can only concatenate str (not "time.struct_time") to str

这个错误说明您在字符串和time.struct_time之间进行了拼接，而这是不允许的。您需要将time.struct_time类型转换为字符串类型，例如使用time.strftime()函数将其格式化为字符串。您可以尝试以下代码来解决这个问题：

import time

# 获取当前时间
now = time.localtime()

# 将 time.struct_time 转换为字符串
formatted_time = time.strftime('%Y-%m-%d %H:%M:%S', now)

# 进行字符串拼接
result = 'The current time is: ' + formatted_time

print(result)

相关问题

试设计算法，将两个顺序表示的字符串s和t连接起来。要求：不能用struct函数

要设计一个算法将两个顺序表示的字符串`s`和`t`连接起来，而不能使用内置的字符串连接函数，我们可以采用逐字符拼接的方式。这里是一个简单的Python示例：

def concatenate_strings(s, t):
    # 创建一个新的空字符串result，用于存储结果
    result = ''
    
    # 遍历字符串s
    for char in s:
        # 将当前字符追加到result中
        result += char
    
    # 再次遍历字符串t
    for char in t:
        # 同样地，将当前字符追加到result中
        result += char
    
    return result

# 示例
s = "Hello"
t = "World!"
concatenated_str = concatenate_strings(s, t)
print(concatenated_str)  # 输出: HelloWorld!

这个算法的工作原理是通过循环访问每个输入字符串中的字符，并依次添加到结果字符串`result`中。虽然这不是最高效的解决方案（因为每次追加操作都需要创建新的字符引用），但对于小字符串来说，这种简单方法是可行的。

把 yolov3.weights 文件转换成 keras 适用的 h5 文件

要将 YOLOv3 的权重文件转换为 Keras 可以使用的 .h5 文件，需要进行以下步骤：

1. 下载并安装 `keras` 和 `tensorflow` 库：

pip install keras tensorflow

2. 下载 YOLOv3 的权重文件 `yolov3.weights` 和配置文件 `yolov3.cfg`。

3. 使用 `yolo_weights_convert.py` 脚本将权重文件转换为 Keras 模型：

python yolo_weights_convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

其中，`yolov3.cfg` 是 YOLOv3 的配置文件路径，`yolov3.weights` 是权重文件路径，`model_data/yolo.h5` 是转换后的 Keras 模型保存路径。

以下是 `yolo_weights_convert.py` 的代码：

import argparse
import numpy as np
import struct
import os

from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, LeakyReLU, UpSampling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.engine.topology import Layer
from keras import backend as K

class YoloLayer(Layer):
    def __init__(self, anchors, max_grid, batch_size, warmup_batches, ignore_thresh, grid_scale,
                 obj_scale, noobj_scale, xywh_scale, class_scale, **kwargs):
        self.ignore_thresh = ignore_thresh
        self.warmup_batches = warmup_batches
        self.anchors = anchors
        self.grid_scale = grid_scale
        self.obj_scale = obj_scale
        self.noobj_scale = noobj_scale
        self.xywh_scale = xywh_scale
        self.class_scale = class_scale
        self.batch_size = batch_size
        self.true_boxes = K.placeholder(shape=(self.batch_size, 1, 1, 1, 50, 4))
        super(YoloLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        super(YoloLayer, self).build(input_shape)

    def get_grid_size(self, net_h, net_w):
        return net_h // 32, net_w // 32

    def call(self, x):
        input_image, y_pred, y_true = x
        self.net_h, self.net_w = input_image.shape.as_list()[1:3]
        self.grid_h, self.grid_w = self.get_grid_size(self.net_h, self.net_w)

        # adjust the shape of the y_predict [batch, grid_h, grid_w, 3, 4+1+80]
        y_pred = K.reshape(y_pred, (self.batch_size, self.grid_h, self.grid_w, 3, 4 + 1 + 80))

        # convert the coordinates to absolute coordinates
        box_xy = K.sigmoid(y_pred[..., :2])
        box_wh = K.exp(y_pred[..., 2:4])
        box_confidence = K.sigmoid(y_pred[..., 4:5])
        box_class_probs = K.softmax(y_pred[..., 5:])

        # adjust the shape of the y_true [batch, 50, 4+1]
        object_mask = y_true[..., 4:5]
        true_class_probs = y_true[..., 5:]

        # true_boxes[..., 0:2] = center, true_boxes[..., 2:4] = wh
        true_boxes = self.true_boxes[..., 0:4]  # shape=[batch, 50, 4]
        true_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h]  # shape=[batch, 50, 2]
        true_wh = true_boxes[..., 2:4] * [self.net_w, self.net_h]  # shape=[batch, 50, 2]
        true_wh_half = true_wh / 2.
        true_mins = true_xy - true_wh_half
        true_maxes = true_xy + true_wh_half

        # calculate the Intersection Over Union (IOU)
        pred_xy = K.expand_dims(box_xy, 4)
        pred_wh = K.expand_dims(box_wh, 4)
        pred_wh_half = pred_wh / 2.
        pred_mins = pred_xy - pred_wh_half
        pred_maxes = pred_xy + pred_wh_half

        intersect_mins = K.maximum(pred_mins, true_mins)
        intersect_maxes = K.minimum(pred_maxes, true_maxes)
        intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
        pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
        true_areas = true_wh[..., 0] * true_wh[..., 1]
        union_areas = pred_areas + true_areas - intersect_areas
        iou_scores = intersect_areas / union_areas

