+ [jbRt^RIHtHtHt^RIt^RIt^RIHtH t ]PPt RtRt!RR4t^RIHt]R,t^RIt]P"P.]P0,t]P4R8dS]P4R8d]P4R8:d^R IHtM^R IHt]].]3,t]]P.]3,tR t!R t"R t#R R lt$^]PJ3Rlt&R!Rlt'R!Rlt(R Rlt)R Rlt*R"Rlt+R#Rlt,!RR4t-Rt.Rt/])R^RR3Rlt0R# ] d]Pt ELi;i ]d]tELi;i ] ]3d]tLi;i ] d]t]tLi;i)$a Perceptual image hashing library Example: >>> from PIL import Image >>> import imagehash >>> hash = imagehash.average_hash(Image.open('test.png')) >>> print(hash) d879f8f89b1bbf >>> otherhash = imagehash.average_hash(Image.open('other.bmp')) >>> print(otherhash) ffff3720200ffff >>> print(hash == otherhash) False >>> print(hash - otherhash) 36 >>> for r in range(1, 30, 5): ... rothash = imagehash.average_hash(Image.open('test.png').rotate(r)) ... print('Rotation by %d: %d Hamming difference' % (r, hash - rothash)) ... Rotation by 1: 2 Hamming difference Rotation by 6: 11 Hamming difference Rotation by 11: 13 Hamming difference Rotation by 16: 17 Hamming difference Rotation by 21: 19 Hamming difference Rotation by 26: 21 Hamming difference >>> )absolute_importdivisionprint_functionN)Image ImageFilterz4.3.2cRPR^VP4,44p\\P!\ V4^, 44pRP \V^4VR7#)z? internal function to make a hex string out of a binary array. c38"TFp\V4xK R#5iNstr).0bs& S/home/luca/screen-checking/.venv/lib/python3.14/site-packages/imagehash/__init__.py '_binary_array_to_hex..Vs8&7c!ff&7z {:0>{width}x}width)joinflattenintnumpyceillenformat)arr bit_stringrs& r_binary_array_to_hexrRs\gg8a#++-&788 UZZJ!+ , -s:q1??cTa]tRt^[toRtRtRtRtRtRt Rt Rt R t R t VtR #) ImageHashzF Hash encapsulation. Can be used for dictionary keys and comparisons. cWnR#r hash)self binary_arrays&&r__init__ImageHash.__init__`s)rcH\VPP44#r )rr$rr%s&r__str__ImageHash.__str__ds dii//1 22rc,\VP4#r )reprr$r*s&r__repr__ImageHash.__repr__gs diirczVf \R4hVPPVPP8wd6\RVPPVPP4h\P !VPP 4VPP 48g4#)NzOther hash must not be None.z&ImageHashes must be of the same shape.) TypeErrorr$sizeshaper count_nonzerorr%others&&r__sub__ImageHash.__sub__js} ] 1 22 YY^^uzz& ;TYY__ejjN^N^ __   TYY..0EJJ4F4F4HH IIrcVfR#\P!VPP4VPP44#NFr array_equalr$rr6s&&r__eq__ImageHash.__eq__rs7 ]    499,,. 0B0B0D EErcVfR#\P!VPP4VPP44'*#r;r<r6s&&r__ne__ImageHash.__ne__xs: ]    tyy002EJJ4F4F4H I IIrc \\VPP44UUu.uFwrV'gK^V^,,NK! upp4#uuppi))sum enumerater$r)r%ivs& r__hash__ImageHash.__hash__~sC Ytyy/@/@/B%C I%CTQqja!a%jj%C I JJ Is A A c.VPP#r )r$r3r*s&r__len__ImageHash.__len__s rr#N)__name__ __module__ __qualname____firstlineno____doc__r'r+r/r8r>rArIrL__static_attributes____classdictcell__ __classdict__s@rr!r![s;3JF J Krr!)Literal)Callablec\\P!\V4^,44pRP \V^4W,R7p\ ^\V4V4Uu.uF q2W3V,NK pp\P !VUUu.uF&qUUu.uFp\\V44NK upNK( upp4p\V4#uupiuupiuuppi)aY Convert a stored hash (hex, as retrieved from str(Imagehash)) back to a Imagehash object. Notes: 1. This algorithm assumes all hashes are either bidimensional arrays with dimensions hash_size * hash_size, or onedimensional arrays with dimensions binbits * 14. 2. This algorithm does not work for hash_size < 2. {:0>{width}b}r) rrsqrtrrrangearrayboolr!)hexstr hash_sizer&rGbit_rowsrowd hash_arrays& r hex_to_hashresCK!O, -&&s62i>S&T49!S=NPY4Z [4Zq! M *4Z [kk(K(3#6#QDQL#6(KL* \6Ks+CC C:C C c 4\\V4^,V, 4pRP\V^4W!,R7p\P!VUu.uFp\ \V44NK up.4V)V,Rp\ V4#uupi)rZrN)rrrrr]r^r!)r_hashsizer`r&rcrds&& rhex_to_flathashris VqH- .