Merge branch 'feature/backend/unify-api-communication'
This commit is contained in:
commit
c26d9222bd
@ -18,7 +18,7 @@ jobs:
|
||||
with:
|
||||
registry: git.kluster.moll.re
|
||||
username: ${{ gitea.repository_owner }}
|
||||
password: ${{ secrets.GITEA_TOKEN}}
|
||||
password: ${{ secrets.DOCKER_PUSH_TOKEN }}
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
1
.gitignore
vendored
Normal file
1
.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
||||
cache/
|
||||
@ -8,5 +8,7 @@ numpy = "*"
|
||||
scipy = "*"
|
||||
fastapi = "*"
|
||||
osmpythontools = "*"
|
||||
pydantic = "*"
|
||||
shapely = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
518
backend/Pipfile.lock
generated
518
backend/Pipfile.lock
generated
@ -1,7 +1,7 @@
|
||||
{
|
||||
"_meta": {
|
||||
"hash": {
|
||||
"sha256": "15cc819367b908ccf7d61e3f2b45574c248556600dfeaa795e19045d6781fe90"
|
||||
"sha256": "0f88c01cde3be9a6332acec33fa0ccf13b6e122a6df8ee5cfefa52ba1e98034f"
|
||||
},
|
||||
"pipfile-spec": 6,
|
||||
"requires": {},
|
||||
@ -40,11 +40,11 @@
|
||||
},
|
||||
"certifi": {
|
||||
"hashes": [
|
||||
"sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f",
|
||||
"sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"
|
||||
"sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
|
||||
"sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"
|
||||
],
|
||||
"markers": "python_version >= '3.6'",
|
||||
"version": "==2024.2.2"
|
||||
"version": "==2024.6.2"
|
||||
},
|
||||
"click": {
|
||||
"hashes": [
|
||||
@ -122,11 +122,11 @@
|
||||
},
|
||||
"email-validator": {
|
||||
"hashes": [
|
||||
"sha256:200a70680ba08904be6d1eef729205cc0d687634399a5924d842533efb824b84",
|
||||
"sha256:97d882d174e2a65732fb43bfce81a3a834cbc1bde8bf419e30ef5ea976370a05"
|
||||
"sha256:561977c2d73ce3611850a06fa56b414621e0c8faa9d66f2611407d87465da631",
|
||||
"sha256:cb690f344c617a714f22e66ae771445a1ceb46821152df8e165c5f9a364582b7"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.1.1"
|
||||
"version": "==2.2.0"
|
||||
},
|
||||
"exceptiongroup": {
|
||||
"hashes": [
|
||||
@ -155,51 +155,51 @@
|
||||
},
|
||||
"fonttools": {
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
@ -667,98 +667,107 @@
|
||||
},
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==3.10.3"
|
||||
"version": "==3.10.5"
|
||||
},
|
||||
"osmpythontools": {
|
||||
"hashes": [
|
||||
@ -769,11 +778,11 @@
|
||||
},
|
||||
"packaging": {
|
||||
"hashes": [
|
||||
"sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5",
|
||||
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|
||||
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|
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|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==24.0"
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==24.1"
|
||||
},
|
||||
"pandas": {
|
||||
"hashes": [
|
||||
@ -887,96 +896,97 @@
|
||||
},
|
||||
"pydantic": {
|
||||
"hashes": [
|
||||
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|
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|
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|
||||
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|
||||
],
|
||||
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|
||||
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|
||||
"version": "==2.7.2"
|
||||
"version": "==2.7.4"
|
||||
},
|
||||
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|
||||
"hashes": [
|
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|
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@ -1120,6 +1130,54 @@
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==1.13.1"
|
||||
},
|
||||
"shapely": {
|
||||
"hashes": [
|
||||
"sha256:011b77153906030b795791f2fdfa2d68f1a8d7e40bce78b029782ade3afe4f2f",
|
||||
"sha256:03152442d311a5e85ac73b39680dd64a9892fa42bb08fd83b3bab4fe6999bfa0",
|
||||
"sha256:05ffd6491e9e8958b742b0e2e7c346635033d0a5f1a0ea083547fcc854e5d5cf",
|
||||
"sha256:0776c92d584f72f1e584d2e43cfc5542c2f3dd19d53f70df0900fda643f4bae6",
|
||||
"sha256:263bcf0c24d7a57c80991e64ab57cba7a3906e31d2e21b455f493d4aab534aaa",
|
||||
"sha256:2fbdc1140a7d08faa748256438291394967aa54b40009f54e8d9825e75ef6113",
|
||||
"sha256:30982f79f21bb0ff7d7d4a4e531e3fcaa39b778584c2ce81a147f95be1cd58c9",
|
||||
"sha256:31c19a668b5a1eadab82ff070b5a260478ac6ddad3a5b62295095174a8d26398",
|
||||
"sha256:3f9103abd1678cb1b5f7e8e1af565a652e036844166c91ec031eeb25c5ca8af0",
|
||||
"sha256:41388321a73ba1a84edd90d86ecc8bfed55e6a1e51882eafb019f45895ec0f65",
|
||||
"sha256:4310b5494271e18580d61022c0857eb85d30510d88606fa3b8314790df7f367d",
|
||||
"sha256:464157509ce4efa5ff285c646a38b49f8c5ef8d4b340f722685b09bb033c5ccf",
|
||||
"sha256:485246fcdb93336105c29a5cfbff8a226949db37b7473c89caa26c9bae52a242",
|
||||
"sha256:489c19152ec1f0e5c5e525356bcbf7e532f311bff630c9b6bc2db6f04da6a8b9",
|
||||
"sha256:4f2ab0faf8188b9f99e6a273b24b97662194160cc8ca17cf9d1fb6f18d7fb93f",
|
||||
"sha256:55a38dcd1cee2f298d8c2ebc60fc7d39f3b4535684a1e9e2f39a80ae88b0cea7",
|
||||
"sha256:58b0ecc505bbe49a99551eea3f2e8a9b3b24b3edd2a4de1ac0dc17bc75c9ec07",
|
||||
"sha256:5af4cd0d8cf2912bd95f33586600cac9c4b7c5053a036422b97cfe4728d2eb53",
|
||||
"sha256:5bbd974193e2cc274312da16b189b38f5f128410f3377721cadb76b1e8ca5328",
|
||||
"sha256:5c4849916f71dc44e19ed370421518c0d86cf73b26e8656192fcfcda08218fbd",
|
||||
"sha256:5dc736127fac70009b8d309a0eeb74f3e08979e530cf7017f2f507ef62e6cfb8",
|
||||
"sha256:63f3a80daf4f867bd80f5c97fbe03314348ac1b3b70fb1c0ad255a69e3749879",
|
||||
"sha256:674d7baf0015a6037d5758496d550fc1946f34bfc89c1bf247cabdc415d7747e",
|
||||
"sha256:6cd4ccecc5ea5abd06deeaab52fcdba372f649728050c6143cc405ee0c166679",
|
||||
"sha256:790a168a808bd00ee42786b8ba883307c0e3684ebb292e0e20009588c426da47",
|
||||
"sha256:7d56ce3e2a6a556b59a288771cf9d091470116867e578bebced8bfc4147fbfd7",
|
||||
"sha256:841f93a0e31e4c64d62ea570d81c35de0f6cea224568b2430d832967536308e6",
|
||||
"sha256:8de4578e838a9409b5b134a18ee820730e507b2d21700c14b71a2b0757396acc",
|
||||
"sha256:92a41d936f7d6743f343be265ace93b7c57f5b231e21b9605716f5a47c2879e7",
|
||||
"sha256:9831816a5d34d5170aa9ed32a64982c3d6f4332e7ecfe62dc97767e163cb0b17",
|
||||
"sha256:994c244e004bc3cfbea96257b883c90a86e8cbd76e069718eb4c6b222a56f78b",
|
||||
"sha256:9dab4c98acfb5fb85f5a20548b5c0abe9b163ad3525ee28822ffecb5c40e724c",
|
||||
"sha256:b79bbd648664aa6f44ef018474ff958b6b296fed5c2d42db60078de3cffbc8aa",
|
||||
"sha256:c3e700abf4a37b7b8b90532fa6ed5c38a9bfc777098bc9fbae5ec8e618ac8f30",
|
||||
"sha256:c52ed79f683f721b69a10fb9e3d940a468203f5054927215586c5d49a072de8d",
|
||||
"sha256:c75c98380b1ede1cae9a252c6dc247e6279403fae38c77060a5e6186c95073ac",
|
||||
"sha256:d2b4431f522b277c79c34b65da128029a9955e4481462cbf7ebec23aab61fc58",
|
||||
"sha256:ddf4a9bfaac643e62702ed662afc36f6abed2a88a21270e891038f9a19bc08fc",
|
||||
"sha256:de0205cb21ad5ddaef607cda9a3191eadd1e7a62a756ea3a356369675230ac35",
|
||||
"sha256:ec555c9d0db12d7fd777ba3f8b75044c73e576c720a851667432fabb7057da6c",
|
||||
"sha256:fb5cdcbbe3080181498931b52a91a21a781a35dcb859da741c0345c6402bf00c"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==2.0.4"
|
||||
},
|
||||
"shellingham": {
|
||||
"hashes": [
|
||||
"sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686",
|
||||
@ -1170,11 +1228,11 @@
|
||||
},
|
||||
"typing-extensions": {
|
||||
"hashes": [
|
||||
"sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8",
|
||||
"sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"
|
||||
"sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
|
||||
"sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.12.0"
|
||||
"version": "==4.12.2"
|
||||
},
|
||||
"tzdata": {
|
||||
"hashes": [
|
||||
@ -1273,11 +1331,11 @@
|
||||
"standard"
|
||||
],
|
||||
"hashes": [
|
||||
"sha256:78fa0b5f56abb8562024a59041caeb555c86e48d0efdd23c3fe7de7a4075bdab",
|
||||
"sha256:f678dec4fa3a39706bbf49b9ec5fc40049d42418716cea52b53f07828a60aa37"
|
||||
"sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81",
|
||||
"sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.30.0"
|
||||
"version": "==0.30.1"
|
||||
},
|
||||
"uvloop": {
|
||||
"hashes": [
|
||||
@ -1474,11 +1532,11 @@
|
||||
},
|
||||
"xarray": {
|
||||
"hashes": [
|
||||
"sha256:7ddedfe2294a0ab00f02d0fbdcb9c6300ec589f3cf436a9c7b7b577a12cd9bcf",
|
||||
"sha256:e0eb1cb265f265126795f388ed9591f3c752f2aca491f6c0576711fd15b708f2"
|
||||
"sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
|
||||
"sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==2024.5.0"
|
||||
"version": "==2024.6.0"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@ -1 +0,0 @@
|
||||
import app.src
|
||||
@ -1,34 +0,0 @@
|
||||
from .src.optimizer import solve_optimization
|
||||
from .src.landmarks_manager import Landmark
|
||||
from fastapi import FastAPI
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
|
||||
@app.get("/optimize/{max_steps}/{print_details}")
|
||||
def main(max_steps: int, print_details: bool):
|
||||
|
||||
# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
|
||||
#max_steps = 16
|
||||
|
||||
|
||||
# Initialize all landmarks (+ start and goal). Order matters here
|
||||
landmarks = []
|
||||
landmarks.append(Landmark("départ", -1, (0, 0)))
|
||||
landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
|
||||
landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
|
||||
landmarks.append(Landmark("louvre", 99, (0,6)))
|
||||
landmarks.append(Landmark("montmartre", 99, (0,10)))
|
||||
landmarks.append(Landmark("concorde", 99, (0,8)))
|
||||
landmarks.append(Landmark("arrivée", -1, (0, 0)))
|
||||
|
||||
|
||||
visiting_order = solve_optimization(landmarks, max_steps, print_details)
|
||||
|
||||
#return visiting_order
|
||||
|
||||
return("max steps :", max_steps, "\n", visiting_order)
|
||||
|
||||
|
||||
"""if __name__ == "__main__":
|
||||
main()"""
|
||||
Binary file not shown.
