| """ |
| Copyright (c) 2019, OptoFidelity OY |
| |
| Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: |
| |
| 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. |
| 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. |
| 3. All advertising materials mentioning features or use of this software must display the following acknowledgement: This product includes software developed by the OptoFidelity OY. |
| 4. Neither the name of the OptoFidelity OY nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. |
| |
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY |
| EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED |
| WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE |
| DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY |
| DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES |
| (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; |
| LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND |
| ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT |
| (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS |
| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| """ |
| |
| import cherrypy |
| import math |
| import numpy as np |
| import numpy.linalg as npl |
| from sqlalchemy.orm import joinedload |
| from genshi.template import MarkupTemplate |
| |
| from TPPTAnalysisSW.testbase import TestBase, testclasscreator |
| from TPPTAnalysisSW.imagefactory import ImageFactory |
| from TPPTAnalysisSW.measurementdb import get_database, MultifingerTapTest, MultifingerTapResults |
| from TPPTAnalysisSW.info.version import Version |
| from TPPTAnalysisSW.utils import Timer |
| from TPPTAnalysisSW.settings import settings |
| import TPPTAnalysisSW.plotinfo as plotinfo |
| import TPPTAnalysisSW.plot_factory as plot_factory |
| import TPPTAnalysisSW.analyzers as analyzers |
| |
| |
| class MultiFingerTapTest(TestBase): |
| """ A dummy test class for use as a template in creating new test classes """ |
| |
| # This is the generator function for the class - it must exist in all derived classes |
| # Just update the id (dummy=99) and class name |
| @staticmethod |
| @testclasscreator(8) |
| def create_testclass(*args, **kwargs): |
| return MultiFingerTapTest(*args, **kwargs) |
| |
| # Init function: make necessary initializations. |
| # Parent function initializes: self.test_id, self.ddttest (dictionary, contains test_type_name) and self.testsession (dictionary) |
| def __init__(self, ddtest_row, *args, **kwargs): |
| """ Initializes a new MultiFingerTapTest class """ |
| super(MultiFingerTapTest, self).__init__(ddtest_row, *args, **kwargs) |
| |
| # Override to make necessary analysis for test session success |
| def runanalysis(self, *args, **kwargs): |
| """ Runs the analysis, return a string containing the test result """ |
| results = self.read_test_results() |
| verdict = "Pass" if results['verdict'] else "Fail" |
| # No measurements - special case |
| if results['verdict'] and results['max_input_offset'] is None: |
| verdict = 'N/A' |
| return verdict |
| |
| # Override to make necessary operations for clearing test results |
| # Clearing the test result from the results table is done elsewhere |
| def clearanalysis(self, *args, **kwargs): |
| """ Clears analysis results """ |
| ImageFactory.delete_images(self.test_id) |
| |
| # Create the test report. Return the created HTML, or raise cherrypy.HTTPError |
| def createreport(self, *args, **kwargs): |
| self.clearanalysis() |
| |
| # Create common template parameters (including ddttest dictionary, testsession dictionary, test_id, test_type_name etc) |
| templateParams = super(MultiFingerTapTest, self).create_common_templateparams(**kwargs) |
| |
| t = Timer() |
| |
| # data for the report |
| results = self.read_test_results() |
| templateParams['results'] = results |
| |
| t.Time("Results") |
| |
| # set the content to be used |
| templateParams['test_page'] = 'test_multifinger_tap.html' |
| templateParams['test_script'] = 'test_page_subplots.js' |
| templateParams['version'] = Version |
| |
| template = MarkupTemplate(open("templates/test_common_body.html")) |
| stream = template.generate(**(templateParams)) |
| t.Time("Markup") |
| |
| verdict = "Pass" if results['verdict'] else "Fail" |
| # No measurements - special case |
| if results['verdict'] and results['max_input_offset'] is None: |
| verdict = 'N/A' |
| |
| return stream.render('xhtml'), verdict |
| |
| # Create images for the report. If the function returns a value, it is used as the new image (including full path) |
| def createimage(self, imagepath, image_name, *args, **kwargs): |
| |
| if image_name == 'passfailgen': |
| t = Timer(1) |
| dbsession = get_database().session() |
