cartoonifier image with opencv using python.pptx

This paper proposes the method that makes an input target image into exaggerated cartoon-like images by using reference images.
To deform a target image, we extract feature points from a target image and define the feature point model on reference images. And then, we apply feature based warping method to this deformation.
For our result be felt more cartoonish, we additionally apply the luminance quantization method and the edge enhancement method to the deformed target image.
At this time, we control intensities of the target image deformation, the luminance quantization and the edge enhancement for the capability that is able to create various results.

1. CONTENTS Abstract Existing system and drawbacks Proposed system and advantages System requirement System Architecture Modules

2. ABSTRACT This paper proposes the method that makes an input target image into exaggerated cartoon-like images by using reference images. To deform a target image, we extract feature points from a target image and define the feature point model on reference images. And then, we apply feature based warping method to this deformation. For our result be felt more cartoonish, we additionally apply the luminance quantization method and the edge enhancement method to the deformed target image. At this time, we control intensities of the target image deformation, the luminance quantization and the edge enhancement for the capability that is able to create various results .

3. EXISTING SYSTEM Before the advent of digital cartoonification techniques, creating a cartoon from an image or a scene involved manual artistic processes. Sketching: Artists would start with a basic sketch using pencils to outline the main features and shapes of the subject. Inking : The sketch would be refined with ink to create clear, bold lines. This step emphasizes the contours and details. Coloring: Artists would use paints, markers, or colored pencils to fill in the areas with colors, often using bright and simplified color palettes. Shading and Highlighting : To give the cartoon a sense of depth, shading and highlights would be added using various techniques

4. Drawbacks: T akes a Long Time: Making cartoons was a slow process that could take hours or days to finish. Requires Skill: You needed a lot of artistic talent to create good cartoons, which made it hard for beginners. Hard to Change: Once you finished a drawing, fixing mistakes was tough. You might have to start over if something went wrong. Uses Physical Materials: Artists needed supplies like paper, pencils, and paints, which could be expensive and took up space

5. proposed system The proposed system for cartoonifying an image using OpenCV involves several steps they are: Read and Grayscale Conversion : The image is read and converted to grayscale. Noise Reduction : A median blur filter is applied to the grayscale image to reduce noise. Edge Detection : Edges are detected using adaptive thresholding.

6. Color Processing : The original image undergoes bilateral filtering to smooth the image while preserving edges, creating a cartoon-like appearance. Combining Edges and Colors : The detected edges are combined with the smoothed image using bitwise operations to overlay the edges onto the smoothed colors . Displaying and Saving : Both the original and cartoonified images are displayed, and the cartoonified image is saved to a file.

7. Advantages: Enhanced Edge Preservation : Better edge definition with bilateral filtering. Improved Performance : Faster processing due to initial downscaling. Better Quality : Smoother and more appealing cartoon effect with multiple filter iterations and careful upscaling. Noise Reduction : Cleaner edge detection through median blur.

8. system requirement Hardware Configuration : Processor - Pentium –III   Speed – 2.4 GHz   RAM - 512 MB (min)   Hard Disk - 20 GB     Software Requirements : Operating System: Windows Coding Language : Python 3.7 Database : SQLite

9. System Architecture

10. Modules Admin Module: View all registered users and their posts. Send motivational messages specifically to depressed users. Monitor and analyze depression statuses through graphs. User Module: Search for and connect with other users. Upload posts in text, image, or .WAV audio formats. Access motivational messages sent by the administrator.