This code performs image histogram visualization and applies some image processing operations using the OpenCV library and Matplotlib in Python. Here's a breakdown of the code:

• The required libraries are imported: numpy for numerical operations, matplotlib.pyplot for plotting, cv2 (OpenCV) for image processing, and os for handling file paths.

• The current working directory is obtained using os.getcwd() and stored in the root variable.

• The image path is created by joining the root path with the filename 'cute_dog.jpg'.

• The visualize_all() function is defined, which encapsulates the code for visualizing histograms and performing image processing operations.

• Inside the visualize_all() function:

  • The image is read using cv.imread() from the specified img_path.

  • The image is converted from BGR to RGB color space using cv.cvtColor(), and the result is stored in imgRGB.

  • A figure with subplots is created using plt.subplots(), with a 3x4 grid of subplots and a specified figure size.

  • The title of the figure is set to 'Image Histogram' using fig.suptitle().

  • The color image is displayed in the first subplot using axes[0, 0].imshow(), and its title is set to 'Color Image'.

  • For each color channel (red, green, blue):

    • The histogram is calculated using cv.calcHist() for the corresponding channel of imgRGB.
    • The histogram is plotted in the respective subplot using axes[0, i+1].plot().
    • The x-axis label is set to 'Image Intensity' using axes[0, i+1].set_xlabel().
    • The y-axis label is set to 'No of Pixels' using axes[0, i+1].set_ylabel().
    • The title of the subplot is set to the uppercase channel name followed by 'Channel' using axes[0, i+1].set_title().

  • A contrast image is created by adjusting the image intensity using cv.addWeighted(), and the result is stored in contrast_image.

  • The contrast image is displayed in the second subplot, and histograms for each color channel are plotted in the subsequent subplots, similar to the previous step.

  • The brightness of the image is reduced by applying a gamma transformation. The value of gamma is set to 2.

  • The image is read again using cv.imread() and converted to a float32 data type. Then, it is divided by 256 to normalize the pixel values.

  • The image is converted to RGB color space, and the result is stored in imgRGB.

  • The gamma transformation is applied to imgRGB using np.power(), and the result is stored in gamma_img.

  • The gamma-reduced image is displayed in the third subplot, and histograms for each color channel are plotted in the subsequent subplots, similar to the previous steps.

  • Finally, plt.tight_layout() is called to improve the spacing between subplots, and plt.show() is used to display the figure.

• The block if __name__ == '__main__': ensures that the visualize_all() function is only executed when the script is run directly, not when it's imported as a module.

Final Output :

Overall, this code reads an image, visualizes its color histogram, applies contrast adjustment, and reduces brightness using gamma transformation. The resulting images and their histograms are displayed using matplotlib