&&s62i>R&Skk,?,QDQL,?@A9*xBWBXY*@sBcxVPR4pVUu.uFp\V4NK pp\V4#uupi)a Convert a stored multihash (hex, as retrieved from str(ImageMultiHash)) back to an ImageMultiHash object. This function is based on hex_to_hash so the same caveats apply. Namely: 1. This algorithm assumes all hashes are either bidimensional arrays with dimensions hash_size * hash_size, or onedimensional arrays with dimensions binbits * 14. 2. This algorithm does not work for hash_size < 2. ,)splitreImageMultiHash)r_rlxhashess& rhex_to_multihashrps: c #( )5a;q>5 )v *s7c .pW^,,p\V4V8wdRp\VPV44h\V^,4FipW^,V^,^,p\ RV,^4pTP \^4Uu.uFqW^V,,^8NK up4Kk \ \P!V44#uupi)a Convert a stored hash (hex, as retrieved from str(Imagehash)) back to a Imagehash object. This method should be used for hashes generated by ImageHash up to version 3.7. For hashes generated by newer versions of ImageHash, hex_to_hash should be used instead. zExpected hex string size of {}.0x) r ValueErrorrr\rappendr!rr])r_r`rcountemsgrGhrHs&& rold_hex_to_hashrxs  1n %K5 *$4;;u%&&  Q U1q519! $(B!**E!H -Hq!Q$hlH -.  %++c"##.sC cV^8d \R4hVPR4PW3\4p\P !V4pV!V4pW48p\ V4#)a Average Hash computation Implementation follows https://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html Step by step explanation: https://web.archive.org/web/20171112054354/https://www.safaribooksonline.com/blog/2013/11/26/image-hashing-with-python/ # noqa: E501 @image must be a PIL instance. @mean how to determine the average luminescence. can try numpy.median instead. ,Hash size must be greater than or equal to 2Lrsconvertresize ANTIALIASrasarrayr!)imager`meanpixelsavgdiffs&&& r average_hashrscMABB s  " "I#99 E --  F|  $rcV^8d \R4h^RIpW,pVPR4PWD3\4p\ P !V4pVPPVPPV^R7^R7pVRV1RV13,p\ P!V4pWx8p \V 4#) Perceptual Hash computation. Implementation follows https://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html @image must be a PIL instance. rzNr{)axis) rs scipy.fftpackr}r~rrrfftpackdctmedianr!) rr`highfreq_factorscipyimg_sizerr dctlowfreqmedrs &&& rphashrsMABB  ' s  " "H#7 C --  }}**6*:C*9*jyj() ||J$rc2^RIpW,pVPR4PWD3\4p\P !V4pVP PV4pVRV1^V^,13,pVP4pWx8p \V 4#)rNr{) rr}r~rrrrrrr!) rr`rrrrrrrrs &&& r phash_simplers  ' s  " "H#7 C --  }} *9*a A o-.$rcV^8d \R4hVPR4PV^,V3\4p\P !V4pVR,VRRR13,8p\ V4#)z Difference Hash computation. following https://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html computes differences horizontally @image must be a PIL instance. rzr{NNNN)rNNr|rr`rrs&& rdhashr0smMABB s  " "IM9#=y I -- u q#2#v&$rcVPR4PW^,3\4p\P!V4pVR,VRR1R3,8p\ V4#)z Difference Hash computation. following https://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html computes differences vertically @image must be a PIL instance. r{rN)rrr)r}r~rrrr!rs&& rdhash_verticalrFsX s  " "I1}#=y I -- u ssAv&$rcR^RIpVeW"^, ,^8XgQR4hMD^\\P!