Binary file not shown.
@ -1,57 +0,0 @@
|
||||
from OSMPythonTools.api import Api
|
||||
from OSMPythonTools.overpass import Overpass
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
# Defines the landmark class (aka some place there is to visit)
|
||||
@dataclass
|
||||
class Landmarkkkk :
|
||||
name : str
|
||||
attractiveness : int
|
||||
id : int
|
||||
|
||||
@dataclass
|
||||
class Landmark :
|
||||
name : str
|
||||
attractiveness : int
|
||||
loc : tuple
|
||||
|
||||
# Converts a OSM id to a landmark
|
||||
def add_from_id(id: int, score: int) :
|
||||
|
||||
try :
|
||||
s = 'way/' + str(id) # prepare string for query
|
||||
obj = api.query(s) # object to add
|
||||
except :
|
||||
s = 'relation/' + str(id) # prepare string for query
|
||||
obj = api.query(s) # object to add
|
||||
|
||||
return Landmarkkkk(obj.tag('name:fr'), score, id) # create Landmark out of it
|
||||
|
||||
|
||||
# take a lsit of tuples (id, score) to generate a list of landmarks
|
||||
def generate_landmarks(ids_and_scores: list) :
|
||||
|
||||
L = []
|
||||
for tup in ids_and_scores :
|
||||
L.append(add_from_id(tup[0], tup[1]))
|
||||
|
||||
return L
|
||||
|
||||
api = Api()
|
||||
|
||||
|
||||
l = (7515426, 70)
|
||||
t = (5013364, 100)
|
||||
n = (201611261, 99)
|
||||
a = (226413508, 50)
|
||||
m = (23762981, 30)
|
||||
|
||||
|
||||
ids_and_scores = [t, l, n, a, m]
|
||||
|
||||
landmarks = generate_landmarks(ids_and_scores)
|
||||
|
||||
|
||||
for obj in landmarks :
|
||||
print(obj)
|
||||
@ -1,323 +0,0 @@
|
||||
from scipy.optimize import linprog
|
||||
import numpy as np
|
||||
from scipy.linalg import block_diag
|
||||
|
||||
|
||||
# landmarks = [Landmark_1, Landmark_2, ...]
|
||||
|
||||
# Convert the solution of the optimization into the list of edges to follow. Order is taken into account
|
||||
def untangle(resx: list) :
|
||||
N = len(resx) # length of res
|
||||
L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
|
||||
n_edges = resx.sum() # number of edges
|
||||
|
||||
order = []
|
||||
nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
|
||||
|
||||
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
|
||||
|
||||
indx = nonzero_tup[0].tolist()
|
||||
indy = nonzero_tup[1].tolist()
|
||||
|
||||
vert = (indx[0], indy[0])
|
||||
|
||||
order.append(vert[0])
|
||||
order.append(vert[1])
|
||||
|
||||
while len(order) < n_edges + 1 :
|
||||
ind = indx.index(vert[1])
|
||||
|
||||
vert = (indx[ind], indy[ind])
|
||||
|
||||
order.append(vert[1])
|
||||
|
||||
return order
|
||||
|
||||
# Just to print the result
|
||||
def print_res(res, landmarks: list, P) :
|
||||
X = abs(res.x)
|
||||
order = untangle(X)
|
||||
things = []
|
||||
|
||||
"""N = int(np.sqrt(len(X)))
|
||||
for i in range(N):
|
||||
print(X[i*N:i*N+N])
|
||||
print("Optimal value:", -res.fun) # Minimization, so we negate to get the maximum
|
||||
print("Optimal point:", res.x)
|
||||
for i,x in enumerate(X) : X[i] = round(x,0)
|
||||
print(order)"""
|
||||
|
||||
if (X.sum()+1)**2 == len(X) :
|
||||
print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
|
||||
else :
|
||||
print('Could not visit all the landmarks, the following order is suggested : ')
|
||||
|
||||
for idx in order :
|
||||
print('- ' + landmarks[idx].name)
|
||||
things.append(landmarks[idx].name)
|
||||
|
||||
steps = path_length(P, abs(res.x))
|
||||
print("\nSteps walked : " + str(steps))
|
||||
|
||||
return things
|
||||
|
||||
# Checks for cases of circular symmetry in the result
|
||||
def has_circle(resx: list) :
|
||||
N = len(resx) # length of res
|
||||
L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
|
||||
n_edges = resx.sum() # number of edges
|
||||
|
||||
|
||||
nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
|
||||
|
||||
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
|
||||
|
||||
indx = nonzero_tup[0].tolist()
|
||||
indy = nonzero_tup[1].tolist()
|
||||
|
||||
|
||||
verts = []
|
||||
|
||||
for i, x in enumerate(indx) :
|
||||
verts.append((x, indy[i]))
|
||||
|
||||
|
||||
for vert in verts :
|
||||
visited = []
|
||||
visited.append(vert)
|
||||
|
||||
while len(visited) < n_edges + 1 :
|
||||
|
||||
try :
|
||||
ind = indx.index(vert[1])
|
||||
|
||||
vert = (indx[ind], indy[ind])
|
||||
|
||||
if vert in visited :
|
||||
return visited
|
||||
else :
|
||||
visited.append(vert)
|
||||
except :
|
||||
break
|
||||
|
||||
return []
|
||||
|
||||
# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
|
||||
def break_sym(landmarks, A_ub, b_ub):
|
||||
L = len(landmarks)
|
||||
upper_ind = np.triu_indices(L,0,L)
|
||||
|
||||
up_ind_x = upper_ind[0]
|
||||
up_ind_y = upper_ind[1]
|
||||
|
||||
for i, _ in enumerate(up_ind_x) :
|
||||
l = [0]*L*L
|
||||
if up_ind_x[i] != up_ind_y[i] :
|
||||
l[up_ind_x[i]*L + up_ind_y[i]] = 1
|
||||
l[up_ind_y[i]*L + up_ind_x[i]] = 1
|
||||
|
||||
A_ub = np.vstack((A_ub,l))
|
||||
b_ub.append(1)
|
||||
|
||||
"""for i in range(7):
|
||||
print(l[i*7:i*7+7])
|
||||
print("\n")"""
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
# Constraint to not have circular paths. Want to go from start -> finish without unconnected loops
|
||||
def break_circle(landmarks, A_ub, b_ub, circle) :
|
||||
N = len(landmarks)
|
||||
l = [0]*N*N
|
||||
|
||||
for index in circle :
|
||||
x = index[0]
|
||||
y = index[1]
|
||||
l[x*N+y] = 1
|
||||
|
||||
A_ub = np.vstack((A_ub,l))
|
||||
b_ub.append(len(circle)-1)
|
||||
|
||||
"""print("\n\nPREVENT CIRCLE")
|
||||
for i in range(7):
|
||||
print(l[i*7:i*7+7])
|
||||
print("\n")"""
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
# Constraint to respect max number of travels
|
||||
def respect_number(landmarks, A_ub, b_ub):
|
||||
h = []
|
||||
for i in range(len(landmarks)) : h.append([1]*len(landmarks))
|
||||
T = block_diag(*h)
|
||||
"""for l in T :
|
||||
for i in range(7):
|
||||
print(l[i*7:i*7+7])
|
||||
print("\n")"""
|
||||
return np.vstack((A_ub, T)), b_ub + [1]*len(landmarks)
|
||||
|
||||
# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
|
||||
def respect_order(landmarks: list, A_eq, b_eq):
|
||||
N = len(landmarks)
|
||||
for i in range(N-1) : # Prevent stacked ones
|
||||
if i == 0 :
|
||||
continue
|
||||
else :
|
||||
l = [0]*N
|
||||
l[i] = -1
|
||||
l = l*N
|
||||
for j in range(N) :
|
||||
l[i*N + j] = 1
|
||||
|
||||
A_eq = np.vstack((A_eq,l))
|
||||
b_eq.append(0)
|
||||
|
||||
"""for i in range(7):
|
||||
print(l[i*7:i*7+7])
|
||||
print("\n")"""
|
||||
|
||||
return A_eq, b_eq
|
||||
|
||||
# Compute manhattan distance between 2 locations
|
||||
def manhattan_distance(loc1: tuple, loc2: tuple):
|
||||
x1, y1 = loc1
|
||||
x2, y2 = loc2
|
||||
return abs(x1 - x2) + abs(y1 - y2)
|
||||
|
||||
# Constraint to not stay in position
|
||||
def init_eq_not_stay(landmarks):
|
||||
L = len(landmarks)
|
||||
l = [0]*L*L
|
||||
|
||||
|
||||
for i in range(L) :
|
||||
for j in range(L) :
|
||||
if j == i :
|
||||
l[j + i*L] = 1
|
||||
l[L-1] = 1 # cannot skip from start to finish
|
||||
#A_eq = np.array([np.array(xi) for xi in A_eq]) # Must convert A_eq into an np array
|
||||
l = np.array(np.array(l))
|
||||
|
||||
"""for i in range(7):
|
||||
print(l[i*7:i*7+7])"""
|
||||
|
||||
return [l], [0]
|
||||
|
||||
# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
|
||||
# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
|
||||
def init_ub_dist(landmarks: list, max_steps: int):
|
||||
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
|
||||
c = []
|
||||
# Coefficients of inequality constraints (left-hand side)
|
||||
A = []
|
||||
for i, spot1 in enumerate(landmarks) :
|
||||
dist_table = [0]*len(landmarks)
|
||||
c.append(-spot1.attractiveness)
|
||||
for j, spot2 in enumerate(landmarks) :
|
||||
dist_table[j] = manhattan_distance(spot1.loc, spot2.loc)
|
||||
A.append(dist_table)
|
||||
c = c*len(landmarks)
|
||||
A_ub = []
|
||||
for line in A :
|
||||
#print(line)
|
||||
A_ub += line
|
||||
return c, A_ub, [max_steps]
|
||||
|
||||
# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
|
||||
def respect_user_mustsee(landmarks: list, A_eq: list, b_eq: list) :
|
||||
L = len(landmarks)
|
||||
H = 0 # sort of heuristic to get an idea of the number of steps needed
|
||||
for i in landmarks :
|
||||
if i.name == "départ" : elem_prev = i # list of all matches
|
||||
for i, elem in enumerate(landmarks) :
|
||||
if elem.attractiveness == -1 :
|
||||
