Image processing application
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This document discusses image processing and its applications. It provides an overview of libraries like OpenCV, SciPy, NumPy, Scikit-image and PIL that can be used for image processing tasks. It then describes some basic image processing operations like reading, showing, writing and filtering images. Finally, it lists various applications of image processing like object recognition, face recognition, medical imaging, agriculture and more. It concludes with an announcement about an upcoming demo session and question/answer session on the topic.
![MASK AN IMAGE - NUMPY
import numpy as np
from skimage import data
import matplotlib.pyplot as plt
image = data.camera()
type(image)
numpy.ndarray #Image is a NumPy array:
mask = image < 87
image[mask]=255
plt.imshow(image, cmap='gray')
pip3 install numpy](https://image.slidesharecdn.com/pantechimageprocessingapplication-200703055929/85/Image-processing-application-11-320.jpg)
Image processing application
- 1. IMAGE PROCESSING APPLICATIONS Pantech Solutions & AIMIT ST. Aloysius College (Autonomous), MANGALORE Organized by
- 2. All Participants, Please fill the Attendance form, Link given in the description
- 3. ABOUT AIMIT ABOUT St Aloysius Institute of Management & Information Technology (AIMIT) is the new campus for MBA and IT programmes offered by St Aloysius College (Autonomous), Mangalore. VISION “Empowering youth through excellence in education to shape a better future for humankind”
- 4. ABOUT PANTECH Online Retail Store Lab equipment, Engineering Kits, Components, Sensors and All level Projects ABOUT Established on 2004 | 7 Branches | 100+ Team R & D Manufacturer of Lab equipment‘s & Development boards | Industrial &Funded projects | Online retail store of engineering products and projects Vision “To Gain Global Leadership in providing Technological Solutions through Sustained Innovation”
- 5. TABLE OF CONTENTS ABOUT IMAGE PROCESSING01 OPENCV & OTHER LIBRARIES BASIC OPERATIONS 02 03 APPLICATIONS OF DIP04 LIVE DEMO05
- 6. ABOUT IMAGE PROCESSING In computer science, digital image processing is the use of a digital computer to process digital images through an algorithm ELECTRONIC EYE
- 7. LIBRARIES SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing Scientific Python NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Numerical Python Mahotas is a includes many algorithms implemented in C++ for speed while operating in numpy arrays and with a very clean Python interface. Mahotas It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. Scikit-image It adds support for opening, manipulating, and saving many different image file formats. PIL OpenCV is a library of programming functions mainly aimed at real-time computer vision. Open CV
- 9. READ, SHOW & WRITE AN IMAGE - OPENCV import cv2 img = cv2.imread(‘image.jpg',0) cv2.imshow(‘show',img) cv2.imwrite(‘photo.jpg',img) cv2.waitKey(0) cv2.destroyAllWindows() pip3 install opencv-python
- 10. IMAGE FILTERING – SCIKIT IMAGE import matplotlib.pyplot as plt from skimage import data,filters image = data.coins() edges = filters.sobel(image) plt.imshow(edges, cmap='gray') pip3 installscikit-image
- 11. MASK AN IMAGE - NUMPY import numpy as np from skimage import data import matplotlib.pyplot as plt image = data.camera() type(image) numpy.ndarray #Image is a NumPy array: mask = image < 87 image[mask]=255 plt.imshow(image, cmap='gray') pip3 install numpy
- 12. BLURING AN IMAGE - SCIPY from scipy import misc,ndimage face = misc.face() blurred_face = ndimage.gaussian_filter(face, sigma=3) very_blurred = ndimage.gaussian_filter(face, sigma=5) plt.imshow(blurred_face) pip3 install scipy
- 13. ENHANCING AN IMAGE - PILLOW from PIL import Image,ImageFilter im = Image.open('image.jpg') im.show() from PIL import ImageEnhance enh = ImageEnhance.Contrast(im) enh.enhance(1.8).show(“Enhanced Image") pip3 install pillow
- 15. 01. OBJECT RECOGNITION Surveillance and security | Traffic monitoring | Video communication
- 16. 02. FACE RECOGNITION Facial Biometrics | Attendance marking | Finding thieves | Receptionist
- 17. 03. AUTONOMOUS VEHICLE Pedestrian detection | Traffic Light detection | Road sign detection | Lane detection
- 18. 04. DISEASE DETECTION Disease detection from Medical Image | Temperature detection from thermal camera
- 19. 05. EMOTION RECOGNITION People emotion monitoring | Lie Detection | Baby Monitoring
- 20. 06. AGRICULTURE Plant disease detection | Fruit & Vegetable classification
- 21. 07. SATELLITE IMAGE ANALYSIS Mapping | Vegetation prediction from satellite image
- 22. 08. ROBOT VISION Localisation | Vision for detection & Recognition
- 23. 09. PATTERN RECOGNITION OCR | Identifying Pattern from Image for Handwritten recognition
- 24. 10. SEARCH ENGINES Image Search | Image plagiarism checker
- 25. 11. TRANSMISSION & ENCODING Image transmission | Video streaming | Steganography
- 26. 12. MEDICAL EQUIPMENTS Gamma ray imaging | PET Scan |X-Ray Imaging |Medical CT Scale | UV Imaging
- 27. 13. MOBILE & CAMERA AI Camera | Bar Code Scanner | Camera based Measurement | Scanner
- 28. 14. DESIGNING 2-D & 3-D Designing applications | 3-D Scanners
- 29. 15. AUGMENTED & VIRTUAL REALITY AR & VR Games | AR based real time image projection | AR applications such as “Lenskart “
- 30. DEMO SESSION
- 31. Q & A SESSION
- 32. THANKS! Do you have any questions? [email protected] www.pantechsolutions.net UPCOMING SESSION Autonomous Vehicle Technology E-Vehicle Technology 5-G Technology