Kitti数据集进行目标检测批处理和shell的入门

KITTI数据集的Python的批处理

#!/usr/bin/env python

# --------------------------------------------------------
# R-FCN
# Copyright (c) 2016 Yuwen Xiong
# Licensed under The MIT License [see LICENSE for details]
# Written by Yuwen Xiong
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse
import json
import pickle

CLASSES = ('__background__',
           '1', '2', '3', '20')

NETS = {'ResNet-101': ('ResNet-101',
                  'resnet101_rfcn_final.caffemodel'),
        'ResNet-50': ('ResNet-50',
                  'resnet50_rfcn_ohem_iter_100000.caffemodel')}

#data = dict()

def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal')
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]

        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
            )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                  thresh),
                  fontsize=14)
    plt.axis('off')
    plt.tight_layout()
    plt.draw()

def demo(net, image_name, ind, direc, real_seq):
    """Detect object classes in an image using pre-computed object proposals."""
    # content = []
    # Load the demo image
    im_file = os.path.join(real_seq, image_name)
    im = cv2.imread(im_file)
    ind_file = 'sequence_%02d' % ind
    dir_file = ind_file + ('/image_%d' % direc)
    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, im)
    timer.toc()
    print ('Detection took {:.3f}s for '
           '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.5 #0.75 
    NMS_THRESH = 0.35
    exist_cls = 0
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4:8]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        thresh = CONF_THRESH

        img_name = image_name.replace('.png', '.txt')
        inds = np.where(dets[:, -1] >= thresh)[0]
        if len(inds) != 0:
            if int(cls) < 20:
                exist_cls = 1
                for i in inds:
                    # img_name = image_name.replace('.jpg', '.txt')    
                    if os.path.exists(ind_file) == False:
                        os.mkdir(ind_file)
                        if os.path.exists(dir_file) == False:
                            os.mkdir(dir_file) 
                    else:
                        if os.path.exists(dir_file) == False:
                            os.mkdir(dir_file)
                    tmp_fid = file(os.path.join(dir_file, img_name), 'a+')
                    bbox = dets[i, :4]
                    score = dets[i, -1]
                    if int(cls) == 1:
                        tmp_fid.write('1 -1 -1 -10 ')
                    elif int(cls) == 2:
                        tmp_fid.write('2 -1 -1 -10 ')
                    else:
                        tmp_fid.write('3 -1 -1 -10 ')

                    tmp_fid.write(str("%.2f"%float(bbox[0])) +  ' ' +  str("%.2f"%float(bbox[1])) + ' ' +  str("%.2f"%float(bbox[2])) +  ' ' +  str("%.2f"%float(bbox[3])) + ' -1 -1 -1 -1000 -1000 -1000 -10 ' +  str("%.2f"%float(score)) + ' \n')
                    tmp_fid.close()
    if exist_cls == 0:
        if os.path.exists(ind_file) == False:
            os.mkdir(ind_file)
            if os.path.exists(dir_file) == False:
                os.mkdir(dir_file)
        else:
            if os.path.exists(dir_file) == False:
                os.mkdir(dir_file)  
        tmp_fid = file(os.path.join(dir_file, img_name), 'a+')
        tmp_fid.close()
        print "There is backgroung"

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--cpu', dest='cpu_mode',
                        help='Use CPU mode (overrides --gpu)',
                        action='store_true')
    parser.add_argument('--net', dest='demo_net', help='Network to use [ResNet-101]',
                        choices=NETS.keys(), default='ResNet-50')

    args = parser.parse_args()

    return args

if __name__ == '__main__':
    cfg.TEST.HAS_RPN = True  # Use RPN for proposals

    args = parse_args()

    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                            'rfcn_end2end', 'test_agnostic.prototxt')
    caffemodel = os.path.join(cfg.DATA_DIR, 'rfcn_models',
                              NETS[args.demo_net][1])

    if not os.path.isfile(caffemodel):
        raise IOError(('{:s} not found.\n').format(caffemodel))

    if args.cpu_mode:
        caffe.set_mode_cpu()
    else:
        caffe.set_mode_gpu()
        caffe.set_device(args.gpu_id)
        cfg.GPU_ID = args.gpu_id
    net = caffe.Net(prototxt, caffemodel, caffe.TEST)

    print '\n\nLoaded network {:s}'.format(caffemodel)

    # Warmup on a dummy image
    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
    for i in xrange(2):
        _, _= im_detect(net, im)
    rt = '/home/YuChen/data-set/dataset/sequences/'

    # process 22 sequences
    for ind in range(22):
        new_seq = rt + ('%02d/' %ind) 
        for direc in range(2):
            real_seq = new_seq + ('image_%d/' %(direc+2))
            print real_seq

            im_names = os.listdir(real_seq)
            for im_name in im_names:
                print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
                print im_name
                demo(net, im_name, ind, direc, real_seq) 

shell的入门知识

在Windows下，可以使用powershell或者是用命令行运行.bat文件，但是在ubuntu下呢？那就是SHELL了。在很多的项目下，我们进行编译的时候会看到build.sh，其实这个东西就是使用shell进行编译的。

shell的入门可以看【1】【2】【3】，在对某个程序进行批处理的时候，其实里面的命令行可以写成：

./exec argc

参考链接：
【1】Shell编程基础：http://wiki.ubuntu.org.cn/Shell%E7%BC%96%E7%A8%8B%E5%9F%BA%E7%A1%80
【2】BASH Manual：https://www.gnu.org/software/bash/manual/
【3】Bash Guide：http://guide.bash.academy/