l = [0]*L*L
|
||||
if elem.name != "arrivée" :
|
||||
for j in range(L) :
|
||||
l[j +i*L] = 1
|
||||
|
||||
else : # This ensures we go to goal
|
||||
for k in range(L-1) :
|
||||
l[k*L+L-1] = 1
|
||||
|
||||
H += manhattan_distance(elem.loc, elem_prev.loc)
|
||||
elem_prev = elem
|
||||
|
||||
"""for i in range(7):
|
||||
print(l[i*7:i*7+7])
|
||||
print("\n")"""
|
||||
|
||||
A_eq = np.vstack((A_eq,l))
|
||||
b_eq.append(1)
|
||||
return A_eq, b_eq, H
|
||||
|
||||
# Computes the path length given path matrix (dist_table) and a result
|
||||
def path_length(P: list, resx: list) :
|
||||
return np.dot(P, resx)
|
||||
|
||||
# Main optimization pipeline
|
||||
def solve_optimization (landmarks, max_steps, printing_details) :
|
||||
|
||||
# SET CONSTRAINTS FOR INEQUALITY
|
||||
c, A_ub, b_ub = init_ub_dist(landmarks, max_steps) # Add the distances from each landmark to the other
|
||||
P = A_ub # store the paths for later. Needed to compute path length
|
||||
A_ub, b_ub = respect_number(landmarks, A_ub, b_ub) # Respect max number of visits.
|
||||
|
||||
# TODO : Problems with circular symmetry
|
||||
A_ub, b_ub = break_sym(landmarks, A_ub, b_ub) # break the symmetry. Only use the upper diagonal values
|
||||
|
||||
# SET CONSTRAINTS FOR EQUALITY
|
||||
A_eq, b_eq = init_eq_not_stay(landmarks) # Force solution not to stay in same place
|
||||
A_eq, b_eq, H = respect_user_mustsee(landmarks, A_eq, b_eq) # Check if there are user_defined must_see. Also takes care of start/goal
|
||||
|
||||
A_eq, b_eq = respect_order(landmarks, A_eq, b_eq) # Respect order of visit (only works when max_steps is limiting factor)
|
||||
|
||||
# Bounds for variables (x can only be 0 or 1)
|
||||
x_bounds = [(0, 1)] * len(c)
|
||||
|
||||
# Solve linear programming problem
|
||||
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
|
||||
|
||||
|
||||
# Raise error if no solution is found
|
||||
if not res.success :
|
||||
|
||||
# Override the max_steps using the heuristic
|
||||
for i, val in enumerate(b_ub) :
|
||||
if val == max_steps : b_ub[i] = H
|
||||
|
||||
# Solve problem again :
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
|
||||
if not res.success :
|
||||
s = "No solution could be found, even when increasing max_steps using the heuristic"
|
||||
return s
|
||||
#raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
|
||||
|
||||
# If there is a solution, we're good to go, just check for
|
||||
else :
|
||||
circle = has_circle(res.x)
|
||||
i = 0
|
||||
|
||||
# Break the circular symmetry if needed
|
||||
while len(circle) != 0 :
|
||||
A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
circle = has_circle(res.x)
|
||||
i += 1
|
||||
|
||||
if printing_details is True :
|
||||
if i != 0 :
|
||||
print(f"Neded to recompute paths {i} times because of unconnected loops...")
|
||||
X = print_res(res, landmarks, P)
|
||||
return X
|
||||
else :
|
||||
return untangle(res.x)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
11
backend/src/amenities/nature.am
Normal file
11
backend/src/amenities/nature.am
Normal file
@ -0,0 +1,11 @@
|
||||
'leisure'='park'
|
||||
geological
|
||||
'natural'='geyser'
|
||||
'natural'='hot_spring'
|
||||
'natural'='arch'
|
||||
'natural'='volcano'
|
||||
'natural'='stone'
|
||||
'tourism'='alpine_hut'
|
||||
'tourism'='viewpoint'
|
||||
'tourism'='zoo'
|
||||
'waterway'='waterfall'
|
||||
2
backend/src/amenities/shopping.am
Normal file
2
backend/src/amenities/shopping.am
Normal file
@ -0,0 +1,2 @@
|
||||
'shop'='department_store'
|
||||
'shop'='mall'
|
||||
8
backend/src/amenities/sightseeing.am
Normal file
8
backend/src/amenities/sightseeing.am
Normal file
@ -0,0 +1,8 @@
|
||||
'tourism'='museum'
|
||||
'tourism'='attraction'
|
||||
'tourism'='gallery'
|
||||
historic
|
||||
'amenity'='planetarium'
|
||||
'amenity'='place_of_worship'
|
||||
'amenity'='fountain'
|
||||
'water'='reflecting_pool'
|
||||
365
backend/src/landmarks_manager.py
Normal file
365
backend/src/landmarks_manager.py
Normal file
@ -0,0 +1,365 @@
|
||||
import math as m
|
||||
import json, os
|
||||
|
||||
from typing import List, Tuple, Optional
|
||||
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
|
||||
|
||||
from structs.landmarks import Landmark, LandmarkType
|
||||
from structs.preferences import Preferences, Preference
|
||||
|
||||
|
||||
SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
|
||||
NATURE = LandmarkType(landmark_type='nature')
|
||||
SHOPPING = LandmarkType(landmark_type='shopping')
|
||||
|
||||
|
||||
# Include the json here
|
||||
# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
|
||||
def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
|
||||
|
||||
l_sights, l_nature, l_shop = get_amenities()
|
||||
L = []
|
||||
|
||||
# List for sightseeing
|
||||
if preferences.sightseeing.score != 0 :
|
||||
L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
|
||||
correct_score(L1, preferences.sightseeing)
|
||||
L += L1
|
||||
|
||||
# List for nature
|
||||
if preferences.nature.score != 0 :
|
||||
L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
|
||||
correct_score(L2, preferences.nature)
|
||||
L += L2
|
||||
|
||||
# List for shopping
|
||||
if preferences.shopping.score != 0 :
|
||||
L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
|
||||
correct_score(L3, preferences.shopping)
|
||||
L += L3
|
||||
|
||||
L = remove_duplicates(L)
|
||||
|
||||
return L, take_most_important(L)
|
||||
|
||||
|
||||
"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
|
||||
|
||||
l_sights, l_nature, l_shop = get_amenities()
|
||||
L = []
|
||||
|
||||
# List for sightseeing
|
||||
if preferences.sightseeing.score != 0 :
|
||||
L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
|
||||
correct_score(L1, preferences.sightseeing)
|
||||
L += L1
|
||||
|
||||
# List for nature
|
||||
if preferences.nature.score != 0 :
|
||||
L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
|
||||
correct_score(L2, preferences.nature)
|
||||
L += L2
|
||||
|
||||
# List for shopping
|
||||
if preferences.shopping.score != 0 :
|
||||
L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
|
||||
correct_score(L3, preferences.shopping)
|
||||
L += L3
|
||||
|
||||
return remove_duplicates(L), take_most_important(L)
|
||||
"""
|
||||
# Helper function to gather the amenities list
|
||||
def get_amenities() -> List[List[str]] :
|
||||
|
||||
# Get the list of amenities from the files
|
||||
sightseeing = get_list('/amenities/sightseeing.am')
|
||||
nature = get_list('/amenities/nature.am')
|
||||
shopping = get_list('/amenities/shopping.am')
|
||||
|
||||
return sightseeing, nature, shopping
|
||||
|
||||
|
||||
# Helper function to read a .am file and generate the corresponding list
|
||||
def get_list(path: str) -> List[str] :
|
||||
|
||||
with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
|
||||
content = f.readlines()
|
||||
|
||||
amenities = []
|
||||
for line in content :
|
||||
amenities.append(line.strip('\n'))
|
||||
|
||||
return amenities
|
||||
|
||||
|
||||
# Take the most important landmarks from the list
|
||||
def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
N_important = parameters['N important']
|
||||
|
||||
L_copy = []
|
||||
L_clean = []
|
||||
scores = [0]*len(L)
|
||||
names = []
|
||||
name_id = {}
|
||||
|
||||
for i, elem in enumerate(L) :
|
||||
if elem.name not in names :
|
||||
names.append(elem.name)
|
||||
name_id[elem.name] = [i]
|
||||
L_copy.append(elem)
|
||||
else :
|
||||
name_id[elem.name] += [i]
|
||||
scores = []
|
||||
for j in name_id[elem.name] :
|
||||
scores.append(L[j].attractiveness)
|
||||
best_id = max(range(len(scores)), key=scores.__getitem__)
|
||||
t = name_id[elem.name][best_id]
|
||||
if t == i :
|
||||
for old in L_copy :
|
||||
if old.name == elem.name :
|
||||
old.attractiveness = L[t].attractiveness
|
||||
|
||||
scores = [0]*len(L_copy)
|
||||
for i, elem in enumerate(L_copy) :
|
||||
scores[i] = elem.attractiveness
|
||||
|
||||
res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
|
||||
|
||||
for i, elem in enumerate(L_copy) :
|
||||
if i in res :
|
||||
L_clean.append(elem)
|
||||
|
||||
return L_clean
|
||||
|
||||
|
||||
# Remove duplicate elements and elements with low score
|
||||
def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
|
||||
"""
|
||||
Removes duplicate landmarks based on their names from the given list.