        # calculate the best IOU, set the object mask and update the class probabilities
        best_ious = K.max(iou_scores, axis=4)
        object_mask_bool = K.cast(best_ious >= self.ignore_thresh, K.dtype(best_ious))
        no_object_mask_bool = 1 - object_mask_bool
        no_object_loss = no_object_mask_bool * box_confidence
        no_object_loss = self.noobj_scale * K.mean(no_object_loss)

        true_box_class = true_class_probs * object_mask
        true_box_confidence = object_mask
        true_box_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h] - pred_mins
        true_box_wh = K.log(true_boxes[..., 2:4] * [self.net_w, self.net_h] / pred_wh)
        true_box_wh = K.switch(object_mask, true_box_wh, K.zeros_like(true_box_wh))  # avoid log(0)=-inf
        true_box_xy = K.switch(object_mask, true_box_xy, K.zeros_like(true_box_xy))  # avoid log(0)=-inf
        box_loss_scale = 2 - true_boxes[..., 2:3] * true_boxes[..., 3:4]

        xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(true_box_xy, box_xy)
        wh_loss = object_mask * box_loss_scale * 0.5 * K.square(true_box_wh - box_wh)
        confidence_loss = true_box_confidence * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          + (1 - true_box_confidence) * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          * no_object_mask_bool
        class_loss = object_mask * K.binary_crossentropy(true_box_class, box_class_probs)

        xy_loss = K.mean(K.sum(xy_loss, axis=[1, 2, 3, 4]))
        wh_loss = K.mean(K.sum(wh_loss, axis=[1, 2, 3, 4]))
        confidence_loss = K.mean(K.sum(confidence_loss, axis=[1, 2, 3, 4]))
        class_loss = K.mean(K.sum(class_loss, axis=[1, 2, 3, 4]))

        loss = self.grid_scale * (xy_loss + wh_loss) + confidence_loss * self.obj_scale + no_object_loss \
               + class_loss * self.class_scale

        # warm up training
        batch_no = K.cast(self.batch_size / 2, dtype=K.dtype(object_mask))
        warmup_steps = self.warmup_batches
        warmup_lr = batch_no / warmup_steps
        batch_no = K.cast(K.minimum(warmup_steps, batch_no), dtype=K.dtype(object_mask))
        lr = self.batch_size / (batch_no * warmup_steps)
        warmup_decay = (1 - batch_no / warmup_steps) ** 4
        lr = lr * (1 - warmup_decay) + warmup_lr * warmup_decay

        self.add_loss(loss)
        self.add_metric(loss, name='loss', aggregation='mean')
        self.add_metric(xy_loss, name='xy_loss', aggregation='mean')
        self.add_metric(wh_loss, name='wh_loss', aggregation='mean')
        self.add_metric(confidence_loss, name='confidence_loss', aggregation='mean')
        self.add_metric(class_loss, name='class_loss', aggregation='mean')
        self.add_metric(lr, name='lr', aggregation='mean')

        return y_pred

    def compute_output_shape(self, input_shape):
        return input_shape[1]

    def get_config(self):
        config = {
            'ignore_thresh': self.ignore_thresh,
            'warmup_batches': self.warmup_batches,
            'anchors': self.anchors,
            'grid_scale': self.grid_scale,
            'obj_scale': self.obj_scale,
            'noobj_scale': self.noobj_scale,
            'xywh_scale': self.xywh_scale,
            'class_scale': self.class_scale
        }
        base_config = super(YoloLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

def _conv_block(inp, convs, skip=True):
    x = inp
    count = 0
    for conv in convs:
        if count == (len(convs) - 2) and skip:
            skip_connection = x
        count += 1
        if conv['stride'] > 1:
            x = ZeroPadding2D(((1, 0), (1, 0)))(x)  # unlike tensorflow darknet prefer left and top paddings
        x = Conv2D(conv['filter'],
                   conv['kernel'],
                   strides=conv['stride'],
                   padding='valid' if conv['stride'] > 1 else 'same',  # unlike tensorflow darknet prefer left and top paddings
                   name='conv_' + str(conv['layer_idx']),
                   use_bias=False if conv['bnorm'] else True)(x)
        if conv['bnorm']:
            x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
        if conv['leaky']:
            x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
    return add([skip_connection, x]) if skip else x

def make_yolov3_model():
    input_image = Input(shape=(None, None, 3))
    true_boxes = Input(shape=(1, 1, 1, 50, 4))
    # Layer  0 => 4
    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                                  {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  5 => 8
    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  9 => 11
    x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 12 => 15
    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 16 => 36
    for i in range(7):
        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_36 = x
    # Layer 37 => 40
    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 41 => 61
    for i in range(7):
        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_61 = x
    # Layer 62 => 65
    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 66 => 74
    for i in range(3):
        x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 75 => 79
    x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}])

    # Layer 80 => 82
    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}], skip=False)

    # Layer 83 => 86
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_61])
    # Layer 87 => 91
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}], skip=False)

    # Layer 92 => 94
    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}],
                          skip=False)

    # Layer 95 => 98
    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_36])
    # Layer 99 => 106
    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter