# python_caffe_train_test

**Repository Path**: bmw360/python_caffe_train_test

## Basic Information

- **Project Name**: python_caffe_train_test
- **Description**: 本文主要是使用caffe python做图片识别的示例包括训练数据lmdb生成，训练，以及模型测试，主要内容如下：

    训练，验证数据lmdb生成，主要包括：样本的预处理 （直方图均衡化，resize），训练样本以及验证样本的lmdb的生成,以及mean_file mean.binaryproto生成


- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-05
- **Last Updated**: 2022-06-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


本文主要是使用caffe python做图片识别的示例包括训练数据lmdb生成，训练，以及模型测试，主要内容如下：


----------


1. 训练，验证数据lmdb生成，主要包括：样本的预处理 （直方图均衡化，resize），训练样本以及验证样本的lmdb的生成,以及mean_file mean.binaryproto生成

2. caffe中模型的定义，主要是修改 [caffe Alexnet 训练文件train_val.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt) ，以及[训练参数文件solver.prototxt ](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/solver.prototxt)，还有[部署文件deploy.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt)

3. 训练验证数据准备完成之后，就是模型的训练

4.  得到训练模型之后，一般会进行本地测试以及从数据库获取url测试然后将结果写到数据库中


----------
先上个[代码](https://github.com/Jayhello/python_caffe_train_test "github代码地址")的框架图，说明见图片（下面会有详细的讲解）:

![这里写图片描述](http://img.blog.csdn.net/20170909195539501?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

下面给出最终的识别结果：


----------


![这里写图片描述](http://img.blog.csdn.net/20170909200020022?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


### 1. 训练，验证数据lmdb生成

1.  对图片进行预处理包括直方图均衡化（Histogram equalization）以及resize到指定的大小,并生成lmdb格式，图片以及对于的标签（label）

2.  按照一定的比例生成，训练样本lmdb以及验证样本lmdb，以及mean_file mean.binaryproto

3. 在测试的时候，我们往往是从数据库中读取url以及id信息，然后将url转化为cv2 可以处理的图片样式，因此我们还要实现将url转化cv2可以处理的图片


### 1.1 图片进行预处理包括直方图均衡化，url->cv2 image 格式
下面通过代码来讲解(文件:  utils->img_process.py)：

```python
# _*_coding:utf-8 _*_

import cv2
import urllib
import numpy as np

IMG_HEIGHT = 227
IMG_WIDTH = 227

# 对图片做直方图均衡化处理
def pre_process_img(img, img_height=IMG_HEIGHT, img_width=IMG_WIDTH):
    # firstly histogram equalization
    img[:, :, 0] = cv2.equalizeHist(img[:, :, 0])
    img[:, :, 1] = cv2.equalizeHist(img[:, :, 1])
    img[:, :, 2] = cv2.equalizeHist(img[:, :, 2])

    # resize image to size
    img = cv2.resize(img, (img_width, img_height), interpolation=cv2.INTER_CUBIC)

    return img

# 通过图片url将其转化为cv2可以处理的形式
def get_cv_img__from_url(url):
    """
    read image from url to cv codec
    :param url:
    :return:
    """
    try:
        url_response = urllib.urlopen(url)
        img_array = np.array(bytearray(url_response.read()), dtype=np.uint8)
        img = cv2.imdecode(img_array, -1)
        return img
    except Exception, e:
        print e
        return None


if __name__ == '__main__':
    url = 'http://www.sanyarb.com.cn/images/attachement/jpg/site2/20161009/A121475977636942_change_ljx6a9_b.jpg'
    img = get_cv_img__from_url(url)
    cv2.imshow("zhan lang", img)

    img = pre_process_img(img)
    cv2.imshow("pre_process_img", img)
    cv2.waitKey()
    pass


```

下面是下载网上的图片，然后对其进行直方图均衡化以及resize的运行的结果：

![这里写图片描述](http://img.blog.csdn.net/20170909200955191?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