|
||||
|
||||
Parameters:
|
||||
L (List[Landmark]): A list of Landmark objects.
|
||||
|
||||
Returns:
|
||||
List[Landmark]: A list of unique Landmark objects based on their names.
|
||||
"""
|
||||
|
||||
L_clean = []
|
||||
names = []
|
||||
|
||||
for landmark in L :
|
||||
if landmark.name in names:
|
||||
continue
|
||||
|
||||
|
||||
else :
|
||||
names.append(landmark.name)
|
||||
L_clean.append(landmark)
|
||||
|
||||
return L_clean
|
||||
|
||||
|
||||
# Correct the score of a list of landmarks by taking into account preference settings
|
||||
def correct_score(L: List[Landmark], preference: Preference) :
|
||||
|
||||
if len(L) == 0 :
|
||||
return
|
||||
|
||||
if L[0].type != preference.type :
|
||||
raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
|
||||
|
||||
for elem in L :
|
||||
elem.attractiveness = int(elem.attractiveness*preference.score/500) # arbitrary computation
|
||||
|
||||
|
||||
# Function to count elements within a certain radius of a location
|
||||
def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int:
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
alpha = (180*radius)/(6371000*m.pi)
|
||||
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
|
||||
|
||||
# Build the query to find elements within the radius
|
||||
radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
|
||||
elementType=['node', 'way', 'relation'])
|
||||
|
||||
try :
|
||||
overpass = Overpass()
|
||||
radius_result = overpass.query(radius_query)
|
||||
return radius_result.countElements()
|
||||
|
||||
except :
|
||||
return None
|
||||
|
||||
|
||||
# Creates a bounding box around given coordinates
|
||||
def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
# Half the side length in km (since it's a square bbox)
|
||||
half_side_length_km = side_length / 2.0
|
||||
|
||||
# Convert distance to degrees
|
||||
lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
|
||||
lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat))) # Adjust for longitude based on latitude
|
||||
|
||||
# Calculate bbox
|
||||
min_lat = lat - lat_diff
|
||||
max_lat = lat + lat_diff
|
||||
min_lon = lon - lon_diff
|
||||
max_lon = lon + lon_diff
|
||||
|
||||
return min_lat, min_lon, max_lat, max_lon
|
||||
|
||||
|
||||
def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
tag_coeff = parameters['tag coeff']
|
||||
park_coeff = parameters['park coeff']
|
||||
church_coeff = parameters['church coeff']
|
||||
radius = parameters['radius close to']
|
||||
bbox_side = parameters['city bbox side']
|
||||
|
||||
# Create bbox around start location
|
||||
bbox = create_bbox(coordinates, bbox_side)
|
||||
|
||||
# Initialize some variables
|
||||
N = 0
|
||||
L = []
|
||||
overpass = Overpass()
|
||||
|
||||
for amenity in list_amenity :
|
||||
query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
|
||||
result = overpass.query(query)
|
||||
N += result.countElements()
|
||||
|
||||
for elem in result.elements():
|
||||
|
||||
name = elem.tag('name') # Add name
|
||||
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
||||
|
||||
# skip if unprecise location
|
||||
if name is None or location[0] is None:
|
||||
continue
|
||||
|
||||
# skip if unused
|
||||
if 'disused:leisure' in elem.tags().keys():
|
||||
continue
|
||||
|
||||
# skip if part of another building
|
||||
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
||||
continue
|
||||
|
||||
else :
|
||||
osm_type = elem.type() # Add type : 'way' or 'relation'
|
||||
osm_id = elem.id() # Add OSM id
|
||||
elem_type = landmarktype # Add the landmark type as 'sightseeing
|
||||
n_tags = len(elem.tags().keys()) # Add number of tags
|
||||
|
||||
# Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
|
||||
if amenity == "'amenity'='place_of_worship'" :
|
||||
score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)
|
||||
elif amenity == "'leisure'='park'" :
|
||||
score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)
|
||||
else :
|
||||
score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
|
||||
|
||||
if score is not None :
|
||||
# Generate the landmark and append it to the list
|
||||
landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
|
||||
L.append(landmark)
|
||||
|
||||
return L
|
||||
|
||||
|
||||
|
||||
"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
|
||||
|
||||
if city_country is None and coordinates is None :
|
||||
raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
|
||||
|
||||
if city_country is not None and coordinates is not None :
|
||||
raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
tag_coeff = parameters['tag coeff']
|
||||
park_coeff = parameters['park coeff']
|
||||
church_coeff = parameters['church coeff']
|
||||
radius = parameters['radius close to']
|
||||
bbox_side = parameters['city bbox side']
|
||||
|
||||
# If city_country is specified :
|
||||
if city_country is not None :
|
||||
nominatim = Nominatim()
|
||||
areaId = nominatim.query(city_country).areaId()
|
||||
bbox = None
|
||||
|
||||
# If coordinates are specified :
|
||||
elif coordinates is not None :
|
||||
bbox = create_bbox(coordinates, bbox_side)
|
||||
areaId = None
|
||||
|
||||
else :
|
||||
raise ValueError("Argument number is not corresponding.")
