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morphologyÚ img_as_ubytez/template-8.pngc C s t j| |ft jS )z8
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Function greates square kernel of size x and y
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)ÚnpZonesÚuint8)ÚxÚy© r ú@/home/samo/programiranje/python/sizif-ocr/aoi_ocr/sid_process.pyÚkernel s r
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c C sâ |d dkrd}n|d dkr$d}nd}t j|t jtdddd}t j|t jtd d
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iterationsé é é é2 z/tmp/sidblock1.pngc S s
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t j| S )N)Úcv2Z contourArea)Úctrr r r Ú<lambda>% s z#find_biggest_blob.<locals>.<lambda>)Úkeyé é( éÿÿÿÿ) r Ú morphologyExÚ
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Ú MORPH_DILATEr ÚimwriteÚ findContoursÚcopyÚ RETR_EXTERNALÚCHAIN_APPROX_SIMPLEÚsortedÚ boundingRect)ÚimageÚoriginal_imageÚsid_maskZ move_leftZimage2Zimage1Úim2ÚctrsÚhierÚ sorted_ctrsr r ÚwÚhr r r Úfind_biggest_blob s" (r/ c C s0 x*t || D ] \}}|dkr ||kr dS q W dS )zÎ
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Function compares student id number with student id mask if the recognised number is valid according to the mask
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:param sid_no:
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:param sid_mask:
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:return: True if they match, else False
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r FT)Úzip)Úsid_nor( ÚsÚesr r r Ú sid_compare+ s r4 c C sò d}t | ||} tjd| tj| j tjtj\}}}t|dd d}x¤t|D ]\} }
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First algorithm. it segments numerals with contours. It works with numbers where individual numerals does not touch.
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:param image:
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:param original_image:
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:param classifier:
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:return: student id as a string
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Ú z/tmp/sid_contour1.pngc S s t j| d S )Nr )r r% )r r r r r G s z%segment_by_contours.<locals>.<lambda>)r r r é é z/tmp/sid_no_{}.pngr g ào@r )r7 r7 r )r/ r r r r! r" r# r$ Ú enumerater% r ÚresizeÚformatÚstrÚpredictÚreshape)r& r' Ú
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classifierr( r1 r) r* r+ r, Úir r r r- r. Úroir r r Úsegment_by_contours8 s" &rA c C sÆ d}t |}t| ||} tjd| | jdd
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Third algorithm. It trys to get biggest "blob" in the image and then it cuts it into individual numbers by force.
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It has some problems with finding individual numbers, so some tweaking must be done!
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:param image:
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:param original_image:
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:param sid_mask:
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:param classifier:
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:return: student id as a string
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r5 z/tmp/sidblock2.pngr r Nr r6 r7 z/tmp/sid_no_{}.pngg ào@)r7 r7 r ) Úlenr/ r r ÚshapeÚintÚranger r9 r: r; r< r= ) r& r' r( r> r1 Zsid_lenÚ imgHeightÚimgWidthZnumWidthr? Únumr r r Úsegment_by_sid_lenZ s &rI c C sH t j|t jtdddd}t|dk }t jd| t jtd}|jddd
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Second attempt. It dilates the image to get all 7 segments wisible as 8888888 then it does pattern matching of 8 with
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pattern image. It works if the scaned gray level is high enough.
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:param image:
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:param original_image:
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:param sid_mask:
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:param classifier:
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:return: student id number as a string
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r é
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)r r z/tmp/sid_3rd1.pngr Nr g è?r5 Téÿ z/tmp/sid_3rd2.pngr6 r7 z/tmp/sid_3no_{}.pngg ào@r r r )r rK rK r )r7 r7 r ) r r Ú MORPH_CLOSEr
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r r ÚimreadÚ template8rC Ú matchTemplateÚTM_CCOEFF_NORMEDr ÚwhereÚcvtColorÚCOLOR_GRAY2BGRr0 ÚappendrB r$ ÚdiffrD ÚarrayÚ rectangler8 r9 r: r; r< r= )r& r' r( r> Z block_imageÚtemplater- r. ÚresÚlocÚcimgÚloc_filtered_xÚloc_filtered_yÚptÚaÚpointsr1 r? rH r r r Úsegment_by_7segmentst sJ
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td d } tjd| t | |||}t |t |ks.t|| rF|jd t| |||}t |t |krpt| |||}|jd t||sdg}|||fS )aÔ
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Tries different approaches on image to get student id number. Firstly clears image of noise and then skeletonizes
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numbers and thickens it until it gets normalized image. It sends it to the segmentation and recognition functions.
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Tweak both MORPH_OPEN lines....
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:param image:
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:param classifier:
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:param sid_mask:
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:return: (student_id, error, warning) student id as a string, list of errors and list of warnings during the recognition
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rK éd z/tmp/enSID0.pngr é )r r r r r6 z/tmp/enSID1.pngrJ g à?z/tmp/enhancedSID.pngz Trying second SID algorithm.zTrying third SID algorithm.z
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rL r Zthinr Z skeletonizerA rB r4 rT ra rI )r& r> r( Zsid_warnZsid_errZimage_originalr1 r r r ÚgetSID¬ s4
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rd )r Únumpyr Zskimager r Ú pkg_resourcesZ templatefileÚresource_filenameÚ__name__rN r
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