在python程序中使用YOLO v3（基于keras）_python和yolo什么关系

在python程序中使用YOLO，可以为YOLO添加python接口，也可以把YOLO的网络框架和权重文件转换成keras或pytorch使用的格式，然后再在python程序中调用。这里介绍基于keras的YOLO调用。

完整项目代码下载地址 ： https://github.com/dcrmg/yolo3-training-keras-master

1. 生成keras的.h5文件

需要：

•  1. yolo网络结构配置文件 .cfg ,如 yolov3.cfg
•  2. yolo训练好的权重文件 .weights ,如 yolov3.weights

 
使用yolo的网络结构配置文件和权重文件转换成keras的.h5文件，转换代码(convert.py)：

# coding: utf-8
#! /usr/bin/env python
"""
Reads Darknet config and weights and creates Keras model with TF backend.
"""
 
import argparse
import configparser
import io
import os
from collections import defaultdict
 
import numpy as np
from keras import backend as K
from keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
                          UpSampling2D, MaxPooling2D, Concatenate)
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.regularizers import l2
from keras.utils.vis_utils import plot_model as plot
 
 
parser = argparse.ArgumentParser(description='Darknet To Keras Converter.')
parser.add_argument('config_path', help='Path to Darknet cfg file.')
parser.add_argument('weights_path', help='Path to Darknet weights file.')
parser.add_argument('output_path', help='Path to output Keras model file.')
 
parser.add_argument(
    '-p',
    '--plot_model',
    help='Plot generated Keras model and save as image.',
    action='store_true')
parser.add_argument(
    '-w',
    '--weights_only',
    help='Save as Keras weights file instead of model file.',
    action='store_true')
 
def unique_config_sections(config_file):
    """Convert all config sections to have unique names.
    Adds unique suffixes to config sections for compability with configparser.
    """
    section_counters = defaultdict(int)
    # output_stream = io.StringIO()
    output_stream = io.BytesIO()
    with open(config_file) as fin:
        for line in fin:
            if line.startswith('['):
                section = line.strip().strip('[]')
                _section = section + '_' + str(section_counters[section])
                section_counters[section] += 1
                line = line.replace(section, _section)
            output_stream.write(line)
    output_stream.seek(0)
    return output_stream
 
# %%
def _main(args):
    config_path = os.path.expanduser(args.config_path)
    weights_path = os.path.expanduser(args.weights_path)
    assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format(
        config_path)
    assert weights_path.endswith(
        '.weights'), '{} is not a .weights file'.format(weights_path)
 
    output_path = os.path.expanduser(args.output_path)
    assert output_path.endswith(
        '.h5'), 'output path {} is not a .h5 file'.format(output_path)
    output_root = os.path.splitext(output_path)[0]
 
    # Load weights and config.
    print('Loading weights.')
    weights_file = open(weights_path, 'rb')
    major, minor, revision = np.ndarray(
        shape=(3, ), dtype='int32', buffer=weights_file.read(12))
    if (major*10+minor)>=2 and major<1000 and minor<1000:
        seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))
    else:
        seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))
    print('Weights Header: ', major, minor, revision, seen)
 
    print('Parsing Darknet config.')
    unique_config_file = unique_config_sections(config_path)
    cfg_parser = configparser.ConfigParser()
    cfg_parser.read_file(unique_config_file)
 
    print('Creating Keras model.')
    input_layer = Input(shape=(None, None, 3))
    prev_layer = input_layer
    all_layers = []
 
    weight_decay = float(cfg_parser['net_0']['decay']
                         ) if 'net_0' in cfg_parser.sections() else 5e-4
    count = 0
    out_index = []
    for section in cfg_parser.sections():
        print('Parsing section {}'.format(section))
        if section.startswith('convolutional'):
            filters = int(cfg_parser[section]['filters'])
            size = int(cfg_parser[section]['size'])
            stride = int(cfg_parser[section]['stride'])
            pad = int(cfg_parser[section]['pad'])
            activation = cfg_parser[section]['activation']
            batch_normalize = 'batch_normalize' in cfg_parser[section]
 
            padding = 'same' if pad == 1 and stride == 1 else 'valid'
 
            # Setting weights.
            # Darknet serializes convolutional weights as:
            # [bias/beta, [gamma, mean, variance], conv_weights]
            prev_layer_shape = K.int_shape(prev_layer)
 
            weights_shape = (size, size, prev_layer_shape[-1], filters)
            darknet_w_shape = (filters, weights_shape[2], size, size)
            weights_size = np.product(weights_shape)
 
            print('conv2d', 'bn'
                  if batch_normalize else '  ', activation, weights_shape)
 
            conv_bias = np.ndarray(
                shape=(filters, ),
                dtype='float32',
                buffer=weights_file.read(filters * 4))
            count += filters
 
            if batch_normalize:
                bn_weights = np.ndarray(
                    shape=(3, filters),
                    dtype='float32',
                    buffer=weights_file.read(filters * 12))
                count += 3 * filters
 
                bn_weight_list = [
                    bn_weights[0],  # scale gamma
                    conv_bias,  # shift beta
                    bn_weights[1],  # running mean
                    bn_weights[2]  # running var
                ]
 
            conv_weights = np.ndarray(
                shape=darknet_w_shape,
                dtype='float32',
                buffer=weights_file.read(weights_size * 4))
            count += weights_size
 
            # DarkNet conv_weights are serialized Caffe-style:
            # (out_dim, in_dim, height, width)
            # We would like to set these to Tensorflow order:
            # (height, width, in_dim, out_dim)
            conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
            conv_weights = [conv_weights] if batch_normalize else [
                conv_weights, conv_bias
            ]
 