\ VP 444,p\ Wa4p\\P!V44p\\P!V44pW^, ,^8XgQR4hW8:gQR4hWx, p VPR4PW"3\4p\P!V4R, p V'dGVPV RVR7p \V 4p V ^;;,^,uu&VPV R4p VPWV R7p V ^,p \P!V 4p W8p\V4#) aD Wavelet Hash computation. based on https://www.kaggle.com/c/avito-duplicate-ads-detection/ @image must be a PIL instance. @hash_size must be a power of 2 and less than @image_scale. @image_scale must be power of 2 and less than image size. By default is equal to max power of 2 for an input image. @mode (see modes in pywt library): 'haar' - Haar wavelets, by default 'db4' - Daubechies wavelets @remove_max_haar_ll - remove the lowest low level (LL) frequency using Haar wavelet. Nzimage_scale is not power of 2zhash_size is not power of 2zhash_size in a wrong ranger{go@haar)level)pywtrrlog2minr3maxr}r~rrwavedec2listwaverec2rr!)rr` image_scalemoderemove_max_haar_llrimage_natural_scale ll_max_levelr dwt_levelrcoeffsdwt_lowrrs&&&&& rwhashrYs]   a (A -N/NN -3uzz#ejj/:;;'3+EJJ{+, UZZ " #]#q(G*GG( ;;;  ! s  " "K#=y I --  % ==|= <& <&)q.) == (& --I- 6 !9 ||G $rc \P!VPR44P4pVPR4P 4Uu.uF'p\P!V4P4NK) upwrEpV^ 8pVP 4pV^U8p\P !V(V4P 4p \P !V(V(4p \P !W^84p \P !W^84p \^V P44p \P!^^^4pV P4'd#\P!WK,VR7wppM&\P!\V4^, 4pV P4'd#\P!WL,VR7wppM&\P!\V4^, 4p^V,p\V^, \VV,44\V^, \V V,44.p\!V4\!V4,FCpVP#\V^, \VV,R,V , 444KE .pVFYpT\%V4Uu.uF9pV^VV, ^, ,,^VV, ,,^8NK; up, pK[ \'\P!V4P)RV344#uupiuupi)a Color Hash computation. Computes fractions of image in intensity, hue and saturation bins: * the first binbits encode the black fraction of the image * the next binbits encode the gray fraction of the remaining image (low saturation) * the next 6*binbits encode the fraction in 6 bins of saturation, for highly saturated parts of the remaining image * the next 6*binbits encode the fraction in 6 bins of saturation, for mildly saturated parts of the remaining image @binbits number of bits to use to encode each pixel fractions r{HSV)binsg?r)rrr}rrlr logical_andrrElinspaceany histogramzerosrrrrrtr\r!reshape)rbinbits intensityrHrws mask_black frac_black mask_gray frac_gray mask_colorsmask_faint_colorsmask_bright_colorschue_binsh_faint_counts_h_bright_countsmaxvaluevaluescountsbitarrayrGs&& r colorhashrs ]]5==- . 6 6 805 e0D0J0J0L M0L1EMM!  $ $ &0L Mq("oo \    {I 6 ; ; =  *yj9&&{ 4DE'' 5EFKOO  NN1c5 )ooa&:J.!;;s8}q01.q'<8L/1KKH  12/ wJ x!|Sh!67 8#hlCPY\dPdLe:f gN#d?&;;;V--HqL#fx&7"&Z>aqQ1q)*Q1-==A>ZZ( %--)112w-@AAE NB[s -M?Mcpa]tRtRtoRtRtRtRtRRltRt R t R t RR lt RR lt RR ltRtVtR#)rmiz This is an image hash containing a list of individual hashes for segments of the image. The matching logic is implemented as described in Efficient Cropping-Resistant Robust Image Hashing cWnR#r segment_hashes)r%ros&&rr'ImageMultiHash.__init__src0VfR#VPV4#r;matchesr6s&&rr>ImageMultiHash.__eq__s ]  e rc.VPV4'*#r rr6s&&rrAImageMultiHash.__ne__s \\%  rNcVPWV4wrE\VP4pV^8XdV#V\VP^,4,p^\V4V, , pWH,p Wi, #)) hash_diffrrfloat) r%r7hamming_cutoffbit_error_rater sum_distancemax_difference max_distance tie_breaker match_scores &&&& rr8ImageMultiHash.__sub__sq..O't**+. \ 3t221566,U<(<78+%+  %%rc\\;QJd".RVP4FNK 54#!RVP444#)c38"TFp\V4xK R#5ir r#)r segments& rr*ImageMultiHash.__hash__..sE1DgDMM1Dr)r$tuplerr*s&rrIImageMultiHash.__hash__s8 eeE1D1DEe FFeE1D1DEE FFrcFRPRVP44#)rkc38"TFp\V4xK R#5ir r )r rns& rr)ImageMultiHash.__str__..s6"5Q#a&&"5r)rrr*s&rr+ImageMultiHash.__str__s 6$"5"56 66rc,\VP4#r )r.rr*s&rr/ImageMultiHash.