|
||||
|
||||
# Initialize some variables
|
||||
N = 0
|
||||
L = []
|
||||
overpass = Overpass()
|
||||
|
||||
for amenity in list_amenity :
|
||||
query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
|
||||
result = overpass.query(query)
|
||||
N += result.countElements()
|
||||
|
||||
for elem in result.elements():
|
||||
|
||||
name = elem.tag('name') # Add name
|
||||
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
||||
|
||||
# skip if unprecise location
|
||||
if name is None or location[0] is None:
|
||||
continue
|
||||
|
||||
# skip if unused
|
||||
if 'disused:leisure' in elem.tags().keys():
|
||||
continue
|
||||
|
||||
# skip if part of another building
|
||||
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
||||
continue
|
||||
|
||||
else :
|
||||
osm_type = elem.type() # Add type : 'way' or 'relation'
|
||||
osm_id = elem.id() # Add OSM id
|
||||
elem_type = landmarktype # Add the landmark type as 'sightseeing
|
||||
n_tags = len(elem.tags().keys()) # Add number of tags
|
||||
|
||||
# Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
|
||||
if amenity == "'amenity'='place_of_worship'" :
|
||||
score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)
|
||||
elif amenity == "'leisure'='park'" :
|
||||
score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)
|
||||
else :
|
||||
score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
|
||||
|
||||
if score is not None :
|
||||
# Generate the landmark and append it to the list
|
||||
landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
|
||||
L.append(landmark)
|
||||
|
||||
return L
|
||||
"""
|
||||
70
backend/src/main.py
Normal file
70
backend/src/main.py
Normal file
@ -0,0 +1,70 @@
|
||||
from optimizer import solve_optimization
|
||||
from refiner import refine_optimization
|
||||
from landmarks_manager import generate_landmarks
|
||||
from structs.landmarks import Landmark
|
||||
from structs.landmarktype import LandmarkType
|
||||
from structs.preferences import Preferences, Preference
|
||||
from fastapi import FastAPI, Query, Body
|
||||
from typing import List
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
|
||||
# Assuming frontend is calling like this :
|
||||
#"http://127.0.0.1:8000/process?param1={param1}¶m2={param2}"
|
||||
@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
|
||||
def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
|
||||
|
||||
if preferences is None :
|
||||
raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
|
||||
if bool(start_lat) ^ bool(start_lon) :
|
||||
raise ValueError("Please provide both latitude and longitude for the starting point")
|
||||
if bool(finish_lat) ^ bool(finish_lon) :
|
||||
raise ValueError("Please provide both latitude and longitude for the finish point")
|
||||
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
if bool(finish_lat) and bool(finish_lon) :
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
else :
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
# Generate the landmarks from the start location
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
|
||||
|
||||
# insert start and finish to the landmarks list
|
||||
landmarks_short.insert(0, start)
|
||||
landmarks_short.append(finish)
|
||||
|
||||
|
||||
# TODO use these parameters in another way
|
||||
max_walking_time = 4 # hours
|
||||
detour = 30 # minutes
|
||||
|
||||
# First stage optimization
|
||||
base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
|
||||
|
||||
# Second stage optimization
|
||||
refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
|
||||
|
||||
return refined_tour
|
||||
|
||||
|
||||
|
||||
# input city, country in the form of 'Paris, France'
|
||||
@app.post("/test2/{city_country}")
|
||||
def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
|
||||
|
||||
landmarks = generate_landmarks(city_country, preferences)
|
||||
|
||||
max_steps = 9000000
|
||||
|
||||
visiting_order = solve_optimization(landmarks, max_steps, True)
|
||||
|
||||
|
||||
|
||||
|
||||
411
backend/src/optimizer.py
Normal file
411
backend/src/optimizer.py
Normal file
@ -0,0 +1,411 @@
|
||||
import numpy as np
|
||||
import json, os
|
||||
|
||||
from typing import List, Tuple
|
||||
from scipy.optimize import linprog
|
||||
from math import radians, sin, cos, acos
|
||||
from shapely import Polygon
|
||||
|
||||
from structs.landmarks import Landmark
|
||||
|
||||
|
||||
# Function to print the result
|
||||
def print_res(L: List[Landmark], L_tot):
|
||||
|
||||
if len(L) == L_tot:
|
||||
print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
|
||||
else :
|
||||
print('Could not visit all the landmarks, the following order is suggested : ')
|
||||
|
||||
dist = 0
|
||||
for elem in L :
|
||||
if elem.name != 'start' :
|
||||
print('- ' + elem.name + ', time to reach = ' + str(elem.time_to_reach))
|
||||
dist += elem.time_to_reach
|
||||
else :
|
||||
print('- ' + elem.name)
|
||||
|
||||
print("\nMinutes walked : " + str(dist))
|
||||
print(f"Visited {len(L)-2} out of {L_tot-2} landmarks")
|
||||
|
||||
|
||||
# Prevent the use of a particular solution
|
||||
def prevent_config(resx, A_ub, b_ub) -> bool:
|
||||
|
||||
for i, elem in enumerate(resx):
|
||||
resx[i] = round(elem)
|
||||
|
||||
N = len(resx) # Number of edges
|
||||
L = int(np.sqrt(N)) # Number of landmarks
|
||||
|
||||
nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
|
||||
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
|
||||
|
||||
ind_a = nonzero_tup[0].tolist()
|
||||
vertices_visited = ind_a
|
||||
vertices_visited.remove(0)
|
||||
|
||||
ones = [1]*L
|
||||
h = [0]*N
|
||||
for i in range(L) :
|
||||
if i in vertices_visited :
|
||||
h[i*L:i*L+L] = ones
|
||||
|
||||
A_ub = np.vstack((A_ub, h))
|
||||
b_ub.append(len(vertices_visited)-1)
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
|
||||
# Prevent the possibility of a given solution bit
|
||||
def break_cricle(circle_vertices: list, L: int, A_ub: list, b_ub: list) -> bool:
|
||||
|
||||
if L-1 in circle_vertices :
|
||||
circle_vertices.remove(L-1)
|
||||
|
||||
h = [0]*L*L
|
||||
for i in range(L) :
|
||||
if i in circle_vertices :
|
||||
h[i*L:i*L+L] = [1]*L
|
||||
|
||||
A_ub = np.vstack((A_ub, h))
|
||||
b_ub.append(len(circle_vertices)-1)
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
|
||||
# Checks if the path is connected, returns a circle if it finds one and the RESULT
|
||||
def is_connected(resx) -> bool:
|
||||
|
||||
# first round the results to have only 0-1 values
|
||||
for i, elem in enumerate(resx):
|
||||
resx[i] = round(elem)
|
||||
|
||||
N = len(resx) # length of res
|
||||
L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
|
||||
n_edges = resx.sum() # number of edges
|
||||
|
||||
nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
|
||||
|
||||
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
|
||||
|
||||
ind_a = nonzero_tup[0].tolist()
|
||||
ind_b = nonzero_tup[1].tolist()
|
||||
|
||||
edges = []
|
||||
edges_visited = []
|
||||
vertices_visited = []
|
||||
|
||||
edge1 = (ind_a[0], ind_b[0])
|
||||
edges_visited.append(edge1)
|
||||
vertices_visited.append(edge1[0])
|
||||
|
||||
for i, a in enumerate(ind_a) :
|
||||
edges.append((a, ind_b[i])) # Create the list of edges
|
||||
|
||||
remaining = edges
|
||||
remaining.remove(edge1)
|
||||
|
||||
break_flag = False
|
||||
while len(remaining) > 0 and not break_flag:
|
||||
for edge2 in remaining :
|
||||
if edge2[0] == edge1[1] :
|
||||
if edge1[1] in vertices_visited :
|
||||
edges_visited.append(edge2)
|
||||
break_flag = True
|
||||
break
|
||||
else :
|
||||
vertices_visited.append(edge1[1])
|
||||
edges_visited.append(edge2)
|
||||
remaining.remove(edge2)
|
||||
edge1 = edge2
|
||||
|
||||
elif edge1[1] == L-1 or edge1[1] in vertices_visited:
|
||||
break_flag = True
|
||||
break
|
||||
|
||||
vertices_visited.append(edge1[1])
|
||||
|
||||
|
||||
if len(vertices_visited) == n_edges +1 :
|
||||
return vertices_visited, []
|
||||
else:
|
||||
return vertices_visited, edges_visited
|
||||
|
||||
|
||||
# Function that returns the distance in meters from one location to another
|
||||
def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
|
||||
|
||||
# Compute the straight-line distance in km
|
||||
if p1 == p2 :
|
||||
return 0, 0
|
||||
else:
|
||||
dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
|
||||
|
||||
# Consider the detour factor for average cityto deterline walking distance (in km)
|
||||
walk_dist = dist*detour
|
||||
|
||||
# Time to walk this distance (in minutes)
|
||||
walk_time = walk_dist/speed*60
|
||||
|
||||
if walk_time > 15 :
|
||||
walk_time = 5*round(walk_time/5)
|
||||
else :
|
||||
walk_time = round(walk_time)
|
||||
|
||||
|
||||
return round(walk_dist, 1), walk_time
|
||||
|
||||
|
||||
# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
|
||||
# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
|
||||
def init_ub_dist(landmarks: List[Landmark], max_steps: int):
|
||||
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
|
||||
c = []
|
||||
# Coefficients of inequality constraints (left-hand side)
|
||||
A_ub = []
|
||||
|
||||
for spot1 in landmarks :
|
||||
dist_table = [0]*len(landmarks)
|
||||
c.append(-spot1.attractiveness)
|
||||
for j, spot2 in enumerate(landmarks) :
|
||||
t = get_distance(spot1.location, spot2.location, detour, speed)[1]
|
||||
dist_table[j] = t
|
||||
A_ub += dist_table
|
||||
c = c*len(landmarks)
|
||||
|
||||
return c, A_ub, [max_steps]
|
||||
|
||||
|
||||
# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
|
||||
def respect_number(L:int, A_ub, b_ub):
|
||||
|
||||
ones = [1]*L
|
||||
zeros = [0]*L
|
||||
for i in range(L) :
|
||||
h = zeros*i + ones + zeros*(L-1-i)
|
||||
A_ub = np.vstack((A_ub, h))
|
||||
b_ub.append(1)
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
max_landmarks = parameters['max landmarks']
|
||||
|
||||
A_ub = np.vstack((A_ub, ones*L))
|
||||
b_ub.append(max_landmarks+1)
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
|
||||
# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
|
||||
def break_sym(L, A_ub, b_ub):
|
||||
upper_ind = np.triu_indices(L,0,L)
|
||||
|
||||
up_ind_x = upper_ind[0]
|
||||
up_ind_y = upper_ind[1]
|
||||
|
||||
for i, _ in enumerate(up_ind_x) :
|
||||
l = [0]*L*L
|
||||
if up_ind_x[i] != up_ind_y[i] :
|
||||
l[up_ind_x[i]*L + up_ind_y[i]] = 1
|
||||
l[up_ind_y[i]*L + up_ind_x[i]] = 1
|
||||
|
||||
A_ub = np.vstack((A_ub,l))
|
||||
b_ub.append(1)
