update

.gitattributes

@@ -1,37 +1,3 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.pt filter=lfs diff=lfs merge=lfs -text
 makeitcolor/pytorch_model.pt filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,174 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,14 +0,0 @@
----
-title: Makeitcolor
-emoji: 📚
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: A Streamlit web application that converts black and white im
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,146 @@
+# must come before any st.* calls
+from streamlit.web.server.health import HealthHandler
+
+def patched_get(self):
+    # force a JSON response
+    self.write({"status": "ok"})
+
+HealthHandler.get = patched_get
+
+
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import os
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from huggingface_hub import snapshot_download
+import io
+import sys
+import asyncio
+import shutil
+
+# Fix for Python 3.12 asyncio issue
+if sys.version_info >= (3, 12) and sys.platform.startswith('win'):
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+def main():
+    st.set_page_config(page_title="Image Colorization", layout="wide")
+    st.title("Black & White to Color Image Converter")
+    
+    # Create examples directory if it doesn't exist
+    examples_dir = "./examples"
+    if not os.path.exists(examples_dir):
+        os.makedirs(examples_dir)
+        
+        example_images = ["image1.jpg", "image2.jpg", "image3.jpg"]
+        for img in example_images:
+            if os.path.exists(img):
+                shutil.copy(img, os.path.join(examples_dir, img))
+    
+    # Check if model is already downloaded
+    model_dir = "./makeitcolor"
+    if not os.path.exists(model_dir):
+        with st.spinner("Downloading model (this might take a few minutes)..."):
+            snapshot_download(repo_id="muhammadnoman76/makeitcolor", local_dir=model_dir, repo_type="model")
+    
+    # Initialize the colorization pipeline
+    try:
+        img_colorization = pipeline(Tasks.image_colorization, model=model_dir)
+        st.success("Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        return
+    
+    # Example images section
+    st.subheader("Try with Example Images")
+    example_col1, example_col2, example_col3 = st.columns(3)
+    
+    with example_col1:
+        if os.path.exists(os.path.join(examples_dir, "image1.jpg")):
+            st.image(os.path.join(examples_dir, "image1.jpg"), caption="Example 1", use_container_width=True)
+            if st.button("Colorize Example 1"):
+                process_image(os.path.join(examples_dir, "image1.jpg"), img_colorization)
+    
+    with example_col2:
+        if os.path.exists(os.path.join(examples_dir, "image2.jpg")):
+            st.image(os.path.join(examples_dir, "image2.jpg"), caption="Example 2", use_container_width=True)
+            if st.button("Colorize Example 2"):
+                process_image(os.path.join(examples_dir, "image2.jpg"), img_colorization)
+    
+    with example_col3:
+        if os.path.exists(os.path.join(examples_dir, "image3.jpg")):
+            st.image(os.path.join(examples_dir, "image3.jpg"), caption="Example 3", use_container_width=True)
+            if st.button("Colorize Example 3"):
+                process_image(os.path.join(examples_dir, "image3.jpg"), img_colorization)
+    
+    st.markdown("---")
+    st.subheader("Upload Your Own Image")
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Upload a black and white image", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Read and display the original image
+        original_image = Image.open(uploaded_file)
+        
+        # Create temporary file path for processing
+        temp_path = "temp_input.jpg"
+        original_image.save(temp_path)
+        
+        process_image(temp_path, img_colorization)
+        
+        # Clean up temp file
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+def process_image(image_path, img_colorization):
+    # Display original image
+    original_image = Image.open(image_path)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("Original Image")
+        st.image(original_image, use_container_width=True)
+    
+    # Colorize the image
+    try:
+        with st.spinner("Colorizing image..."):
+            result = img_colorization(image_path)
+            colorized_image = result['output_img']
+            
+            # Convert BGR to RGB for display
+            colorized_image_rgb = cv2.cvtColor(colorized_image, cv2.COLOR_BGR2RGB)
+            
+            with col2:
+                st.subheader("Colorized Image")
+                st.image(colorized_image_rgb, use_container_width=True)
+            
+            # Add download button for colorized image
+            colorized_pil = Image.fromarray(colorized_image_rgb)
+            buf = io.BytesIO()
+            colorized_pil.save(buf, format="PNG")
+            byte_im = buf.getvalue()
+            
+            st.download_button(
+                label="Download Colorized Image",
+                data=byte_im,
+                file_name="colorized_image.png",
+                mime="image/png"
+            )
+    except Exception as e:
+        st.error(f"Error during colorization: {e}")
+
+
+if __name__ == "__main__":
+    main()
+
+st.markdown("---")
+st.markdown("""
+    <div style="text-align: center; padding: 10px; background-color: #f0f2f6; border-radius: 5px;">
+        <p>Developed by <a href="https://www.linkedin.com/in/muhammad-noman76/" target="_blank">Muhammad Noman</a> | 
+        Contact: muhammadnomanshafiq76@gmail.com</p>
+    </div>
+""", unsafe_allow_html=True)