__repr__s d!! ""rc $aVf*VfRp\VP^,4V,p.pVPF;o\V3RlVP44pWR8dK*VPV4K= \V4\ V43#)a Gets the difference between two multi-hashes, as a tuple. The first element of the tuple is the number of matching segments, and the second element is the sum of the hamming distances of matching hashes. NOTE: Do not order directly by this tuple, as higher is better for matches, and worse for hamming cutoff. :param other_hash: The image multi hash to compare against :param hamming_cutoff: The maximum hamming distance to a region hash in the target hash :param bit_error_rate: Percentage of bits which can be incorrect, an alternative to the hamming cutoff. The default of 0.25 means that the segment hashes can be up to 25% different g?c36<"TFpSV, xK R#5ir )r other_segment_hash segment_hashs& rr+ImageMultiHash.hash_diff..s"7%%%7s)rrrrtrE)r% other_hashrr distanceslowest_distancers&&&& @rrImageMultiHash.hash_diffsN++A./.@>)))l(77?&  O$* YY ''rc 2VPWV4wrVWR8#)a Checks whether this hash matches another crop resistant hash, `other_hash`. :param other_hash: The image multi hash to compare against :param region_cutoff: The minimum number of regions which must have a matching hash :param hamming_cutoff: The maximum hamming distance to a region hash in the target hash :param bit_error_rate: Percentage of bits which can be incorrect, an alternative to the hamming cutoff. The default of 0.25 means that the segment hashes can be up to 25% different )r)r%r region_cutoffrrrrs&&&&& rrImageMultiHash.matchess~~j.I*'  !!rc .aaa\VVVV3RlR7#)a Returns the hash in a list which is the best match to the current hash :param other_hashes: A list of image multi hashes to compare against :param hamming_cutoff: The maximum hamming distance to a region hash in the target hash :param bit_error_rate: Percentage of bits which can be incorrect, an alternative to the hamming cutoff. Defaults to 0.25 if unset, which means the hash can be 25% different c*<SPVSS4#r )r8)rrrr%s&r+ImageMultiHash.best_match..s$,,z>>Rr)key)r)r% other_hashesrrsf&ffr best_matchImageMultiHash.best_matchs R rr)NN)rNN)rNrOrPrQrRr'r>rAr8rIr+r/rrrrSrTrUs@rrmrmsB  ! &G7#(8 "  rrmc\4p\4p\P!\P!V44p\ V^,4pVP V4VP 4p\4pVF@pVwrV ^, V 3V ^,V 3W^, 3W^,3.p VPV 4KB VPW4V'gV#\4pVFWpW,'d6VP V4VP V4VP V4KFVP V4KY K)aK Finds a region and returns a set of pixel coordinates for it. :param remaining_pixels: A numpy bool array, with True meaning the pixels are remaining to segment :param segmented_pixels: A set of pixel coordinates which have already been assigned to segment. This will be updated with the new pixels added to the returned segment. ) setr transposenonzeroraddcopyupdatedifference_update) remaining_pixelssegmented_pixels in_region not_in_regionavailable_pixelsstart new_pixelstry_nextpixelrny neighbourss && r _find_regionr!sUOOEMM2B$CD " # unn U(e 41UAJUAJAJAJ :  ??: -=  u*e MM%NN5e rcVPwr4W8p\P!VPR\R7p.p\ 4pTP \ V4U u.uFp RV 3NK up 4TP \ V4U u.uFqR3NK up 4TP \ V4U u.uFqV 3NK up 4TP \ V4U u.uFqV3NK up 4\P!WV4P4'dR\P!WV4p \W4p \V 4V8dVPV 4V Fp RWl&K K{\P!V4p \V4W4,8dR\P!W4p \W4p \V 4V8dVPV 4V Fp RWl&K KgV#uup iuup iuup iuup i)a Finds all the regions within an image pixel array, and returns a list of the regions. Note: