diff --git a/backend/src/sandbox/get_streets.py b/backend/src/sandbox/get_streets.py index 50e61ce..67bbd0c 100644 --- a/backend/src/sandbox/get_streets.py +++ b/backend/src/sandbox/get_streets.py @@ -7,6 +7,7 @@ from sklearn.decomposition import PCA import matplotlib.pyplot as plt from pydantic import BaseModel from OSMPythonTools.overpass import Overpass, overpassQueryBuilder +from OSMPythonTools.cachingStrategy import CachingStrategy, JSON from math import sin, cos, sqrt, atan2, radians @@ -177,7 +178,7 @@ def fit_lines(points, labels): else : loc = ShoppingLocation( type='street', - centroid=tuple(centroid), + centroid=tuple((centroid[1], centroid[0])), importance = len(cluster_points), start=start_point, end=end_point @@ -199,24 +200,13 @@ def create_landmark(shopping_location: ShoppingLocation): # Define the bounding box for a given radius around the coordinates lat, lon = shopping_location.centroid - bbox = ("around:500", str(lat), str(lon)) + bbox = ("around:1000", str(lat), str(lon)) overpass = Overpass() + # CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR) # Query neighborhoods and shopping malls - query = overpassQueryBuilder( - bbox = bbox, - elementType = ['node'], - selector = ['"place"="suburb"'], - includeCenter = True, - out = 'body' - ) - - try: - result = overpass.query(query) - print(f'query OK with {len(result.elements())} elements') - except Exception as e: - raise Exception("query unsuccessful") + selectors = ['"place"~"^(suburb|neighborhood|city_block)$"', '"shop"="mall"'] min_dist = float('inf') new_name = 'Shopping Area' @@ -224,21 +214,45 @@ def create_landmark(shopping_location: ShoppingLocation): osm_id = 0 osm_type = 'node' - for elem in result.elements(): - location = (elem.centerLat(), elem.centerLon()) - print(f"Distance : {get_distance(shopping_location.centroid, location)}") - if get_distance(shopping_location.centroid, location) < min_dist : - new_name = elem.tag('name') - osm_type = elem.type() # Add type: 'way' or 'relation' - osm_id = elem.id() # Add OSM id + for sel in selectors : + query = overpassQueryBuilder( + bbox = bbox, + elementType = ['node', 'way', 'relation'], + selector = sel, + includeCenter = True, + out = 'body' + ) - print("closer thing found") + try: + result = overpass.query(query) + # print(f'query OK with {len(result.elements())} elements') + except Exception as e: + raise Exception("query unsuccessful") - # add english name if it exists - try : - new_name_en = elem.tag('name:en') - except: - pass + for elem in result.elements(): + + location = (elem.lat(), elem.lon()) + + if location[0] is None : + location = (elem.centerLat(), elem.centerLon()) + if location[0] is None : + continue + + # print(f"Distance : {get_distance(shopping_location.centroid, location)}") + d = get_distance(shopping_location.centroid, location) + if d < min_dist : + min_dist = d + new_name = elem.tag('name') + osm_type = elem.type() # Add type: 'way' or 'relation' + osm_id = elem.id() # Add OSM id + + # print("closer thing found") + + # add english name if it exists + try : + new_name_en = elem.tag('name:en') + except: + pass return Landmark( name=new_name, @@ -253,7 +267,9 @@ def create_landmark(shopping_location: ShoppingLocation): # Extract points -points = extract_points('lyon_data.json') +points = extract_points('newyork_data.json') + +# print(len(points)) ######## Create a figure with 1 row and 3 columns for side-by-side plots fig, axes = plt.subplots(1, 3, figsize=(15, 5)) @@ -280,6 +296,7 @@ axes[1].set_title('DBSCAN Clusters') axes[1].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=20) axes[1].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=7, label='Noise') +# Keep the 5 biggest clusters clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels) # Fit lines @@ -289,26 +306,26 @@ corners, locations = fit_lines(clustered_points, clustered_labels) ######## Plot clustered points in normal size and noise points separately axes[2].