from pyzbar.pyzbar import decode
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from .sid_process import getSID
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import cv2
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import numpy as np
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import os
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import pkg_resources
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markerfile = '/template-sq.png' # always use slash
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markerfilename = pkg_resources.resource_filename(__name__, markerfile)
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class Paper:
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def __init__(self, filename=None, sid_classifier=None, settings=None, output_path="/tmp"):
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self.filename = filename
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self.output_path=output_path
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self.invalid = None
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self.QRData = None
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self.settings = {"answer_threshold": 0.25} if settings is None else settings
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self.errors = []
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self.warnings = []
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self.sid = None
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self.sid_classifier = sid_classifier
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if filename is not None:
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self.loadImage(filename)
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self.runOcr()
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def loadImage(self, filename, rgbchannel=0):
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self.img = cv2.imread(filename, rgbchannel)
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if self.img is None:
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self.errors.append("File could not be loaded!")
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self.invalid = True
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return
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self.imgHeight, self.imgWidth = self.img.shape[0:2]
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def saveImage(self, filename="/tmp/debug_image.png"):
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cv2.imwrite(filename, self.img)
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def runOcr(self):
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if self.invalid == True:
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return
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self.decodeQRandRotate()
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self.imgTreshold()
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cv2.imwrite('/tmp/debug_threshold.png', self.bwimg)
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skewAngle = 0
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# try:
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# skewAngle=self.getSkewAngle()
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# except:
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# self.errors.append("Could not determine skew angle!")
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# self.rotateAngle(skewAngle)
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self.generateAnswerMatrix()
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self.saveImage()
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def decodeQRandRotate(self):
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if self.invalid == True:
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return
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blur = cv2.blur(self.img, (3, 3))
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d = decode(blur)
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self.img = blur
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if len(d) == 0:
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self.errors.append("QR code could not be found!")
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self.data = None
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self.invalid = True
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return
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if(len(d)>1): #if there are multiple codes, get first ean or qr code available.
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for dd in d:
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if(dd.type=="EAN13" or dd.type=="QR"):
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d[0]=dd
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break
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self.QRDecode = d
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self.QRData = d[0].data
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xpos = d[0].rect.left
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ypos = d[0].rect.top
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# check if image is rotated wrongly
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if xpos > self.imgHeight / 2.0 and ypos > self.imgWidth / 2.0:
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self.rotateAngle(180)
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def rotateAngle(self, angle=0):
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# rot_mat = cv2.getRotationMatrix2D(
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# (self.imgHeight / 2, self.imgWidth / 2), angle, 1.0
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# )
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rot_mat = cv2.getRotationMatrix2D(
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(self.imgWidth / 2, self.imgHeight / 2), angle, 1.0
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)
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result = cv2.warpAffine(
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self.img,
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rot_mat,
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(self.imgWidth, self.imgHeight),
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flags=cv2.INTER_CUBIC,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=(255, 255, 255),
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)
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self.img = result
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self.imgHeight, self.imgWidth = self.img.shape[0:2]
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# todo, make better tresholding
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def imgTreshold(self):
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(self.thresh, self.bwimg) = cv2.threshold(
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self.img, 128, 255,
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cv2.THRESH_BINARY | cv2.THRESH_OTSU
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)
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def getSkewAngle(self):
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neg = 255 - self.bwimg # get negative image
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cv2.imwrite("/tmp/debug_1.png", neg)
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angle_counter = 0 # number of angles
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angle = 0.0 # collects sum of angles
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cimg = cv2.cvtColor(self.img, cv2.COLOR_GRAY2BGR)
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# get all the Hough lines
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for line in cv2.HoughLinesP(neg, 1, np.pi / 180, 325):
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x1, y1, x2, y2 = line[0]
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cv2.line(cimg, (x1, y1), (x2, y2), (0, 0, 255), 2)
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# calculate the angle (in radians)
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this_angle = np.arctan2(y2 - y1, x2 - x1)
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if this_angle and abs(this_angle) <= 10:
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# filtered zero degree and outliers
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angle += this_angle
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angle_counter += 1
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# the skew is calculated of the mean of the total angles, #try block helps with division by zero.
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try:
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skew = np.rad2deg(
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angle / angle_counter
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) # the 1.2 factor is just experimental....
