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- BFMatch(img1, img2)
- Brute-Force Matcher - Matches
Params:
* img1 - Query Image
* img2 - Train Image
Returns:
* Matches
- adaptiveBilateralFilter(img, ksize=(5, 5), sigmaSpace=None)
- Adaptive Bilateral Filtering
Params:
* img - image
* ksize - OPTIONAL - Kernal Size, def: (5,5)
* sigmaSpace - OPTIONAL - Filter sigma in coord space. def: None
- adaptiveEqHist(img, clipLimit=2.0, tileGridSize=(8, 8))
- CLAHE (Contrast Limited Adaptive Histogram Equalization) - Improves contrast via Adaptive Histogram Equalization
Params:
* img - image
* clipLimit - OPTIONAL - contrast limit to clip pixels before hist eq, def is 2.0
* tileGridSize - (row, col) blocks that are equalized - def is (8,8)
Returns:
* Equalized Image
- adaptiveGaussianThreshold(img)
- Returns an img after Adaptive Gaussian Thresholding
Params:
* img - image
Returns:
* Image that has undergone Adaptive Gaussian Thresholding
- adaptiveMeanThreshold(img)
- Returns an img after Adaptive Mean Thresholding
Params:
* img - image
Returns:
* Image that has undergone Adaptive Mean Thresholding
- add(img1, img2)
- Image Addition - add two images
Params:
* img1 - image 1
* img2 - image 2 or a scalar quantity
Returns:
* Addition of img1 and img2
- addBorder(img, flag=0, top=10, bottom=10, left=10, right=10, color=(255, 0, 0))
- Adds a border to the image
Params:
* img - image to add border
* flag - OPTIONAL - border type flag, def is CONSTANT (0)
* top - OPTIONAL - top pixel width
* bottom - OPTIONAL - bottom pixel width
* right - OPTIONAL - right pixel width
* left - OPTIONAL - left pixel width
* color - OPTIONAL - only used if flag == cv2.BORDER_CONSTANT
Returns:
* img with specified border
- addWeight(img1, wt1, img2, wt2, gamma=0)
- Image Blending - Added Weighting
Params:
* img1 - first image
* wt1 - weight of first image
* img2 - second img
* wt2 - weight of second image
* gamma - OPTIONAL - def is 0
Returns:
* weighted addition of imgs
- affineTransform(img, pts, newPts)
- Affine Transformation - translates a list of points to a new set of points while keeping
all parallel lines in original img parallel in output
Params:
* img - image
* pts - points of reference in original image
* newPts - points to be translated to
Returns:
* Shifted img under an Affine Transformation
- applyColorMap(img, flag)
- Apply a Color map to an img
Params:
* img - image
* flag - string flag or corresponding int found in getColorMapFlags()
Returns:
* img in color map
- applyHSV(img)
- Apply a HSV Color Map to img
Params:
* img - image
Returns:
* HSV img
- applyJET(img)
- Apply a JET Color Map to img
Params:
* img - image
Returns:
* JET img
- approxSinCurve(angles, data, exp=1)
- Approximates a Sin Curve to given Data using the Least Squares Solutions Method
Params:
* angles - List of angles
* data - list of data points
* exp - exponent for sin, i.e. sin, sin^2, sin^3; def: 1
Returns:
* {"data", "amplitude", "phase shift", "vertical shift"} dictionary
- area(cnt)
- Returns area of cnt
Params:
* cnt - contour
Returns:
* Area of cnt
- aspectRatio(cnt)
- Returns the Aspect Ratio (Ratio of width to height of bounding rectangle)
Params:
* cnt - contour
Returns:
* Aspect Ratio of cnt
- axis(cnt)
- Returns the Major and Minor axis lengths of the nearest fitting ellipse for the contour
Params:
* cnt - contour
Returns:
* (Major Axis, Minor Axis) - lengths of Major and Minor Axis ellipse for a given contour
- backProjection(roi, target)
- Backprojection - Finds objects of interest in an image
Params:
* roi - Region of Interest Img
* target - target img
Returns:
* Img of Result
- backgroundSubtract(img, flag=0)
- EXPERIMENTAL
Background Subtraction Methods
Params:
* img - Image
* flag - OPTIONAL - algorithm select; <0 - MOG2, 0- MOG, >0-GMG; def: 0
Returns:
* Background Subtracted Mask
- bgr2hsv(BGR)
- Finds the HSV value from a BGR value, good for using ot track
Params:
* BGR - BGR value i.e. [X, Y, X]
Returns:
* HSV - HSV value corresponding to it.
