• 企业400电话
  • 微网小程序
  • AI电话机器人
  • 电商代运营
  • 全 部 栏 目

    企业400电话 网络优化推广 AI电话机器人 呼叫中心 网站建设 商标✡知产 微网小程序 电商运营 彩铃•短信 增值拓展业务
    教你怎么用python删除相似度高的图片

    1. 前言

    因为输入是视频,切完帧之后都是连续图片,所以我的目录结构如下:

    其中frame_output是视频切帧后的保存路径,1和2文件夹分别对应两个是视频切帧后的图片。

    2. 切帧代码如下:

    #encoding:utf-8
    import os
    import sys
    import cv2
    
    video_path = '/home/pythonfile/video/'  # 绝对路径,video下有两段视频
    out_frame_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'frame_output')  #frame_output是视频切帧后的保存路径
    if not os.path.exists(out_frame_path):
        os.makedirs(out_frame_path)
    print('out_frame_path', out_frame_path)
    files = []
    list1 = os.listdir(video_path)
    print('list', list1)
    for i in range(len(list1)):
        item = os.path.join(video_path, list1[i])
        files.append(item)
    print('files',files)
    for k,file in enumerate(files):
        frame_dir = os.path.join(out_frame_path, '%d'%(k+1))
        if not os.path.exists(frame_dir):
            os.makedirs(frame_dir)
        cap = cv2.VideoCapture(file)
        j = 0
        print('start prossing NO.%d video' % (k + 1))
        while True:
            ret, frame = cap.read()
            j += 1
            if ret:
            #每三帧保存一张
                if j % 3 == 0:
                    cv2.imwrite(os.path.join(frame_dir, '%d.jpg'%j), frame)
            else:
                cap.release()
                break
        print('prossed NO.%d video'%(k+1))
    
    

    3. 删除相似度高的图片

    # coding: utf-8
    import os
    import cv2
    # from skimage.measure import compare_ssim
    # from skimage.metrics import _structural_similarity
    from skimage.metrics import structural_similarity as ssim
    
    def delete(filename1):
        os.remove(filename1)
    
    
    def list_all_files(root):
        files = []
        list = os.listdir(root)
        # os.listdir()方法:返回指定文件夹包含的文件或子文件夹名字的列表。该列表顺序以字母排序
        for i in range(len(list)):
            element = os.path.join(root, list[i])
            # 需要先使用python路径拼接os.path.join()函数,将os.listdir()返回的名称拼接成文件或目录的绝对路径再传入os.path.isdir()和os.path.isfile().
            if os.path.isdir(element):  # os.path.isdir()用于判断某一对象(需提供绝对路径)是否为目录
                # temp_dir = os.path.split(element)[-1]
                # os.path.split分割文件名与路径,分割为data_dir和此路径下的文件名,[-1]表示只取data_dir下的文件名
                files.append(list_all_files(element))
    
            elif os.path.isfile(element):
                files.append(element)
        # print('2',files)
        return files
    
    
    def ssim_compare(img_files):
        count = 0
        for currIndex, filename in enumerate(img_files):
            if not os.path.exists(img_files[currIndex]):
                print('not exist', img_files[currIndex])
                break
            img = cv2.imread(img_files[currIndex])
            img1 = cv2.imread(img_files[currIndex + 1])
            #进行结构性相似度判断
            # ssim_value = _structural_similarity.structural_similarity(img,img1,multichannel=True)
            ssim_value = ssim(img,img1,multichannel=True)
            if ssim_value > 0.9:
                #基数
                count += 1
                imgs_n.append(img_files[currIndex + 1])
                print('big_ssim:',img_files[currIndex], img_files[currIndex + 1], ssim_value)
            # 避免数组越界
            if currIndex+1 >= len(img_files)-1:
                break
        return count
    
    
    if __name__ == '__main__':
        path = '/home/dj/pythonfile/frame_output/'
    
        img_path = path
        imgs_n = []
       
        all_files = list_all_files(path) #返回包含完整路径的所有图片名的列表
        print('1',len(all_files))
       
        for files in all_files:
            # 根据文件名排序,x.rfind('/')是从右边寻找第一个‘/'出现的位置,也就是最后出现的位置
            # 注意sort和sorted的区别,sort作用于原列表,sorted生成新的列表,且sorted可以作用于所有可迭代对象
            files.sort(key = lambda x: int(x[x.rfind('/')+1:-4]))#路径中包含“/”
            # print(files)
            img_files = []
            for img in files:
                if img.endswith('.jpg'):
                    # 将所有图片名都放入列表中
                    img_files.append(img)
            count = ssim_compare(img_files)
            print(img[:img.rfind('/')],"路径下删除的图片数量为:",count)
        for image in imgs_n:
            delete(image)
    