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except:
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skew = 0
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cv2.imwrite("/tmp/debug_2.png", cimg)
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return skew
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def locateUpMarkers(self, threshold=0.85, height=200):
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template = cv2.imread(markerfilename, 0)
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w, h = template.shape[::-1]
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crop_img = self.bwimg[0:height, :]
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res = cv2.matchTemplate(crop_img, template, cv2.TM_CCOEFF_NORMED)
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loc = np.where(res >= threshold)
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cimg = cv2.cvtColor(crop_img, cv2.COLOR_GRAY2BGR)
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# remove false matching of the squares in qr code
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loc_filtered_x = []
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loc_filtered_y = []
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if len(loc[0]) == 0:
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min_y = -1
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else:
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min_y = np.min(loc[0])
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for pt in zip(*loc[::-1]):
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if pt[1] < min_y + 20:
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loc_filtered_y.append(pt[1])
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loc_filtered_x.append(pt[0])
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# order by x coordinate
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loc_filtered_x, loc_filtered_y = zip(
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*sorted(zip(loc_filtered_x, loc_filtered_y))
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)
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# loc=[loc_filtered_y,loc_filtered_x]
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# remove duplicates
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a = np.diff(loc_filtered_x) > 40
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a = np.append(a, True)
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loc_filtered_x = np.array(loc_filtered_x)
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loc_filtered_y = np.array(loc_filtered_y)
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loc = [loc_filtered_y[a], loc_filtered_x[a]]
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for pt in zip(*loc[::-1]):
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cv2.rectangle(cimg, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2)
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cv2.imwrite("/tmp/debug_3.png", cimg)
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self.xMarkerLocations = loc
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return loc
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def locateRightMarkers(self, threshold=0.85, width=200):
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template = cv2.imread(markerfilename, 0)
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w, h = template.shape[::-1]
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crop_img = self.bwimg[:, -width:]
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cv2.imwrite('/tmp/debug_right.png', crop_img)
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res = cv2.matchTemplate(crop_img, template, cv2.TM_CCOEFF_NORMED)
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loc = np.where(res >= threshold)
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cimg = cv2.cvtColor(crop_img, cv2.COLOR_GRAY2BGR)
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# remove false matching of the squares in qr code
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loc_filtered_x = []
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loc_filtered_y = []
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if len(loc[1]) == 0:
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min_x = -1
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else:
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max_x = np.max(loc[1])
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for pt in zip(*loc[::-1]):
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if pt[1] > max_x - 20:
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loc_filtered_y.append(pt[1])
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loc_filtered_x.append(pt[0])
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# order by y coordinate
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try:
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loc_filtered_y, loc_filtered_x = zip(
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*sorted(zip(loc_filtered_y, loc_filtered_x))
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)
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except:
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self.yMarkerLocations=[np.array([1,1]),np.array([1,2])]
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return self.yMarkerLocations
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# loc=[loc_filtered_y,loc_filtered_x]
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# remove duplicates
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a = np.diff(loc_filtered_y) > 40
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a = np.append(a, True)
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loc_filtered_x = np.array(loc_filtered_x)
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loc_filtered_y = np.array(loc_filtered_y)
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loc = [loc_filtered_y[a], loc_filtered_x[a]]
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for pt in zip(*loc[::-1]):
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cv2.rectangle(cimg, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2)
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cv2.imwrite("/tmp/debug_4.png", cimg)
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self.yMarkerLocations = [loc[0], loc[1] + self.imgWidth - width]
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return self.yMarkerLocations
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def generateAnswerMatrix(self):
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self.locateUpMarkers()
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self.locateRightMarkers()
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roixoff = 4
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roiyoff = 0
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roiwidth = 55
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roiheight = roiwidth
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totpx = roiwidth * roiheight
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cimg = cv2.cvtColor(self.img, cv2.COLOR_GRAY2BGR)
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self.answerMatrix = []
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for y in self.yMarkerLocations[0]:
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oneline = []
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for x in self.xMarkerLocations[1]:
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roi = self.bwimg[
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y - roiyoff : y + int(roiheight - roiyoff),
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x - roixoff : x + int(roiwidth - roixoff),
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]
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# cv2.imwrite('ans_x'+str(x)+'_y_'+str(y)+'.png',roi)
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black = totpx - cv2.countNonZero(roi)
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oneline.append(black / totpx)
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cv2.rectangle(cimg, (x - roixoff,y - roiyoff), (x + int(roiwidth - roixoff),y + int(roiheight - roiyoff)), (0, 255, 255), 2)
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cv2.imwrite('/tmp/debug_answers.png',cimg)
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self.answerMatrix.append(oneline)
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def get_enhanced_sid(self):
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if self.sid_classifier is None:
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return "x"
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if self.settings is not None:
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sid_mask = self.settings.get("sid_mask", None)
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es, err, warn = getSID(
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self.img[
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int(0.04 * self.imgHeight) : int(0.095 * self.imgHeight),
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int(0.65 * self.imgWidth) : int(0.95 * self.imgWidth),
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],
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self.sid_classifier,
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sid_mask,
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)
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[self.errors.append(e) for e in err]
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[self.warnings.append(w) for w in warn]
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return es
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def get_code_data(self):
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if self.QRData is None:
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self.errors.append("Could not read QR or EAN code! Not an exam?")
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retval = {
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"exam_id": None,
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"page_no": None,
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"paper_id": None,
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"faculty_id": None,
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"sid": None,
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}
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return retval
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qrdata = bytes.decode(self.QRData, "utf8")
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if self.QRDecode[0].type == "EAN13":
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return {
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"exam_id": int(qrdata[0:7]),
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"page_no": int(qrdata[7])+1,
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"paper_id": int(qrdata[-5:-1]),
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"faculty_id": None,
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"sid": None,
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}
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else:
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data = qrdata.split(",")
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retval = {
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"exam_id": int(data[1]),
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"page_no": int(data[3]),
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"paper_id": int(data[2]),
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"faculty_id": int(data[0]),
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"sid": None
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}
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if len(data) > 4:
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retval["sid"] = data[4]
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return retval
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def get_paper_ocr_data(self):
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data = self.get_code_data()
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data["qr"] = bytes.decode(self.QRData, 'utf8')
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data["errors"] = self.errors
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data["warnings"] = self.warnings
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data["up_position"] = (
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list(self.xMarkerLocations[0] / self.imgWidth),
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list(self.xMarkerLocations[1] / self.imgHeight),
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)
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data["right_position"] = (
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list(self.yMarkerLocations[0] / self.imgWidth),
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list(self.yMarkerLocations[1] / self.imgHeight),
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)
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data["ans_matrix"] = (
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(np.array(self.answerMatrix) > self.settings["answer_threshold"]) * 1
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).tolist()
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if data["sid"] is None and data["page_no"] == 1:
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data["sid"] = self.get_enhanced_sid()
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output_filename=os.path.join(self.output_path, '.'.join(self.filename.split('/')[-1].split('.')[:-1])+".png")
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cv2.imwrite(output_filename, self.img)
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data['output_filename']=output_filename
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print(np.array(self.answerMatrix))
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return data
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