PYTHON PROJECTS WITH SOURCE CODE

>> Soybean Plan Leaf Diseases Detection Using Neural Network Python Project Source Code

>> Optical Character Recognition Using Image Processing Python OpenCV Project with Source Code

>> Facial Emotion Recognition Using Image Processing | Python Project Source Code | IEEE Based Project

>> Python Code On Image Watermarking Full Project With Source Code | IEEE Based Project | Final Year Project

>> Audio Steganography Using Python Project Source Code || Hiding Text In Audio Using Python Project

>> Image Steganography for Hiding Message in Image Using Image Processing | Python Project Source Code | IEEE Based Project

>> Image Compression Using Python Project || Image Compression Using Python Project With Source Code

>> Brain Tumor Detection Using Image Processing Python Project With Source Code | IEEE Based Projects

>> Image Encryption Decryption Using AES Algorithm Python Project With Source Code | IEEE Based Projects

>> Python Code On Image Steganography For Hiding Secret Image In Cover Image

>> Plant Disease Detection Using Convolutional Neural Network CNN Full Python  Project With Source Code || IEEE Based Projects

>> Age and Gender Recognition using WebCam Python Project Source Code || Age and Gender Recognition using Deep Learning

>> Message Cryptography Using Python Project with Source Code

>> Real Time Face Recognition Using Python | Python Code for Face Recognition Using WebCam

>> Python Code for Social Distance Detection (Covid19) Full Project With Source Code

>> Python Code for Digit Recognition Using Image Processing Full Project Source Code

>> AES Based Message Encryption and Decryption Using Python Project Source Code

>> Barcode Recognition Using Python Project Source Code

>> Image Steganography Using Python Source Code - Hiding Message in Image Using Python Project

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FingerNail Disease Detection Using Image Processing Matlab Project Source Code | IEEE Based Projects

ABSTRACT

          This project gives idea to predict diseases using the color of the nail at early stage of diagnosis. The main aim of our project is to analyze the disease without causing harm to humans. In earlier traditional system of disease detection, doctors observe the nails of patients and will predict the disease. Many diseases can be identified by analyzing nails of patients. But it is difficult for human eyes to differentiate the slight changes in color. So it is less accurate and time consuming. Our proposed system can be quite useful to overcome this issue since it is fully computer based. The input to the proposed system is image of nail. The system will process the nail image and will extract the nail’s features to diagnose the disease. Human nail consist of various features, our proposed system uses nail color changes to diagnose the disease. Here, first training set data is prepared from nail images of patients with specific diseases. This training data set is compared with extracted feature from input nail image to obtain the result. In our experiment, we found that training set data are correctly matched with color feature of nail image results. It is focused on the system of image recognition on the basis of color analysis. The proposed system is based on the algorithm which automatically extracts only nails area from scanned back side of palm (Region of Interest). These selected pixels are processed for further analysis using median filters. The system is fully computer based, so even small discontinuities in color values are observed, and we can detect color changes in the initial stage of disease.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Blood Cancer Detection Using Neural Network | Matlab Project Source Code | IEEE Based Project

ABSTRACT

        Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques and a fuzzy inference system is use in this study as promising modalities for detection of different types of blood cancer. The accuracy rate of the diagnosis of blood cancer by using the fuzzy system will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. The proposed method is using Wavelet Transformation for image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering for final decision of blood cancer based on the number of different cells.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Face Emotion Recognition Using Image Processing | Python Project Source Code | IEEE Based Project

ABSTRACT

            This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Python Code On Image Watermarking Full Project With Source Code | IEEE Based Project | Final Year Project

ABSTRACT

        Digital Image watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The Image watermarking is most popular method for copyright protection by discrete wavelet transform which performs two level decomposition of original cover image and watermark image is embedded in lowest level sub band of cover image.

PROJECT OUTPUT

PROJECT VIDEO

Contact: 

Prof. Roshan P. Helonde 

Mobile: +91-7276355704 

WhatsApp: +91-7276355704 

Email: roshanphelonde@rediffmail.com

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Brain Tumor Detection Using Image Processing Python Project With Source Code | IEEE Based Projects

ABSTRACT

          Brain tumors are the most common issue in children. Approximately 3,410 children and adolescents under age 20 are diagnosed with primary brain tumors each year. Brain tumors, either malignant or benign, that originate in the cells of the brain. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. In this work we used Brain Tumor Detection Using Image Processing.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Python Code for Plant Disease Detection Using CNN Convolutional Neural Network Full Source Code || IEEE Based Projects

 ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This project proposes an approach for plant leaf disease detection and classification on plants using image processing. The algorithm presented has three basic steps: Image Pre-processing and analysis Recognition of plant disease. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Convolutional  Neural Network CNN for classification. 

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Matlab Code for Key Based AES Image Encryption Decryption | AES Based Image Encryption Using Matlab Project

 ABSTRACT

            During the last decade information security has become the major issue. The encrypting and decrypting of the data has been widely investigated because the demand for the better encryption and decryption of the data is gradually increased for getting the better security for the communication between the devices more privately. The Image Encryption Decryption play a major role for the fulfillment for this demand. The purpose of this project is to provide the better as well as more secure communication system by enhancing the strength of Advance Encryption Standard (AES) algorithm. AES algorithm was known for providing the best security without any limitations.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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