makeitcolor/README.md

@@ -0,0 +1,121 @@
+---
+license: apache-2.0
+---
+# MakeItColor: Image Colorization Model
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10raIuCBUhKCPqIuL_HiSQmkJJ9jbu2VC?usp=sharing)
+
+## Overview
+
+**MakeItColor** is a deep learning model designed for automatic image colorization. It transforms grayscale images into vivid, realistic colorized outputs using a PyTorch-based Convolutional Neural Network (CNN) architecture integrated with the ModelScope framework.
+
+This model builds upon the work of [DDColor](https://github.com/piddnad/DDColor), utilizing a dual-encoder approach and trained on the **ImageNet-Val5k** dataset.
+
+## Features
+
+- **Task**: Image Colorization
+- **Framework**: PyTorch, ModelScope
+- **Architecture**: Convolutional Neural Network (CNN)
+- **Input**: Grayscale images (single-channel)
+- **Output**: Colorized images (RGB format)
+
+## Installation
+
+Ensure you have **Python 3.7+** installed. Then, install the required dependencies:
+
+```bash
+pip install opencv-python
+pip install modelscope==1.12.0 
+pip install datasets==2.14.7
+pip install pillow
+pip install numpy
+```
+
+## Usage
+
+### ModelScope Pipeline
+
+```python
+import cv2
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from huggingface_hub import snapshot_download
+
+# Download the model files to a local directory
+snapshot_download(repo_id="muhammadnoman76/makeitcolor", local_dir="./makeitcolor", repo_type="model")
+
+# Initialize the colorization pipeline
+img_colorization = pipeline(Tasks.image_colorization, model='./makeitcolor')
+
+# Load a grayscale image
+img_path = 'input.jpg'
+
+# Run colorization
+result = img_colorization(img_path)
+
+# Save the colorized image
+cv2.imwrite('result.png', result['output_img'])
+```
+
+> **Note**: 
+> - Ensure that the input image (`input.jpg`) is a proper grayscale (single-channel) image.
+> - The output (`result.png`) will be a standard RGB image.
+
+## Google Colab
+
+For an interactive demonstration, try our [Google Colab notebook](https://colab.research.google.com/drive/10raIuCBUhKCPqIuL_HiSQmkJJ9jbu2VC?usp=sharing).
+
+## Model Files
+
+The repository contains:
+- `pytorch_model.pt`: Pre-trained model weights
+- `configuration.json`: Model configuration file for ModelScope integration
+- `README.md`: Documentation
+
+## Requirements
+
+### Hardware
+- CPU (supported)
+- GPU (recommended for faster inference)
+
+### Software Dependencies
+- `modelscope`
+- `opencv-python`
+- `torch`
+
+## Input Format
+
+- Grayscale images (`.png`, `.jpg`, etc.)
+
+### Example Workflow
+
+1. Prepare a grayscale image (e.g., `input.jpg`)
+2. Run the provided example code
+3. Check the output file (`result.png`) for the colorized result
+
+## Limitations
+
+- May struggle with highly complex, ambiguous, or abstract grayscale images
+- Performance and output quality depend on the clarity and details of the input
+- Primarily optimized for natural images; results may vary for synthetic or artistic inputs
+
+## Credits
+
+This work builds upon and was inspired by the [DDColor project](https://github.com/piddnad/DDColor).  
+**MakeItColor** leverages a dual-encoder strategy from DDColor and is trained on the **ImageNet-Val5k** dataset.
+
+Special thanks to the creators of DDColor for their foundational contributions.
+
+## License
+
+This project is licensed under the **Apache License 2.0**.
+
+## Contact
+
+For issues, questions, or feedback:
+- Open an issue on the Hugging Face repository
+- Contact the maintainer directly at: [muhammadnomanshafiq76@gmail.com](mailto:muhammadnomanshafiq76@gmail.com)
+
+---
+
+**Developed by Muhammad Noman**

makeitcolor/configuration.json

@@ -0,0 +1,67 @@
+{
+    "framework": "pytorch",
+
+    "task": "image-colorization",
+
+    "pipeline": {
+        "type": "ddcolor-image-colorization"
+    },
+
+    "model": {
+        "type": "ddcolor"
+    },
+
+    "dataset": {
+        "name": "imagenet-val5k-image",
+        "dataroot_gt": "val5k/",
+        "filename_tmpl": "{}",
+        "scale": 1,
+        "gt_size": 256
+    },
+
+    "train": {
+        "dataloader": {
+            "batch_size_per_gpu": 4,
+            "workers_per_gpu": 4,
+            "shuffle": true
+        },
+        "optimizer": {
+            "type": "AdamW",
+            "lr": 1e-6,
+            "weight_decay": 0.01,
+            "betas": [0.9, 0.99]
+        },
+        "lr_scheduler": {
+            "type": "CosineAnnealingLR",
+            "T_max": 200000,
+            "eta_min": 1e-7
+        },
+        "max_epochs": 2,
+        "hooks": [{
+                "type": "CheckpointHook",
+                "interval": 1
+            },
+            {
+                "type": "TextLoggerHook",
+                "interval": 1
+            },
+            {
+                "type": "IterTimerHook"
+            },
+            {
+                "type": "EvaluationHook",
+                "interval": 1
+            }
+        ]
+    },
+
+    "evaluation": {
+        "dataloader": {
+            "batch_size_per_gpu": 8,
+            "workers_per_gpu": 1,
+            "shuffle": false
+        },
+        "metrics": "image-colorization-metric"
+    }
+
+}

makeitcolor/pytorch_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17c460d7e55b32a598370621d77173be59e03c24b0823f06821db23a50c263ce
+size 911950059

packages.txt

@@ -0,0 +1,4 @@
+libgl1-mesa-glx
+libglib2.0-0
+libgtk2.0-0
+curl

requirements.txt

@@ -0,0 +1,8 @@
+timm
+streamlit>=1.24.0
+opencv-python>=4.7.0
+modelscope==1.12.0
+datasets==2.14.7
+pillow>=9.5.0
+numpy>=1.24.0
+huggingface-hub>=0.16.4