----------


### 1.2 图片按照一定的比例生成训练样本以及验证样本lmdb]

```python
# _*_coding:utf-8 _*_

import sys
sys.path.insert(0, '../../caffe_train_test/')
import os
import glob
import random
import numpy as np

import cv2

import caffe
from caffe.proto import caffe_pb2
import lmdb

from utils.img_process import *

# 根据图片和标签转化为对应的lmdb格式
def make_datum(img, label):
    # image is numpy.ndarray format. BGR instead of RGB
    return caffe_pb2.Datum(
        channels=3,
        width=IMG_HEIGHT,
        height=IMG_WIDTH,
        label=label,
        data=np.rollaxis(img, 2).tostring())


# 创建lmdb的基类
class GenerateLmdb(object):

    def __init__(self, img_path):
        """
        img_path -> multiple calss directory
        like, class_1, class_2, class_3....
        each class has corresponding class image like class_1_1.png
        :param img_path:
        """
        # get all the images in different class directory
        # 获取到多有的图片列表
        self.img_lst = glob.glob(os.path.join(img_path, '*', '*.png'))
        print 'input_img list num is %s' % len(self.img_lst)
        # shuffle all the images
        # 需要对列表乱序
        random.shuffle(self.img_lst)

	# 根据标签，比例生成训练lmdb以及验证lmdb
    def generate_lmdb(self, label_lst, percentage, train_path, validation_path):
        """
        label_lst like ['class_1', 'class_2', 'class_3', .....]
        percentage like is 5 (4/5) then 80% be train image, (1/5) 20% be validation image
        train_path like that '/data/train/train_lmdb'
        validation_path like '/data/train/validation_lmdb'
        """
        print 'now generate train lmdb'
        self._generate_lmdb(label_lst, percentage, True, train_path)
        print 'now generate validation lmdb'
        self._generate_lmdb(label_lst, percentage, False, validation_path)

        print '\n generate all images'

    def _generate_lmdb(self, label_lst, percentage, b_train, input_path):
        """
        b_train is True means to generate train lmdb, or validation lmdb
        """
        output_db = lmdb.open(input_path, map_size=int(1e12))
        with output_db.begin(write=True) as in_txn:
            for idx, img_path in enumerate(self.img_lst):

                # create train data
                if b_train:
                    # !=0 means validation data then skip loop
                    if idx % percentage != 0:
                        continue
                # create validation data
                else:
                    # ==0 means train data then skip
                    if idx % percentage == 0:
                        continue

                img = cv2.imread(img_path, cv2.IMREAD_COLOR)
                img = pre_process_img(img)
                # path like that '../../class_1/0001.png'
                # so img_path.split('/')[-2] -> class_1
                label = label_lst.index(img_path.split('/')[-2])
                datum = make_datum(img, label)
                in_txn.put('{:0>5d}'.format(idx), datum.SerializeToString())
                print '{:0>5d}'.format(idx) + '->label: ', label, " " + img_path

        output_db.close()


def get_label_lst_by_dir(f_dir):
    """
    f_dir like 'home/user/class', sub dir 'class_1', 'class_2'...'class_n'
    :return: ['class_1', 'class_2'...'class_n']
    """
    return os.listdir(f_dir)

if __name__ == '__main__':
    img_path = '../../ad_train/'
    cl = GenerateLmdb(img_path)

    train_lmdb = '/data6/light/storm_1_1/images/ad_train_py/input_data/train_lmdb'
    validation_lmdb = '/data6/light/storm_1_1/images/ad_train_py/input_data/validation_lmdb'

	# 删除原有的lmdb文件
    os.system('rm -rf  ' + train_lmdb)
    os.system('rm -rf  ' + validation_lmdb)

    input_path = '/data6/light/storm_1_1/images/ad_train/'
    label_lst = get_label_lst_by_dir(input_path)
    print 'label_lst is: %s' % ', '.join(label_lst)

    # (1/10)10% to be validation data, 90% to be train data
    # 1/10的文件为验证lmdb, 9/10为训练lmdb
    percentage = 10

    cl.generate_lmdb(label_lst, percentage, train_lmdb, validation_lmdb)

    pass


