How sahniaditya007/sar-isro works
SAR-ISRO combines a React upload interface and Flask colorization API with separate image-analysis scripts. In the documented web workflow, a user uploads a SAR image, the backend enhances and colorizes it, then returns a PNG for display. The README also documents depth estimation, classification, zero-shot classification, segmentation, and basic processing as model capabilities; they are not wired into the web API in the sampled code. Their paths are supported by the README and tree, but their internal calls were not inspected.
Main entry points: ImageUpload.js Image upload · app.py Flask API · App.js App shell · index.js React entry
Components 15
Web interface
- React entry
sar-image-colorizer/src/index.js - App shell
sar-image-colorizer/src/App.js - Image upload
sar-image-colorizer/src/components/ImageUpload.js
Colorization API
- Flask API
app.py - Decode grayscale
app.py - Contrast enhancement
app.py - Color mapping
app.py - Image blending
app.py - PNG response
app.py
AI model tools
- Depth estimation
AI-Models/AImodel.py - Basic processing
AI-Models/basic.py - Image classification
AI-Models/classification.py - Zero-shot classification
AI-Models/zeroshotClassification.py - Image segmentation
AI-Models/segmentation.py
Other components
- Image user
Connections 12
- Image user → Image upload: selects and uploads
- React entry → App shell: renders
- App shell → Image upload: renders
- Image upload → Flask API: POSTs image
- Flask API → Decode grayscale: calls processing
- Decode grayscale → Contrast enhancement: returns grayscale
- Contrast enhancement → Color mapping: passes equalized image
- Color mapping → Image blending: provides mapped image
- Image blending → PNG response: provides combined image
- PNG response → Flask API: returns PNG
- Flask API → Image upload: sends image response
- Image upload → Image user: displays result