SiamMask附加：miniconada文件&更便捷的运行摄像头读取的文件

目录

1. 问题一 ： 激活创建python虚拟环境（PS:我电脑上装了anaconda运行SSD+sort, 我另外安装了miniconda来运行siammask）
2. 问题二： 在.bashrc文件中添加使用miniconda的路径 （PS:我电脑上装了anaconda运行SSD+sort, 我另外安装了miniconda来运行siammask）
3. 知识点一 : source ~/.bashrc来激活bashrc
4. 知识点二 : chmod +x RunDemo.sh 来赋予脚本文件执行权限
5. 修改一 ： 使用RunDemo.sh文件解决运行多条设置代码问题
6. 修改二 ： 写改demo.py将读取图像帧修改为读取摄像头视频

问题一：（已解决）

文件在我电脑的安装路径
/home/hp/miniconda3/envs/env_py36

激活方式source activate env_py36 替代了如下步骤 激活了miniconada的虚拟环境

 

问题二：（已解决）

注意：在home下ctrl+h显示隐藏文件，打开.bashrc文件，最后修改

1:
如下写法/home/hp/miniconda3/bin在前，
export PATH="/home/hp/miniconda3/bin:$PATH"
因而先运行虚拟miniconda.

2:
如果需要用原来的装了tenseoflow的python需要改写
export PATH="$PATH：/home/hp/miniconda3/bin"
这样先运行我原来的python

问题三：（已解决）

找不到tools模块
解决办法：只需要完成红线部分，赋予地址即可，不然找不到

进入SiamMask文件夹，读取当前路径，给予PAYTHONPATH当前路径

知识点一：

1 每次更改bashrc之后ctrl+s保存
2 另外当前终端需要source ~/.bashrc来激活其中内容。
3 或者可以重新开启终端，上述步骤会自动运行

知识点二：

chmod +x的意思就是给执行权限

如下参考下面博客：
https://blog.csdn.net/u012106306/article/details/80436911

LINUX下不同的文件类型有不同的颜色，这里

举例

蓝色表示目录;
绿色表示可执行文件;
红色表示压缩文件;
浅蓝色表示链接文件;
灰色表示其它文件;

因此如果只是普通的上传start.sh这个文件，那么上传之后也就是一个普通的文件，不能执行

PS：UBUNTU终端下并没有变化一直蓝色

修改一：

通过RunDemo.sh脚本文件，自动运行多重指令(PS:赋予.sh文件权限 chmod +x RunDemo.sh)

如图放置

RunDemo.sh文件内容如下：

#cp ./DownloadModel/* ./SiamMask-master/experiments/siammask/
#source activate env_py36
#上面两条命令要提前在终端里执行完毕
cd SiamMask

export SiamMask=$PWD
# bash make.sh
export PYTHONPATH=$PWD:$PYTHONPATH
cd $SiamMask/experiments/siammask
# export PYTHONPATH=$PWD:$PYTHONPATH
python ../../tools/demo.py --resume SiamMask_DAVIS.pth --config config_davis.json

参考作者如下说明

修改二：

先是原版本：demo.py

# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import glob

from tools.test import *

parser = argparse.ArgumentParser(description='PyTorch Tracking Demo')

parser.add_argument('--resume', default='', type=str, required=True,
                    metavar='PATH',help='path to latest checkpoint (default: none)')
parser.add_argument('--config', dest='config', default='config_davis.json',
                    help='hyper-parameter of SiamMask in json format')
parser.add_argument('--base_path', default='../../data/tennis', help='datasets')
args = parser.parse_args()

if __name__ == '__main__':
    # Setup device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    torch.backends.cudnn.benchmark = True

    # Setup Model
    cfg = load_config(args)
    from custom import Custom
    siammask = Custom(anchors=cfg['anchors'])
    if args.resume:
        assert isfile(args.resume), '{} is not a valid file'.format(args.resume)
        siammask = load_pretrain(siammask, args.resume)

    siammask.eval().to(device)

    # Parse Image file
    img_files = sorted(glob.glob(join(args.base_path, '*.jp*')))
    ims = [cv2.imread(imf) for imf in img_files]

    # Select ROI
    cv2.namedWindow("SiamMask", cv2.WND_PROP_FULLSCREEN)
    # cv2.setWindowProperty("SiamMask", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
    try:
        init_rect = cv2.selectROI('SiamMask', ims[0], False, False)
        x, y, w, h = init_rect
    except:
        exit()

    toc = 0
    for f, im in enumerate(ims):
        tic = cv2.getTickCount()
        if f == 0:  # init
            target_pos = np.array([x + w / 2, y + h / 2])
            target_sz = np.array([w, h])
            state = siamese_init(im, target_pos, target_sz, siammask, cfg['hp'])  # init tracker
        elif f > 0:  # tracking
            state = siamese_track(state, im, mask_enable=True, refine_enable=True)  # track
            location = state['ploygon'].flatten()
            mask = state['mask'] > state['p'].seg_thr

            im[:, :, 2] = (mask > 0) * 255 + (mask == 0) * im[:, :, 2]
            cv2.polylines(im, [np.int0(location).reshape((-1, 1, 2))], True, (0, 255, 0), 3)
            cv2.imshow('SiamMask', im)
            key = cv2.waitKey(1)
            if key > 0:
                break

        toc += cv2.getTickCount() - tic
    toc /= cv2.getTickFrequency()
    fps = f / toc
    print('SiamMask Time: {:02.1f}s Speed: {:3.1f}fps (with visulization!)'.format(toc, fps))

