import cv2
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import numpy as np
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from skimage import morphology,img_as_ubyte
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from sklearn import svm
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from sklearn.externals import joblib
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"""
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(1) The text is an array of chars (in row-major order) where
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* each char can be one of the following:
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* 'x': hit
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* 'o': miss
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* ' ': don't-care
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* (2) When the origin falls on a hit or miss, use an upper case
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* char (e.g., 'X' or 'O') to indicate it. When the origin
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* falls on a don't-care, indicate this with a 'C'.
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* The string must have exactly one origin specified.
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* (3) The advantage of this method is that the text can be input
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* in a format that shows the 2D layout of the Sel; e.g.,
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:::: AND ::::
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(10) The sequence string is formatted as follows:
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* ~ An arbitrary number of operations, each separated
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* by a '+' character. White space is ignored.
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* ~ Each operation begins with a case-independent character
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* specifying the operation:
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* d or D (dilation)
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* e or E (erosion)
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* o or O (opening)
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* c or C (closing)
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* r or R (rank binary reduction)
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* x or X (replicative binary expansion)
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* b or B (add a border of 0 pixels of this size)
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* ~ The args to the morphological operations are bricks of hits,
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* and are formatted as a.b, where a and b are horizontal and
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* vertical dimensions, rsp.
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* ~ The args to the reduction are a sequence of up to 4 integers,
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* each from 1 to 4.
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* ~ The arg to the expansion is a power of two, in the set
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* {2, 4, 8, 16}.
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* (11) An example valid sequence is:
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* "b32 + o1.3 + C3.1 + r23 + e2.2 + D3.2 + X4"
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* In this example, the following operation sequence is carried out:
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* * b32: Add a 32 pixel border around the input image
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* * o1.3: Opening with vert sel of length 3 (e.g., 1 x 3)
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* * C3.1: Closing with horiz sel of length 3 (e.g., 3 x 1)
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* * r23: Two successive 2x2 reductions with rank 2 in the first
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* and rank 3 in the second. The result is a 4x reduced pix.
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* * e2.2: Erosion with a 2x2 sel (origin will be at x,y: 0,0)
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* * d3.2: Dilation with a 3x2 sel (origin will be at x,y: 1,0)
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* * X4: 4x replicative expansion, back to original resolution
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"""
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def kernel(x, y):
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return np.ones((x, y), np.uint8)
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def getSID(image, classifier):
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image=255-image
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image=img_as_ubyte(image>100)
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cv2.imwrite("enSID0.png", image)
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# Remove noise
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image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(2,2), iterations=1)
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# Closing. Connect non connected parts
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image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(5, 3), iterations=4)
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# Again noise removal after closing
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image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(8,8), iterations=1)
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# Skeletonization
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image = img_as_ubyte(morphology.thin(image>128))
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cv2.imwrite("enSID1.png",image)
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# Stub removal (might not be necessary if thinning instead of skeletonize is used above
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# Making lines stronger
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image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(5, 5), iterations=1)
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image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(10, 10))
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# Thining again
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image = img_as_ubyte(morphology.skeletonize(image>0.5))
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image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(10, 10))
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im2,ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
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#classifier = joblib.load('filename.joblib')
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sid_no=""
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for i, ctr in enumerate(sorted_ctrs):
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# Get bounding box
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x, y, w, h = cv2.boundingRect(ctr)
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# Getting ROI
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if(w<h/2):
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sid_no=sid_no+"1"
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continue
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roi = image[y:y+h, x:x+w]
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roi = img_as_ubyte(roi < 128)
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roi = cv2.resize(roi,(32,32))
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#cv2.rectangle(image,(x,y),( x + w, y + h ),(0,255,0),2)
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cv2.imwrite('sid_no_{}.png'.format(i), roi)
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sid_no=sid_no+str(classifier.predict(roi.reshape(1,-1)/255.0)[0])
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print(sid_no)
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return image
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