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They also found that the empirical distributions of passenger"},{"category_id":15,"poly":[880.0,205.0,1552.0,205.0,1552.0,236.0,880.0,236.0],"score":0.99,"text":"incidence times (by time of day) had peaks just before the respec-"},{"category_id":15,"poly":[880.0,234.0,1553.0,234.0,1553.0,264.0,880.0,264.0],"score":0.99,"text":"tive average bus departure times. They hypothesized the existence"},{"category_id":15,"poly":[881.0,264.0,1345.0,264.0,1345.0,296.0,881.0,296.0],"score":0.98,"text":"of three classes of passengers: with proportion"},{"category_id":15,"poly":[1362.0,264.0,1552.0,264.0,1552.0,296.0,1362.0,296.0],"score":0.95,"text":"passengers whose"},{"category_id":15,"poly":[880.0,295.0,1552.0,295.0,1552.0,325.0,880.0,325.0],"score":1.0,"text":"time of incidence is causally coincident with that of a bus departure"},{"category_id":15,"poly":[880.0,326.0,1555.0,326.0,1555.0,355.0,880.0,355.0],"score":0.99,"text":"(e.g., because they saw the approaching bus from their home or a"},{"category_id":15,"poly":[881.0,356.0,1195.0,356.0,1195.0,388.0,881.0,388.0],"score":0.99,"text":"shop window); with proportion"},{"category_id":15,"poly":[1279.0,356.0,1553.0,356.0,1553.0,388.0,1279.0,388.0],"score":0.99,"text":", passengers who time their"},{"category_id":15,"poly":[882.0,388.0,1552.0,388.0,1552.0,416.0,882.0,416.0],"score":0.99,"text":"arrivals to minimize expected waiting time; and with proportion"},{"category_id":15,"poly":[1021.0,418.0,1553.0,418.0,1553.0,447.0,1021.0,447.0],"score":1.0,"text":", passengers who are randomly incident. The authors"},{"category_id":15,"poly":[881.0,448.0,989.0,448.0,989.0,477.0,881.0,477.0],"score":1.0,"text":"found that"},{"category_id":15,"poly":[1008.0,448.0,1553.0,448.0,1553.0,477.0,1008.0,477.0],"score":1.0,"text":"was positively correlated with the potential reduction"},{"category_id":15,"poly":[880.0,479.0,1552.0,479.0,1552.0,507.0,880.0,507.0],"score":1.0,"text":"in waiting time (compared with arriving randomly) that resulted"},{"category_id":15,"poly":[882.0,510.0,1551.0,510.0,1551.0,536.0,882.0,536.0],"score":0.97,"text":"from knowledge of the timetable and of service reliability. They also"},{"category_id":15,"poly":[881.0,539.0,943.0,539.0,943.0,568.0,881.0,568.0],"score":1.0,"text":"found"},{"category_id":15,"poly":[963.0,539.0,1553.0,539.0,1553.0,568.0,963.0,568.0],"score":0.99,"text":"to be higher in the peak commuting periods rather than in"},{"category_id":15,"poly":[881.0,568.0,1554.0,568.0,1554.0,599.0,881.0,599.0],"score":0.98,"text":"the off-peak periods, indicating more awareness of the timetable or"},{"category_id":15,"poly":[881.0,599.0,1323.0,599.0,1323.0,627.0,881.0,627.0],"score":0.98,"text":"historical reliability, or both, by commuters."},{"category_id":15,"poly":[905.0,1452.0,1551.0,1452.0,1551.0,1483.0,905.0,1483.0],"score":0.99,"text":"Furth and Muller study the issue in a theoretical context and gener-"},{"category_id":15,"poly":[883.0,1485.0,1553.0,1485.0,1553.0,1514.0,883.0,1514.0],"score":1.0,"text":"ally agree with the above findings (2). They are primarily concerned"},{"category_id":15,"poly":[882.0,1513.0,1553.0,1513.0,1553.0,1545.0,882.0,1545.0],"score":0.99,"text":"with the use of data from automatic vehicle-tracking systems to assess"},{"category_id":15,"poly":[880.0,1545.0,1553.0,1545.0,1553.0,1574.0,880.0,1574.0],"score":0.99,"text":"the impacts of reliability on passenger incidence behavior and wait-"},{"category_id":15,"poly":[881.0,1577.0,1551.0,1577.0,1551.0,1606.0,881.0,1606.0],"score":0.98,"text":"ing times. They propose that passengers will react to unreliability by"},{"category_id":15,"poly":[883.0,1608.0,1551.0,1608.0,1551.0,1637.0,883.0,1637.0],"score":1.0,"text":"departing earlier than they would with reliable services. Randomly"},{"category_id":15,"poly":[880.0,1636.0,1554.0,1636.0,1554.0,1669.0,880.0,1669.0],"score":1.0,"text":"incident unaware passengers will experience unreliability as a more"},{"category_id":15,"poly":[882.0,1669.0,1553.0,1669.0,1553.0,1697.0,882.0,1697.0],"score":0.99,"text":"dispersed distribution of headways and simply allocate additional"},{"category_id":15,"poly":[880.0,1699.0,1551.0,1699.0,1551.0,1726.0,880.0,1726.0],"score":0.97,"text":"time to their trip plan to improve the chance of arriving at their des-"},{"category_id":15,"poly":[881.0,1730.0,1551.0,1730.0,1551.0,1759.0,881.0,1759.0],"score":0.98,"text":"tination on time. Aware passengers, whose incidence is not entirely"},{"category_id":15,"poly":[880.0,1760.0,1552.0,1760.0,1552.0,1789.0,880.0,1789.0],"score":0.99,"text":"random, will react by timing their incidence somewhat earlier than"},{"category_id":15,"poly":[882.0,1792.0,1550.0,1792.0,1550.0,1818.0,882.0,1818.0],"score":0.99,"text":"the scheduled departure time to increase their chance of catching the"},{"category_id":15,"poly":[883.0,1823.0,1552.0,1823.0,1552.0,1849.0,883.0,1849.0],"score":0.99,"text":"desired service. The authors characterize these reactions as the costs"},{"category_id":15,"poly":[883.0,1853.0,1031.0,1853.0,1031.0,1880.0,883.0,1880.0],"score":0.95,"text":"of unreliability."},{"category_id":15,"poly":[907.0,630.0,1553.0,630.0,1553.0,658.0,907.0,658.0],"score":1.0,"text":"Bowman and Turnquist built on the concept of aware and unaware"},{"category_id":15,"poly":[881.0,662.0,1136.0,662.0,1136.0,690.0,881.0,690.0],"score":0.99,"text":"passengers of proportions"},{"category_id":15,"poly":[1155.0,662.0,1196.0,662.0,1196.0,690.0,1155.0,690.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[1264.0,662.0,1553.0,662.0,1553.0,690.0,1264.0,690.0],"score":0.99,"text":",respectively. They proposed"},{"category_id":15,"poly":[881.0,692.0,1208.0,692.0,1208.0,719.0,881.0,719.0],"score":0.99,"text":"a utility-based model to estimate"},{"category_id":15,"poly":[1226.0,692.0,1552.0,692.0,1552.0,719.0,1226.0,719.0],"score":1.0,"text":"and the distribution of incidence"},{"category_id":15,"poly":[880.0,721.0,1554.0,721.0,1554.0,751.0,880.0,751.0],"score":0.99,"text":"times, and thus the mean waiting time, of aware passengers over"},{"category_id":15,"poly":[880.0,752.0,1553.0,752.0,1553.0,780.0,880.0,780.0],"score":0.98,"text":"a given headway as a function of the headway and reliability of"},{"category_id":15,"poly":[880.0,782.0,1081.0,782.0,1081.0,812.0,880.0,812.0],"score":0.99,"text":"bus departure times"},{"category_id":15,"poly":[1113.0,782.0,1552.0,782.0,1552.0,812.0,1113.0,812.0],"score":0.99,"text":". They observed seven bus stops in Chicago,"},{"category_id":15,"poly":[882.0,813.0,1553.0,813.0,1553.0,841.0,882.0,841.0],"score":0.98,"text":"Illinois, each served by a single (different) bus route, between 6:00"},{"category_id":15,"poly":[882.0,844.0,923.0,844.0,923.0,871.0,882.0,871.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[1017.0,844.0,1550.0,844.0,1550.0,871.0,1017.0,871.0],"score":0.97,"text":".for 5 to 10 days each. The bus routes had headways"},{"category_id":15,"poly":[882.0,874.0,955.0,874.0,955.0,902.0,882.0,902.0],"score":0.95,"text":"of 5to"},{"category_id":15,"poly":[1033.0,874.0,1553.0,874.0,1553.0,902.0,1033.0,902.0],"score":0.98,"text":"and a range of reliabilities. The authors found that"},{"category_id":15,"poly":[882.0,906.0,1553.0,906.0,1553.0,933.0,882.0,933.0],"score":0.99,"text":"actual average waiting time was substantially less than predicted"},{"category_id":15,"poly":[881.0,935.0,1443.0,935.0,1443.0,963.0,881.0,963.0],"score":1.0,"text":"by the random incidence model. They estimated that"},{"category_id":15,"poly":[1462.0,935.0,1553.0,935.0,1553.0,963.0,1462.0,963.0],"score":0.96,"text":"was not"},{"category_id":15,"poly":[881.0,966.0,1552.0,966.0,1552.0,994.0,881.0,994.0],"score":0.98,"text":"statistically significantly different from 1.0, which they explain by"},{"category_id":15,"poly":[880.0,994.0,1552.0,994.0,1552.0,1025.0,880.0,1025.0],"score":0.99,"text":"the fact that all observations were taken during peak commuting"},{"category_id":15,"poly":[880.0,1027.0,1552.0,1027.0,1552.0,1054.0,880.0,1054.0],"score":0.99,"text":"times. Their model predicts that the longer the headway and the"},{"category_id":15,"poly":[881.0,1058.0,1554.0,1058.0,1554.0,1086.0,881.0,1086.0],"score":0.99,"text":"more reliable the departures, the more peaked the distribution of"},{"category_id":15,"poly":[881.0,1088.0,1553.0,1088.0,1553.0,1115.0,881.0,1115.0],"score":0.98,"text":"incidence times will be and the closer that peak will be to the next"},{"category_id":15,"poly":[882.0,1119.0,1552.0,1119.0,1552.0,1148.0,882.0,1148.0],"score":1.0,"text":"scheduled departure time. This prediction demonstrates what they"},{"category_id":15,"poly":[882.0,1149.0,1552.0,1149.0,1552.0,1176.0,882.0,1176.0],"score":0.99,"text":"refer to as a safety margin that passengers add to reduce the