    4. 导入skimage.measure import compare_ssim出错的解决方法:

    from skimage.measure import compare_ssim

    改为

    from skimage.metrics import _structural_similarity

    5. structural_similarity.py的源码

    from warnings import warn
    import numpy as np
    from scipy.ndimage import uniform_filter, gaussian_filter
    
    from ..util.dtype import dtype_range
    from ..util.arraycrop import crop
    from .._shared.utils import warn, check_shape_equality
    
    __all__ = ['structural_similarity']
    
    
    def structural_similarity(im1, im2,
                              *,
                              win_size=None, gradient=False, data_range=None,
                              multichannel=False, gaussian_weights=False,
                              full=False, **kwargs):
        """
        Compute the mean structural similarity index between two images.
    
        Parameters
        ----------
        im1, im2 : ndarray
            Images. Any dimensionality with same shape.
        win_size : int or None, optional
            The side-length of the sliding window used in comparison. Must be an
            odd value. If `gaussian_weights` is True, this is ignored and the
            window size will depend on `sigma`.
        gradient : bool, optional
            If True, also return the gradient with respect to im2.
        data_range : float, optional
            The data range of the input image (distance between minimum and
            maximum possible values). By default, this is estimated from the image
            data-type.
        multichannel : bool, optional
            If True, treat the last dimension of the array as channels. Similarity
            calculations are done independently for each channel then averaged.
        gaussian_weights : bool, optional
            If True, each patch has its mean and variance spatially weighted by a
            normalized Gaussian kernel of width sigma=1.5.
        full : bool, optional
            If True, also return the full structural similarity image.
    
        Other Parameters
        ----------------
        use_sample_covariance : bool
            If True, normalize covariances by N-1 rather than, N where N is the
            number of pixels within the sliding window.
        K1 : float
            Algorithm parameter, K1 (small constant, see [1]_).
        K2 : float
            Algorithm parameter, K2 (small constant, see [1]_).
        sigma : float
            Standard deviation for the Gaussian when `gaussian_weights` is True.
    
        Returns
        -------
        mssim : float
            The mean structural similarity index over the image.
        grad : ndarray
            The gradient of the structural similarity between im1 and im2 [2]_.
            This is only returned if `gradient` is set to True.
        S : ndarray
            The full SSIM image.  This is only returned if `full` is set to True.
    
        Notes
        -----
        To match the implementation of Wang et. al. [1]_, set `gaussian_weights`
        to True, `sigma` to 1.5, and `use_sample_covariance` to False.
    
        .. versionchanged:: 0.16
            This function was renamed from ``skimage.measure.compare_ssim`` to
            ``skimage.metrics.structural_similarity``.
    
        References
        ----------
        .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R.,  Simoncelli, E. P.
           (2004). Image quality assessment: From error visibility to
           structural similarity. IEEE Transactions on Image Processing,
           13, 600-612.
           https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
           :DOI:`10.1109/TIP.2003.819861`
    
        .. [2] Avanaki, A. N. (2009). Exact global histogram specification
           optimized for structural similarity. Optical Review, 16, 613-621.
           :arxiv:`0901.0065`
           :DOI:`10.1007/s10043-009-0119-z`
    
        """
        check_shape_equality(im1, im2)
    
        if multichannel:
            # loop over channels
            args = dict(win_size=win_size,
                        gradient=gradient,
                        data_range=data_range,
                        multichannel=False,
                        gaussian_weights=gaussian_weights,
                        full=full)
            args.update(kwargs)
            nch = im1.shape[-1]
            mssim = np.empty(nch)
            if gradient:
                G = np.empty(im1.shape)
            if full:
                S = np.empty(im1.shape)
            for ch in range(nch):
                ch_result = structural_similarity(im1[..., ch],
                                                  im2[..., ch], **args)
                if gradient and full:
                    mssim[..., ch], G[..., ch], S[..., ch] = ch_result
                elif gradient:
                    mssim[..., ch], G[..., ch] = ch_result
                elif full:
                    mssim[..., ch], S[..., ch] = ch_result
                else:
                    mssim[..., ch] = ch_result
            mssim = mssim.mean()
            if gradient and full:
                return mssim, G, S
            elif gradient:
                return mssim, G
            elif full:
                return mssim, S
            else:
                return mssim
    
