| """ |
| 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, MultifingerSwipeTest, MultifingerSwipeResults |
| 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 MultiFingerSwipeTest(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(5) |
| def create_testclass(*args, **kwargs): |
| return MultiFingerSwipeTest(*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 MultiFingerSwipeTest class """ |
| super(MultiFingerSwipeTest, 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 = results['missing_swipes_verdict'] and results['errors_verdict'] |
| if verdict: |
| if results['offset_verdict'] is None: |
| verdict = None |
| elif results['jitter_verdict'] is None: |
| # what to do??? |
| verdict = results['offset_verdict'] |
| else: |
| verdict = results['offset_verdict'] and results['jitter_verdict'] |
| |
| return "N/A" if verdict is None else "Pass" if verdict else "Fail" |
| |
| # 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(MultiFingerSwipeTest, 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_swipe.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 = results['missing_swipes_verdict'] and results['errors_verdict'] |
| if verdict: |
| if results['offset_verdict'] is None: |
| verdict = None |
| elif results['jitter_verdict'] is None: |
| # what to do??? |
| verdict = results['offset_verdict'] |
| else: |
| verdict = results['offset_verdict'] and results['jitter_verdict'] |
| |
| return stream.render('xhtml'), "N/A" if verdict is None else "Pass" if verdict else "Fail" |
| |
| # 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 == 'swpgen': |
| 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) |
| passed_points = [] |
| failed_points = [] |
| lines = [] |
| for multiswipe in results['swipes']: |
| for finger in multiswipe['fingers']: |
| for points in finger['passed_points'].values(): |
| passed_points.extend(points) |
| for points in finger['failed_points'].values(): |
| failed_points.extend(points) |
| lines.append((finger['swipe_start'], finger['swipe_end'])) |
| pinfo = {'passed_points': passed_points, |
| 'failed_points': failed_points, |
| 'lines': lines} |
| t.Time("Results") |
| title = 'Preview: Multifinger swipe overview ' + self.dut['program'] |
| plot_factory.plot_swipes_on_target(imagepath, pinfo, dutinfo, *args, title=title, **kwargs) |
| t.Time("Image") |
| elif image_name == 'swpdtls': |
| t = Timer(1) |
| dbsession = get_database().session() |
| dutinfo = plotinfo.TestDUTInfo(testdut_id=self.dut['id'], dbsession=dbsession) |
| results = self.read_swipe_results(args[0], dbsession=dbsession, dutinfo=dutinfo, **kwargs) |
| t.Time("Results") |
| title = 'Preview: Multifinger swipe details ID:' + args[0] |
| plot_factory.plot_multifinger_swipedetails(imagepath, results, dutinfo, 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(MultifingerSwipeTest).filter(MultifingerSwipeTest.test_id == self.test_id). \ |
| order_by(MultifingerSwipeTest.id).options(joinedload('multi_finger_swipe_results')).all() |
| |
| s.Time("DB Results") |
| swipes = [] |
| errors = set() |
| max_offset = None |
| max_jitter = None |
| missing_swipes = 0 |
| total_swipes = 0 |
| swipe_id = 0 |
| |
| for multiswipe in results: |
| swipe = self.calculate_swipe_details(multiswipe, dutinfo, **kwargs) |
| swipe_id += 1 |
| swipe['id'] = swipe_id |
| swipes.append(swipe) |
| |
| # Calculate common parameters |
| errors = errors.union(swipe['errors']) |
| max_offset = swipe['max_offset'] if max_offset is None else max(swipe['max_offset'], max_offset) |
| max_jitter = swipe['max_jitter'] if max_jitter is None else max(swipe['max_jitter'], max_jitter) |
