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UrbanCode (v0.2.1)

A Python package for street view image perception analysis, providing tools for feature extraction and comfort prediction.

Related Research

Thermal Comfort in Sight: Thermal Affordance and Its Visual Assessment

Features

Street View Image (SVI) Analysis

  • Semantic segmentation
  • Object detection
  • Color feature extraction
  • Scene recognition
  • Perception analysis (thermal_comfort, visual_comfort, safety, etc.)

Examples

1. Street View Image Feature Extraction

examples/test_svi_image_feature.ipynb

  • Demonstrates how to extract various features from street view images
  • Includes semantic segmentation, object detection, color analysis, and scene recognition
  • Shows how to process multiple images and save results

2. Street View Image Comfort Prediction

examples/test_svi_comfort_prediction.ipynb

  • Shows how to predict comfort scores from street view images
  • Demonstrates the use of the comfort function for both single images and folders
  • Includes visualization of perception metrics
  • Automatically normalizes perception scores to 0-5 range

Installation

pip install urbancode

Usage

Feature Extraction

import urbancode as uc
import pandas as pd

# Process a folder of images
df = uc.svi.filename("path/to/folder")
df = uc.svi.segmentation(df, folder_path="path/to/folder")
df = uc.svi.object_detection(df, folder_path="path/to/folder")
df = uc.svi.color(df, folder_path="path/to/folder")
df = uc.svi.scene_recognition(df, folder_path="path/to/folder")

# Save results
df.to_csv("svi_results.csv", index=False)

Comfort Prediction

import urbancode as uc

# Process a single image
df = uc.svi.comfort("path/to/image.jpg", mode='image')

# Process a folder of images
df = uc.svi.comfort("path/to/folder", mode='folder')

# Save results
df.to_csv("comfort_results.csv", index=False)

Perception Metrics

The comfort function returns a DataFrame with the following perception metrics (normalized to 0-5 range):

  • thermal_comfort
  • visual_comfort
  • temp_intensity
  • sun_intensity
  • humidity_inference
  • wind_inference
  • traffic_flow
  • greenery_rate
  • shading_area
  • material_comfort
  • imageability
  • enclosure
  • human_scale
  • transparency
  • complexity
  • safe
  • lively
  • beautiful
  • wealthy
  • boring
  • depressing

Metadata

Release files for urbancode 0.2.3

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Source distribution for urbancode 0.2.3
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Table of built distributions (wheels) for urbancode 0.2.3
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urbancode-0.2.3-py3-none-any.whl Python 3 none any Details

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