| | |
| | | # todo, make better tresholding |
| | | def imgTreshold(self): |
| | | (self.thresh, self.bwimg) = cv2.threshold( |
| | | self.img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU |
| | | self.img, 128, 255, |
| | | cv2.THRESH_BINARY | cv2.THRESH_OTSU |
| | | ) |
| | | |
| | | def getSkewAngle(self): |
| | |
| | | es, err, warn = getSID( |
| | | self.img[ |
| | | int(0.04 * self.imgHeight) : int(0.095 * self.imgHeight), |
| | | int(0.7 * self.imgWidth) : int(0.99 * self.imgWidth), |
| | | int(0.65 * self.imgWidth) : int(0.95 * self.imgWidth), |
| | | ], |
| | | self.sid_classifier, |
| | | sid_mask, |
| | |
| | | data = qrdata.split(",") |
| | | retval = { |
| | | "exam_id": int(data[1]), |
| | | "page_no": int(data[3])+1, |
| | | "page_no": int(data[3]), |
| | | "paper_id": int(data[2]), |
| | | "faculty_id": int(data[0]), |
| | | "sid": None |
| | |
| | | data["errors"] = self.errors |
| | | data["warnings"] = self.warnings |
| | | data["up_position"] = ( |
| | | list(self.xMarkerLocations[1] / self.imgWidth), |
| | | list(self.yMarkerLocations[1] / self.imgHeight), |
| | | list(self.xMarkerLocations[0] / self.imgWidth), |
| | | list(self.xMarkerLocations[1] / self.imgHeight), |
| | | ) |
| | | data["right_position"] = ( |
| | | list(self.xMarkerLocations[1] / self.imgWidth), |
| | | list(self.yMarkerLocations[0] / self.imgWidth), |
| | | list(self.yMarkerLocations[1] / self.imgHeight), |
| | | ) |
| | | data["ans_matrix"] = ( |
| | | (np.array(self.answerMatrix) > self.settings["answer_threshold"]) * 1 |
| | | ).tolist() |
| | | if data["sid"] is None and data["page_no"] == 2: |
| | | if data["sid"] is None and data["page_no"] == 1: |
| | | data["sid"] = self.get_enhanced_sid() |
| | | output_filename=os.path.join(self.output_path, '.'.join(self.filename.split('/')[-1].split('.')[:-1])+".png") |
| | | cv2.imwrite(output_filename, self.img) |