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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 math |
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class Paper: |
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def __init__(self, filename=None, sid_classifier=None, settings=None): |
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self.filename = filename |
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self.invalid = None |
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self.QRData = None |
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self.settings={'answer_treshold':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="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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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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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, 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("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("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("template.png", 0) |
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w, h = template.shape[::-1] |
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crop_img = self.img[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("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("template.png", 0) |
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w, h = template.shape[::-1] |
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crop_img = self.img[:, -width:] |
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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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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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# 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("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 = 10 |
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roiyoff = 5 |
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roiwidth = 50 |
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roiheight = roiwidth |
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totpx = roiwidth * roiheight |
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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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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(0.7 * self.imgWidth) : int(0.99 * 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 = {'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 {'exam_id': int(qrdata[0:7]), |
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'page_no': int(qrdata[7]), |
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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={'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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} |
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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']=self.QRData |
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data['errors']=self.errors |
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data['warnings']=self.warnings |
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data['up_position']=(list(self.xMarkerLocations[1]/self.imgWidth), list(self.yMarkerLocations[1]/self.imgHeight)) |
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data['right_position']=(list(self.xMarkerLocations[1]/self.imgWidth), list(self.yMarkerLocations[1]/self.imgHeight)) |
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data['ans_matrix']=((np.array(self.answerMatrix)>self.settings['answer_treshold'])*1).tolist() |
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if data['sid'] is None: |
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data['sid']=self.get_enhanced_sid() |
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return data |