| dutinfo = plotinfo.TestDUTInfo(testdut_id=self.dut['id'], dbsession=dbsession) |
| results = self.read_test_results(dutinfo=dutinfo, dbsession=dbsession) |
| pinfo = {'passed_points': [tap['targetpoints'][0] for tap in results['taps'] if tap['verdict']], |
| 'failed_points': [tap['targetpoints'][0] for tap in results['taps'] if not tap['verdict']], |
| } |
| t.Time("Results") |
| title = 'Preview: Multifinger Tap overview ' + self.dut['program'] |
| plot_factory.plot_passfail_on_target(imagepath, pinfo, dutinfo, *args, title=title, **kwargs) |
| t.Time("Image") |
| elif image_name == 'passfaildet': |
| t = Timer(1) |
| dbsession = get_database().session() |
| dutinfo = plotinfo.TestDUTInfo(testdut_id=self.dut['id'], dbsession=dbsession) |
| results = self.read_test_results(dutinfo=dutinfo, dbsession=dbsession) |
| t.Time("Results") |
| title = 'Preview: Multifinger Tap detailed overview ' + self.dut['program'] |
| plot_factory.plot_multifinger_p2p(imagepath, results, dutinfo, *args, title=title, **kwargs) |
| t.Time("Image") |
| elif image_name == 'p2pdxdy': |
| t = Timer(1) |
| results = self.read_dxdy_results(**kwargs) |
| t.Time("Results") |
| # Avoid glitches - use 2.0 limit for display |
| plot_factory.plot_dxdy_graph(imagepath, results, 2.0, *args, **kwargs) |
| t.Time("Image") |
| elif image_name == 'tapdtls': |
| t = Timer(1) |
| results = self.read_tap_results(args[0], **kwargs) |
| t.Time("Results") |
| title = 'Preview: Multifinger Tap details' |
| plot_factory.plot_multifinger_tapdetails(imagepath, results, *args, title=title, **kwargs) |
| t.Time("Image") |
| else: |
| raise cherrypy.HTTPError(message="No such image in the report") |
| |
| return None |
| |
| def read_test_results(self, dutinfo=None, dbsession=None, **kwargs): |
| if dbsession is None: |
| dbsession = get_database().session() |
| if dutinfo is None: |
| dutinfo = plotinfo.TestDUTInfo(testdut_id=self.dut['id'], dbsession=dbsession) |
| |
| s = Timer(2) |
| results = dbsession.query(MultifingerTapTest).filter(MultifingerTapTest.test_id == self.test_id). \ |
| order_by(MultifingerTapTest.id).options(joinedload('multi_finger_tap_results')).all() |
| |
| s.Time("DB Results") |
| taps = [] |
| errors = set() |
| max_offset = None |
| missing_inputs = 0 |
| missing_edge_inputs = 0 |
| total_points = 0 |
| point_id = 0 |
| |
| for multitap in results: |
| tap = self.calculate_tap_details(multitap, dutinfo, **kwargs) |
| point_id += 1 |
| tap['id'] = point_id |
| taps.append(tap) |
| |
| # Calculate common parameters |
| errors = errors.union(tap['errors']) |
| if tap['max_input_offset'] is not None: |
| max_offset = tap['max_input_offset'] if max_offset is None else max(tap['max_input_offset'], max_offset) |
| missing_inputs += tap['missing_inputs'] |
| total_points += tap['num_fingers'] |
| |
| s.Time("Analysis") |
| |
| results = {'taps': taps, |
| 'errors': errors, |
| 'maxposerror': float(settings['maxposerror']), |
| 'verdict': (max_offset <= settings['maxposerror'] |
| and missing_inputs - missing_edge_inputs <= settings['maxmissing'] |
| and len(errors) == 0), |
| 'max_input_offset_verdict': 'N/A' if max_offset is None else 'Pass' if max_offset <= settings[ |
| 'maxposerror'] else 'Fail', |
| 'max_input_offset': max_offset, |
| 'edge_analysis_done': False, |
| 'missing_inputs': missing_inputs, |
| 'missing_inputs_verdict': (missing_inputs - missing_edge_inputs <= settings['maxmissing']), |
| 'total_points': total_points, |
| 'images': [(ImageFactory.create_image_name(self.test_id, 'passfailgen'), |
| ImageFactory.create_image_name(self.test_id, 'passfailgen', 'detailed')), |
| (ImageFactory.create_image_name(self.test_id, 'passfaildet'), |
| ImageFactory.create_image_name(self.test_id, 'passfaildet', 'detailed')), |
| (ImageFactory.create_image_name(self.test_id, 'p2pdxdy'), |
| ImageFactory.create_image_name(self.test_id, 'p2pdxdy', 'detailed')), |
| ], |
| } |
| |
| return results |
| |
| def read_dxdy_results(self, **kwargs): |
| results = self.read_test_results(**kwargs) |
| |
| # Parse failed and passed points |
| passed_points = [] |
| failed_points = [] |
| for tap in results['taps']: |
| for finger in tap['fingers']: |
| for id in finger['points'].keys(): |
| passed_points.extend([(finger['target'], p) for d, p |
| in zip(finger['distances'][id], finger['points'][id]) |
| if d <= finger['maxposerror']]) |
| failed_points.extend([(finger['target'], p) for d, p |
| in zip(finger['distances'][id], finger['points'][id]) |
| if d > finger['maxposerror']]) |
| |
| results['passed_points'] = passed_points |
| results['failed_points'] = failed_points |
| |
| return results |
| |