chance"},{"category_id":15,"poly":[883.0,1181.0,1552.0,1181.0,1552.0,1206.0,883.0,1206.0],"score":0.98,"text":"of missing their bus when the service is known to be somewhat"},{"category_id":15,"poly":[882.0,1210.0,1551.0,1210.0,1551.0,1238.0,882.0,1238.0],"score":0.98,"text":"unreliable. Such a safety margin can also result from unreliability in"},{"category_id":15,"poly":[881.0,1242.0,1553.0,1242.0,1553.0,1269.0,881.0,1269.0],"score":0.99,"text":"passengers' journeys to the public transport stop or station. Bowman"},{"category_id":15,"poly":[882.0,1271.0,1553.0,1271.0,1553.0,1299.0,882.0,1299.0],"score":0.99,"text":"and Turnquist conclude from their model that the random incidence"},{"category_id":15,"poly":[880.0,1301.0,1551.0,1301.0,1551.0,1331.0,880.0,1331.0],"score":0.99,"text":"model underestimates the waiting time benefits of improving reli-"},{"category_id":15,"poly":[882.0,1332.0,1552.0,1332.0,1552.0,1362.0,882.0,1362.0],"score":0.99,"text":"ability and overestimates the waiting time benefits of increasing ser-"},{"category_id":15,"poly":[883.0,1363.0,1552.0,1363.0,1552.0,1392.0,883.0,1392.0],"score":0.99,"text":"vice frequency. This is because as reliability increases passengers"},{"category_id":15,"poly":[882.0,1394.0,1552.0,1394.0,1552.0,1422.0,882.0,1422.0],"score":0.99,"text":"can better predict departure times and so can time their incidence to"},{"category_id":15,"poly":[882.0,1423.0,1159.0,1423.0,1159.0,1452.0,882.0,1452.0],"score":0.99,"text":"decrease their waiting time."},{"category_id":15,"poly":[175.0,235.0,819.0,235.0,819.0,264.0,175.0,264.0],"score":0.99,"text":"After briefly introducing the random incidence model, which is"},{"category_id":15,"poly":[149.0,265.0,818.0,265.0,818.0,295.0,149.0,295.0],"score":0.98,"text":"often assumed to hold at short headways, the balance of this section"},{"category_id":15,"poly":[148.0,298.0,818.0,298.0,818.0,324.0,148.0,324.0],"score":0.98,"text":"reviews six studies of passenger incidence behavior that are moti-"},{"category_id":15,"poly":[148.0,327.0,818.0,327.0,818.0,356.0,148.0,356.0],"score":1.0,"text":"vated by understanding the relationships between service headway,"},{"category_id":15,"poly":[146.0,355.0,820.0,355.0,820.0,388.0,146.0,388.0],"score":0.99,"text":"service reliability, passenger incidence behavior, and passenger"},{"category_id":15,"poly":[149.0,388.0,818.0,388.0,818.0,414.0,149.0,414.0],"score":1.0,"text":"waiting time in a more nuanced fashion than is embedded in the"},{"category_id":15,"poly":[149.0,419.0,818.0,419.0,818.0,445.0,149.0,445.0],"score":1.0,"text":"random incidence assumption (2). Three of these studies depend on"},{"category_id":15,"poly":[147.0,447.0,818.0,447.0,818.0,477.0,147.0,477.0],"score":0.99,"text":"manually collected data, two studies use data from AFC systems,"},{"category_id":15,"poly":[148.0,479.0,819.0,479.0,819.0,507.0,148.0,507.0],"score":0.99,"text":"and one study analyzes the issue purely theoretically. These studies"},{"category_id":15,"poly":[147.0,509.0,819.0,509.0,819.0,537.0,147.0,537.0],"score":0.99,"text":"reveal much about passenger incidence behavior, but all are found"},{"category_id":15,"poly":[147.0,538.0,820.0,538.0,820.0,567.0,147.0,567.0],"score":0.99,"text":"to be limited in their general applicability by the methods with"},{"category_id":15,"poly":[150.0,569.0,818.0,569.0,818.0,597.0,150.0,597.0],"score":0.99,"text":"which they collect information about passengers and the services"},{"category_id":15,"poly":[147.0,599.0,458.0,599.0,458.0,630.0,147.0,630.0],"score":1.0,"text":"those passengers intend to use."},{"category_id":15,"poly":[150.0,1219.0,212.0,1219.0,212.0,1247.0,150.0,1247.0],"score":1.0,"text":"where"},{"category_id":15,"poly":[264.0,1219.0,817.0,1219.0,817.0,1247.0,264.0,1247.0],"score":0.99,"text":"is the probabilistic expectation of some random variable"},{"category_id":15,"poly":[168.0,1248.0,209.0,1248.0,209.0,1275.0,168.0,1275.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[283.0,1248.0,601.0,1248.0,601.0,1275.0,283.0,1275.0],"score":0.97,"text":"is the coefficient of variation of"},{"category_id":15,"poly":[625.0,1248.0,818.0,1248.0,818.0,1275.0,625.0,1275.0],"score":0.96,"text":".a unitless measure"},{"category_id":15,"poly":[148.0,1277.0,345.0,1277.0,345.0,1307.0,148.0,1307.0],"score":0.97,"text":"of the variability of"},{"category_id":15,"poly":[370.0,1277.0,477.0,1277.0,477.0,1307.0,370.0,1307.0],"score":0.99,"text":"defined as"},{"category_id":15,"poly":[906.0,1883.0,1552.0,1883.0,1552.0,1910.0,906.0,1910.0],"score":0.98,"text":"Luethi et al. continued with the analysis of manually collected"},{"category_id":15,"poly":[880.0,1909.0,1552.0,1909.0,1552.0,1945.0,880.0,1945.0],"score":0.99,"text":"data on actual passenger behavior (6). They use the language"},{"category_id":15,"poly":[883.0,1945.0,1552.0,1945.0,1552.0,1972.0,883.0,1972.0],"score":0.99,"text":"of probability to describe two classes of passengers. The first is"},{"category_id":15,"poly":[881.0,1973.0,1552.0,1973.0,1552.0,2003.0,881.0,2003.0],"score":1.0,"text":"timetable-dependent passengers (i.e., the aware passengers), whose"},{"category_id":15,"poly":[881.0,2006.0,1552.0,2006.0,1552.0,2033.0,881.0,2033.0],"score":1.0,"text":"incidence behavior is affected by awareness (possibly gained"},{"category_id":15,"poly":[149.0,748.0,817.0,748.0,817.0,774.0,149.0,774.0],"score":1.0,"text":"One characterization of passenger incidence behavior is that of ran-"},{"category_id":15,"poly":[148.0,777.0,818.0,777.0,818.0,806.0,148.0,806.0],"score":0.99,"text":"dom incidence (3). The key assumption underlying the random inci-"},{"category_id":15,"poly":[148.0,807.0,818.0,807.0,818.0,836.0,148.0,836.0],"score":0.99,"text":"dence model is that the process of passenger arrivals to the public"},{"category_id":15,"poly":[148.0,837.0,819.0,837.0,819.0,866.0,148.0,866.0],"score":0.99,"text":"transport service is independent from the vehicle departure process"},{"category_id":15,"poly":[148.0,868.0,818.0,868.0,818.0,897.0,148.0,897.0],"score":1.0,"text":"of the service. This implies that passengers become incident to the"},{"category_id":15,"poly":[149.0,899.0,817.0,899.0,817.0,925.0,149.0,925.0],"score":0.99,"text":"service at a random time, and thus the instantaneous rate of passen-"},{"category_id":15,"poly":[148.0,928.0,820.0,928.0,820.0,957.0,148.0,957.0],"score":1.0,"text":"ger arrivals to the service is uniform over a given period of time. Let"},{"category_id":15,"poly":[174.0,956.0,214.0,956.0,214.0,990.0,174.0,990.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[239.0,956.0,818.0,956.0,818.0,990.0,239.0,990.0],"score":0.99,"text":"be random variables representing passenger waiting times"},{"category_id":15,"poly":[148.0,988.0,818.0,988.0,818.0,1016.0,148.0,1016.0],"score":1.0,"text":"and service headways, respectively. Under the random incidence"},{"category_id":15,"poly":[149.0,1019.0,818.0,1019.0,818.0,1048.0,149.0,1048.0],"score":0.98,"text":"assumption and the assumption that vehicle capacity is not a binding"},{"category_id":15,"poly":[149.0,1050.0,726.0,1050.0,726.0,1076.0,149.0,1076.0],"score":0.99,"text":"constraint, a classic result of transportation science is that"},{"category_id":15,"poly":[146.0,1793.0,818.0,1793.0,818.0,1822.0,146.0,1822.0],"score":0.98,"text":" Jolliffe and Hutchinson studied bus passenger incidence in South"},{"category_id":15,"poly":[147.0,1825.0,696.0,1825.0,696.0,1852.0,147.0,1852.0],"score":0.97,"text":"London suburbs (5). They observed 10 bus stops for"},{"category_id":15,"poly":[735.0,1825.0,817.0,1825.0,817.0,1852.0,735.0,1852.0],"score":1.0,"text":"perday"},{"category_id":15,"poly":[148.0,1855.0,819.0,1855.0,819.0,1881.0,148.0,1881.0],"score":1.0,"text":"over 8 days, recording the times of passenger incidence and actual"},{"category_id":15,"poly":[148.0,1884.0,819.0,1884.0,819.0,1912.0,148.0,1912.0],"score":0.98,"text":"and scheduled bus departures. They limited their stop selection to"},{"category_id":15,"poly":[146.0,1913.0,819.0,1913.0,819.0,1945.0,146.0,1945.0],"score":1.0,"text":"those served by only a single bus route with a single service pat-"},{"category_id":15,"poly":[147.0,1945.0,819.0,1945.0,819.0,1974.0,147.0,1974.0],"score":0.98,"text":"tern so as to avoid ambiguity about which service a passenger was"},{"category_id":15,"poly":[147.0,1972.0,820.0,1972.0,820.0,2006.0,147.0,2006.0],"score":0.98,"text":"waiting for. The authors found that the actual average passenger"},{"category_id":15,"poly":[149.0,2005.0,323.0,2005.0,323.0,2033.0,149.0,2033.0],"score":0.96,"text":"waitingtimewas"},{"category_id":15,"poly":[374.0,2005.0,819.0,2005.0,819.0,2033.0,374.0,2033.0],"score":1.0,"text":"less than predicted by the random incidence"},{"category_id":15,"poly":[148.0,686.0,625.0,686.0,625.0,721.0,148.0,721.0],"score":0.99,"text":"Random Passenger Incidence