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README.md

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 ---
-license: cc-by-4.0
+language: en
+license: mit
+tags:
+  - gan
+  - cgan
+  - keras
+  - tensorflow
+  - computer-vision
+  - image-processing
+  - astronomy
+  - galaxy-morphology
+  - image-segmentation
+pipeline_tag: image-to-image
+library_name: keras
+datasets:
+  - desi-legacy-survey
 ---
+
+# Galaxy Image Simplification using Generative AI
+
+This repository hosts the pretrained models for **Galaxy Image Simplification using Generative AI**, a pipeline that converts complex galaxy images into simplified, skeletonized representations suitable for quantitative morphology analysis.
+
+The pipeline combines:
+
+- A **ResNet-based classifier** to select **spiral galaxies**
+- A **conditional GAN (cGAN)** to produce initial arm masks
+- A **post-processing cGAN** to smooth and connect broken arm segments
+
+These models were trained on images from the **DESI Legacy Survey** with manually annotated spiral arms.
+
+---
+
+## Model Sources
+
+- **Code & full project:**  
+  https://github.com/SaiTeja-Erukude/galaxy-image-simplification-using-genai
+
+---
+
+## Files in this repository
+
+| File name                        | Type          | Description                                                       |
+|----------------------------------|---------------|-------------------------------------------------------------------|
+| `models/galaxy_classifier_resnet50.h5`  | Keras model   | ResNet-based binary classifier: spiral vs. non-spiral galaxy      |
+| `models/galaxy_simplifier_cgan.h5`      | Keras model   | Conditional GAN: galaxy RGB image ➜ initial arm-highlighted image |
+| `models/postprocess_cgan.h5`            | Keras model   | Conditional GAN: initial mask ➜ refined, smooth/connected mask    |
+| `predict.py`                            | Python script | Full inference pipeline (classification ➜ simplifier cGAN ➜ post-cGAN) |
+| `graphical_abstract.jpg`                | Image         | Graphical abstract / high-level overview of the Galaxy Simplifier pipeline |
+| `requirements.txt`                      | Text file     | Python dependencies needed for running inference            |
+| `README.md`	                          | Markdown	  | Model card and usage instructions (this file) |
+
+---
+
+## Intended Use
+
+### What this model does
+
+Given an optical galaxy image (RGB, 256×256):
+
+1. **ResNet classifier (`galaxy_classifier_resnet50.h5`)**
+   - Predicts whether the galaxy is a **spiral**.
+   - Outputs a 2-class softmax:  
+     - class `0` – non-spiral / other  
+     - class `1` – spiral  
+   - Typical usage: apply a confidence threshold on the spiral class (e.g. `p_spiral > 0.65`) before running the GAN pipeline.
+
+2. **Skeletonization cGAN (`galaxy_simplifier_cgan.h5`)**
+   - Input: original RGB galaxy image (normalized to `[-1, 1]`).
+   - Output: image where **white lines** track the spiral arms (initial skeleton-like mask).
+
+3. **Post-processing cGAN (`postprocess_cgan.h5`)**
+   - Input: initial cGAN output.
+   - Output: refined mask with **smoother and better-connected arm structures**.
+   - This can be further processed with classical image processing (thresholding, skeletonization, dilation) to produce final binary masks.
+
+### Primary use cases
+
+- Large-scale **spiral galaxy selection** and morphology analysis
+- Measuring arm geometry, pitch angles, and other structural properties
+- Building catalogs of simplified galaxy images from wide-field surveys
+
+### Not intended for
+
+- General-purpose image generation outside the astronomy domain
+- High-fidelity photometric modeling or pixel-perfect reconstruction of galaxies
+
+---
+
+## How to use
+
+You can either:
+
+- use your **own inference script**, or  
+- use the provided minimalistic `inference.py`.
+
+---
+
+## Citation
+If you use this code, models, or catalog in your research, please cite:
+
+```bibtex
+@article{erukude2025galaxy,
+  title={Galaxy image simplification using Generative AI},
+  author={Erukude, Sai Teja and Shamir, Lior},
+  journal={Astronomy and Computing},
+  pages={100990},
+  year={2025},
+  publisher={Elsevier}
+}
+```

