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：
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#encoding:utf-8import osimport sysimport cv2video_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
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# coding: utf-8import osimport cv2# from skimage.measure import compare_ssim# from skimage.metrics import _structural_similarityfrom skimage.metrics import structural_similarity as ssimdef 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 filesdef 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 countif __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：
将
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from skimage.measure import compare_ssim |
改为
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from skimage.metrics import _structural_similarity |
5. structural_similarity.py的源码
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from warnings import warnimport numpy as npfrom scipy.ndimage import uniform_filter, gaussian_filterfrom ..util.dtype import dtype_rangefrom ..util.arraycrop import cropfrom .._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 |
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原文链接：https://blog.csdn.net/DJames23/article/details/116430898