| missing_swipes += swipe['missing_swipes'] |
| total_swipes += swipe['num_fingers'] |
| |
| s.Time("Analysis") |
| |
| results = {'swipes': swipes, |
| 'errors': errors, |
| 'offset_verdict': None if max_offset is None else max_offset <= settings['maxoffset'], |
| 'jitter_verdict': None if max_jitter is None else max_jitter <= settings['maxjitter'], |
| 'max_offset': max_offset, |
| 'max_jitter': max_jitter, |
| 'edge_analysis_done': False, |
| 'total_swipes': total_swipes, |
| 'missing_swipes': missing_swipes, |
| 'missing_swipes_verdict': (missing_swipes <= settings['maxmissingswipes']), |
| 'errors_verdict': len(errors) == 0, |
| 'images': [(ImageFactory.create_image_name(self.test_id, 'swpgen'), |
| ImageFactory.create_image_name(self.test_id, 'swpgen', 'detailed')), |
| ], |
| } |
| |
| return results |
| |
| def read_swipe_results(self, swipe_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) |
| |
| multiswipe = dbsession.query(MultifingerSwipeTest).filter(MultifingerSwipeTest.id == swipe_id). \ |
| options(joinedload('multi_finger_swipe_results')).first() |
| |
| return self.calculate_swipe_details(multiswipe, dutinfo, **kwargs) |
| |
| def calculate_swipe_details(self, multiswipe, dutinfo, **kwargs): |
| # Transfer swipe info to individual swipes |
| start_point, end_point = analyzers.robot_to_target([(multiswipe.start_x, multiswipe.start_y), |
| (multiswipe.end_x, multiswipe.end_y)], dutinfo) |
| separation_x = multiswipe.separation_distance * math.cos(math.radians(multiswipe.separation_angle)) |
| separation_y = multiswipe.separation_distance * math.sin(math.radians(multiswipe.separation_angle)) |
| start_points_x = [start_point[0] + i * separation_x for i in range(multiswipe.number_of_fingers)] |
| start_points_y = [start_point[1] + i * separation_y for i in range(multiswipe.number_of_fingers)] |
| end_points_x = [end_point[0] + i * separation_x for i in range(multiswipe.number_of_fingers)] |
| end_points_y = [end_point[1] + i * separation_y for i in range(multiswipe.number_of_fingers)] |
| startpoints = list(zip(start_points_x, start_points_y)) |
| endpoints = list(zip(end_points_x, end_points_y)) |
| |
| allpoints = analyzers.panel_to_target([(p.panel_x, p.panel_y) for p in multiswipe.multi_finger_swipe_results], |
| dutinfo) |
| swipepoints = analyzers.target_to_swipe(allpoints, startpoints[0], endpoints[0]) |
| swipestarts = analyzers.target_to_swipe(startpoints, startpoints[0], endpoints[0]) |
| fingerids = np.array([p.finger_id for p in multiswipe.multi_finger_swipe_results]) |
| |
| swipe_errors = set() |
| # Check if we have the correct number of finger ids |
| uniqids = np.unique(fingerids) |
| if len(uniqids) > multiswipe.number_of_fingers: |
| swipe_errors.add('Too many fingers were detected in input') |
| |
| pointsbyid = {} |
| swipepointsbyid = {} |
| for id in uniqids: |
| pointsbyid[id] = [p for pid, p in zip(fingerids, allpoints) if pid == id] |
| swipepointsbyid[id] = np.array([np.array(p) for pid, p in zip(fingerids, swipepoints) if pid == id]) |
| |
| # Map the finger ids in the database to the id's in the points list |
| max_jitter = None |
| fingers = [] |
| finger_offsets = [] |
| missing_fingers = 0 |
| |
| fingerids = self.find_fingerids(swipestarts, swipepointsbyid, swipe_errors) |
| for startpoint, endpoint, swipestart, ids_in_place in zip(startpoints, endpoints, swipestarts, fingerids): |
| fingerpoints = {} |
| swipepoints = {} |
| passed = {} |
| failed = {} |
| jitters = {} |
| results = [] |
| finger = {'swipe_start': startpoint, |
| 'swipe_end': endpoint, |
| 'points': fingerpoints, |