 

再是改进后的直接读取视频：demo.py

# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import glob
from tools.test import *

parser = argparse.ArgumentParser(description='PyTorch Tracking Demo')

parser.add_argument('--resume', default='', type=str, required=True,
                    metavar='PATH',help='path to latest checkpoint (default: none)')
parser.add_argument('--config', dest='config', default='config_davis.json',
                    help='hyper-parameter of SiamMask in json format')
parser.add_argument('--base_path', default='../../data/tennis', help='datasets')
args = parser.parse_args()

if __name__ == '__main__':
    # Setup device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    torch.backends.cudnn.benchmark = True

    # Setup Model
    cfg = load_config(args)
    from custom import Custom
    siammask = Custom(anchors=cfg['anchors'])
    if args.resume:
        assert isfile(args.resume), '{} is not a valid file'.format(args.resume)
        siammask = load_pretrain(siammask, args.resume)

    siammask.eval().to(device)

    # Parse Image file
    img_files = sorted(glob.glob(join(args.base_path, '*.jp*')))
    ims = [cv2.imread(imf) for imf in img_files]

    ###Modify###
    VeryBig=999999999  # 用于将视频框调整到最大
    Cap = cv2.VideoCapture(0)  # 设置读取摄像头
    # Cap.set(cv2.CAP_PROP_FRAME_WIDTH,VeryBig)
    # Cap.set(cv2.CAP_PROP_FRAME_HEIGHT,VeryBig)
    ret, frame = Cap.read()  # 读取帧
    ims = [frame] # 把frame放入列表格式的frame， 因为原文是将每帧图片放入列表
    ###Modify###

    # Select ROI
    cv2.namedWindow("SiamMask", cv2.WND_PROP_FULLSCREEN)
    # cv2.setWindowProperty("SiamMask", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
    try:
        init_rect = cv2.selectROI('SiamMask', ims[0], False, False)
        x, y, w, h = init_rect
    except:
        exit()

    toc = 0
###Modify### 此部分被下面替换
    # for f, im in enumerate(ims):  # 列表中逐个提取图片
    #     tic = cv2.getTickCount()
    #     if f == 0:  # init
    #         target_pos = np.array([x + w / 2, y + h / 2])
    #         target_sz = np.array([w, h])
    #         state = siamese_init(im, target_pos, target_sz, siammask, cfg['hp'])  # init tracker
    #     elif f > 0:  # tracking
    #         state = siamese_track(state, im, mask_enable=True, refine_enable=True)  # track
    #         location = state['ploygon'].flatten()
    #         mask = state['mask'] > state['p'].seg_thr

    #         im[:, :, 2] = (mask > 0) * 255 + (mask == 0) * im[:, :, 2]
    #         cv2.polylines(im, [np.int0(location).reshape((-1, 1, 2))], True, (0, 255, 0), 3)
    #         cv2.imshow('SiamMask', im)
    #         key = cv2.waitKey(1)
    #         if key > 0:
    #             break

    #     toc += cv2.getTickCount() - tic
    
    ###Modify###
    im=frame
    f=0
    target_pos = np.array([x + w / 2, y + h / 2])
    target_sz = np.array([w, h])
    state = siamese_init(im, target_pos, target_sz, siammask, cfg['hp'])  # init tracker
    while(True):
        tic = cv2.getTickCount()
        ret, im = Cap.read()  # 逐个提取frame
        if (ret==False):
            break;
        state = siamese_track(state, im, mask_enable=True, refine_enable=True)  # track
        location = state['ploygon'].flatten()
        mask = state['mask'] > state['p'].seg_thr

        im[:, :, 2] = (mask > 0) * 255 + (mask == 0) * im[:, :, 2]
        cv2.polylines(im, [np.int0(location).reshape((-1, 1, 2))], True, (0, 255, 0), 3)
        cv2.imshow('SiamMask', im)
        key = cv2.waitKey(1)
        if key > 0:
            break

        toc += cv2.getTickCount() - tic
        f=f+1
    ###Modify###
    
        

        
    toc /= cv2.getTickFrequency()
    fps = f / toc
    print('SiamMask Time: {:02.1f}s Speed: {:3.1f}fps (with visulization!)'.format(toc, fps))