        K1 = kwargs.pop('K1', 0.01)
        K2 = kwargs.pop('K2', 0.03)
        sigma = kwargs.pop('sigma', 1.5)
        if K1  0:
            raise ValueError("K1 must be positive")
        if K2  0:
            raise ValueError("K2 must be positive")
        if sigma  0:
            raise ValueError("sigma must be positive")
        use_sample_covariance = kwargs.pop('use_sample_covariance', True)
    
        if gaussian_weights:
            # Set to give an 11-tap filter with the default sigma of 1.5 to match
            # Wang et. al. 2004.
            truncate = 3.5
    
        if win_size is None:
            if gaussian_weights:
                # set win_size used by crop to match the filter size
                r = int(truncate * sigma + 0.5)  # radius as in ndimage
                win_size = 2 * r + 1
            else:
                win_size = 7   # backwards compatibility
    
        if np.any((np.asarray(im1.shape) - win_size)  0):
            raise ValueError(
                "win_size exceeds image extent.  If the input is a multichannel "
                "(color) image, set multichannel=True.")
    
        if not (win_size % 2 == 1):
            raise ValueError('Window size must be odd.')
    
        if data_range is None:
            if im1.dtype != im2.dtype:
                warn("Inputs have mismatched dtype.  Setting data_range based on "
                     "im1.dtype.", stacklevel=2)
            dmin, dmax = dtype_range[im1.dtype.type]
            data_range = dmax - dmin
    
        ndim = im1.ndim
    
        if gaussian_weights:
            filter_func = gaussian_filter
            filter_args = {'sigma': sigma, 'truncate': truncate}
        else:
            filter_func = uniform_filter
            filter_args = {'size': win_size}
    
        # ndimage filters need floating point data
        im1 = im1.astype(np.float64)
        im2 = im2.astype(np.float64)
    
        NP = win_size ** ndim
    
        # filter has already normalized by NP
        if use_sample_covariance:
            cov_norm = NP / (NP - 1)  # sample covariance
        else:
            cov_norm = 1.0  # population covariance to match Wang et. al. 2004
    
        # compute (weighted) means
        ux = filter_func(im1, **filter_args)
        uy = filter_func(im2, **filter_args)
    
        # compute (weighted) variances and covariances
        uxx = filter_func(im1 * im1, **filter_args)
        uyy = filter_func(im2 * im2, **filter_args)
        uxy = filter_func(im1 * im2, **filter_args)
        vx = cov_norm * (uxx - ux * ux)
        vy = cov_norm * (uyy - uy * uy)
        vxy = cov_norm * (uxy - ux * uy)
    
        R = data_range
        C1 = (K1 * R) ** 2
        C2 = (K2 * R) ** 2
    
        A1, A2, B1, B2 = ((2 * ux * uy + C1,
                           2 * vxy + C2,
                           ux ** 2 + uy ** 2 + C1,
                           vx + vy + C2))
        D = B1 * B2
        S = (A1 * A2) / D
    
        # to avoid edge effects will ignore filter radius strip around edges
        pad = (win_size - 1) // 2
    
        # compute (weighted) mean of ssim
        mssim = crop(S, pad).mean()
    
        if gradient:
            # The following is Eqs. 7-8 of Avanaki 2009.
            grad = filter_func(A1 / D, **filter_args) * im1
            grad += filter_func(-S / B2, **filter_args) * im2
            grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D,
                                **filter_args)
            grad *= (2 / im1.size)
    
            if full:
                return mssim, grad, S
            else:
                return mssim, grad
        else:
            if full:
                return mssim, S
            else:
                return mssim
    

    到此这篇关于教你怎么用python删除相似度高的图片的文章就介绍到这了,更多相关python删除相似度高的图片内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!

    您可能感兴趣的文章:
    • python查找重复图片并删除(图片去重)
    • Python实现的删除重复文件或图片功能示例【去重】
    • 使用python opencv对目录下图片进行去重的方法
    • 使用python如何删除同一文件夹下相似的图片
    • python删除文件夹下相同文件和无法打开的图片
    • python判断图片宽度和高度后删除图片的方法
    • 用python删除文件夹中的重复图片(图片去重)
    上一篇:Python实现K-means聚类算法并可视化生成动图步骤详解
    下一篇:python中必会的四大高级数据类型(字符,元组,列表,字典)
  • 相关文章
  • 

    © 2016-2020 巨人网络通讯 版权所有

    《增值电信业务经营许可证》 苏ICP备15040257号-8

    教你怎么用python删除相似度高的图片 教你,怎么,用,python,删除,