|
||||
|
||||
return A_ub, b_ub
|
||||
|
||||
|
||||
# Constraint to not stay in position. Removes d11, d22, d33, etc.
|
||||
def init_eq_not_stay(L: int):
|
||||
l = [0]*L*L
|
||||
|
||||
for i in range(L) :
|
||||
for j in range(L) :
|
||||
if j == i :
|
||||
l[j + i*L] = 1
|
||||
|
||||
l = np.array(np.array(l))
|
||||
|
||||
return [l], [0]
|
||||
|
||||
|
||||
# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
|
||||
def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
|
||||
L = len(landmarks)
|
||||
|
||||
for i, elem in enumerate(landmarks) :
|
||||
if elem.must_do is True and elem.name not in ['finish', 'start']:
|
||||
l = [0]*L*L
|
||||
for j in range(L) : # sets the horizontal ones (go from)
|
||||
l[j +i*L] = 1 # sets the vertical ones (go to) double check if good
|
||||
|
||||
for k in range(L-1) :
|
||||
l[k*L+L-1] = 1
|
||||
|
||||
A_eq = np.vstack((A_eq,l))
|
||||
b_eq.append(2)
|
||||
|
||||
return A_eq, b_eq
|
||||
|
||||
|
||||
# Constraint to ensure start at start and finish at goal
|
||||
def respect_start_finish(L: int, A_eq: list, b_eq: list):
|
||||
ls = [1]*L + [0]*L*(L-1) # sets only horizontal ones for start (go from)
|
||||
ljump = [0]*L*L
|
||||
ljump[L-1] = 1 # Prevent start finish jump
|
||||
lg = [0]*L*L
|
||||
ll = [0]*L*(L-1) + [1]*L
|
||||
for k in range(L-1) : # sets only vertical ones for goal (go to)
|
||||
ll[k*L] = 1
|
||||
if k != 0 : # Prevent the shortcut start -> finish
|
||||
lg[k*L+L-1] = 1
|
||||
|
||||
|
||||
A_eq = np.vstack((A_eq,ls))
|
||||
A_eq = np.vstack((A_eq,ljump))
|
||||
A_eq = np.vstack((A_eq,lg))
|
||||
A_eq = np.vstack((A_eq,ll))
|
||||
b_eq.append(1)
|
||||
b_eq.append(0)
|
||||
b_eq.append(1)
|
||||
b_eq.append(0)
|
||||
|
||||
return A_eq, b_eq
|
||||
|
||||
|
||||
# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
|
||||
def respect_order(N: int, A_eq, b_eq):
|
||||
for i in range(N-1) : # Prevent stacked ones
|
||||
if i == 0 or i == N-1: # Don't touch start or finish
|
||||
continue
|
||||
else :
|
||||
l = [0]*N
|
||||
l[i] = -1
|
||||
l = l*N
|
||||
for j in range(N) :
|
||||
l[i*N + j] = 1
|
||||
|
||||
A_eq = np.vstack((A_eq,l))
|
||||
b_eq.append(0)
|
||||
|
||||
return A_eq, b_eq
|
||||
|
||||
|
||||
# Computes the time to reach from each landmark to the next
|
||||
def add_time_to_reach(order: List[int], landmarks: List[Landmark])->List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour_factor = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
j = 0
|
||||
L = []
|
||||
prev = landmarks[0]
|
||||
|
||||
while(len(L) != len(order)) :
|
||||
|
||||
elem = landmarks[order[j]]
|
||||
if elem != prev :
|
||||
elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
|
||||
elem.must_do = True
|
||||
L.append(elem)
|
||||
prev = elem
|
||||
j += 1
|
||||
|
||||
return L
|
||||
|
||||
|
||||
def add_time_to_reach_simple(ordered_visit: List[Landmark])-> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour_factor = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
L = []
|
||||
prev = ordered_visit[0]
|
||||
L.append(prev)
|
||||
total_dist = 0
|
||||
|
||||
for elem in ordered_visit[1:] :
|
||||
elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
|
||||
elem.must_do = True
|
||||
L.append(elem)
|
||||
prev = elem
|
||||
total_dist += get_distance(elem.location, prev.location, detour_factor, speed)[1]
|
||||
|
||||
|
||||
return L, total_dist
|
||||
|
||||
|
||||
# Main optimization pipeline
|
||||
def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
|
||||
|
||||
L = len(landmarks)
|
||||
|
||||
# SET CONSTRAINTS FOR INEQUALITY
|
||||
c, A_ub, b_ub = init_ub_dist(landmarks, max_steps) # Add the distances from each landmark to the other
|
||||
A_ub, b_ub = respect_number(L, A_ub, b_ub) # Respect max number of visits (no more possible stops than landmarks).
|
||||
A_ub, b_ub = break_sym(L, A_ub, b_ub) # break the 'zig-zag' symmetry
|
||||
|
||||
# SET CONSTRAINTS FOR EQUALITY
|
||||
A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
|
||||
A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq) # Check if there are user_defined must_see. Also takes care of start/goal
|
||||
A_eq, b_eq = respect_start_finish(L, A_eq, b_eq) # Force start and finish positions
|
||||
A_eq, b_eq = respect_order(L, A_eq, b_eq) # Respect order of visit (only works when max_steps is limiting factor)
|
||||
|
||||
# SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
|
||||
x_bounds = [(0, 1)]*L*L
|
||||
|
||||
# Solve linear programming problem
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
|
||||
# Raise error if no solution is found
|
||||
if not res.success :
|
||||
raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
|
||||
|
||||
# If there is a solution, we're good to go, just check for connectiveness
|
||||
else :
|
||||
order, circle = is_connected(res.x)
|
||||
i = 0
|
||||
timeout = 80
|
||||
while len(circle) != 0 and i < timeout:
|
||||
A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
|
||||
A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
order, circle = is_connected(res.x)
|
||||
if len(circle) == 0 :
|
||||
break
|
||||
print(i)
|
||||
i += 1
|
||||
|
||||
if i == timeout :
|
||||
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
|
||||
|
||||
# Add the times to reach and stop optimizing
|
||||
L = add_time_to_reach(order, landmarks)
|
||||
|
||||
if printing_details is True :
|
||||
if i != 0 :
|
||||
print(f"Neded to recompute paths {i} times because of unconnected loops...")
|
||||
print_res(L, len(landmarks))
|
||||
print("\nTotal score : " + str(int(-res.fun)))
|
||||
|
||||
return L
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
8
backend/src/parameters/landmarks_manager.params
Normal file
8
backend/src/parameters/landmarks_manager.params
Normal file
@ -0,0 +1,8 @@
|
||||
{
|
||||
"city bbox side" : 10,
|
||||
"radius close to" : 27.5,
|
||||
"church coeff" : 0.6,
|
||||
"park coeff" : 1.5,
|
||||
"tag coeff" : 100,
|
||||
"N important" : 40
|
||||
}
|
||||
5
backend/src/parameters/optimizer.params
Normal file
5
backend/src/parameters/optimizer.params
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"detour factor" : 1.4,
|
||||
"average walking speed" : 4.8,
|
||||
"max landmarks" : 10
|
||||
}
|
||||
293
backend/src/refiner.py
Normal file
293
backend/src/refiner.py
Normal file
@ -0,0 +1,293 @@
|
||||
from collections import defaultdict
|
||||
from heapq import heappop, heappush
|
||||
from itertools import permutations
|
||||
import os, json
|
||||
|
||||
from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
|
||||
from typing import List, Tuple
|
||||
from math import pi
|
||||
|
||||
from structs.landmarks import Landmark
|
||||
from landmarks_manager import take_most_important
|
||||
from optimizer import solve_optimization, add_time_to_reach_simple, print_res, get_distance
|
||||
|
||||
|
||||
def create_corridor(landmarks: List[Landmark], width: float) :
|
||||
|
||||
corrected_width = (180*width)/(6371000*pi)
|
||||
|
||||
path = create_linestring(landmarks)
|
||||
obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
|
||||
|
||||
return obj
|
||||
|
||||
|
||||
def create_linestring(landmarks: List[Landmark])->List[Point] :
|
||||
|
||||
points = []
|
||||
|
||||
for landmark in landmarks :
|
||||
points.append(Point(landmark.location))
|
||||
|
||||
return LineString(points)
|
||||
|
||||
|
||||
def is_in_area(area: Polygon, coordinates) -> bool :
|
||||
point = Point(coordinates)
|
||||
return point.within(area)
|
||||
|
||||
|
||||
def is_close_to(location1: Tuple[float], location2: Tuple[float]):
|
||||
"""Determine if two locations are close by rounding their coordinates to 3 decimals."""