            # Handle activation.
            act_fn = None
            if activation == 'leaky':
                pass  # Add advanced activation later.
            elif activation != 'linear':
                raise ValueError(
                    'Unknown activation function `{}` in section {}'.format(
                        activation, section))
 
            # Create Conv2D layer
            if stride>1:
                # Darknet uses left and top padding instead of 'same' mode
                prev_layer = ZeroPadding2D(((1,0),(1,0)))(prev_layer)
            conv_layer = (Conv2D(
                filters, (size, size),
                strides=(stride, stride),
                kernel_regularizer=l2(weight_decay),
                use_bias=not batch_normalize,
                weights=conv_weights,
                activation=act_fn,
                padding=padding))(prev_layer)
 
            if batch_normalize:
                conv_layer = (BatchNormalization(
                    weights=bn_weight_list))(conv_layer)
            prev_layer = conv_layer
 
            if activation == 'linear':
                all_layers.append(prev_layer)
            elif activation == 'leaky':
                act_layer = LeakyReLU(alpha=0.1)(prev_layer)
                prev_layer = act_layer
                all_layers.append(act_layer)
 
        elif section.startswith('route'):
            ids = [int(i) for i in cfg_parser[section]['layers'].split(',')]
            layers = [all_layers[i] for i in ids]
            if len(layers) > 1:
                print('Concatenating route layers:', layers)
                concatenate_layer = Concatenate()(layers)
                all_layers.append(concatenate_layer)
                prev_layer = concatenate_layer
            else:
                skip_layer = layers[0]  # only one layer to route
                all_layers.append(skip_layer)
                prev_layer = skip_layer
 
        elif section.startswith('maxpool'):
            size = int(cfg_parser[section]['size'])
            stride = int(cfg_parser[section]['stride'])
            all_layers.append(
                MaxPooling2D(
                    pool_size=(size, size),
                    strides=(stride, stride),
                    padding='same')(prev_layer))
            prev_layer = all_layers[-1]
 
        elif section.startswith('shortcut'):
            index = int(cfg_parser[section]['from'])
            activation = cfg_parser[section]['activation']
            assert activation == 'linear', 'Only linear activation supported.'
            all_layers.append(Add()([all_layers[index], prev_layer]))
            prev_layer = all_layers[-1]
 
        elif section.startswith('upsample'):
            stride = int(cfg_parser[section]['stride'])
            assert stride == 2, 'Only stride=2 supported.'
            all_layers.append(UpSampling2D(stride)(prev_layer))
            prev_layer = all_layers[-1]
 
        elif section.startswith('yolo'):
            out_index.append(len(all_layers)-1)
            all_layers.append(None)
            prev_layer = all_layers[-1]
 
        elif section.startswith('net'):
            pass
 
        else:
            raise ValueError(
                'Unsupported section header type: {}'.format(section))
 
    # Create and save model.
    if len(out_index)==0: out_index.append(len(all_layers)-1)
    model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])
    print(model.summary())
    if args.weights_only:
        model.save_weights('{}'.format(output_path))
        print('Saved Keras weights to {}'.format(output_path))
    else:
        model.save('{}'.format(output_path))
        print('Saved Keras model to {}'.format(output_path))
 
    # Check to see if all weights have been read.
    remaining_weights = len(weights_file.read()) / 4
    weights_file.close()
    print('Read {} of {} from Darknet weights.'.format(count, count +
                                                       remaining_weights))
    if remaining_weights > 0:
        print('Warning: {} unused weights'.format(remaining_weights))
 
    if args.plot_model:
        plot(model, to_file='{}.png'.format(output_root), show_shapes=True)
        print('Saved model plot to {}.png'.format(output_root))
 
 
if __name__ == '__main__':
    _main(parser.parse_args())

使用方法：

python convert.py yolov3.cfg yolov3.weights yolov3.h5

转换过程输出：

Loading weights.
('Weights Header: ', 0, 2, 0, array([1600]))
Parsing Darknet config.
Creating Keras model.
Parsing section net_0
Parsing section convolutional_0
('conv2d', 'bn', u'leaky', (3, 3, 3, 32))
……
……
conv2d_59 (Conv2D)              (None, None, None, 1 18450       leaky_re_lu_58[0][0]             
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, None, None, 1 9234        leaky_re_lu_65[0][0]             
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, None, None, 1 4626        leaky_re_lu_72[0][0]             
==================================================================================================
Total params: 61,576,342
Trainable params: 61,523,734
Non-trainable params: 52,608
__________________________________________________________________________________________________
None
Saved Keras model to yolov3.h5
Read 61576342 of 61576342 from Darknet weights.

执行完成之后在当前目录下生成yolov3.h5文件。

2. python + keras + yolo检测

需要：

•  1. 类别标签文件yolo_classes.txt ，如有三个分类 dog、cat、tiger 则classes.txt内容为 dog cat tiger（每个类别一行）
•  2. anchors文件 yolo_anchors.txt
•  3. 步骤一中生成的 yolov3.h5

注意：yolo_anchors.txt记录了yolov3通过k-means聚类方法产生的9个anchors(锚点)信息，使用的训练图像大小是416×416,如果 你的训练图片大小不一样，这个yolo_anchors.txt文件需要重新生成，最好使用跟作者一样的416×416的图像训练。

检测代码略，请见项目代码。

通过调整YOLO类 self.score 的大小控制检出目标的阈值。

200个样本简单训练了400轮的检测效果：