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https://github.com/kr1shnasomani/colourpulse
Font style detection using TensorFlow and Keras
https://github.com/kr1shnasomani/colourpulse
computer-vision deep-learning keras neural-network numpy opencv pypi tensorflow
Last synced: about 1 month ago
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Font style detection using TensorFlow and Keras
- Host: GitHub
- URL: https://github.com/kr1shnasomani/colourpulse
- Owner: kr1shnasomani
- Created: 2025-01-05T19:02:06.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-01-13T06:48:57.000Z (about 1 month ago)
- Last Synced: 2025-01-13T07:33:11.681Z (about 1 month ago)
- Topics: computer-vision, deep-learning, keras, neural-network, numpy, opencv, pypi, tensorflow
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
ColourPulse
The code performs automatic colorization of black-and-white images using a pre-trained deep learning model. It loads the model and cluster centers, processes the input image, predicts color channels, combines them with the luminance channel, and converts the result to a colorized image.
## Execution Guide:
1. Run the following command line in the terminal:
```
pip install opencv-python opencv-contrib-python numpy
```
2. Enter the path of the black and white images in the code, also enter the output directory.
3. Download the following models and paste their path in the code:
a. [colorization_deploy_v2.prototxt](https://github.com/kr1shnasomani/ColourPulse/blob/main/model/colorization_deploy_v2.prototxt)
b. [colorization_release_v2.caffemodel](https://www.dropbox.com/s/dx0qvhhp5hbcx7z/colorization_release_v2.caffemodel?dl=1)
c. [pts_in_hull.npy](https://github.com/kr1shnasomani/ColourPulse/blob/main/model/pts_in_hull.npy)
4. Run the code and it will output a colour image
## Model Prediction:
Image Input:
Image Output:
## Overview:
This script implements **automatic colorization of black-and-white images** using a pre-trained deep learning model in OpenCV. Below is a step-by-step breakdown:
#### 1. **Library Imports**
- The script uses essential libraries:
- `numpy` for numerical computations.
- `cv2` (OpenCV) for image processing.
- `os` for file path management.
#### 2. **Model Paths**
- Specifies the paths to the required model files:
- `prototxt`: Defines the network architecture.
- `caffemodel`: Contains the pre-trained weights.
- `npy`: Stores cluster centers for color distribution.
- Ensures paths are dynamically adjusted and checks the existence of critical files.
#### 3. **Model Initialization**
- Loads the pre-trained model (`.prototxt` and `.caffemodel`) using OpenCV's `cv2.dnn.readNetFromCaffe`.
- Loads the color cluster centers from the `.npy` file.
- Modifies the network by adding cluster centers as 1x1 convolutions.
#### 4. **Colorization Function (`colorize_image`)**
- **Input**: Path to a black-and-white image.
- **Steps**:
1. Reads the input image and verifies its existence.
2. Converts the image to the **LAB color space**, where:
- `L`: Lightness (input channel for colorization).
- `a` and `b`: Color channels (predicted by the model).
3. Preprocesses the image:
- Normalizes pixel values.
- Resizes the image to match the model input dimensions.
- Extracts and adjusts the `L` channel.
4. Feeds the processed `L` channel to the network.
5. Predicts the `a` and `b` channels and resizes them to the original image dimensions.
6. Combines the original `L` channel with the predicted `a` and `b` channels.
7. Converts the LAB image back to the **BGR color space** for display.
8. Clamps pixel values to the valid range and converts the image to 8-bit format.
### Key Features:
- Utilizes OpenCV's **DNN module** to load and process pre-trained deep learning models.
- Automatically converts and colorizes black-and-white images using the **LAB color space**.
- Provides robust error handling for missing files or invalid inputs.