|
||||
absx = abs(location1[0] - location2[0])
|
||||
absy = abs(location1[1] - location2[1])
|
||||
|
||||
return absx < 0.001 and absy < 0.001
|
||||
#return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
|
||||
|
||||
|
||||
def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
|
||||
|
||||
i = 1
|
||||
while i < len(landmarks):
|
||||
j = i+1
|
||||
while j < len(landmarks):
|
||||
if is_close_to(landmarks[i].location, landmarks[j].location) and landmarks[i].name not in ['start', 'finish'] and landmarks[j].name not in ['start', 'finish']:
|
||||
# If they are not adjacent, move the j-th element to be adjacent to the i-th element
|
||||
if j != i + 1:
|
||||
landmarks.insert(i + 1, landmarks.pop(j))
|
||||
break # Move to the next i-th element after rearrangement
|
||||
j += 1
|
||||
i += 1
|
||||
|
||||
return landmarks
|
||||
|
||||
"""
|
||||
def find_shortest_path(landmarks: List[Landmark]) -> List[Landmark]:
|
||||
|
||||
# Read from data
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
# Step 1: Build the graph
|
||||
graph = defaultdict(list)
|
||||
for i in range(len(landmarks)):
|
||||
for j in range(len(landmarks)):
|
||||
if i != j:
|
||||
distance = get_distance(landmarks[i].location, landmarks[j].location, detour, speed)[1]
|
||||
graph[i].append((distance, j))
|
||||
|
||||
# Step 2: Dijkstra's algorithm to find the shortest path from start to finish
|
||||
start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
|
||||
finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
|
||||
|
||||
distances = {i: float('inf') for i in range(len(landmarks))}
|
||||
previous_nodes = {i: None for i in range(len(landmarks))}
|
||||
distances[start_idx] = 0
|
||||
priority_queue = [(0, start_idx)]
|
||||
|
||||
while priority_queue:
|
||||
current_distance, current_index = heappop(priority_queue)
|
||||
|
||||
if current_distance > distances[current_index]:
|
||||
continue
|
||||
|
||||
for neighbor_distance, neighbor_index in graph[current_index]:
|
||||
distance = current_distance + neighbor_distance
|
||||
|
||||
if distance < distances[neighbor_index]:
|
||||
distances[neighbor_index] = distance
|
||||
previous_nodes[neighbor_index] = current_index
|
||||
heappush(priority_queue, (distance, neighbor_index))
|
||||
|
||||
# Step 3: Backtrack from finish to start to find the path
|
||||
path = []
|
||||
current_index = finish_idx
|
||||
while current_index is not None:
|
||||
path.append(landmarks[current_index])
|
||||
current_index = previous_nodes[current_index]
|
||||
path.reverse()
|
||||
|
||||
return path
|
||||
"""
|
||||
"""
|
||||
def total_path_distance(path: List[Landmark], detour, speed) -> float:
|
||||
total_distance = 0
|
||||
for i in range(len(path) - 1):
|
||||
total_distance += get_distance(path[i].location, path[i + 1].location, detour, speed)[1]
|
||||
return total_distance
|
||||
"""
|
||||
|
||||
|
||||
def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
|
||||
|
||||
# Read from data
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
# Step 1: Find 'start' and 'finish' landmarks
|
||||
start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
|
||||
finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
|
||||
|
||||
start_landmark = landmarks[start_idx]
|
||||
finish_landmark = landmarks[finish_idx]
|
||||
|
||||
|
||||
# Step 2: Create a list of unvisited landmarks excluding 'start' and 'finish'
|
||||
unvisited_landmarks = [lm for i, lm in enumerate(landmarks) if i not in [start_idx, finish_idx]]
|
||||
|
||||
# Step 3: Initialize the path with the 'start' landmark
|
||||
path = [start_landmark]
|
||||
coordinates = [landmarks[start_idx].location]
|
||||
|
||||
current_landmark = start_landmark
|
||||
|
||||
# Step 4: Use nearest neighbor heuristic to visit all landmarks
|
||||
while unvisited_landmarks:
|
||||
nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_distance(current_landmark.location, lm.location, detour, speed)[1])
|
||||
path.append(nearest_landmark)
|
||||
coordinates.append(nearest_landmark.location)
|
||||
current_landmark = nearest_landmark
|
||||
unvisited_landmarks.remove(nearest_landmark)
|
||||
|
||||
# Step 5: Finally add the 'finish' landmark to the path
|
||||
path.append(finish_landmark)
|
||||
coordinates.append(landmarks[finish_idx].location)
|
||||
|
||||
path_poly = Polygon(coordinates)
|
||||
|
||||
return path, path_poly
|
||||
|
||||
def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
|
||||
|
||||
second_order_landmarks = []
|
||||
visited_names = []
|
||||
area = create_corridor(visited_landmarks, width)
|
||||
|
||||
for visited in visited_landmarks :
|
||||
visited_names.append(visited.name)
|
||||
|
||||
for landmark in all_landmarks :
|
||||
if is_in_area(area, landmark.location) and landmark.name not in visited_names:
|
||||
second_order_landmarks.append(landmark)
|
||||
|
||||
return take_most_important(second_order_landmarks, len(visited_landmarks))
|
||||
|
||||
|
||||
|
||||
"""def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
|
||||
|
||||
minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
|
||||
|
||||
if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
|
||||
|
||||
full_set = base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish)
|
||||
full_set.append(base_tour[-1]) # add finish back
|
||||
|
||||
new_tour = solve_optimization(full_set, max_time, print_infos)
|
||||
|
||||
return new_tour"""
|
||||
|
||||
|
||||
def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
|
||||
|
||||
# Read from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
max_landmarks = parameters['max landmarks']
|
||||
|
||||
if len(base_tour)-2 >= max_landmarks :
|
||||
return base_tour
|
||||
|
||||
|
||||
minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
|
||||
|
||||
if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
|
||||
|
||||
# full set of visitable landmarks
|
||||
full_set = base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish)
|
||||
full_set.append(base_tour[-1]) # add finish back
|
||||
|
||||
# get a new tour
|
||||
new_tour = solve_optimization(full_set, max_time, False)
|
||||
new_tour, new_dist = add_time_to_reach_simple(new_tour)
|
||||
|
||||
"""#if base_tour[0].location == base_tour[-1].location :
|
||||
if False :
|
||||
coords = [] # Coordinates of the new tour
|
||||
coords_dict = {} # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
|
||||
|
||||
# Iterate through the new tour without finish
|
||||
for elem in new_tour[:-1] :
|
||||
coords.append(Point(elem.location))
|
||||
coords_dict[elem.location] = elem # if start = goal, only finish remains
|
||||
|
||||
# Create a concave polygon using the coordinates
|
||||
better_tour_poly = concave_hull(MultiPoint(coords)) # Create concave hull with "core" of tour leaving out start and finish
|
||||
xs, ys = better_tour_poly.exterior.xy
|
||||
|
||||
# reverse the xs and ys
|
||||
xs.reverse()
|
||||
ys.reverse()
|
||||
|
||||
better_tour = [] # List of ordered visit
|
||||
name_index = {} # Maps the name of a landmark to its index in the concave polygon
|
||||
|
||||
# Loop through the polygon and generate the better (ordered) tour
|
||||
for i,x in enumerate(xs[:-1]) :
|
||||
better_tour.append(coords_dict[tuple((x,ys[i]))])
|
||||
name_index[coords_dict[tuple((x,ys[i]))].name] = i
|
||||
|
||||
|
||||
# Scroll the list to have start in front again
|
||||
start_index = name_index['start']
|
||||
better_tour = better_tour[start_index:] + better_tour[:start_index]
|
||||
|
||||
# Append the finish back and correct the time to reach
|
||||
better_tour.append(new_tour[-1])
|
||||
|
||||
# Rearrange only if polygon
|
||||
better_tour = rearrange(better_tour)
|
||||
|
||||
# Add the time to reach
|
||||
better_tour = add_time_to_reach_simple(better_tour)
|
||||
"""
|
||||
|
||||
|
||||
"""
|
||||
if not better_poly.is_simple :
|
||||
|
||||
coords_dict = {}
|
||||
better_tour2 = []
|
||||
for elem in better_tour :
|
||||
coords_dict[elem.location] = elem
|
||||
|
||||
better_poly2 = better_poly.buffer(0)
|
||||
new_coords = better_poly2.exterior.coords[:]
|
||||
start_coords = base_tour[0].location
|
||||
start_index = new_coords.
|
||||
|
||||
#for point in new_coords :
|
||||
"""
|
||||
|
||||
|
||||
better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
|
||||
better_tour, better_dist = add_time_to_reach_simple(better_tour)
|
||||
|
||||
if new_dist < better_dist :
|
||||
final_tour = new_tour
|
||||
else :
|
||||
final_tour = better_tour
|
||||
|
||||
if print_infos :
|
||||
print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
|
||||
print("\nRefined tour (result of second stage optimization): ")
|
||||
print_res(final_tour, len(full_set))
|
||||
|
||||
|
||||
|
||||
return final_tour
|
||||
20
backend/src/structs/landmarks.py
Normal file
20
backend/src/structs/landmarks.py
Normal file
@ -0,0 +1,20 @@
|
||||
from typing import Optional
|
||||
from pydantic import BaseModel
|
||||
|
||||
from .landmarktype import LandmarkType
|
||||
|
||||
|
||||
|
||||
# Output to frontend
|
||||
class Landmark(BaseModel) :
|
||||
name : str
|
||||
type: LandmarkType # De facto mapping depending on how the query was executed with overpass. Should still EXACTLY correspond to the preferences
|
||||
location : tuple
|
||||
osm_type : str
|
||||
osm_id : int
|
||||
attractiveness : int
|
||||
must_do : bool
|
||||
n_tags : int
|
||||
time_to_reach : Optional[int] = 0
|
||||
|
||||
|
||||
4
backend/src/structs/landmarktype.py
Normal file
4
backend/src/structs/landmarktype.py
Normal file
@ -0,0 +1,4 @@
|
||||
from pydantic import BaseModel
|
||||
|
||||
class LandmarkType(BaseModel):
|
||||
landmark_type: str
|
||||
28
backend/src/structs/preferences.py
Normal file
28
backend/src/structs/preferences.py
Normal file
@ -0,0 +1,28 @@
|
||||
from pydantic import BaseModel
|
||||
from .landmarktype import LandmarkType
|
||||
|
||||
class Preference(BaseModel) :
|
||||
name: str
|
||||
type: LandmarkType # should match the attributes of the Preferences class
|
||||
score: int # score could be from 1 to 5
|
||||
|
||||
# Input for optimization
|
||||
class Preferences(BaseModel) :
|
||||
# Sightseeing / History & Culture (Musées, bâtiments historiques, opéras, églises)
|
||||
sightseeing : Preference
|
||||
|
||||
# Nature (parcs, jardins, rivières, plages)
|
||||
nature: Preference
|
||||
|
||||
# Shopping (diriger plutôt vers des zones / rues commerçantes)
|
||||
shopping : Preference
|
||||
|
||||
""" # Food (price low or high. Combien on veut dépenser pour manger à midi/soir)
|
||||
food_budget : Preference
|
||||
|
||||
# Tolérance au détour (ce qui détermine (+ ou -) le chemin emprunté)
|
||||
detour_tol : Preference"""
|
||||
|
||||
|
||||
|
||||
|
||||
116
backend/src/tester.py
Normal file
116
backend/src/tester.py
Normal file
@ -0,0 +1,116 @@
|
||||
import pandas as pd
|
||||
|
||||
from typing import List
|
||||
from landmarks_manager import generate_landmarks
|
||||
from fastapi.encoders import jsonable_encoder
|
||||
|
||||
from optimizer import solve_optimization
|
||||
from refiner import refine_optimization
|
||||
from structs.landmarks import Landmark
|
||||
from structs.landmarktype import LandmarkType
|
||||
from structs.preferences import Preferences, Preference
|
||||
|
||||
|
||||
# Helper function to create a .txt file with results
|
||||
def write_data(L: List[Landmark], file_name: str):
|
||||
|
||||
data = pd.DataFrame()
|
||||
i = 0
|
||||
|
||||
for landmark in L :
|
||||
data[i] = jsonable_encoder(landmark)
|
||||
i += 1
|
||||
|
||||
data.to_json(file_name, indent = 2, force_ascii=False)
|
||||
|
||||
def test3(city_country: str) -> List[Landmark]:
|
||||
|
||||
|
||||
preferences = Preferences(
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