Behavior"},{"category_id":15,"poly":[151.0,1434.0,213.0,1434.0,213.0,1462.0,151.0,1462.0],"score":0.99,"text":"where"},{"category_id":15,"poly":[246.0,1434.0,521.0,1434.0,521.0,1462.0,246.0,1462.0],"score":0.98,"text":"is the standard deviation of"},{"category_id":15,"poly":[580.0,1434.0,816.0,1434.0,816.0,1462.0,580.0,1462.0],"score":0.96,"text":".The second expression"},{"category_id":15,"poly":[148.0,1466.0,819.0,1466.0,819.0,1493.0,148.0,1493.0],"score":0.99,"text":"in Equation 1 is particularly useful because it expresses the mean"},{"category_id":15,"poly":[146.0,1496.0,819.0,1496.0,819.0,1525.0,146.0,1525.0],"score":0.99,"text":"passenger waiting time as the sum of two components: the waiting"},{"category_id":15,"poly":[148.0,1526.0,818.0,1526.0,818.0,1553.0,148.0,1553.0],"score":0.98,"text":"time caused by the mean headway (i.e., the reciprocal of service fre-"},{"category_id":15,"poly":[147.0,1557.0,819.0,1557.0,819.0,1584.0,147.0,1584.0],"score":0.99,"text":"quency) and the waiting time caused by the variability of the head-"},{"category_id":15,"poly":[148.0,1588.0,818.0,1588.0,818.0,1612.0,148.0,1612.0],"score":0.97,"text":"ways (which is one measure of service reliability). When the service"},{"category_id":15,"poly":[148.0,1617.0,817.0,1617.0,817.0,1644.0,148.0,1644.0],"score":1.0,"text":"is perfectly reliable with constant headways, the mean waiting time"},{"category_id":15,"poly":[148.0,1646.0,472.0,1646.0,472.0,1677.0,148.0,1677.0],"score":0.99,"text":"will be simply half the headway."},{"category_id":15,"poly":[151.0,176.0,817.0,176.0,817.0,204.0,151.0,204.0],"score":0.99,"text":"dependent on the service headway and the reliability of the departure"},{"category_id":15,"poly":[147.0,205.0,652.0,205.0,652.0,236.0,147.0,236.0],"score":0.99,"text":"time of the service to which passengers are incident."},{"category_id":15,"poly":[149.0,1735.0,702.0,1735.0,702.0,1767.0,149.0,1767.0],"score":0.98,"text":"More Behaviorally Realistic Incidence Models"},{"category_id":15,"poly":[1519.0,98.0,1554.0,98.0,1554.0,125.0,1519.0,125.0],"score":1.0,"text":"53"},{"category_id":15,"poly":[148.0,98.0,322.0,98.0,322.0,123.0,148.0,123.0],"score":1.0,"text":"Frumin and Zhao"}],"page_info":{"page_no":0,"height":2200,"width":1700}}]}
tests/unittest/test_tools/assets/cli_dev/cli_test_01.model.json deleted 100644 → 0
View file @ cad4c585
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They also found that the empirical distributions of passenger"},{"category_id":15,"poly":[880.0,205.0,1552.0,205.0,1552.0,236.0,880.0,236.0],"score":0.99,"text":"incidence times (by time of day) had peaks just before the respec-"},{"category_id":15,"poly":[880.0,234.0,1553.0,234.0,1553.0,264.0,880.0,264.0],"score":0.99,"text":"tive average bus departure times. They hypothesized the existence"},{"category_id":15,"poly":[881.0,264.0,1345.0,264.0,1345.0,296.0,881.0,296.0],"score":0.98,"text":"of three classes of passengers: with proportion"},{"category_id":15,"poly":[1362.0,264.0,1552.0,264.0,1552.0,296.0,1362.0,296.0],"score":0.95,"text":"passengers whose"},{"category_id":15,"poly":[880.0,295.0,1552.0,295.0,1552.0,325.0,880.0,325.0],"score":1.0,"text":"time of incidence is causally coincident with that of a bus departure"},{"category_id":15,"poly":[880.0,326.0,1555.0,326.0,1555.0,355.0,880.0,355.0],"score":0.99,"text":"(e.g., because they saw the approaching bus from their home or a"},{"category_id":15,"poly":[881.0,356.0,1195.0,356.0,1195.0,388.0,881.0,388.0],"score":0.99,"text":"shop window); with proportion"},{"category_id":15,"poly":[1279.0,356.0,1553.0,356.0,1553.0,388.0,1279.0,388.0],"score":0.99,"text":", passengers who time their"},{"category_id":15,"poly":[882.0,388.0,1552.0,388.0,1552.0,416.0,882.0,416.0],"score":0.99,"text":"arrivals to minimize expected waiting time; and with proportion"},{"category_id":15,"poly":[1021.0,418.0,1553.0,418.0,1553.0,447.0,1021.0,447.0],"score":1.0,"text":", passengers who are randomly incident. The authors"},{"category_id":15,"poly":[881.0,448.0,989.0,448.0,989.0,477.0,881.0,477.0],"score":1.0,"text":"found that"},{"category_id":15,"poly":[1008.0,448.0,1553.0,448.0,1553.0,477.0,1008.0,477.0],"score":1.0,"text":"was positively correlated with the potential reduction"},{"category_id":15,"poly":[880.0,479.0,1552.0,479.0,1552.0,507.0,880.0,507.0],"score":1.0,"text":"in waiting time (compared with arriving randomly) that resulted"},{"category_id":15,"poly":[882.0,510.0,1551.0,510.0,1551.0,536.0,882.0,536.0],"score":0.97,"text":"from knowledge of the timetable and of service reliability. They also"},{"category_id":15,"poly":[881.0,539.0,943.0,539.0,943.0,568.0,881.0,568.0],"score":1.0,"text":"found"},{"category_id":15,"poly":[963.0,539.0,1553.0,539.0,1553.0,568.0,963.0,568.0],"score":0.99,"text":"to be higher in the peak commuting periods rather than in"},{"category_id":15,"poly":[881.0,568.0,1554.0,568.0,1554.0,599.0,881.0,599.0],"score":0.98,"text":"the off-peak periods, indicating more awareness of the timetable or"},{"category_id":15,"poly":[881.0,599.0,1323.0,599.0,1323.0,627.0,881.0,627.0],"score":0.98,"text":"historical reliability, or both, by commuters."},{"category_id":15,"poly":[905.0,1452.0,1551.0,1452.0,1551.0,1483.0,905.0,1483.0],"score":0.99,"text":"Furth and Muller study the issue in a theoretical context and gener-"},{"category_id":15,"poly":[883.0,1485.0,1553.0,1485.0,1553.0,1514.0,883.0,1514.0],"score":1.0,"text":"ally agree with the above findings (2). They are primarily concerned"},{"category_id":15,"poly":[882.0,1513.0,1553.0,1513.0,1553.0,1545.0,882.0,1545.0],"score":0.99,"text":"with the use of data from automatic vehicle-tracking systems to assess"},{"category_id":15,"poly":[880.0,1545.0,1553.0,1545.0,1553.0,1574.0,880.0,1574.0],"score":0.99,"text":"the impacts of reliability on passenger incidence behavior and wait-"},{"category_id":15,"poly":[881.0,1577.0,1551.0,1577.0,1551.0,1606.0,881.0,1606.0],"score":0.98,"text":"ing times. They propose that passengers will react to unreliability by"},{"category_id":15,"poly":[883.0,1608.0,1551.0,1608.0,1551.0,1637.0,883.0,1637.0],"score":1.0,"text":"departing earlier than they would with reliable services. Randomly"},{"category_id":15,"poly":[880.0,1636.0,1554.0,1636.0,1554.0,1669.0,880.0,1669.0],"score":1.0,"text":"incident unaware passengers will experience unreliability as a more"},{"category_id":15,"poly":[882.0,1669.0,1553.0,1669.0,1553.0,1697.0,882.0,1697.0],"score":0.99,"text":"dispersed distribution of headways and simply allocate additional"},{"category_id":15,"poly":[880.0,1699.0,1551.0,1699.0,1551.0,1726.0,880.0,1726.0],"score":0.97,"text":"time to their trip plan to improve the chance of arriving at their des-"},{"category_id":15,"poly":[881.0,1730.0,1551.0,1730.0,1551.0,1759.0,881.0,1759.0],"score":0.98,"text":"tination on time. Aware passengers, whose incidence is not entirely"},{"category_id":15,"poly":[880.0,1760.0,1552.0,1760.0,1552.0,1789.0,880.0,1789.0],"score":0.99,"text":"random, will react by timing their incidence somewhat earlier than"},{"category_id":15,"poly":[882.0,1792.0,1550.0,1792.0,1550.0,1818.0,882.0,1818.0],"score":0.99,"text":"the scheduled departure time to increase their chance of catching the"},{"category_id":15,"poly":[883.0,1823.0,1552.0,1823.0,1552.0,1849.0,883.0,1849.0],"score":0.99,"text":"desired service. The authors characterize these reactions as the costs"},{"category_id":15,"poly":[883.0,1853.0,1031.0,1853.0,1031.0,1880.0,883.0,1880.0],"score":0.95,"text":"of unreliability."},{"category_id":15,"poly":[907.0,630.0,1553.0,630.0,1553.0,658.0,907.0,658.0],"score":1.0,"text":"Bowman and Turnquist built on the concept of aware and unaware"},{"category_id":15,"poly":[881.0,662.0,1136.0,662.0,1136.0,690.0,881.0,690.0],"score":0.99,"text":"passengers of proportions"},{"category_id":15,"poly":[1155.0,662.0,1196.0,662.0,1196.0,690.0,1155.0,690.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[1264.0,662.0,1553.0,662.0,1553.0,690.0,1264.0,690.0],"score":0.99,"text":",respectively. They proposed"},{"category_id":15,"poly":[881.0,692.0,1208.0,692.0,1208.0,719.0,881.0,719.0],"score":0.99,"text":"a utility-based model to estimate"},{"category_id":15,"poly":[1226.0,692.0,1552.0,692.0,1552.0,719.0,1226.0,719.0],"score":1.0,"text":"and the distribution of incidence"},{"category_id":15,"poly":[880.0,721.0,1554.0,721.0,1554.0,751.0,880.0,751.0],"score":0.99,"text":"times, and thus the mean waiting time, of aware passengers over"},{"category_id":15,"poly":[880.0,752.0,1553.0,752.0,1553.0,780.0,880.0,780.0],"score":0.98,"text":"a given headway as a function of the headway and reliability of"},{"category_id":15,"poly":[880.0,782.0,1081.0,782.0,1081.0,812.0,880.0,812.0],"score":0.99,"text":"bus departure times"},{"category_id":15,"poly":[1113.0,782.0,1552.0,782.0,1552.0,812.0,1113.0,812.0],"score":0.99,"text":". They observed seven bus stops in Chicago,"},{"category_id":15,"poly":[882.0,813.0,1553.0,813.0,1553.0,841.0,882.0,841.0],"score":0.98,"text":"Illinois, each served by a single (different) bus route, between 6:00"},{"category_id":15,"poly":[882.0,844.0,923.0,844.0,923.0,871.0,882.0,871.