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-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 ))
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3. 删除相似度高的图片
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# 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)
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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
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改为
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from skimage.metrics import _structural_similarity
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5. structural_similarity.py的源码
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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
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原文链接:https://blog.csdn.net/DJames23/article/details/116430898