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working algo for clusters
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Helldragon67 2024-12-02 22:22:35 +01:00
parent 4397e36be6
commit 06d2f4c8aa
12 changed files with 149400 additions and 171473 deletions
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1
backend/.pylintrc
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@ -440,6 +440,7 @@ disable=raw-checker-failed,
use-implicit-booleaness-not-comparison-to-string,
use-implicit-booleaness-not-comparison-to-zero,
import-error
line-too-long
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option

1
backend/Pipfile
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@ -24,3 +24,4 @@ pywikibot = "*"
pymemcache = "*"
fastapi-cli = "*"
scikit-learn = "*"
pyqt6 = "*"

53
backend/Pipfile.lock generated
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@ -1,7 +1,7 @@
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@ -1092,6 +1092,57 @@
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698
backend/src/sandbox/colmar_data.json Normal file
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@ -0,0 +1,698 @@
{
"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": "2024-12-02T21:14:59Z",
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},
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"addr:street": "Rue des Clefs",
"brand": "Un Jour Ailleurs",
"brand:wikidata": "Q105106211",
"clothes": "women",
"name": "Un Jour Ailleurs",
"opening_hours": "Mo-Fr 10:00-19:00; Sa 10:00-18:30",
"phone": "+33368318572",
"shop": "clothes",
"website": "https://boutique.unjourailleurs.com/fr/mode-femme/boutique-colmar-76"
},
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"addr:postcode": "68000",
"addr:street": "Rue des Clefs",
"brand": "Timberland",
"brand:wikidata": "Q1539185",
"name": "Timberland",
"opening_hours": "Mo-Sa 10:00-19:00",
"phone": "+33389298650",
"shop": "clothes"
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]
}

208
backend/src/sandbox/get_streets.py
View File

@ -1,4 +1,210 @@
import numpy as np
import json
import os
from typing import Optional, Literal
from sklearn.cluster import DBSCAN
from sklearn.linear_model import RANSACRegressor
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from pydantic import BaseModel
class ShoppingLocation(BaseModel):
type: Literal['street', 'area']
importance: int
centroid: tuple
start: Optional[list] = None
end: Optional[list] = None
end: Optional[tuple] = None
def extract_points(filestr: str) :
"""
Extract points from geojson file.
Returns :
np.array containing the points
"""
points = []
with open(os.path.dirname(__file__) + '/' + filestr, 'r') as f:
geojson = json.load(f)
for feature in geojson['features']:
if feature['geometry']['type'] == 'Point':
centroid = feature['geometry']['coordinates']
points.append(centroid)
elif feature['geometry']['type'] == 'Polygon':
centroid = np.array(feature['geometry']['coordinates'][0][0])
points.append(centroid)
# Convert the list of points to a NumPy array
return np.array(points)
def filter_clusters(cluster_points, cluster_labels):
"""
Remove clusters of less importance.
"""
label_counts = np.bincount(cluster_labels)
# Step 3: Get the indices (labels) of the 5 largest clusters
top_5_labels = np.argsort(label_counts)[-5:] # Get the largest 5 clusters
# Step 4: Filter points to keep only the points in the top 5 clusters
filtered_cluster_points = []
filtered_cluster_labels = []
for label in top_5_labels:
filtered_cluster_points.append(cluster_points[cluster_labels == label])
filtered_cluster_labels.append(np.full((label_counts[label],), label)) # Replicate the label
# Concatenate filtered clusters into a single array
return np.vstack(filtered_cluster_points), np.concatenate(filtered_cluster_labels)
def fit_lines(points, labels):
"""
Fit lines to identified clusters.
"""
all_x = []
all_y = []
lines = []
locations = []
for label in set(labels):
cluster_points = points[labels == label]
# If there's not enough points, skip
if len(cluster_points) < 2:
continue
# Apply PCA to find the principal component (i.e., the line of best fit)
pca = PCA(n_components=1)
pca.fit(cluster_points)
direction = pca.components_[0]
centroid = pca.mean_
# Project the cluster points onto the principal direction (line direction)
projections = np.dot(cluster_points - centroid, direction)
# Get the range of the projections to find the approximate length of the cluster
cluster_length = projections.max() - projections.min()
# Now adjust `t` so that it scales with the cluster length
t = np.linspace(-cluster_length / 2.75, cluster_length / 2.75, 10)
# Calculate the start and end of the line based on min/max projections
start_point = centroid[0] + t*direction[0]
end_point = centroid[1] + t*direction[1]
# Store the line
lines.append((start_point, end_point))
# For visualization, store the points
all_x.append(min(start_point))
all_x.append(max(start_point))
all_y.append(min(end_point))
all_y.append(max(end_point))
if np.linalg.norm(t) <= 0.0045 :
loc = ShoppingLocation(
type='area',
centroid=tuple(centroid),
importance = len(cluster_points)
)
else :
loc = ShoppingLocation(
type='street',
centroid=tuple(centroid),
start=start_point,
end=end_point,
importance = len(cluster_points)
)
locations.append(loc)
xmin = min(all_x)
xmax = max(all_x)
ymin = min(all_y)
ymax = max(all_y)
corners = (xmin, xmax, ymin, ymax)
return corners, locations
# Extract points
points = extract_points('strasbourg_data.json')
# Create a figure with 1 row and 3 columns for side-by-side plots
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Plot 0: Raw data points
axes[0].set_title('Raw Data')
axes[0].scatter(points[:, 0], points[:, 1], color='blue', s=20)
# Apply DBSCAN to find clusters. Choose different settings for different cities.
if len(points) > 400 :
dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree') # for large cities
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
labels = dbscan.fit_predict(points)
# Separate clustered points and noise points
clustered_points = points[labels != -1]
clustered_labels = labels[labels != -1]
noise_points = points[labels == -1]
# Plot n°1: DBSCAN Clustering Results
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')
clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels)
# Fit lines
corners, locations = fit_lines(clustered_points, clustered_labels)
(xmin, xmax, ymin, ymax) = corners
# 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].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=10, label='Noise')
# Step 4: 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)
# print(8)
axes[2].set_title('PCA Fitted Lines on Clusters')
# print(all_x)
# Adjust the axis limit for previous plots
axes[0].set_xlim(xmin-0.01, xmax+0.01)
axes[0].set_ylim(ymin-0.01, ymax+0.01)
axes[1].set_xlim(xmin-0.01, xmax+0.01)
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)
# Adjust layout for better spacing
plt.tight_layout()
# Show the plots
plt.show()

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backend/src/tests/test_invalid_input.py
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@ -33,19 +33,19 @@ def invalid_client():
([91, 181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 423),
}, 422),
([-91, 181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 423),
}, 422),
([91, -181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 423),
}, 422),
([-91, -181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 423),
}, 422),
]
)
def test_input(invalid_client, start, preferences, status_code): # pylint: disable=redefined-outer-name