```

下面是实践的运行截图（这个代码好早前就运行了，这次写bolg做了一些处理）下面是一个三分类的目录（前面做过十几中的分类，这里写bolg，做了简化）
类别标签是： ad_text(文字广告)， ad_web（网页广告），others（其他类）

类别目录如下：

![分类目录](http://img.blog.csdn.net/20170909202101550?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

下面是输出的label列表：

![这里写图片描述](http://img.blog.csdn.net/20170909202034630?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

下面是运行 `python create_lmdb.py` 的部分日志结果（为了简便做了很多处理）

![这里写图片描述](http://img.blog.csdn.net/20170909202526070?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

下面是最终生成的lmdb文件：

![这里写图片描述](http://img.blog.csdn.net/20170909202706259?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

到此我们生成了，caffe训练需要的lmdb文件


### 1.3 mean_file mean.binaryproto

```python

# _*_ coding:utf-8

import os

# 生成，生成mean_binaryproto文件的字符串命令
def get_mean_cmd(mean_tool_path, train_lmdb_path, mean_binaryproto_path):
    # create train command
    return '%s -backend=lmdb %s %s ' % (mean_tool_path, train_lmdb_path, mean_binaryproto_path)


if __name__ == '__main__':
	# caffe mean 工具的路径
    mean_tool_path = '/home/ubuntu/caffe/build/tools/compute_image_mean'
    train_lmdb_path = '/home/xiongyu/input/train_lmdb'
    mean_binaryproto_path = '/home/xiongyu/input/mean.binaryproto'

    cmd = get_mean_cmd(mean_tool_path, train_lmdb_path, mean_binaryproto_path)
    print cmd

	# 执行生成命令
    os.system(cmd)

```

cmd合成的字符串

![这里写图片描述](http://img.blog.csdn.net/20170909205300908?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

实际生成的结果

![这里写图片描述](http://img.blog.csdn.net/20170909205312198?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

----------


### 2. caffe中模型的配置文件的定义以及说明


----------


### 2.1 训练模型定义

caffe中模型的定义，主要是修改 [caffe Alexnet 训练文件train_val.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt)
。主要修改mean_file mean.binaryproto，source train lmdb 路径，


![这里写图片描述](http://img.blog.csdn.net/20170909203645666?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


![这里写图片描述](http://img.blog.csdn.net/20170909203657259?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


![这里写图片描述](http://img.blog.csdn.net/20170909203707971?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


![这里写图片描述](http://img.blog.csdn.net/20170909203719180?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

由于这个示例主要讲的是3分类，因此还要修改num_output为3（记得修改对应的  **`部署文件`**）

![这里写图片描述](http://img.blog.csdn.net/20170909203731319?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


### 2.2 部署文件

[部署文件deploy.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt) 记得修改对应的num_output为3和训练文件一致


![这里写图片描述](http://img.blog.csdn.net/20170909205722029?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

### 2.3 训练运行参数文件

[训练运行参数文件solver.prototxt ](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/solver.prototxt)

```
net: "/data6/light/storm_1_1/images/ad_train_py/caffe_model/caffenet_train_val_1.prototxt"
test_iter: 1000
# 每1000次做一次验证
test_interval: 1000
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 2500
display: 50
# 最大迭代次数
max_iter: 30000
momentum: 0.9
# 权重衰减因子
weight_decay: 0.0005
# 每训练6000次生成一次模型快照
snapshot: 5000
# 模型快照前缀
snapshot_prefix: "/data6/light/storm_1_1/images/ad_train_py/caffe_model/caffe_model_1"
# GPU模式
solver_mode: GPU

```

下面看下最终生成的模型文件（文件太大删除了很多，只保留一个运行时的）

![这里写图片描述](http://img.blog.csdn.net/20170909210408514?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