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[1017.0,844.0,1550.0,844.0,1550.0,871.0,1017.0,871.0],"score":0.97,"text":".for 5 to 10 days each. The bus routes had headways"},{"category_id":15,"poly":[882.0,874.0,955.0,874.0,955.0,902.0,882.0,902.0],"score":0.95,"text":"of 5to"},{"category_id":15,"poly":[1033.0,874.0,1553.0,874.0,1553.0,902.0,1033.0,902.0],"score":0.98,"text":"and a range of reliabilities. The authors found that"},{"category_id":15,"poly":[882.0,906.0,1553.0,906.0,1553.0,933.0,882.0,933.0],"score":0.99,"text":"actual average waiting time was substantially less than predicted"},{"category_id":15,"poly":[881.0,935.0,1443.0,935.0,1443.0,963.0,881.0,963.0],"score":1.0,"text":"by the random incidence model. They estimated that"},{"category_id":15,"poly":[1462.0,935.0,1553.0,935.0,1553.0,963.0,1462.0,963.0],"score":0.96,"text":"was not"},{"category_id":15,"poly":[881.0,966.0,1552.0,966.0,1552.0,994.0,881.0,994.0],"score":0.98,"text":"statistically significantly different from 1.0, which they explain by"},{"category_id":15,"poly":[880.0,994.0,1552.0,994.0,1552.0,1025.0,880.0,1025.0],"score":0.99,"text":"the fact that all observations were taken during peak commuting"},{"category_id":15,"poly":[880.0,1027.0,1552.0,1027.0,1552.0,1054.0,880.0,1054.0],"score":0.99,"text":"times. Their model predicts that the longer the headway and the"},{"category_id":15,"poly":[881.0,1058.0,1554.0,1058.0,1554.0,1086.0,881.0,1086.0],"score":0.99,"text":"more reliable the departures, the more peaked the distribution of"},{"category_id":15,"poly":[881.0,1088.0,1553.0,1088.0,1553.0,1115.0,881.0,1115.0],"score":0.98,"text":"incidence times will be and the closer that peak will be to the next"},{"category_id":15,"poly":[882.0,1119.0,1552.0,1119.0,1552.0,1148.0,882.0,1148.0],"score":1.0,"text":"scheduled departure time. This prediction demonstrates what they"},{"category_id":15,"poly":[882.0,1149.0,1552.0,1149.0,1552.0,1176.0,882.0,1176.0],"score":0.99,"text":"refer to as a safety margin that passengers add to reduce the chance"},{"category_id":15,"poly":[883.0,1181.0,1552.0,1181.0,1552.0,1206.0,883.0,1206.0],"score":0.98,"text":"of missing their bus when the service is known to be somewhat"},{"category_id":15,"poly":[882.0,1210.0,1551.0,1210.0,1551.0,1238.0,882.0,1238.0],"score":0.98,"text":"unreliable. Such a safety margin can also result from unreliability in"},{"category_id":15,"poly":[881.0,1242.0,1553.0,1242.0,1553.0,1269.0,881.0,1269.0],"score":0.99,"text":"passengers' journeys to the public transport stop or station. Bowman"},{"category_id":15,"poly":[882.0,1271.0,1553.0,1271.0,1553.0,1299.0,882.0,1299.0],"score":0.99,"text":"and Turnquist conclude from their model that the random incidence"},{"category_id":15,"poly":[880.0,1301.0,1551.0,1301.0,1551.0,1331.0,880.0,1331.0],"score":0.99,"text":"model underestimates the waiting time benefits of improving reli-"},{"category_id":15,"poly":[882.0,1332.0,1552.0,1332.0,1552.0,1362.0,882.0,1362.0],"score":0.99,"text":"ability and overestimates the waiting time benefits of increasing ser-"},{"category_id":15,"poly":[883.0,1363.0,1552.0,1363.0,1552.0,1392.0,883.0,1392.0],"score":0.99,"text":"vice frequency. This is because as reliability increases passengers"},{"category_id":15,"poly":[882.0,1394.0,1552.0,1394.0,1552.0,1422.0,882.0,1422.0],"score":0.99,"text":"can better predict departure times and so can time their incidence to"},{"category_id":15,"poly":[882.0,1423.0,1159.0,1423.0,1159.0,1452.0,882.0,1452.0],"score":0.99,"text":"decrease their waiting time."},{"category_id":15,"poly":[175.0,235.0,819.0,235.0,819.0,264.0,175.0,264.0],"score":0.99,"text":"After briefly introducing the random incidence model, which is"},{"category_id":15,"poly":[149.0,265.0,818.0,265.0,818.0,295.0,149.0,295.0],"score":0.98,"text":"often assumed to hold at short headways, the balance of this section"},{"category_id":15,"poly":[148.0,298.0,818.0,298.0,818.0,324.0,148.0,324.0],"score":0.98,"text":"reviews six studies of passenger incidence behavior that are moti-"},{"category_id":15,"poly":[148.0,327.0,818.0,327.0,818.0,356.0,148.0,356.0],"score":1.0,"text":"vated by understanding the relationships between service headway,"},{"category_id":15,"poly":[146.0,355.0,820.0,355.0,820.0,388.0,146.0,388.0],"score":0.99,"text":"service reliability, passenger incidence behavior, and passenger"},{"category_id":15,"poly":[149.0,388.0,818.0,388.0,818.0,414.0,149.0,414.0],"score":1.0,"text":"waiting time in a more nuanced fashion than is embedded in the"},{"category_id":15,"poly":[149.0,419.0,818.0,419.0,818.0,445.0,149.0,445.0],"score":1.0,"text":"random incidence assumption (2). Three of these studies depend on"},{"category_id":15,"poly":[147.0,447.0,818.0,447.0,818.0,477.0,147.0,477.0],"score":0.99,"text":"manually collected data, two studies use data from AFC systems,"},{"category_id":15,"poly":[148.0,479.0,819.0,479.0,819.0,507.0,148.0,507.0],"score":0.99,"text":"and one study analyzes the issue purely theoretically. These studies"},{"category_id":15,"poly":[147.0,509.0,819.0,509.0,819.0,537.0,147.0,537.0],"score":0.99,"text":"reveal much about passenger incidence behavior, but all are found"},{"category_id":15,"poly":[147.0,538.0,820.0,538.0,820.0,567.0,147.0,567.0],"score":0.99,"text":"to be limited in their general applicability by the methods with"},{"category_id":15,"poly":[150.0,569.0,818.0,569.0,818.0,597.0,150.0,597.0],"score":0.99,"text":"which they collect information about passengers and the services"},{"category_id":15,"poly":[147.0,599.0,458.0,599.0,458.0,630.0,147.0,630.0],"score":1.0,"text":"those passengers intend to use."},{"category_id":15,"poly":[150.0,1219.0,212.0,1219.0,212.0,1247.0,150.0,1247.0],"score":1.0,"text":"where"},{"category_id":15,"poly":[264.0,1219.0,817.0,1219.0,817.0,1247.0,264.0,1247.0],"score":0.99,"text":"is the probabilistic expectation of some random variable"},{"category_id":15,"poly":[168.0,1248.0,209.0,1248.0,209.0,1275.0,168.0,1275.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[283.0,1248.0,601.0,1248.0,601.0,1275.0,283.0,1275.0],"score":0.97,"text":"is the coefficient of variation of"},{"category_id":15,"poly":[625.0,1248.0,818.0,1248.0,818.0,1275.0,625.0,1275.0],"score":0.96,"text":".a unitless measure"},{"category_id":15,"poly":[148.0,1277.0,345.0,1277.0,345.0,1307.0,148.0,1307.0],"score":0.97,"text":"of the variability of"},{"category_id":15,"poly":[370.0,1277.0,477.0,1277.0,477.0,1307.0,370.0,1307.0],"score":0.99,"text":"defined as"},{"category_id":15,"poly":[906.0,1883.0,1552.0,1883.0,1552.0,1910.0,906.0,1910.0],"score":0.98,"text":"Luethi et al. continued with the analysis of manually collected"},{"category_id":15,"poly":[880.0,1909.0,1552.0,1909.0,1552.0,1945.0,880.0,1945.0],"score":0.99,"text":"data on actual passenger behavior (6). They use the language"},{"category_id":15,"poly":[883.0,1945.0,1552.0,1945.0,1552.0,1972.0,883.0,1972.0],"score":0.99,"text":"of probability to describe two classes of passengers. The first is"},{"category_id":15,"poly":[881.0,1973.0,1552.0,1973.0,1552.0,2003.0,881.0,2003.0],"score":1.0,"text":"timetable-dependent passengers (i.e., the aware passengers), whose"},{"category_id":15,"poly":[881.0,2006.0,1552.0,2006.0,1552.0,2033.0,881.0,2033.0],"score":1.0,"text":"incidence behavior is affected by awareness (possibly gained"},{"category_id":15,"poly":[149.0,748.0,817.0,748.0,817.0,774.0,149.0,774.0],"score":1.0,"text":"One characterization of passenger incidence behavior is that of ran-"},{"category_id":15,"poly":[148.0,777.0,818.0,777.0,818.0,806.0,148.0,806.0],"score":0.99,"text":"dom incidence (3). The key assumption underlying the random inci-"},{"category_id":15,"poly":[148.0,807.0,818.0,807.0,818.0,836.0,148.0,836.0],"score":0.99,"text":"dence model is that the process of passenger arrivals to the public"},{"category_id":15,"poly":[148.0,837.0,819.0,837.0,819.0,866.0,148.0,866.0],"score":0.99,"text":"transport service is independent from the vehicle departure process"},{"category_id":15,"poly":[148.0,868.0,818.0,868.0,818.0,897.0,148.0,897.0],"score":1.0,"text":"of the service. This implies that passengers become incident to the"},{"category_id":15,"poly":[149.0,899.0,817.0,899.0,817.0,925.0,149.0,925.0],"score":0.99,"text":"service at a random time, and thus the instantaneous rate of passen-"},{"category_id":15,"poly":[148.0,928.0,820.0,928.0,820.0,957.0,148.0,957.0],"score":1.0,"text":"ger arrivals to the service is uniform over a given period of time. Let"},{"category_id":15,"poly":[174.0,956.0,214.0,956.0,214.0,990.0,174.0,990.0],"score":1.0,"text":"and"},{"category_id":15,"poly":[239.0,956.0,818.0,956.0,818.0,990.0,239.0,990.0],"score":0.99,"text":"be