Added CLI for python version

README.md

@@ -3,7 +3,38 @@
 <img src="https://github.com/soumik12345/colorization-using-optimization/workflows/test/badge.svg" alt="build-failing">
 
 Python and C++ implementations of a user-guided image/video colorization technique as proposed by the paper 
-[Colorization Using Optimization](https://dl.acm.org/doi/10.1145/1015706.1015780).
+[Colorization Using Optimization](https://dl.acm.org/doi/10.1145/1015706.1015780). The algorithm is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. This premise is formalized using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. While using this alogorithm, an artist only needs to annotate the image with a few color scribbles or visual clues, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. The annotation can be done using any drawing tool such as [JSPaint](https://jspaint.app/) or [Gimp](https://www.gimp.org/).
+
+## Instructions
+
+### Instructions for running python version
+
+1. Create a virtualenv using:
+    - `virtualenv venv --python=python3`
+    - `source venv/bin/activate`
+    - `pip install -r requirements.txt`
+
+2. Colorize images using the CLI:
+    ```
+    Options:
+        --original_image TEXT    Original Image Path
+        --visual_clue TEXT       Visual Clue Image Path
+        --result_path TEXT       Colorized Image Path (without file extensions)
+        -i, --use_itercative     Use Iterative Mode
+        --epochs INTEGER         Number of epochs for Iterative Mode
+        --log_intervals INTEGER  Log Interval
+        --help                   Show this message and exit.
+    ```
+
+### Instructions to build C++ version
+
+1. Install dependencies using `sh install.sh`
+
+2. Create a build directory `mkdir build && cd build`
+
+3. Generate makefiles and compile using `cmake .. && make`
+
+4. Run the executable using `./colorization [input-image] [visual-clues] [result] [gamma] [threshold]`
 
 ## Results
 

colorize.py

@@ -0,0 +1,39 @@
+import cv2
+import click
+
+from colorization import Colorizer, IterativeColorizer
+
+
+@click.command()
+@click.option('--original_image', help='Original Image Path')
+@click.option('--visual_clue', help='Visual Clue Image Path')
+@click.option('--result_path', default='./result', help='Colorized Image Path (without file extensions)')
+@click.option('--use_itercative', '-i', is_flag=True, help='Use Iterative Mode')
+@click.option('--epochs', default=500, help='Number of epochs for Iterative Mode')
+@click.option('--log_intervals', default=100, help='Log Interval')
+def colorize(original_image, visual_clue, result_path, use_itercative, epochs, log_intervals):
+    if use_itercative:
+        colorizer = Colorizer(
+            gray_image_file=original_image,
+            visual_clues_file=visual_clue
+        )
+        colorizer.plot_inputs()
+        result = colorizer.colorize()
+        colorizer.plot_results(result)
+        cv2.imwrite(result_path + '.png', result)
+    else:
+        colorizer = IterativeColorizer(
+            original_image=original_image,
+            visual_clues=visual_clue
+        )
+        colorizer.plot_inputs()
+        colorizer.colorize(
+            epochs=epochs, log_interval=log_intervals
+        )
+        colorizer.plot_results(log_intervals=log_intervals)
+        for i, result in enumerate(colorizer.result_history):
+            cv2.imwrite(result_path + '{}.png'.format(i + 1), result)
+
+
+if __name__ == '__main__':
+    colorize()