Update 2020 🎉

Author: imneonizer

README.md

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+# How to find if an image is bright or dark?
+### Concept
+
+- Input image is resized to `10x10` pixel, to reduce the computation.
+- Convert it to `LAB` color space to access the luminous channel which is independent of colors.
+- Normalize pixel values to be in range of `0 - 1`.
+- Compare the `mean` value of pixels with an `threshold` value.
+
+### External modules
+
+```
+>> pip install opencv-python
+>> pip install numpy
+```
+### How to run
+
+- **Demo**: [Colab Notebook](https://colab.research.google.com/github/imneonizer/How-to-find-if-an-image-is-bright-or-dark/blob/master/demo.ipynb)
+
+- **Execute program**
+
+  ````
+  python run.py
+  ````
+
+  - It will read images from `images` directory and classify it as bright or dark.
+  - It will separate and save images to  `output/bright` directory and `output/dark` directory accordingly.
+
+- **Sample Dark Images**
+
+  <table>
+      <tr>
+          <img src="images/c76f1.jpg", height=220>
+          <img src="images/c2e07.jpg", height=220>
+      </tr>
+  </table>
+
+- **Sample Bright Images**
+
+  <table>
+      <tr>
+          <img src="images/0acbf.jpg", height=220>
+          <img src="images/2d37f.jpg", height=220>
+      </tr>
+  </table>
+
+- The function calculate brightness level in range of 0-1, hence you can decide on a threshold to treat images either as bright or dark.
+
+  ````python
+  def isbright(image, dim=10, thresh=0.5):
+      # Resize image to 10x10
+      image = cv2.resize(image, (dim, dim))
+      # Convert color space to LAB format and extract L channel
+      L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB))
+      # Normalize L channel by dividing all pixel values with maximum pixel value
+      L = L/np.max(L)
+      # Return True if mean is greater than thresh else False
+      return np.mean(L) > thresh
+  ````
+
+- For a better result you can tweak with `dim` parameter, it will preserve more pixels while calculating brightness levels.
+
+- Don't forget to star the repository.
+

Readme.md

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