random variables representing passenger waiting times"},{"category_id":15,"poly":[148.0,988.0,818.0,988.0,818.0,1016.0,148.0,1016.0],"score":1.0,"text":"and service headways, respectively. Under the random incidence"},{"category_id":15,"poly":[149.0,1019.0,818.0,1019.0,818.0,1048.0,149.0,1048.0],"score":0.98,"text":"assumption and the assumption that vehicle capacity is not a binding"},{"category_id":15,"poly":[149.0,1050.0,726.0,1050.0,726.0,1076.0,149.0,1076.0],"score":0.99,"text":"constraint, a classic result of transportation science is that"},{"category_id":15,"poly":[146.0,1793.0,818.0,1793.0,818.0,1822.0,146.0,1822.0],"score":0.98,"text":" Jolliffe and Hutchinson studied bus passenger incidence in South"},{"category_id":15,"poly":[147.0,1825.0,696.0,1825.0,696.0,1852.0,147.0,1852.0],"score":0.97,"text":"London suburbs (5). They observed 10 bus stops for"},{"category_id":15,"poly":[735.0,1825.0,817.0,1825.0,817.0,1852.0,735.0,1852.0],"score":1.0,"text":"perday"},{"category_id":15,"poly":[148.0,1855.0,819.0,1855.0,819.0,1881.0,148.0,1881.0],"score":1.0,"text":"over 8 days, recording the times of passenger incidence and actual"},{"category_id":15,"poly":[148.0,1884.0,819.0,1884.0,819.0,1912.0,148.0,1912.0],"score":0.98,"text":"and scheduled bus departures. They limited their stop selection to"},{"category_id":15,"poly":[146.0,1913.0,819.0,1913.0,819.0,1945.0,146.0,1945.0],"score":1.0,"text":"those served by only a single bus route with a single service pat-"},{"category_id":15,"poly":[147.0,1945.0,819.0,1945.0,819.0,1974.0,147.0,1974.0],"score":0.98,"text":"tern so as to avoid ambiguity about which service a passenger was"},{"category_id":15,"poly":[147.0,1972.0,820.0,1972.0,820.0,2006.0,147.0,2006.0],"score":0.98,"text":"waiting for. The authors found that the actual average passenger"},{"category_id":15,"poly":[149.0,2005.0,323.0,2005.0,323.0,2033.0,149.0,2033.0],"score":0.96,"text":"waitingtimewas"},{"category_id":15,"poly":[374.0,2005.0,819.0,2005.0,819.0,2033.0,374.0,2033.0],"score":1.0,"text":"less than predicted by the random incidence"},{"category_id":15,"poly":[148.0,686.0,625.0,686.0,625.0,721.0,148.0,721.0],"score":0.99,"text":"Random Passenger Incidence Behavior"},{"category_id":15,"poly":[151.0,1434.0,213.0,1434.0,213.0,1462.0,151.0,1462.0],"score":0.99,"text":"where"},{"category_id":15,"poly":[246.0,1434.0,521.0,1434.0,521.0,1462.0,246.0,1462.0],"score":0.98,"text":"is the standard deviation of"},{"category_id":15,"poly":[580.0,1434.0,816.0,1434.0,816.0,1462.0,580.0,1462.0],"score":0.96,"text":".The second expression"},{"category_id":15,"poly":[148.0,1466.0,819.0,1466.0,819.0,1493.0,148.0,1493.0],"score":0.99,"text":"in Equation 1 is particularly useful because it expresses the mean"},{"category_id":15,"poly":[146.0,1496.0,819.0,1496.0,819.0,1525.0,146.0,1525.0],"score":0.99,"text":"passenger waiting time as the sum of two components: the waiting"},{"category_id":15,"poly":[148.0,1526.0,818.0,1526.0,818.0,1553.0,148.0,1553.0],"score":0.98,"text":"time caused by the mean headway (i.e., the reciprocal of service fre-"},{"category_id":15,"poly":[147.0,1557.0,819.0,1557.0,819.0,1584.0,147.0,1584.0],"score":0.99,"text":"quency) and the waiting time caused by the variability of the head-"},{"category_id":15,"poly":[148.0,1588.0,818.0,1588.0,818.0,1612.0,148.0,1612.0],"score":0.97,"text":"ways (which is one measure of service reliability). When the service"},{"category_id":15,"poly":[148.0,1617.0,817.0,1617.0,817.0,1644.0,148.0,1644.0],"score":1.0,"text":"is perfectly reliable with constant headways, the mean waiting time"},{"category_id":15,"poly":[148.0,1646.0,472.0,1646.0,472.0,1677.0,148.0,1677.0],"score":0.99,"text":"will be simply half the headway."},{"category_id":15,"poly":[151.0,176.0,817.0,176.0,817.0,204.0,151.0,204.0],"score":0.99,"text":"dependent on the service headway and the reliability of the departure"},{"category_id":15,"poly":[147.0,205.0,652.0,205.0,652.0,236.0,147.0,236.0],"score":0.99,"text":"time of the service to which passengers are incident."},{"category_id":15,"poly":[149.0,1735.0,702.0,1735.0,702.0,1767.0,149.0,1767.0],"score":0.98,"text":"More Behaviorally Realistic Incidence Models"},{"category_id":15,"poly":[1519.0,98.0,1554.0,98.0,1554.0,125.0,1519.0,125.0],"score":1.0,"text":"53"},{"category_id":15,"poly":[148.0,98.0,322.0,98.0,322.0,123.0,148.0,123.0],"score":1.0,"text":"Frumin and Zhao"}],"page_info":{"page_no":0,"height":2200,"width":1700}}]
\ No newline at end of file
tests/unittest/test_tools/test_cli.py deleted 100644 → 0
View file @ cad4c585
import os
import shutil
import tempfile
from click.testing import CliRunner
from magic_pdf.tools.cli import cli
def test_cli_pdf():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/tools'
filename = 'cli_test_01'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir='/tmp/magic_pdf/unittest/tools')
# run
runner = CliRunner()
result = runner.invoke(
cli,
[
'-p',
'tests/unittest/test_tools/assets/cli/pdf/cli_test_01.pdf',
'-o',
temp_output_dir,
],
)
# check
assert result.exit_code == 0
base_output_dir = os.path.join(temp_output_dir, 'cli_test_01/auto')
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
assert os.path.exists(os.path.join(base_output_dir, 'images')) is True
assert os.path.isdir(os.path.join(base_output_dir, 'images')) is True
assert os.path.exists(os.path.join(base_output_dir, f'{filename}_content_list.json')) is True
# teardown
shutil.rmtree(temp_output_dir)
def test_cli_path():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/tools'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir='/tmp/magic_pdf/unittest/tools')
# run
runner = CliRunner()
result = runner.invoke(
cli, ['-p', 'tests/unittest/test_tools/assets/cli/path', '-o', temp_output_dir]
)
# check
assert result.exit_code == 0
filename = 'cli_test_01'
base_output_dir = os.path.join(temp_output_dir, 'cli_test_01/auto')
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
assert os.path.exists(os.path.join(base_output_dir, 'images')) is True
assert os.path.isdir(os.path.join(base_output_dir, 'images')) is True
assert os.path.exists(os.path.join(base_output_dir, f'{filename}_content_list.json')) is True
base_output_dir = os.path.join(temp_output_dir, 'cli_test_02/auto')
filename = 'cli_test_02'
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 5000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
assert os.path.exists(os.path.join(base_output_dir, 'images')) is True
assert os.path.isdir(os.path.join(base_output_dir, 'images')) is True
assert os.path.exists(os.path.join(base_output_dir, f'{filename}_content_list.json')) is True
# teardown
shutil.rmtree(temp_output_dir)
tests/unittest/test_tools/test_cli_dev.py deleted 100644 → 0
View file @ cad4c585
import os
import shutil
import tempfile
from click.testing import CliRunner
from magic_pdf.tools import cli_dev
def test_cli_pdf():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/tools'
filename = 'cli_test_01'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir='/tmp/magic_pdf/unittest/tools')
# run
runner = CliRunner()
result = runner.invoke(
cli_dev.cli,
[
'pdf',
'-p',
'tests/unittest/test_tools/assets/cli/pdf/cli_test_01.pdf',
'-j',
'tests/unittest/test_tools/assets/cli_dev/cli_test_01.model.json',
'-o',
temp_output_dir,
],
)
# check
assert result.exit_code == 0
base_output_dir = os.path.join(temp_output_dir, 'cli_test_01/auto')
r = os.stat(os.path.join(base_output_dir, f'{filename}_content_list.json'))
assert r.st_size > 5000
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
assert os.path.exists(os.path.join(base_output_dir, 'images')) is True
assert os.path.isdir(os.path.join(base_output_dir, 'images')) is True
# teardown
shutil.rmtree(temp_output_dir)
def test_cli_jsonl():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/tools'
filename = 'cli_test_01'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir='/tmp/magic_pdf/unittest/tools')
def mock_read_s3_path(s3path):
with open(s3path, 'rb') as f:
return f.read()
cli_dev.read_s3_path = mock_read_s3_path # mock
# run
runner = CliRunner()
result = runner.invoke(
cli_dev.cli,
[
'jsonl',
'-j',
'tests/unittest/test_tools/assets/cli_dev/cli_test_01.jsonl',
'-o',
temp_output_dir,
],
)
# check
assert result.exit_code == 0
base_output_dir = os.path.join(temp_output_dir, 'cli_test_01/auto')
r = os.stat(os.path.join(base_output_dir, f'{filename}_content_list.json'))
assert r.st_size > 5000
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
assert os.path.exists(os.path.join(base_output_dir, 'images')) is True
assert os.path.isdir(os.path.join(base_output_dir, 'images')) is True
# teardown
shutil.rmtree(temp_output_dir)
tests/unittest/test_tools/test_common.py deleted 100644 → 0
View file @ cad4c585
import os
import shutil
import tempfile
import pytest
from magic_pdf.tools.common import do_parse