----------


### 3. 训练验证数据准备完成之后，就是模型的训练

代码类似与mean 文件的生成，这里就不解释了

> command |& tee out.log
， 将结果输出到标准输出流以及out.log文件中



```python
# _*_ coding:utf-8

import os


def get_train_cmd(caffe_path, solver_path, log_path):
    # create train command
    return '%s train --solver %s |& tee %s ' % (caffe_path, solver_path, log_path)


if __name__ == '__main__':

    caffe_path = "/home/xiongyu/caffe/build/tools/caffe"
    solver_path = "/home/xiongyu/caffe_models/caffe_model_1/solver_1.prototxt"
    log_path = "/home/xiongyu/caffe_models/caffe_model_1/model_1_train.log"

    train = get_train_cmd(caffe_path, solver_path, log_path)

    print train
    # use caffe to train model
    os.system(train)

    pass

```

下面是训练时的部分截图：

![这里写图片描述](http://img.blog.csdn.net/20170909211336774?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


----------


### 4.  本地测试以及从数据库获取url测试然后将结果写到数据库中


----------


### 4.1 测试基类文件predict_base.py

为了保证代码的模块性，测试的便捷性，这个基类提供给测试本地文件以及数据库文件调用

```python
# _*_coding:utf-8 _*_

import sys
sys.path.insert(0, '../../caffe_train_test/')
import os
import glob
import cv2
import caffe
import lmdb
import numpy as np
from caffe.proto import caffe_pb2

from utils.img_process import *


class CaffePredict(object):

    def __init__(self, b_gpu, mean_path, deploy_path, model_path):
		# cpu或者是gpu模式
        if b_gpu:
            caffe.set_mode_gpu()
        else:
            caffe.set_mode_cpu()

        mean_blob = caffe_pb2.BlobProto()
        with open(mean_path) as f:
            mean_blob.ParseFromString(f.read())

        mean_array = np.asarray(mean_blob.data, dtype=np.float32).\
            reshape((mean_blob.channels, mean_blob.height, mean_blob.width))

        self.net = caffe.Net(deploy_path, model_path, caffe.TEST)

        # Define image transformers
        self.transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
        self.transformer.set_mean('data', mean_array)
        # puts the channel as the first dimention
        self.transformer.set_transpose('data', (2, 0, 1))

	# predict只需要输入cv2 image格式图片即可
    def predict(self, img):
        img = pre_process_img(img)
        self.net.blobs['data'].data[...] = self.transformer.preprocess('data', img)
        out = self.net.forward()
        pred_probas = out['prob']

        # predict result
        ret_lst = [round(f, 4) for f in pred_probas[0].tolist()]
        return ret_lst

# 获取默认的caffe模型
def get_default_caffe_predict():
    # Read model architecture and trained model's weights
    mean_path = "/data6/light/storm_1_1/images/ad_train_py/input_data/mean.binaryproto"
    deploy_path = "/data6/light/storm_1_1/images/ad_train_py/caffe_model/caffenet_deploy_1.prototxt"
    model_path = "/data6/light/storm_1_1/images/ad_train_py/caffe_model/caffe_model_1_iter_10000.caffemodel"
    b_gpu = True
    caffe_predict = CaffePredict(b_gpu, mean_path, deploy_path, model_path)
    return caffe_predict


if __name__ == '__main__':
	# 使用默认的模型识别
    caffe_predict = get_default_caffe_predict()

    img_path = '/data6/light/storm_1_1/images/ad_train_py/test_data/0.png'
    img = cv2.imread(img_path, cv2.IMREAD_COLOR)
    print caffe_predict.predict(img)

    pass

```

识别一张图片，运行结果如下：

![这里写图片描述](http://img.blog.csdn.net/20170909212031010?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

### 4.2 测试本地目录所有图片文件

`predict_from_local.py` 读取目录下的所有文件，并输出识别结果

```python
import sys
sys.path.insert(0, '../../caffe_train_test/')
from predict_base import CaffePredict, get_default_caffe_predict
import glob
import cv2


def get_img_lst(img_dir):
    """
    img_dir: /data6/light/storm_1_1/images/ad_train_py/test_data/
    lots of images like '0.jpg, 1.jpg ......'
    """
    return glob.glob(img_dir + "*.png")


def predict_all():
    path = '/data6/light/storm_1_1/images/ad_train_py/test_data/'
    img_lst = get_img_lst(path)
    caffe_predict = get_default_caffe_predict()

    for path in img_lst:
        try:
            img = cv2.imread(path, cv2.IMREAD_COLOR)
            # caffe_predict.predict is not thread safe,so can't be used in multiple thread
            # python is dummy multiple threads
            ret_lst = caffe_predict.predict(img)
            print path, ret_lst
        except Exception, e:
            print e


if __name__ == '__main__':
    predict_all()
    pass

