useful dataset for cv

http://rogerioferis.com/VisualRecognitionAndSearch2014/Resources.html

how to debug matlab code in linxu system

x = rand(10,10);
y = rand(10,5);
z = x * y;
keyboard; % you can interactively inspect x, y, z here
z = sort(z);

To leave keyboard mode, you can type dbquit to exit the program, or return to continue executing the program.

feature reduction code

http://www.cad.zju.edu.cn/home/dengcai/Data/DimensionReduction.html

CloudCV: Object Detection

http://cloudcv.org/objdetect/

Benchmarks and challenges like PASCAL VOC and ImageNet have played a crucial role in advancing computer vision algorithms. However, with minor exceptions, such challenges also result in massive duplication of effort, with each research group developing its own infrastructure and code-base. In fact, warnings of fragmentation and lack of code reuse have repeatedly been among the top observations by forward-looking NSF funded workshops [1,2].

CloudCV can help unify fragmented efforts by being a unified data and code repository.

extract features using caffe

http://www.cnblogs.com/platero/p/3967208.html

feat_helper_pb2.py

# Generated by the protocol buffer compiler.  DO NOT EDIT!

from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)



DESCRIPTOR = descriptor.FileDescriptor(
  name='datum.proto',
  package='feat_extract',
  serialized_pb='\n\x0b\x64\x61tum.proto\x12\x0c\x66\x65\x61t_extract\"i\n\x05\x44\x61tum\x12\x10\n\x08\x63hannels\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12\r\n\x05label\x18\x05 \x01(\x05\x12\x12\n\nfloat_data\x18\x06 \x03(\x02')




_DATUM = descriptor.Descriptor(
  name='Datum',
  full_name='feat_extract.Datum',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  fields=[
    descriptor.FieldDescriptor(
      name='channels', full_name='feat_extract.Datum.channels', index=0,
      number=1, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='height', full_name='feat_extract.Datum.height', index=1,
      number=2, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='width', full_name='feat_extract.Datum.width', index=2,
      number=3, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='data', full_name='feat_extract.Datum.data', index=3,
      number=4, type=12, cpp_type=9, label=1,
      has_default_value=False, default_value="",
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='label', full_name='feat_extract.Datum.label', index=4,
      number=5, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
    descriptor.FieldDescriptor(
      name='float_data', full_name='feat_extract.Datum.float_data', index=5,
      number=6, type=2, cpp_type=6, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      options=None),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  options=None,
  is_extendable=False,
  extension_ranges=[],
  serialized_start=29,
  serialized_end=134,
)

DESCRIPTOR.message_types_by_name['Datum'] = _DATUM

class Datum(message.Message):
  __metaclass__ = reflection.GeneratedProtocolMessageType
  DESCRIPTOR = _DATUM
  
  # @@protoc_insertion_point(class_scope:feat_extract.Datum)


# @@protoc_insertion_point(module_scope)


leveldb2mat.py

import leveldb
import feat_helper_pb2
import numpy as np
import scipy.io as sio
import time

def main(argv):
    leveldb_name = sys.argv[1]
    print "%s" % sys.argv[1]
    batch_num = int(sys.argv[2]);
    batch_size = int(sys.argv[3]);
    window_num = batch_num*batch_size;

    start = time.time()
    if 'db' not in locals().keys():
        db = leveldb.LevelDB(leveldb_name)
        datum = feat_helper_pb2.Datum()

    ft = np.zeros((window_num, int(sys.argv[4])))
    for im_idx in range(window_num):
        datum.ParseFromString(db.Get('%d' %(im_idx)))
        ft[im_idx, :] = datum.float_data

    print 'time 1: %f' %(time.time() - start)
    sio.savemat(sys.argv[5], {'feats':ft})
    print 'time 2: %f' %(time.time() - start)
    print 'done!'

    #leveldb.DestroyDB(leveldb_name)

if __name__ == '__main__':
    import sys

    main(sys.argv)

#!/usr/bin/env sh
# args for EXTRACT_FEATURE
TOOL=../../build/tools
MODEL=../../examples/imagenet/caffe_reference_imagenet_model #下载得到的caffe model
PROTOTXT=../../examples/_temp/imagenet_val.prototxt # 网络定义
LAYER=conv1 # 提取层的名字，如提取fc7等
LEVELDB=../../examples/_temp/features_conv1 # 保存的leveldb路径
BATCHSIZE=10

# args for LEVELDB to MAT
DIM=290400 # 需要手工计算feature长度
OUT=../../examples/_temp/features.mat #.mat文件保存路径
BATCHNUM=1 # 有多少哥batch， 本例只有两张图， 所以只有一个batch

$TOOL/extract_features.bin  $MODEL $PROTOTXT $LAYER $LEVELDB $BATCHSIZE
python leveldb2mat.py $LEVELDB $BATCHNUM  $BATCHSIZE $DIM $OUT 

caffe extract features - tool batch size and model batch size

https://github.com/BVLC/caffe/issues/585#issuecomment-51856173
Caffe operates on batches of data in GPU mode to reduce the overhead of communicating between the host and device. The feature extraction example batch_size is set to 50, and when the tool is called it is given 10 iterations as its argument, so it is actually processing 500 inputs. Set the batch_size and iteration arg to both be 1 to process a single image, although that's less efficient in throughput.

batch_size is set to 50
the tool batch size is set to 10 then it will extract 500 image features
the tool batch size is set to 20 then it will extract 1000 image features

decaffen Getting an error: unrecognized command line option ‘-Wl’

https://github.com/UCB-ICSI-Vision-Group/decaf-release/issues/3