@pytest.mark.parametrize('method', ['auto', 'txt', 'ocr'])
def test_common_do_parse(method):
import magic_pdf.model as model_config
model_config.__use_inside_model__ = True
# setup
unitest_dir = '/tmp/magic_pdf/unittest/tools'
filename = 'fake'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir='/tmp/magic_pdf/unittest/tools')
# run
with open('tests/unittest/test_tools/assets/common/cli_test_01.pdf', 'rb') as f:
bits = f.read()
do_parse(temp_output_dir,
filename,
bits, [],
method,
False,
f_dump_content_list=True)
# check
base_output_dir = os.path.join(temp_output_dir, f'fake/{method}')
r = os.stat(os.path.join(base_output_dir, f'{filename}_content_list.json'))
assert r.st_size > 5000
r = os.stat(os.path.join(base_output_dir, f'{filename}.md'))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, f'{filename}_middle.json'))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, f'{filename}_model.json'))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, f'{filename}_origin.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_layout.pdf'))
assert r.st_size > 400000
r = os.stat(os.path.join(base_output_dir, f'{filename}_spans.pdf'))
assert r.st_size > 400000
os.path.exists(os.path.join(base_output_dir, 'images'))
os.path.isdir(os.path.join(base_output_dir, 'images'))
# teardown
shutil.rmtree(temp_output_dir)
tests/unittest/test_unit.py deleted 100644 → 0
View file @ cad4c585
import os
import pytest
from magic_pdf.libs.boxbase import (__is_overlaps_y_exceeds_threshold,
_is_bottom_full_overlap, _is_in,
_is_in_or_part_overlap,
_is_in_or_part_overlap_with_area_ratio,
_is_left_overlap, _is_part_overlap,
_is_vertical_full_overlap, _left_intersect,
_right_intersect, bbox_distance,
bbox_relative_pos, calculate_iou,
calculate_overlap_area_2_minbox_area_ratio,
calculate_overlap_area_in_bbox1_area_ratio,
find_bottom_nearest_text_bbox,
find_left_nearest_text_bbox,
find_right_nearest_text_bbox,
find_top_nearest_text_bbox,
get_bbox_in_boundary,
get_minbox_if_overlap_by_ratio)
from magic_pdf.libs.commons import get_top_percent_list, join_path, mymax
from magic_pdf.libs.config_reader import get_s3_config
from magic_pdf.libs.path_utils import parse_s3path
# 输入一个列表，如果列表空返回0，否则返回最大元素
@pytest.mark.parametrize('list_input, target_num',
[
([0, 0, 0, 0], 0),
([0], 0),
([1, 2, 5, 8, 4], 8),
([], 0),
([1.1, 7.6, 1.009, 9.9], 9.9),
([1.0 * 10 ** 2, 3.5 * 10 ** 3, 0.9 * 10 ** 6], 0.9 * 10 ** 6),
])
def test_list_max(list_input: list, target_num) -> None:
"""
list_input: 输入列表元素，元素均为数字类型
"""
assert target_num == mymax(list_input)
# 连接多个参数生成路径信息，使用"/"作为连接符，生成的结果需要是一个合法路径
@pytest.mark.parametrize('path_input, target_path', [
(['https:', '', 'www.baidu.com'], 'https://www.baidu.com'),
(['https:', 'www.baidu.com'], 'https:/www.baidu.com'),
(['D:', 'file', 'pythonProject', 'demo' + '.py'], 'D:/file/pythonProject/demo.py'),
])
def test_join_path(path_input: list, target_path: str) -> None:
"""
path_input: 输入path的列表，列表元素均为字符串
"""
assert target_path == join_path(*path_input)
# 获取列表中前百分之多少的元素
@pytest.mark.parametrize('num_list, percent, target_num_list', [
([], 0.75, []),
([-5, -10, 9, 3, 7, -7, 0, 23, -1, -11], 0.8, [23, 9, 7, 3, 0, -1, -5, -7]),
([-5, -10, 9, 3, 7, -7, 0, 23, -1, -11], 0, []),
([-5, -10, 9, 3, 7, -7, 0, 23, -1, -11, 28], 0.8, [28, 23, 9, 7, 3, 0, -1, -5])
])
def test_get_top_percent_list(num_list: list, percent: float, target_num_list: list) -> None:
"""
num_list: 数字列表，列表元素为数字
percent: 占比，float, 向下取证
"""
assert target_num_list == get_top_percent_list(num_list, percent)
# 输入一个s3路径，返回bucket名字和其余部分(key)
@pytest.mark.parametrize('s3_path, target_data', [
('s3://bucket/path/to/my/file.txt', 'bucket'),
('s3a://bucket1/path/to/my/file2.txt', 'bucket1'),
# ("/path/to/my/file1.txt", "path"),
# ("bucket/path/to/my/file2.txt", "bucket"),
])
def test_parse_s3path(s3_path: str, target_data: str):
"""
s3_path: s3路径
如果为无效路径，则返回对应的bucket名字和其余部分
如果为异常路径 例如：file2.txt，则报异常
"""
bucket_name, key = parse_s3path(s3_path)
assert target_data == bucket_name
# 2个box是否处于包含或者部分重合关系。
# 如果某边界重合算重合。
# 部分边界重合，其他在内部也算包含
@pytest.mark.parametrize('box1, box2, target_bool', [
((120, 133, 223, 248), (128, 168, 269, 295), True),
((137, 53, 245, 157), (134, 11, 200, 147), True), # 部分重合
((137, 56, 211, 116), (140, 66, 202, 199), True), # 部分重合
((42, 34, 69, 65), (42, 34, 69, 65), True), # 部分重合
((39, 63, 87, 106), (37, 66, 85, 109), True), # 部分重合
((13, 37, 55, 66), (7, 46, 49, 75), True), # 部分重合
((56, 83, 85, 104), (64, 85, 93, 106), True), # 部分重合
((12, 53, 48, 94), (14, 53, 50, 94), True), # 部分重合
((43, 54, 93, 131), (55, 82, 77, 106), True), # 包含
((63, 2, 134, 71), (72, 43, 104, 78), True), # 包含
((25, 57, 109, 127), (26, 73, 49, 95), True), # 包含
((24, 47, 111, 115), (34, 81, 58, 106), True), # 包含
((34, 8, 105, 83), (76, 20, 116, 45), True), # 包含
])
def test_is_in_or_part_overlap(box1: tuple, box2: tuple, target_bool: bool) -> None:
"""
box1: 坐标数组
box2: 坐标数组
"""
assert target_bool == _is_in_or_part_overlap(box1, box2)
# 如果box1在box2内部，返回True
# 如果是部分重合的，则重合面积占box1的比例大于阈值时候返回True
@pytest.mark.parametrize('box1, box2, target_bool', [
((35, 28, 108, 90), (47, 60, 83, 96), False), # 包含 box1 up box2, box2 多半,box1少半
((65, 151, 92, 177), (49, 99, 105, 198), True), # 包含 box1 in box2
((80, 62, 112, 84), (74, 40, 144, 111), True), # 包含 box1 in box2
((65, 88, 127, 144), (92, 102, 131, 139), False), # 包含 box2 多半，box1约一半
((92, 102, 131, 139), (65, 88, 127, 144), True), # 包含 box1 多半
((100, 93, 199, 168), (169, 126, 198, 165), False), # 包含 box2 in box1
((26, 75, 106, 172), (65, 108, 90, 128), False), # 包含 box2 in box1
((28, 90, 77, 126), (35, 84, 84, 120), True), # 相交 box1多半，box2多半
((37, 6, 69, 52), (28, 3, 60, 49), True), # 相交 box1多半，box2多半
((94, 29, 133, 60), (84, 30, 123, 61), True), # 相交 box1多半，box2多半
])
def test_is_in_or_part_overlap_with_area_ratio(box1: tuple, box2: tuple, target_bool: bool) -> None:
out_bool = _is_in_or_part_overlap_with_area_ratio(box1, box2)
assert target_bool == out_bool
# box1在box2内部或者box2在box1内部返回True。如果部分边界重合也算作包含。
@pytest.mark.parametrize('box1, box2, target_bool', [
# ((), (), "Error"), # Error
((65, 151, 92, 177), (49, 99, 105, 198), True), # 包含 box1 in box2
((80, 62, 112, 84), (74, 40, 144, 111), True), # 包含 box1 in box2
((76, 140, 154, 277), (121, 326, 192, 384), False), # 分离
((65, 88, 127, 144), (92, 102, 131, 139), False), # 包含 box2 多半，box1约一半
((92, 102, 131, 139), (65, 88, 127, 144), False), # 包含 box1 多半
((68, 94, 118, 120), (68, 90, 118, 122), True), # 包含，box1 in box2 两边x相切
((69, 94, 118, 120), (68, 90, 118, 122), True), # 包含，box1 in box2 一边x相切
((69, 114, 118, 122), (68, 90, 118, 122), True), # 包含，box1 in box2 一边y相切
# ((100, 93, 199, 168), (169, 126, 198, 165), True), # 包含 box2 in box1 Error
# ((26, 75, 106, 172), (65, 108, 90, 128), True), # 包含 box2 in box1 Error
# ((38, 94, 122, 120), (68, 94, 118, 120), True), # 包含，box2 in box1 两边y相切 Error
# ((68, 34, 118, 158), (68, 94, 118, 120), True), # 包含，box2 in box1 两边x相切 Error
# ((68, 34, 118, 158), (68, 94, 84, 120), True), # 包含，box2 in box1 一边x相切 Error
# ((27, 94, 118, 158), (68, 94, 84, 120), True), # 包含，box2 in box1 一边y相切 Error
])
def test_is_in(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _is_in(box1, box2)
# 仅仅是部分包含关系，返回True，如果是完全包含关系则返回False
@pytest.mark.parametrize('box1, box2, target_bool', [
((65, 151, 92, 177), (49, 99, 105, 198), False), # 包含 box1 in box2
((80, 62, 112, 84), (74, 40, 144, 111), False), # 包含 box1 in box2
# ((76, 140, 154, 277), (121, 326, 192, 384), False), # 分离 Error
((76, 140, 154, 277), (121, 277, 192, 384), True), # 外相切
((65, 88, 127, 144), (92, 102, 131, 139), True), # 包含 box2 多半，box1约一半
((92, 102, 131, 139), (65, 88, 127, 144), True), # 包含 box1 多半
((68, 94, 118, 120), (68, 90, 118, 122), False), # 包含，box1 in box2 两边x相切
((69, 94, 118, 120), (68, 90, 118, 122), False), # 包含，box1 in box2 一边x相切
((69, 114, 118, 122), (68, 90, 118, 122), False), # 包含，box1 in box2 一边y相切
# ((26, 75, 106, 172), (65, 108, 90, 128), False), # 包含 box2 in box1 Error
# ((38, 94, 122, 120), (68, 94, 118, 120), False), # 包含，box2 in box1 两边y相切 Error
# ((68, 34, 118, 158), (68, 94, 84, 120), False), # 包含，box2 in box1 一边x相切 Error
])
def test_is_part_overlap(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _is_part_overlap(box1, box2)
# left_box右侧是否和right_box左侧有部分重叠
@pytest.mark.parametrize('box1, box2, target_bool', [
(None, None, False),
((88, 81, 222, 173), (60, 221, 123, 358), False), # 分离
((121, 149, 184, 289), (172, 130, 230, 268), True), # box1 left bottom box2 相交
((172, 130, 230, 268), (121, 149, 184, 289), False), # box2 left bottom box1 相交
((109, 68, 182, 146), (215, 188, 277, 253), False), # box1 top left box2 分离
((117, 53, 222, 176), (174, 142, 298, 276), True), # box1 left top box2 相交
((174, 142, 298, 276), (117, 53, 222, 176), False), # box2 left top box1 相交
((65, 88, 127, 144), (92, 102, 131, 139), True), # box1 left box2 y:box2 in box1
((92, 102, 131, 139), (65, 88, 127, 144), False), # box2 left box1 y:box1 in box2
