muneeswaris1991 Goto Github PK

edge-detection-with-background-subtraction

Edge detection done first and give the input to the background subtraction.

edge-extract

extracting edges in the video

erosion-and-dilation-of-image

fire-base-workshop

IWSC master- fire base workshop

fire-detection-system

Detecting fire in the video

flask-app-builder-

frame-differencing

frames-separation-and-frame-rate

gloabal-trade-analysis

Tableau prpject

gmm-background-subtraction-using-webcamera

Webcamera: GMM background subtraction

grayscale-conversion

convert RBG to grayscale

gui

GUI (Graphical User Interface) using python

helloworld

For beginners in open source!!

identified-video-motions

image-contrast

Image contrast

image-enhancer

intersection-of-union-iou-

An IOU differs from a promissory note in that an IOU is not a negotiable instrument and does not specify repayment terms such as the time of repayment. IOUs usually specify the debtor, the amount owed, and sometimes the creditor.

iou-metrics

Intersection of Union

k12---learning-syllabus

K12 - Learning Syllabus

knn-background-subtraction

KNN Background Subtraction OpenCV Python. fgbg = cv.createBackgroundSubtractorKNN(detectShadows=False) This is another algorithm for background subtraction, known as KNN. It also takes detect shadows argument as True or False. This OpenCV function will initialize background subtraction. Reading Frames. ret, frame = cap.read()

lpdb

metrics---psnr-ssim-brisque

BRISQUE is statistically better than the full-reference peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) and highly competitive with respect to all present-day distortion-generic NR IQA algorithms. PSNR and SSIM are the two most representative FR methods, BIQI , BRISQUE and NIQE are the state-of-the-art NR IQA methods. to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) and highly competitive with respect to all present-day distortion-generic NR IQA algorithms.

movielens-case-study

Datascience project 2

no-reference-iqa-

Image quality assessment ( IQA) is employed to estimate the degree of distortion. Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains. (2) The proposed model predicted the lane-based queue length in real time--the prediction included incremental queue accumulation ( IQA ), queue trajectory, maximum queue, and residual queue.

nyc311-project

DataScience

object-and-edge

object-detection

object-detection-at-motion

Videos can be treated as stack of pictures called frames. Here I am comparing different frames(pictures) to the first frame which should be static(No movements initially). We compare two images by comparing the intensity value of each pixels. Gray Frame : In Gray frame the image is a bit blur and in grayscale we did so because, In gray pictures there is only one intensity value whereas in RGB(Red, Green and Blue) image thre are three intensity values. So it would be easy to calculate the intensity difference in grayscale. Difference Frame : Difference frame shows the difference of intensities of first frame to the current frame. Threshold Frame : If the intensity difference for a particular pixel is more than 30(in my case) then that pixel will be white and if the difference is less than 30 that pixel will be black. Color Frame : In this frame you can see the color images in color frame along with green contour around the moving objects.

object-detector

optical-flow