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=30) - +axes[2].set_title('PCA Fitted Lines on Clusters') # Create a list of Landmarks for the shopping things shopping_landmarks = [] for loc in locations : + axes[2].scatter(loc.centroid[0], loc.centroid[1], color='lime', marker='x', s=200, linewidth=3) landmark = create_landmark(loc) shopping_landmarks.append(landmark) - print(f"{landmark.name} is a shopping area with a score of {landmark.attractiveness}") - + + ####### Plot the detected lines in the final plot ####### -for loc in locations: - if loc.type == 'street' : - line_x = loc.start - line_y = loc.end - axes[2].plot(line_x, line_y, color='lime', linewidth=3) - else : - axes[2].scatter(loc.centroid[0], loc.centroid[1], color='None', edgecolors='lime', s=200, linewidth=3) +# for loc in locations: +# if loc.type == 'street' : +# line_x = loc.start +# line_y = loc.end +# axes[2].plot(line_x, line_y, color='lime', linewidth=3) +# else : + -axes[2].set_title('PCA Fitted Lines on Clusters') axes[0].set_xlim(xmin-0.01, xmax+0.01) axes[0].set_ylim(ymin-0.01, ymax+0.01) @@ -319,5 +336,11 @@ axes[1].set_ylim(ymin-0.01, ymax+0.01) axes[2].set_xlim(xmin-0.01, xmax+0.01) axes[2].set_ylim(ymin-0.01, ymax+0.01) -# plt.tight_layout() -# plt.show() + +print("\n\n\n") +for landmark in shopping_landmarks : + print(f"{landmark.name} is a shopping area with a score of {landmark.attractiveness}") + + +plt.tight_layout() +plt.show() diff --git a/backend/src/sandbox/newyork_data.json b/backend/src/sandbox/newyork_data.json new file mode 100644 index 0000000..e40935c --- /dev/null +++ b/backend/src/sandbox/newyork_data.json @@ -0,0 +1,42085 @@ +{ + "type": "FeatureCollection", + "generator": "overpass-turbo", + "copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.", + "timestamp": "2024-12-03T15:13:15Z", + "features": [ + { + "type": "Feature", + "properties": { + "@id": "node/357645476", + "name": "Alnoor Boutique", + "phone": "+1-718-802-1514", + "shop": "clothes" + }, + "geometry": { + "type": "Point", + "coordinates": [ + -73.9929109, + 40.6900102 + ] + }, + "id": "node/357645476" + }, + { + "type": "Feature", + "properties": { + "@id": "node/368047907", + "name": "Porta Bella", + "opening_hours": "Mo-Th 10:30-19:30; Fr 10:00-20:00; Sa 10:00-20:30; Su 10:00-19:00", + "phone": "+1-718-765-1450", + "shop": "clothes", + "website": "https://www.portabellastores.com" + }, + "geometry": { + "type": "Point", + "coordinates": [ + -74.0115417, + 40.6437244 + ] + }, + "id": "node/368047907" + }, + { + "type": "Feature", + "properties": { + "@id": "node/663072765", + "addr:city": "New York", + "addr:housenumber": "595", + "addr:postcode": "10017", + "addr:street": "5th Avenue", + "brand": "Ted Baker", + "brand:wikidata": "Q2913458", + "name": "Ted Baker", + "shop": "clothes" + }, + "geometry": { + "type": "Point", + "coordinates": [ + -73.9778626, + 40.7573031 + ] + }, + "id": "node/663072765" + }, + { + "type": "Feature", + "properties": { + "@id": "node/663084339", + "brand": "H&M", + "brand:wikidata": "Q188326", + "name": "H&M", + "shop": "clothes" + }, + "geometry": { + "type": "Point", + "coordinates": [ + -73.9805923, + 40.7535383 + ] + }, + "id": "node/663084339" + }, + { + "type": "Feature", + "properties": { + "@id": "node/663084489", + "brand": "Zara", + "brand:wikidata": "Q147662", + "name": "Zara", + "shop": "clothes", + "wheelchair": "yes" + }, + "geometry": { + "type": "Point", + "coordinates": [ + -73.9809753, + 40.7537116 + ] + }, + "id": "node/663084489" + }, + { + "type": "Feature", + "properties": { + "@id": "node/886395953", + "addr:housenumber": "716", + "addr:street": "Broadway", + "brand": "Buffalo Exchange", + "brand:wikidata": "Q4985721", + "name": "Buffalo 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