((182, 130, 230, 268), (121, 149, 174, 289), False), # box2 left box1 分离
((1, 10, 26, 45), (3, 4, 20, 39), True), # box1 bottom box2 x:box2 in box1
])
def test_left_intersect(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _left_intersect(box1, box2)
# left_box左侧是否和right_box右侧部分重叠
@pytest.mark.parametrize('box1, box2, target_bool', [
(None, None, False),
((88, 81, 222, 173), (60, 221, 123, 358), False), # 分离
((121, 149, 184, 289), (172, 130, 230, 268), False), # box1 left bottom box2 相交
((172, 130, 230, 268), (121, 149, 184, 289), True), # box2 left bottom box1 相交
((109, 68, 182, 146), (215, 188, 277, 253), False), # box1 top left box2 分离
((117, 53, 222, 176), (174, 142, 298, 276), False), # box1 left top box2 相交
((174, 142, 298, 276), (117, 53, 222, 176), True), # box2 left top box1 相交
((65, 88, 127, 144), (92, 102, 131, 139), False), # box1 left box2 y:box2 in box1
# ((92, 102, 131, 139), (65, 88, 127, 144), True), # box2 left box1 y:box1 in box2 Error
((182, 130, 230, 268), (121, 149, 174, 289), False), # box2 left box1 分离
# ((1, 10, 26, 45), (3, 4, 20, 39), False), # box1 bottom box2 x:box2 in box1 Error
])
def test_right_intersect(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _right_intersect(box1, box2)
# x方向上：要么box1包含box2, 要么box2包含box1。不能部分包含
# y方向上：box1和box2有重叠
@pytest.mark.parametrize('box1, box2, target_bool', [
# (None, None, False), # Error
((35, 28, 108, 90), (47, 60, 83, 96), True), # box1 top box2, x:box2 in box1, y:有重叠
((35, 28, 98, 90), (27, 60, 103, 96), True), # box1 top box2, x:box1 in box2, y:有重叠
((57, 77, 130, 210), (59, 219, 119, 293), False), # box1 top box2, x: box2 in box1, y:无重叠
((47, 60, 83, 96), (35, 28, 108, 90), True), # box2 top box1, x:box1 in box2, y:有重叠
((27, 60, 103, 96), (35, 28, 98, 90), True), # box2 top box1, x:box2 in box1, y:有重叠
((59, 219, 119, 293), (57, 77, 130, 210), False), # box2 top box1, x: box1 in box2, y:无重叠
((35, 28, 55, 90), (57, 60, 83, 96), False), # box1 top box2, x:无重叠, y:有重叠
((47, 60, 63, 96), (65, 28, 108, 90), False), # box2 top box1, x:无重叠, y:有重叠
])
def test_is_vertical_full_overlap(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _is_vertical_full_overlap(box1, box2)
# 检查box1下方和box2的上方有轻微的重叠，轻微程度收到y_tolerance的限制
@pytest.mark.parametrize('box1, box2, target_bool', [
(None, None, False),
((35, 28, 108, 90), (47, 89, 83, 116), True), # box1 top box2, y:有重叠
((35, 28, 108, 90), (47, 60, 83, 96), False), # box1 top box2, y:有重叠且过多
((57, 77, 130, 210), (59, 219, 119, 293), False), # box1 top box2, y:无重叠
((47, 60, 83, 96), (35, 28, 108, 90), False), # box2 top box1, y:有重叠且过多
((27, 89, 103, 116), (35, 28, 98, 90), False), # box2 top box1, y:有重叠
((59, 219, 119, 293), (57, 77, 130, 210), False), # box2 top box1, y:无重叠
])
def test_is_bottom_full_overlap(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _is_bottom_full_overlap(box1, box2)
# 检查box1的左侧是否和box2有重叠
@pytest.mark.parametrize('box1, box2, target_bool', [
(None, None, False),
((88, 81, 222, 173), (60, 221, 123, 358), False), # 分离
# ((121, 149, 184, 289), (172, 130, 230, 268), False), # box1 left bottom box2 相交 Error
# ((172, 130, 230, 268), (121, 149, 184, 289), True), # box2 left bottom box1 相交 Error
((109, 68, 182, 146), (215, 188, 277, 253), False), # box1 top left box2 分离
((117, 53, 222, 176), (174, 142, 298, 276), False), # box1 left top box2 相交
# ((174, 142, 298, 276), (117, 53, 222, 176), True), # box2 left top box1 相交 Error
# ((65, 88, 127, 144), (92, 102, 131, 139), False), # box1 left box2 y:box2 in box1 Error
((1, 10, 26, 45), (3, 4, 20, 39), True), # box1 middle bottom box2 x:box2 in box1
])
def test_is_left_overlap(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == _is_left_overlap(box1, box2)
# 查两个bbox在y轴上是否有重叠，并且该重叠区域的高度占两个bbox高度更低的那个超过阈值
@pytest.mark.parametrize('box1, box2, target_bool', [
# (None, None, "Error"), # Error
((51, 69, 192, 147), (75, 48, 132, 187), True), # y: box1 in box2
((51, 39, 192, 197), (75, 48, 132, 187), True), # y: box2 in box1
((88, 81, 222, 173), (60, 221, 123, 358), False), # y: box1 top box2
((109, 68, 182, 196), (215, 188, 277, 253), False), # y: box1 top box2 little
((109, 68, 182, 196), (215, 78, 277, 253), True), # y: box1 top box2 more
((109, 68, 182, 196), (215, 138, 277, 213), False), # y: box1 top box2 more but lower overlap_ratio_threshold
((109, 68, 182, 196), (215, 138, 277, 203), True), # y: box1 top box2 more and more overlap_ratio_threshold
])
def test_is_overlaps_y_exceeds_threshold(box1: tuple, box2: tuple, target_bool: bool) -> None:
assert target_bool == __is_overlaps_y_exceeds_threshold(box1, box2)
# Determine the coordinates of the intersection rectangle
@pytest.mark.parametrize('box1, box2, target_num', [
# (None, None, "Error"), # Error
((88, 81, 222, 173), (60, 221, 123, 358), 0.0), # 分离
((76, 140, 154, 277), (121, 326, 192, 384), 0.0), # 分离
((142, 109, 238, 164), (134, 211, 224, 270), 0.0), # 分离
((109, 68, 182, 196), (175, 138, 277, 213), 0.024475524475524476), # 相交
((56, 90, 170, 219), (103, 212, 171, 304), 0.02288586346557361), # 相交
((109, 126, 204, 245), (130, 127, 232, 186), 0.33696071621517326), # 相交
((109, 126, 204, 245), (110, 127, 232, 206), 0.5493822593770807), # 相交
((76, 140, 154, 277), (121, 277, 192, 384), 0.0) # 相切
])
def test_calculate_iou(box1: tuple, box2: tuple, target_num: float) -> None:
assert target_num == calculate_iou(box1, box2)
# 计算box1和box2的重叠面积占最小面积的box的比例
@pytest.mark.parametrize('box1, box2, target_num', [
# (None, None, "Error"), # Error
((142, 109, 238, 164), (134, 211, 224, 270), 0.0), # 分离
((88, 81, 222, 173), (60, 221, 123, 358), 0.0), # 分离
((76, 140, 154, 277), (121, 326, 192, 384), 0.0), # 分离
((76, 140, 154, 277), (121, 277, 192, 384), 0.0), # 相切
((109, 126, 204, 245), (110, 127, 232, 206), 0.7704918032786885), # 相交
((56, 90, 170, 219), (103, 212, 171, 304), 0.07496803069053709), # 相交
((121, 149, 184, 289), (172, 130, 230, 268), 0.17841079460269865), # 相交
((51, 69, 192, 147), (75, 48, 132, 187), 0.5611510791366906), # 相交
((117, 53, 222, 176), (174, 142, 298, 276), 0.12636469221835075), # 相交
((102, 60, 233, 203), (70, 190, 220, 319), 0.08188757807078417), # 相交
((109, 126, 204, 245), (130, 127, 232, 186), 0.7254901960784313), # 相交
])
def test_calculate_overlap_area_2_minbox_area_ratio(box1: tuple, box2: tuple, target_num: float) -> None:
assert target_num == calculate_overlap_area_2_minbox_area_ratio(box1, box2)
# 计算box1和box2的重叠面积占bbox1的比例
@pytest.mark.parametrize('box1, box2, target_num', [
# (None, None, "Error"), # Error
((142, 109, 238, 164), (134, 211, 224, 270), 0.0), # 分离
((88, 81, 222, 173), (60, 221, 123, 358), 0.0), # 分离
((76, 140, 154, 277), (121, 326, 192, 384), 0.0), # 分离
((76, 140, 154, 277), (121, 277, 192, 384), 0.0), # 相切
((142, 109, 238, 164), (134, 164, 224, 270), 0.0), # 相切
((109, 126, 204, 245), (110, 127, 232, 206), 0.6568774878372402), # 相交
((56, 90, 170, 219), (103, 212, 171, 304), 0.03189174486604107), # 相交
((121, 149, 184, 289), (172, 130, 230, 268), 0.1619047619047619), # 相交
((51, 69, 192, 147), (75, 48, 132, 187), 0.40425531914893614), # 相交
((117, 53, 222, 176), (174, 142, 298, 276), 0.12636469221835075), # 相交
((102, 60, 233, 203), (70, 190, 220, 319), 0.08188757807078417), # 相交
((109, 126, 204, 245), (130, 127, 232, 186), 0.38620079610791685), # 相交
])
def test_calculate_overlap_area_in_bbox1_area_ratio(box1: tuple, box2: tuple, target_num: float) -> None:
assert target_num == calculate_overlap_area_in_bbox1_area_ratio(box1, box2)
# 计算两个bbox重叠的面积占最小面积的box的比例，如果比例大于ratio，则返回小的那个bbox,否则返回None
@pytest.mark.parametrize('box1, box2, ratio, target_box', [
# (None, None, 0.8, "Error"), # Error
((142, 109, 238, 164), (134, 211, 224, 270), 0.0, None), # 分离
((109, 126, 204, 245), (110, 127, 232, 206), 0.5, (110, 127, 232, 206)),
((56, 90, 170, 219), (103, 212, 171, 304), 0.5, None),
((121, 149, 184, 289), (172, 130, 230, 268), 0.5, None),
((51, 69, 192, 147), (75, 48, 132, 187), 0.5, (75, 48, 132, 187)),
((117, 53, 222, 176), (174, 142, 298, 276), 0.5, None),
((102, 60, 233, 203), (70, 190, 220, 319), 0.5, None),
((109, 126, 204, 245), (130, 127, 232, 186), 0.5, (130, 127, 232, 186)),
])
def test_get_minbox_if_overlap_by_ratio(box1: tuple, box2: tuple, ratio: float, target_box: list) -> None:
assert target_box == get_minbox_if_overlap_by_ratio(box1, box2, ratio)
# 根据boundry获取在这个范围内的所有的box的列表，完全包含关系
@pytest.mark.parametrize('boxes, boundary, target_boxs', [
# ([], (), "Error"), # Error
([], (110, 340, 209, 387), []),
([(142, 109, 238, 164)], (134, 211, 224, 270), []), # 分离
([(109, 126, 204, 245), (110, 127, 232, 206)], (105, 116, 258, 300), [(109, 126, 204, 245), (110, 127, 232, 206)]),
([(109, 126, 204, 245), (110, 127, 232, 206)], (105, 116, 258, 230), [(110, 127, 232, 206)]),
([(81, 280, 123, 315), (282, 203, 342, 247), (183, 100, 300, 155), (46, 99, 133, 148), (33, 156, 97, 211),
(137, 29, 287, 87)], (80, 90, 249, 200), []),