| def read_tap_results(self, tap_id, dbsession=None, dutinfo=None, **kwargs): |
| if dbsession is None: |
| dbsession = get_database().session() |
| if dutinfo is None: |
| dutinfo = plotinfo.TestDUTInfo(testdut_id=self.dut['id'], dbsession=dbsession) |
| |
| multitap = dbsession.query(MultifingerTapTest).filter(MultifingerTapTest.id == tap_id). \ |
| options(joinedload('multi_finger_tap_results')).first() |
| |
| return self.calculate_tap_details(multitap, dutinfo, **kwargs) |
| |
| def calculate_tap_details(self, multitap, dutinfo, **kwargs): |
| missing_tap_inputs = 0 |
| |
| # Transfer the tap info to individual points |
| robot_point = analyzers.robot_to_target((multitap.robot_x, multitap.robot_y), dutinfo) |
| points_x = [ |
| robot_point[0] + i * multitap.separation_distance * math.cos(math.radians(multitap.separation_angle)) |
| for i in range(multitap.number_of_fingers)] |
| points_y = [ |
| robot_point[1] + i * multitap.separation_distance * math.sin(math.radians(multitap.separation_angle)) |
| for i in range(multitap.number_of_fingers)] |
| targetpoints = list(zip(points_x, points_y)) |
| |
| allpoints = analyzers.panel_to_target([(p.panel_x, p.panel_y) for p in multitap.multi_finger_tap_results], |
| dutinfo) |
| fingerids = np.array([p.finger_id for p in multitap.multi_finger_tap_results]) |
| |
| taperrors = set() |
| # Check if we have the correct number of finger ids |
| uniqids = np.unique(fingerids) |
| if len(uniqids) > multitap.number_of_fingers: |
| taperrors.add('Too many fingers were detected in input') |
| |
| pointsbyid = {} |
| for id in uniqids: |
| pointsbyid[id] = np.array([np.array(p) for pid, p in zip(fingerids, allpoints) if pid == id]) |
| |
| # Map the finger ids in the database to the id's in the points list |
| max_tap_offset = None |
| fingers = [] |
| finger_offsets = [] |
| |
| fingerids = self.find_fingerids(targetpoints, pointsbyid, taperrors) |
| for target_point, ids_in_place in zip(targetpoints, fingerids): |
| fingerpoints = {} |
| distances = {} |
| finger = {'target': target_point, |
| 'maxposerror': float(settings['maxposerror']), |
| 'points': fingerpoints, |
| 'distances': distances, |
| 'verdict': False} |
| fingers.append(finger) |
| if ids_in_place is None or len(ids_in_place) == 0: |
| # Missing finger |
| # taperrors.add('Not all fingers were detected in input') |
| finger_offsets.append(None) |
| missing_tap_inputs += 1 |
| continue |
| |
| max_finger_offset = None |
| for id in ids_in_place: |
| fingerpoints[id] = pointsbyid[id] |
| fdistances = npl.norm(fingerpoints[id] - target_point, axis=1) |
| distances[id] = [analyzers.round_dec(d) for d in fdistances] |
| max_id_offset = analyzers.round_dec(np.max(fdistances)) |
| max_finger_offset = max_id_offset if max_finger_offset is None else max(max_id_offset, |
| max_finger_offset) |
| finger['max_input_error'] = max_finger_offset |
| finger_offsets.append(max_finger_offset) |
| |
| if max_finger_offset <= settings['maxposerror']: |
| finger['verdict'] = True |
| # else: |
| # taperrors.add('Maximum offset exceeded') |
| |
| max_tap_offset = None |
| # Find max offset from non-None offsets |
| if finger_offsets.count(None) < len(finger_offsets): |
| max_tap_offset = max([o for o in finger_offsets if o is not None]) |
| |
| verdict = (max_tap_offset is not None and max_tap_offset <= settings['maxposerror'] and len(taperrors) == 0) |
| tap = {'num_fingers': multitap.number_of_fingers, |
| 'targetpoints': targetpoints, |
| 'missing_inputs': missing_tap_inputs, |
| 'offsets': finger_offsets, |
| 'fingerids': fingerids, |
| 'fingers': fingers, |
| 'max_input_offset': max_tap_offset, |
| 'errors': taperrors, |
| 'verdict': verdict, |
| 'verdict_text': 'N/A' if max_tap_offset is None else 'Pass' if verdict else 'Fail', |
| 'image': ImageFactory.create_image_name(self.test_id, 'tapdtls', str(multitap.id))} |
| |
| return tap |
| |
| def find_fingerids(self, targetpoints, pointsbyid, taperrors): |
| ''' Find finger ids for the points sorted by ids. Returns an array, |
| where each id gives the finger_id for the specified point in targetpoints ''' |
| |
| numids = len(pointsbyid.keys()) |
| numpoints = len(targetpoints) |
| |
| if numids == 0: |
| # No measurements found |
| return [None] * numpoints |
| |
| # Find the distances from each median point per id to each of the target points |
| distances = {} |
| # print str(targetpoints) |
| for id in pointsbyid.keys(): |
| # For each fingerid check the closest target point |
| median = np.median(pointsbyid[id], axis=0) |
| # print str(median) |
| dists = [npl.norm(median - p) for p in targetpoints] |
| distances[id] = dists |
| |
| return analyzers.find_closest_id_match(distances) |
