
# data= np.expand_dims(f_1, axis=0)
# result=model_sec.predict(data)
# print(result)
#第二个模型可以完全预测，没有问题