示例#1
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def trainConstructModel():
    model = construct_refine_model()
    y_train = keras.utils.to_categorical(train[1], num_classes)
    y_test = keras.utils.to_categorical(test[1], num_classes)
    import b_load_model_weights

    b_load_model_weights.load_model_weights(model)

    # (1) 数据探索
    #  data explore
    import c_visualize_image

    resized = c_visualize_image.visualize_images()
    import d_inference_image
    out = d_inference_image.inference_image(model, resized)

    # 转换为Channel First
    batch = np.transpose(resized, (2, 0, 1))
    # 将图像转换到-1到1区间
    batch = 2 * (batch / 255.) - 1
    # 测试单张图片,需要将一个数据转换为数组batch数据
    batch = np.expand_dims(batch, axis=0)

    label = np.expand_dims(out, axis=0)

    model.compile(loss='mean_squared_error',
                  optimizer='adadelta',
                  metrics=['accuracy'])

    t = now()
    model.fit(batch,
              label,
              batch_size=batch_size,
              epochs=1,
              verbose=1,
              validation_data=(x_test, y_test))


#construct_yolo_model()
示例#2
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# (0) 构建YOLO实时检测模型
# construct the tiny-yolo model
import a_construct_yolo_model

model = a_construct_yolo_model.construct_yolo_model()

import b_load_model_weights

b_load_model_weights.load_model_weights(model)

# (1) 数据探索
#  data explore
import c_visualize_image

resized = c_visualize_image.visualize_images()

# (2) 图像模型推断
# model inference the image
import d_inference_image

out = d_inference_image.inference_image(model, resized)

# (3) YOLO模型预测结果转换为box坐标
# ineference result convert to box
import e_convert_to_box

boxes = e_convert_to_box.regression_convert_to_box(out)

# (4) 单图像上进行车辆检测
# visualize car detection image results
# (0) 构建YOLO实时检测模型
# construct the tiny-yolo model
import a_construct_yolo_model

model = a_construct_yolo_model.construct_yolo_model()
#model.summary()

import b_load_model_weights

b_load_model_weights.load_model_weights(model)

# (1) 数据探索
#  data explore
import c_visualize_image

resized = c_visualize_image.visualize_images(
    imagePath='./test_images2/test1.jpg')

# (2) 图像模型推断
# model inference the image
import d_inference_image

out = d_inference_image.inference_image(model, resized)
print(out)

# (3) YOLO模型预测结果转换为box坐标
# ineference result convert to box
import e_convert_to_box

boxes = e_convert_to_box.regression_convert_to_box(out, class_num_case=12)

# (4) 单图像上进行车辆检测