inference.py

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+import os
+import numpy as np
+from   keras.models              import load_model
+from   keras.preprocessing.image import load_img, img_to_array
+from   matplotlib                import pyplot
+
+
+######################
+# Configuration
+######################
+RESNET_PATH     = "path_to_resnet50_model.h5"
+CGAN_PATH       = "path_to_cgan_model.h5"
+POST_CGAN_PATH  = "path_to_postprocess_cgan_model.h5"  # <--- NEW
+
+DATA_PATH       = "path_to_test_dir"
+OUTPUT_PATH     = "path_to_output_dir"
+
+HEIGHT, WIDTH   = 256, 256
+TARGET_SIZE     = (HEIGHT, WIDTH)
+BATCH_SIZE      = 32
+
+os.makedirs(OUTPUT_PATH, exist_ok=True)
+
+
+# Load the models
+resnet_model = load_model(RESNET_PATH)
+print("Resnet50 loaded successfully!")
+
+cgan_model = load_model(CGAN_PATH)
+print("cGAN loaded successfully!")
+
+post_cgan_model = load_model(POST_CGAN_PATH)
+print("Post-processing cGAN loaded successfully!")
+
+
+######################
+#
+######################
+def load_and_preprocess(img_path: str, model: str = "resnet") -> np.ndarray:
+    """
+    Desc:
+        Load an image from disk and preprocess it for input into a deep learning model.
+    Args:
+        img_path (str): Path to the image file.
+        model (str): The model type to preprocess for. 
+                     "resnet" uses scaling to [0,1], other models use [-1,1] normalization.
+    Returns:
+        np.ndarray: Preprocessed image ready for model input.
+    """
+    img = load_img(img_path, target_size=TARGET_SIZE)
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    if model == "resnet":
+        return img_array / 255.0
+    # for "cgan" and "post_cgan" we assume [-1, 1] normalization
+    return (img_array - 127.5) / 127.5
+    
+
+######################
+#
+######################
+def plot_generated_image(gen_image: np.ndarray, filename: str) -> None:
+    """
+    Save a generated image to disk after rescaling it from [-1, 1] to [0, 1].
+    Args:
+        gen_image (np.ndarray): The generated image array, expected shape (1, H, W, C).
+        filename (str): The filename to save the image as (including extension, e.g., "image.png").
+    Returns:
+        None
+    """
+    # Scale from [-1,1] to [0,1]
+    gen_image = (gen_image + 1) / 2.0
+
+    # Save the generated image
+    output_filename = os.path.join(OUTPUT_PATH, filename)
+    pyplot.imsave(output_filename, gen_image[0])
+
+
+all_ctr    = 0
+spiral_ctr = 0
+
+# === Loop through images ===
+for filename in os.listdir(DATA_PATH):
+    if not filename.lower().endswith(('.jpg', '.jpeg', '.png')):
+        continue
+
+    img_path = os.path.join(DATA_PATH, filename)
+    all_ctr += 1
+
+    # Step 1: Classify with ResNet50
+    resnet_input = load_and_preprocess(img_path, model="resnet")
+    resnet_preds = resnet_model.predict(resnet_input, verbose=0)
+
+    predicted_class = np.argmax(resnet_preds, axis=1)[0]
+    if predicted_class == 1:  # Spiral galaxy
+
+        if resnet_preds[0][1] > 0.65:    # Confidence threshold
+
+            # Step 2: Process with first cGAN (skeletonization)
+            cgan_input   = load_and_preprocess(img_path, model="cgan")
+            cgan_output  = cgan_model.predict(cgan_input, verbose=0)
+
+            # Step 3: Post-process with second cGAN (smoothing/connecting lines)
+            post_output  = post_cgan_model.predict(cgan_output, verbose=0)
+
+            # Step 4: Save final post-processed output
+            plot_generated_image(post_output, filename)
+            spiral_ctr += 1
+
+    print(f"Found '{spiral_ctr}' spiral galaxies in '{all_ctr}' images.")

models/galaxy_classifier_resnet50.h5

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models/galaxy_simplifier_cgan.h5

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models/postprocess_cgan.h5

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requirements.txt

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+absl-py==2.2.2
+albucore==0.0.17
+albumentations==1.4.18
+annotated-types==0.7.0
+astunparse==1.6.3
+cachetools==5.5.2
+certifi==2025.1.31
+charset-normalizer==3.4.1
+colorama==0.4.6
+contourpy==1.1.1
+cycler==0.12.1
+eval_type_backport==0.2.2
+flatbuffers==25.2.10
+fonttools==4.57.0
+gast==0.4.0
+google-auth==2.38.0
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+grpcio==1.70.0
+h5py==3.11.0
+idna==3.10
+imageio==2.35.1
+importlib_metadata==8.5.0
+importlib_resources==6.4.5
+keras==2.13.1
+kiwisolver==1.4.7
+lazy_loader==0.4
+libclang==18.1.1
+Markdown==3.7
+MarkupSafe==2.1.5
+matplotlib==3.7.5
+networkx==3.1
+numpy==1.24.4
+oauthlib==3.2.2
+opencv-python==4.11.0.86
+opencv-python-headless==4.11.0.86
+opt_einsum==3.4.0
+packaging==24.2
+pillow==10.4.0
+protobuf==4.25.6
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.10.6
+pydantic_core==2.27.2
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+PyWavelets==1.4.1
+PyYAML==6.0.2
+requests==2.32.3
+requests-oauthlib==2.0.0
+rsa==4.9
+scikit-image==0.21.0
+scipy==1.10.1
+six==1.17.0
+tensorboard==2.13.0
+tensorboard-data-server==0.7.2
+tensorflow==2.13.0
+tensorflow-estimator==2.13.0
+tensorflow-intel==2.13.0
+tensorflow-io-gcs-filesystem==0.31.0
+tensorflow_keras==0.1
+termcolor==2.4.0
+tifffile==2023.7.10
+tqdm==4.67.1
+typing_extensions==4.13.1
+urllib3==2.2.3
+Werkzeug==3.0.6
+wrapt==1.17.2
+zipp==3.20.2