```

运行结果如下：

![这里写图片描述](http://img.blog.csdn.net/20170909212335843?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


### 4.3 测试数据库所有图片文件

当然在实际的运行中我们往往测试几十万张图片，一般上传到服务器也很麻烦（图片要下载下来，然后打包在sz到linux目录，这样很麻烦而且，打包文件太大的话上传到服务器往往报错）。所以我们一般在数据库上面读取url然后识别，在把识别的结果写回到数据库，例如这样：

![这里写图片描述](http://img.blog.csdn.net/20170909213000172?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

```python
# _*_ coding:utf-8 _*_

import sys
sys.path.insert(0, '../../caffe_train_test/')
from utils.DbBse import DbService, get_default_db
from utils.img_process import get_cv_img__from_url
from predict_base import CaffePredict, get_default_caffe_predict


def predict_from_db():
    """
    get all the url and id from database and
    then predict, write predict result to database
    :return:
    """
    db = get_default_db()

    # [(1, 'http://xxx.1.jpg'), (2, 'http://xxx.2.jpg).....]
    url_id_lst = db.get_ad_info()

    print 'url_id_lst length is %s: ' % len(url_id_lst)
    print 'url_id_lst first is', url_id_lst[0]

    caffe_predict = get_default_caffe_predict()

    for item in url_id_lst:
        img = get_cv_img__from_url(item[1])
        if img is None:
            continue

        ret_lst = caffe_predict.predict(img)
        # item[0] is id
        ret_lst.append(item[0])
        # write result to database
        print item[1], ret_lst
        db.update_ad_info(ret_lst)


if __name__ == '__main__':
    predict_from_db()
    pass

```

下面是运行结果：

![这里写图片描述](http://img.blog.csdn.net/20170909213045877?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

![这里写图片描述](http://img.blog.csdn.net/20170909213105846?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvaGFsdW9sdW8yMTE=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)


> http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
>
> https://software.intel.com/en-us/articles/training-and-deploying-deep-learning-networks-with-caffe-optimized-for-intel-architecture


# English README project framework
1. caffe_model:the training and deploy prototxt files

2. train_data_generate:generate training lmdb, validation lmdb, and mean_binaryproto

3. caffe_train: training caffe model

4. caffe_model_test:test model recognition results, both local files, and files from database

5. utils:image process fucntion, url(of image) to cv2 format, database process

### The directory -> caffe model
------------

[caffe Alexnet training file train_val.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt), change the input lmdb path

[parameters files solver.prototxt ](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/solver.prototxt), change input path

[deploy file deploy.prototxt](https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt) change output_num like training prototxt files


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/caffe_traintxt_1.jpg)


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/caffe_traintxt_2.jpg)


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/caffe_traintxt_3.jpg)


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/caffe_traintxt_4.jpg)


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/caffe_traintxt_5.jpg)


### utils
------------

read image from url coded in cv2 format


![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/img_url_2%20(2).jpg)


### caffe_train.py, create_mean_binaryproto.py
------------
generate train command and run train

create_mean_binaryproto.py create mean binary proto file

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/mean_1.png)

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/mean_2.png)

### caffe model test
------------

base predict class

The below demo predict one image

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/predict_base_1.jpg)

The below demo predict images from local directory

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/predict_from_local_1.jpg)


The below demo predict images from database and write recognition results to database

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/predict_from_db_1.jpg)

![](https://github.com/Jayhello/python_caffe_train_test/blob/master/run_images/predict_from_db_2.jpg)


-----------------

You can see all the explantion in this [bolg](http://blog.csdn.net/haluoluo211/article/details/77918156)


Cited some contents from the below two articles.

>http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/

>https://software.intel.com/en-us/articles/training-and-deploying-deep-learning-networks-with-caffe-optimized-for-intel-architecture