([(81, 280, 123, 315), (282, 203, 342, 247), (183, 100, 300, 155), (46, 99, 133, 148), (33, 156, 97, 211),
(137, 29, 287, 87)], (30, 20, 349, 320),
[(81, 280, 123, 315), (282, 203, 342, 247), (183, 100, 300, 155), (46, 99, 133, 148), (33, 156, 97, 211),
(137, 29, 287, 87)]),
([(81, 280, 123, 315), (282, 203, 342, 247), (183, 100, 300, 155), (46, 99, 133, 148), (33, 156, 97, 211),
(137, 29, 287, 87)], (30, 20, 200, 320),
[(81, 280, 123, 315), (46, 99, 133, 148), (33, 156, 97, 211)]),
])
def test_get_bbox_in_boundary(boxes: list, boundary: tuple, target_boxs: list) -> None:
assert target_boxs == get_bbox_in_boundary(boxes, boundary)
# 寻找上方距离最近的box,margin 4个单位， x方向有重合，y方向最近的
@pytest.mark.parametrize('pymu_blocks, obj_box, target_boxs', [
([{'bbox': (81, 280, 123, 315)}, {'bbox': (282, 203, 342, 247)}, {'bbox': (183, 100, 300, 155)},
{'bbox': (46, 99, 133, 148)}, {'bbox': (33, 156, 97, 211)},
{'bbox': (137, 29, 287, 87)}], (81, 280, 123, 315), {'bbox': (33, 156, 97, 211)}),
# ([{"bbox": (168, 120, 263, 159)},
# {"bbox": (231, 61, 279, 159)},
# {"bbox": (35, 85, 136, 110)},
# {"bbox": (228, 193, 347, 225)},
# {"bbox": (144, 264, 188, 323)},
# {"bbox": (62, 37, 126, 64)}], (228, 193, 347, 225),
# [{"bbox": (168, 120, 263, 159)}, {"bbox": (231, 61, 279, 159)}]), # y：方向最近的有两个，x: 两个均有重合 Error
([{'bbox': (35, 85, 136, 159)},
{'bbox': (168, 120, 263, 159)},
{'bbox': (231, 61, 279, 118)},
{'bbox': (228, 193, 347, 225)},
{'bbox': (144, 264, 188, 323)},
{'bbox': (62, 37, 126, 64)}], (228, 193, 347, 225),
{'bbox': (168, 120, 263, 159)},), # y:方向最近的有两个，x:只有一个有重合
([{'bbox': (239, 115, 379, 167)},
{'bbox': (33, 237, 104, 262)},
{'bbox': (124, 288, 168, 325)},
{'bbox': (242, 291, 379, 340)},
{'bbox': (55, 117, 121, 154)},
{'bbox': (266, 183, 384, 217)}, ], (124, 288, 168, 325), {'bbox': (55, 117, 121, 154)}),
([{'bbox': (239, 115, 379, 167)},
{'bbox': (33, 237, 104, 262)},
{'bbox': (124, 288, 168, 325)},
{'bbox': (242, 291, 379, 340)},
{'bbox': (55, 117, 119, 154)},
{'bbox': (266, 183, 384, 217)}, ], (124, 288, 168, 325), None), # x没有重合
([{'bbox': (80, 90, 249, 200)},
{'bbox': (183, 100, 240, 155)}, ], (183, 100, 240, 155), None), # 包含
])
def test_find_top_nearest_text_bbox(pymu_blocks: list, obj_box: tuple, target_boxs: dict) -> None:
assert target_boxs == find_top_nearest_text_bbox(pymu_blocks, obj_box)
# 寻找下方距离自己最近的box, x方向有重合，y方向最近的
@pytest.mark.parametrize('pymu_blocks, obj_box, target_boxs', [
([{'bbox': (165, 96, 300, 114)},
{'bbox': (11, 157, 139, 201)},
{'bbox': (124, 208, 265, 262)},
{'bbox': (124, 283, 248, 306)},
{'bbox': (39, 267, 84, 301)},
{'bbox': (36, 89, 114, 145)}, ], (165, 96, 300, 114), {'bbox': (124, 208, 265, 262)}),
([{'bbox': (187, 37, 303, 49)},
{'bbox': (2, 227, 90, 283)},
{'bbox': (158, 174, 200, 212)},
{'bbox': (259, 174, 324, 228)},
{'bbox': (205, 61, 316, 97)},
{'bbox': (295, 248, 374, 287)}, ], (205, 61, 316, 97), {'bbox': (259, 174, 324, 228)}), # y有两个最近的, x只有一个重合
# ([{"bbox": (187, 37, 303, 49)},
# {"bbox": (2, 227, 90, 283)},
# {"bbox": (259, 174, 324, 228)},
# {"bbox": (205, 61, 316, 97)},
# {"bbox": (295, 248, 374, 287)},
# {"bbox": (158, 174, 209, 212)}, ], (205, 61, 316, 97),
# [{"bbox": (259, 174, 324, 228)}, {"bbox": (158, 174, 209, 212)}]), # x有重合，y有两个最近的 Error
([{'bbox': (287, 132, 398, 191)},
{'bbox': (44, 141, 163, 188)},
{'bbox': (132, 191, 240, 241)},
{'bbox': (81, 25, 142, 67)},
{'bbox': (74, 297, 116, 314)},
{'bbox': (77, 84, 224, 107)}, ], (287, 132, 398, 191), None), # x没有重合
([{'bbox': (80, 90, 249, 200)},
{'bbox': (183, 100, 240, 155)}, ], (183, 100, 240, 155), None), # 包含
])
def test_find_bottom_nearest_text_bbox(pymu_blocks: list, obj_box: tuple, target_boxs: dict) -> None:
assert target_boxs == find_bottom_nearest_text_bbox(pymu_blocks, obj_box)
# 寻找左侧距离自己最近的box, y方向有重叠，x方向最近
@pytest.mark.parametrize('pymu_blocks, obj_box, target_boxs', [
([{'bbox': (80, 90, 249, 200)}, {'bbox': (183, 100, 240, 155)}], (183, 100, 240, 155), None), # 包含
([{'bbox': (28, 90, 77, 126)}, {'bbox': (35, 84, 84, 120)}], (35, 84, 84, 120), None), # y:重叠，x:重叠大于2
([{'bbox': (28, 90, 77, 126)}, {'bbox': (75, 84, 134, 120)}], (75, 84, 134, 120), {'bbox': (28, 90, 77, 126)}),
# y:重叠，x:重叠小于等于2
([{'bbox': (239, 115, 379, 167)},
{'bbox': (33, 237, 104, 262)},
{'bbox': (124, 288, 168, 325)},
{'bbox': (242, 291, 379, 340)},
{'bbox': (55, 113, 161, 154)},
{'bbox': (266, 123, 384, 217)}], (266, 123, 384, 217), {'bbox': (55, 113, 161, 154)}), # y重叠，x left
# ([{"bbox": (136, 219, 268, 240)},
# {"bbox": (169, 115, 268, 181)},
# {"bbox": (33, 237, 104, 262)},
# {"bbox": (124, 288, 168, 325)},
# {"bbox": (55, 117, 161, 154)},
# {"bbox": (266, 183, 384, 217)}], (266, 183, 384, 217),
# [{"bbox": (136, 219, 267, 240)}, {"bbox": (169, 115, 267, 181)}]), # y有重叠，x重叠小于2或者在left Error
])
def test_find_left_nearest_text_bbox(pymu_blocks: list, obj_box: tuple, target_boxs: dict) -> None:
assert target_boxs == find_left_nearest_text_bbox(pymu_blocks, obj_box)
# 寻找右侧距离自己最近的box, y方向有重叠，x方向最近
@pytest.mark.parametrize('pymu_blocks, obj_box, target_boxs', [
([{'bbox': (80, 90, 249, 200)}, {'bbox': (183, 100, 240, 155)}], (183, 100, 240, 155), None), # 包含
([{'bbox': (28, 90, 77, 126)}, {'bbox': (35, 84, 84, 120)}], (28, 90, 77, 126), None), # y:重叠，x:重叠大于2
([{'bbox': (28, 90, 77, 126)}, {'bbox': (75, 84, 134, 120)}], (28, 90, 77, 126), {'bbox': (75, 84, 134, 120)}),
# y:重叠，x:重叠小于等于2
([{'bbox': (239, 115, 379, 167)},
{'bbox': (33, 237, 104, 262)},
{'bbox': (124, 288, 168, 325)},
{'bbox': (242, 291, 379, 340)},
{'bbox': (55, 113, 161, 154)},
{'bbox': (266, 123, 384, 217)}], (55, 113, 161, 154), {'bbox': (239, 115, 379, 167)}), # y重叠，x right
# ([{"bbox": (169, 115, 298, 181)},
# {"bbox": (169, 219, 268, 240)},
# {"bbox": (33, 177, 104, 262)},
# {"bbox": (124, 288, 168, 325)},
# {"bbox": (55, 117, 161, 154)},
# {"bbox": (266, 183, 384, 217)}], (33, 177, 104, 262),
# [{"bbox": (169, 115, 298, 181)}, {"bbox": (169, 219, 268, 240)}]), # y有重叠，x重叠小于2或者在right Error
])
def test_find_right_nearest_text_bbox(pymu_blocks: list, obj_box: tuple, target_boxs: dict) -> None:
assert target_boxs == find_right_nearest_text_bbox(pymu_blocks, obj_box)
# 判断两个矩形框的相对位置关系 (left, right, bottom, top)
@pytest.mark.parametrize('box1, box2, target_box', [
# (None, None, "Error"), # Error
((80, 90, 249, 200), (183, 100, 240, 155), (False, False, False, False)), # 包含
# ((124, 81, 222, 173), (60, 221, 123, 358), (False, True, False, True)), # 分离，右上 Error
((142, 109, 238, 164), (134, 211, 224, 270), (False, False, False, True)), # 分离，上
# ((51, 69, 192, 147), (205, 198, 282, 297), (True, False, False, True)), # 分离，左上 Error
# ((101, 149, 164, 289), (172, 130, 230, 268), (True, False, False, False)), # 分离，左 Error
# ((69, 196, 124, 285), (130, 127, 232, 186), (True, False, True, False)), # 分离，左下 Error
((103, 212, 171, 304), (56, 90, 170, 209), (False, False, True, False)), # 分离，下
# ((124, 367, 222, 415), (60, 221, 123, 358), (False, True, True, False)), # 分离，右下 Error
# ((172, 130, 230, 268), (101, 149, 164, 289), (False, True, False, False)), # 分离，右 Error
])
def test_bbox_relative_pos(box1: tuple, box2: tuple, target_box: tuple) -> None:
assert target_box == bbox_relative_pos(box1, box2)
# 计算两个矩形框的距离
"""
受bbox_relative_pos方法的影响，左右相反，这里计算结果全部受影响，在错误的基础上计算出了正确的结果
"""
@pytest.mark.parametrize('box1, box2, target_num', [
# (None, None, "Error"), # Error
((80, 90, 249, 200), (183, 100, 240, 155), 0.0), # 包含
((142, 109, 238, 164), (134, 211, 224, 270), 47.0), # 分离，上
((103, 212, 171, 304), (56, 90, 170, 209), 3.0), # 分离，下
((101, 149, 164, 289), (172, 130, 230, 268), 8.0), # 分离，左
((172, 130, 230, 268), (101, 149, 164, 289), 8.0), # 分离，右
((80.3, 90.8, 249.0, 200.5), (183.8, 100.6, 240.2, 155.1), 0.0), # 包含
((142.3, 109.5, 238.9, 164.2), (134.4, 211.2, 224.8, 270.1), 47.0), # 分离，上
((103.5, 212.6, 171.1, 304.8), (56.1, 90.9, 170.6, 209.2), 3.4), # 分离，下
((101.1, 149.3, 164.9, 289.0), (172.1, 130.1, 230.5, 268.5), 7.2), # 分离，左
((172.1, 130.3, 230.1, 268.1), (101.2, 149.9, 164.3, 289.1), 7.8), # 分离，右
((124.3, 81.1, 222.5, 173.8), (60.3, 221.5, 123.0, 358.9), 47.717711596429254), # 分离，右上
((51.2, 69.31, 192.5, 147.9), (205.0, 198.1, 282.98, 297.09), 51.73287156151299), # 分离，左上
((124.3, 367.1, 222.9, 415.7), (60.9, 221.4, 123.2, 358.6), 8.570880934886448), # 分离，右下
((69.9, 196.2, 124.1, 285.7), (130.0, 127.3, 232.6, 186.1), 11.69700816448377), # 分离，左下
])
def test_bbox_distance(box1: tuple, box2: tuple, target_num: float) -> None:
assert target_num - bbox_distance(box1, box2) < 1
@pytest.mark.skip(reason='skip')
# 根据bucket_name获取s3配置ak,sk,endpoint
def test_get_s3_config() -> None:
bucket_name = os.getenv('bucket_name')
target_data = os.getenv('target_data')
assert convert_string_to_list(target_data) == list(get_s3_config(bucket_name))
def convert_string_to_list(s):
cleaned_s = s.strip("'")
items = cleaned_s.split(',')
cleaned_items = [item.strip() for item in items]
return cleaned_items
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