| 'passed_points': passed, |
| 'failed_points': failed, |
| 'swipe_points': swipepoints, |
| 'jitters': jitters, |
| 'results': results, |
| 'verdict': False} |
| fingers.append(finger) |
| if ids_in_place is None: |
| # Missing finger |
| # swipe_errors.add('Not all fingers were detected in input') |
| finger_offsets.append(None) |
| missing_fingers += 1 |
| continue |
| |
| max_finger_offset = None |
| max_finger_jitter = None |
| for id in ids_in_place: |
| fingerpoints[id] = pointsbyid[id] |
| swipepoints[id] = swipepointsbyid[ |
| id] - swipestart # Transform coordinates to individual swipe coordinates |
| results = analyzers.analyze_swipe_jitter(swipepointsbyid[id], float(settings['jittermask'])) |
| jitters[id] = results['jitters'] |
| offsets = np.abs(swipepointsbyid[id][:, 1] - swipestart[1]) |
| max_id_offset = analyzers.round_dec(np.max(offsets)) |
| results['max_offset'] = max_id_offset |
| # Passed/failed points to visualization |
| passfail_values = [analyzers.round_dec(o) <= settings['maxoffset'] for p, o in |
| zip(swipepoints[id], offsets)] |
| passed[id] = [fingerpoints[id][i] for (i, t) in enumerate(passfail_values) if t] |
| failed[id] = [fingerpoints[id][i] for (i, t) in enumerate(passfail_values) if not t] |
| max_finger_offset = max_id_offset if max_finger_offset is None else max(max_id_offset, |
| max_finger_offset) |
| max_id_jitter = analyzers.round_dec(results['max_jitter']) |
| max_finger_jitter = max_id_jitter if max_finger_jitter is None else max(max_id_jitter, |
| max_finger_jitter) |
| finger['max_offset'] = max_finger_offset |
| finger_offsets.append(max_finger_offset) |
| finger['max_jitter'] = max_finger_jitter |
| if max_finger_jitter is not None: |
| max_jitter = max_finger_jitter if max_jitter is None else max(max_jitter, max_finger_jitter) |
| |
| if max_finger_offset <= settings['maxoffset']: |
| finger['verdict'] = True |
| # else: |
| # swipe_errors.add('Maximum offset exceeded') |
| |
| max_offset = None |
| # Find max offset from non-None offsets |
| if finger_offsets.count(None) < len(finger_offsets): |
| max_offset = max([o for o in finger_offsets if o is not None]) |
| |
| if max_offset is None: |
| verdict = None |
| verdict_text = 'N/A' |
| verdict_class = '' |
| else: |
| verdict = max_offset <= settings['maxoffset'] and max_jitter <= settings['maxjitter'] and ( |
| len(swipe_errors) == 0) |
| verdict_text = 'Pass' if verdict else 'Fail' |
| verdict_class = 'passed' if verdict else 'failed' |
| |
| swipe = {'num_fingers': multiswipe.number_of_fingers, |
| 'startpoints': startpoints, |
| 'endpoints': endpoints, |
| 'missing_swipes': missing_fingers, |
| 'offsets': finger_offsets, |
| 'fingerids': fingerids, |
| 'fingers': fingers, |
| 'max_offset': max_offset, |
| 'max_jitter': max_jitter, |
| 'errors': swipe_errors, |
| 'verdict': verdict, |
| 'verdict_text': verdict_text, |
| 'verdict_class': verdict_class, |
| 'image': ImageFactory.create_image_name(self.test_id, 'swpdtls', str(multiswipe.id))} |
| |
| return swipe |
| |
| def find_fingerids(self, swipestarts, swipepointsbyid, swipe_errors): |
| ''' 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(swipepointsbyid.keys()) |
| numpoints = len(swipestarts) |
| |
| 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 swipepointsbyid.keys(): |
| # For each fingerid check the closest target swipe - very easy in swipe coordinates |
| median_offset = np.median([p[1] for p in swipepointsbyid[id]]) |
| dists = [np.abs(median_offset - p[1]) for p in swipestarts] |
| distances[id] = dists |
| |
| return analyzers.find_closest_id_match(distances) |