score = 5),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 0),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 5))
|
||||
|
||||
coordinates = None
|
||||
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
|
||||
|
||||
#write_data(landmarks)
|
||||
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
|
||||
test = landmarks_short
|
||||
|
||||
test.insert(0, start)
|
||||
test.append(finish)
|
||||
|
||||
max_walking_time = 2 # hours
|
||||
|
||||
visiting_list = solve_optimization(test, max_walking_time*60, True)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def test4(coordinates: tuple[float, float]) -> List[Landmark]:
|
||||
|
||||
|
||||
preferences = Preferences(
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
score = 5),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 5),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 5))
|
||||
|
||||
|
||||
# Create start and finish
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
# Generate the landmarks from the start location
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
|
||||
#write_data(landmarks, "landmarks.txt")
|
||||
|
||||
# Insert start and finish to the landmarks list
|
||||
landmarks_short.insert(0, start)
|
||||
landmarks_short.append(finish)
|
||||
|
||||
# TODO use these parameters in another way
|
||||
max_walking_time = 2 # hours
|
||||
detour = 30 # minutes
|
||||
|
||||
# First stage optimization
|
||||
base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
|
||||
|
||||
# Second stage optimization
|
||||
refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
|
||||
|
||||
return refined_tour
|
||||
|
||||
|
||||
#test4(tuple((48.8344400, 2.3220540))) # Café Chez César
|
||||
#test4(tuple((48.8375946, 2.2949904))) # Point random
|
||||
test4(tuple((47.377859, 8.540585))) # Zurich HB
|
||||
#test3('Vienna, Austria')
|
||||
9730
landmarks.txt
Normal file
9730
landmarks.txt
Normal file
File diff suppressed because it is too large
Load Diff
482
minor_landmarks.txt
Normal file
482
minor_landmarks.txt
Normal file
@ -0,0 +1,482 @@
|
||||
{
|
||||
"0":{
|
||||
"name":"Atelier Brancusi",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8614029,
|
||||
2.3519903
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":55503399,
|
||||
"attractiveness":13,
|
||||
"must_do":false,
|
||||
"n_tags":13,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"1":{
|
||||
"name":"Musée de Cluny",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8506604,
|
||||
2.3437398
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":56640163,
|
||||
"attractiveness":18,
|
||||
"must_do":false,
|
||||
"n_tags":18,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"2":{
|
||||
"name":"Musée du Luxembourg",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8485965,
|
||||
2.3340156
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":170226810,
|
||||
"attractiveness":15,
|
||||
"must_do":false,
|
||||
"n_tags":15,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"3":{
|
||||
"name":"Musée national Eugène Delacroix",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8546662,
|
||||
2.3353836
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":395785603,
|
||||
"attractiveness":15,
|
||||
"must_do":false,
|
||||
"n_tags":15,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"4":{
|
||||
"name":"Hôtel de Sully",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.854635,
|
||||
2.3638126
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":403146,
|
||||
"attractiveness":14,
|
||||
"must_do":false,
|
||||
"n_tags":13,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"5":{
|
||||
"name":"Hôtel de la Monnaie",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.856403,
|
||||
2.3388625
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":967664,
|
||||
"attractiveness":11,
|
||||
"must_do":false,
|
||||
"n_tags":11,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"6":{
|
||||
"name":"Musée Bourdelle",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8432078,
|
||||
2.3186583
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":1212876,
|
||||
"attractiveness":23,
|
||||
"must_do":false,
|
||||
"n_tags":23,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"7":{
|
||||
"name":"Musée Carnavalet",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8576819,
|
||||
2.3627147
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":2405955,
|
||||
"attractiveness":25,
|
||||
"must_do":false,
|
||||
"n_tags":25,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"8":{
|
||||
"name":"Pont Neuf",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8565248,
|
||||
2.3408132
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":53574149,
|
||||
"attractiveness":16,
|
||||
"must_do":false,
|
||||
"n_tags":15,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"9":{
|
||||
"name":"Jardin du Luxembourg",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8467137,
|
||||
2.3363649
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":128206209,
|
||||
"attractiveness":35,
|
||||
"must_do":false,
|
||||
"n_tags":23,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"10":{
|
||||
"name":"Cathédrale Notre-Dame de Paris",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8529372,
|
||||
2.3498701
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":201611261,
|
||||
"attractiveness":55,
|
||||
"must_do":false,
|
||||
"n_tags":54,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"11":{
|
||||
"name":"Maison à l'enseigne du Faucheur",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8558553,
|
||||
2.3568266
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":1123456865,
|
||||
"attractiveness":13,
|
||||
"must_do":false,
|
||||
"n_tags":11,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"12":{
|
||||
"name":"Maison à l'enseigne du Mouton",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8558431,
|
||||
2.3568914
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":1123456866,
|
||||
"attractiveness":13,
|
||||
"must_do":false,
|
||||
"n_tags":11,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"13":{
|
||||
"name":"Palais de Justice de Paris",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8556537,
|
||||
2.3446072
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":536982,
|
||||
"attractiveness":24,
|
||||
"must_do":false,
|
||||
"n_tags":24,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"14":{
|
||||
"name":"Mémorial des Martyrs de la Déportation",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8517365,
|
||||
2.3524734
|
||||
],
|
||||
"osm_type":"relation",
|
||||
"osm_id":9396191,
|
||||
"attractiveness":21,
|
||||
"must_do":false,
|
||||
"n_tags":21,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"15":{
|
||||
"name":"Fontaine des Innocents",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8606368,
|
||||
2.3480233
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":52469222,
|
||||
"attractiveness":16,
|
||||
"must_do":false,
|
||||
"n_tags":15,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"16":{
|
||||
"name":"Hôtel de Sens",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8535257,
|
||||
2.3588733
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":55541122,
|
||||
"attractiveness":21,
|
||||
"must_do":false,
|
||||
"n_tags":20,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"17":{
|
||||
"name":"Hôtel d'Ourscamp",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8555465,
|
||||
2.3571206
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":55620201,
|
||||
"attractiveness":10,
|
||||
"must_do":false,
|
||||
"n_tags":9,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"18":{
|
||||
"name":"Hôtel de Choiseul-Praslin",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8478008,
|
||||
2.3203873
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":65756922,
|
||||
"attractiveness":12,
|
||||
"must_do":false,
|
||||
"n_tags":12,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"19":{
|
||||
"name":"Chapelle Notre-Dame-des-Anges",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8462836,
|
||||
2.3235558
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":219378497,
|
||||
"attractiveness":16,
|
||||
"must_do":false,
|
||||
"n_tags":16,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"20":{
|
||||
"name":"Fontaine du Palmier",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8575005,
|
||||
2.3472864
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":261092850,
|
||||
"attractiveness":23,
|
||||
"must_do":false,
|
||||
"n_tags":14,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"21":{
|
||||
"name":"Église Saint-Séverin",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8520913,
|
||||
2.3457237
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":19740659,
|
||||
"attractiveness":15,
|
||||
"must_do":false,
|
||||
"n_tags":25,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"22":{
|
||||
"name":"Église Orthodoxe Roumaine des Saints Archanges",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.849488,
|
||||
2.3471975
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":55366403,
|
||||
"attractiveness":10,
|
||||
"must_do":false,
|
||||
"n_tags":16,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"23":{
|
||||
"name":"Église Saint-Merri",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8590777,
|
||||
2.3508448
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":55742120,
|
||||
"attractiveness":16,
|
||||
"must_do":false,
|
||||
"n_tags":26,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"24":{
|
||||
"name":"Église Saint-Germain des Prés",
|
||||
"type":{
|
||||
"landmark_type":"sightseeing"
|
||||
},
|
||||
"location":[
|
||||
48.8539667,
|
||||
2.334463
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":62287173,
|
||||
"attractiveness":12,
|
||||
"must_do":false,
|
||||
"n_tags":21,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"25":{
|
||||
"name":"Square de la Tour Saint-Jacques",
|
||||
"type":{
|
||||
"landmark_type":"nature"
|
||||
},
|
||||
"location":[
|
||||
48.857913,
|
||||
2.3487608
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":15895172,
|
||||
"attractiveness":19,
|
||||
"must_do":false,
|
||||
"n_tags":12,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"26":{
|
||||
"name":"Square Jean XXIII",
|
||||
"type":{
|
||||
"landmark_type":"nature"
|
||||
},
|
||||
"location":[
|
||||
48.8523775,
|
||||
2.3503406
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":20444455,
|
||||
"attractiveness":23,
|
||||
"must_do":false,
|
||||
"n_tags":15,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"27":{
|
||||
"name":"Square Félix Desruelles",
|
||||
"type":{
|
||||
"landmark_type":"nature"
|
||||
},
|
||||
"location":[
|
||||
48.8536974,
|
||||
2.3345069
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":62287123,
|
||||
"attractiveness":12,
|
||||
"must_do":false,
|
||||
"n_tags":7,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"28":{
|
||||
"name":"Jardin des Rosiers – Joseph-Migneret",
|
||||
"type":{
|
||||
"landmark_type":"nature"
|
||||
},
|
||||
"location":[
|
||||
48.8572946,
|
||||
2.3602516
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":365878540,
|
||||
"attractiveness":14,
|
||||
"must_do":false,
|
||||
"n_tags":8,
|
||||
"time_to_reach":0
|
||||
},
|
||||
"29":{
|
||||
"name":"Jardin du Père-Armand-David",
|
||||
"type":{
|
||||
"landmark_type":"nature"
|
||||
},
|
||||
"location":[
|
||||
48.8478674,
|
||||
2.3220972
|
||||
],
|
||||
"osm_type":"way",
|
||||
"osm_id":588324415,
|
||||
"attractiveness":10,
|
||||
"must_do":false,
|
||||
"n_tags":7,
|
||||
"time_to_reach":0
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user