Slightly different segmentations are produced when using pillow version 6 vs. >=7, due to a change in rounding in the greyscale conversion. :param pixels: A numpy array of the pixel brightnesses. :param segment_threshold: The brightness threshold to use when differentiating between hills and valleys. :param min_segment_size: The minimum number of pixels for a segment. T)dtypeFr) r4rfullr^rr r\ bitwise_andrrrrtinvert)rsegment_thresholdmin_segment_size img_width img_heightthreshold_pixelsunassigned_pixelssegmentsalready_segmentedzrrpixthreshold_pixels_is&&& r_find_all_segmentsr+Ms .ZZ d$? UE*,=>,=qB7,=>?E),<=,53DE3Daq>3DEFE)4DE4Dqz?4DEF )=AACC&&'7K ) ='\$$ ??7 c!ll#34  66&&'9M ) ='\$$ ??7 c! 5?=EEs G- G25 G7 G<ii,cVP4pVPR4PWU3\4pVP \ P !44P \ P!44p\P!V4P\P4p\WsV4pV'g&R V^, V^, 30p VPV 4V'd\VRRR7RVp.p VFp VPwr\!V 4V, p\!V 4V, p\#RV 44V,p\#RV 44V,p\%RV 44^,V,p\%R V 44^,V,pVP'VVVV34pV PV!V44K \)V 4#) a Creates a CropResistantHash object, by the algorithm described in the paper "Efficient Cropping-Resistant Robust Image Hashing". DOI 10.1109/ARES.2014.85 This algorithm partitions the image into bright and dark segments, using a watershed-like algorithm, and then does an image hash on each segment. This makes the image much more resistant to cropping than other algorithms, with the paper claiming resistance to up to 50% cropping, while most other algorithms stop at about 5% cropping. Note: Slightly different segmentations are produced when using pillow version 6 vs. >=7, due to a change in rounding in the greyscale conversion. This leads to a slightly different result. :param image: The image to hash :param hash_func: The hashing function to use :param limit_segments: If you have storage requirements, you can limit to hashing only the M largest segments :param segment_threshold: Brightness threshold between hills and valleys. This should be static, putting it between peak and through dynamically breaks the matching :param min_segment_size: Minimum number of pixels for a hashable segment :param segmentation_image_size: Size which the image is resized to before segmentation r{c\V4#r )r)rs&rr%crop_resistant_hash..sCFrT)rreverseNc32"TF q^,xK R#5irNrr coords& rr&crop_resistant_hash.. ,G5AhhGc32"TF q^,xK R#5irNrr2s& rrr4r5r6c32"TF q^,xK R#5ir1rr2s& rrr4-WEQxxWr6c32"TF q^,xK R#5ir8rr2s& rrr4r:r6)rr)r r}r~rfilterr GaussianBlur MedianFilterrr]astypefloat32r+rtsortedr3rrrcroprm)r hash_funclimit_segmentsr r!segmentation_image_size orig_imagerr&full_image_segmentrororig_worig_hscale_wscale_hmin_ymin_xmax_ymax_x bounding_boxs&&&&&& rcrop_resistant_hashrQ}s6jjl s  " "$;#UW` a k..0 1 8 89Q9Q9S T ++e  # #EMM 2 v:J K !81!<>UXY>Y Z[ //$% H"2D A/> R( W??.& &M3 3' &M3 3' ,G , ,w 6% ,G , ,w 6% -W- - 1W <% -W- - 1W <%%u!=>,-- ,'($ vr)rdb4)rS)rS r)rSrTr))rUrg)rUNrT)rS)1rR __future__rrrsysrPILrr ResamplingLANCZOSrAttributeError __version__rr!typingrW WhashMode ImportErrorr numpy.typingNDArraybool_r version_inforXcollections.abcrMeanFuncHashFuncr2rerirprxrrrrrrrrrmrr+rQrrrrgs<A@ "    % %   F@))Z ] # <<   , v !c&6&6)&C& wi& '( u{{mY. /(($$*#$%**8.(,&/d3Bl]]@)X-d  Ak__H   $   ( (sGD<E+E&"E7<EE E#"E#& E43E47 FF