- bilateralFilter(img, d=9, sigmaColor=75, sigmaSpace=75)
- Bilateral Filtering
Params:
* img - Image
* d - OPTIONAL - diameter of each pixel neighborhood. def: 9
* sigmaColor - OPTIONAL - Filter sigma in color space. def: 75
* sigmaSpace - OPTIONAL - Filter sigma in coord space. def: 75
Returns:
* Bilateral Filtered Img
- binaryThreshold(img, threshVal=127, maxVal=255, invert=True)
- Returns a binary threshold of the image
Params:
* img - image to threshold
* threshVal - OPTIONAL - threshold value to classify pixels Default 127
* maxVal - OPTIONAL - value to be given if pixel over threshVal. Default 255
* invert - OPTIONAL - True means inverts binary threshold (white to black, vice versa), false otherwise. Default True
Returns:
* threshold image
- bitAnd(img1, img2=None, maskVar=None)
- Bitwise Ops - And
Params:
* img1 - first input array/scalar
* img2 - OPTIONAL - second input array/scalar; def is usually img1
* maskVar - OPTIONAL - mask of img; def of None
Returns:
* Bitwise And of imgs
- bitNot(img)
- Bitwise Ops - Not
Params:
* img - input img
Returns:
* Bitwise Not of imgs
- bitOr(img1, img2=None, mask=None)
- Bitwise Ops - Or
Params:
* img1 - first input array/scalar
* img2 - OPTIONAL - second input array/scalar; def is usually img1
* mask - OPTIONAL - mask of img; def is None
Returns:
* Bitwise Or of imgs
- bitXor(img1, img2=None, mask=None)
- Bitwise Ops - Exclusive Or
Params:
* img1 - first input array/scalar
* img2 - OPTIONAL - second input array/scalar; def is usually img1
* mask - OPTIONAL - mask of img; def is None
Returns:
* Bitwise Exclusive Or of imgs
- blackhat(img, kernel=(5, 5))
- Black Hat - Difference btw closing of img and input img
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
Returns:
* BlackHat Img
- blur(img, kernel=(5, 5))
- Image Blur
Params:
* img - image
* kernel - OPTIONAL - size of kernel, def is (5,5)
Returns:
* Blurred Image
- boundingRect(cnt)
- Calculates the up-right bounding rectangle of a point set / cnt
Params:
* cnt - Contour
Returns:
* Bounding Rectangle
- boundingRectPoints(cnt)
- Returns the points to a rectangle bounding cnt
Params:
* cnt - Contour
Returns:
* points to bouding rectangle
- caget(PV)
- Retrieves and Returns the value of the named PV
Params:
* PV - PV name to get
Returns:
* Value of PV
- cainfo(PV)
- Returns a string of info about a PV
Params:
* PV - The PV Value to retrieve info from
Returns:
* String of info about PV
- capture()
- Captures and returns an image from Camera
Returns:
* Captured Img
- captureDisplay(title='Frame')
- Capture and display an image from Camera
Params:
* title - OPTIONAL - name of display pop up, def: 'Frame'
- caput(PV, value)
- Sets the Value of the Named PV with Value
Params:
* PV - PV to set
* value - Value to set PV to
- centroid(cnt)
- Returns the centroid to a given contour
Params:
* cnt - contour
Returns:
* (cx, cy) -> Pixel Position of the Centroid
- centroidFloat(cnt)
- Returns the centroid as a float for exactness
Params:
* cnt - contour
Returns:
* (cx, cy) -> centroid coord, as a float for exactness
- closing(img, kernel=(5, 5))
- Closing - Dilation followed by Erosion.
Useful for closing small holes inside foreground objects,
or small black points on object
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
Returns:
* closed img
- cntInfo(img, cnt)
- Returns pertinent information regarding a contour
Params:
* img - image
* cnt - contour
Returns:
* dictionary of cnt info
- cntInfoMult(img, cnts)
- Returns a list of all cnt info for a list of contours
Here, cnts[0] -> cntProp[0]; i.e. all contour indexes map to info index
Params:
* img - image containing contours
* cnts - list of contours
Returns:
* contour information in list format
- coherenceFilter(img, sigma=11, str_sigma=11, blend=0.5, iter_n=4)
- Applies a Coherence-enhancing filter onto an img
Params:
* img- image
* sigma - OPTIONAL - def: 11
* str_sigma - OPTIONAL - def: 11
* blend - OPTIONAL - def: 0.5
* iter_n - OPTIONAL - number of iterations; def: 4
Returns:
* Filtered Img
- colorFlags(filterStr='')
- Returns the list of acceptable flags for color conversion
used for change colormap fnc
Params:
* filter - OPTIONAL - filters flags to specified string
Returns:
* List of Color Flags
- contourApprox(cnt, epsilon=0.005)
- Approimates a contour to a given epsilon
Params:
* cnt - contour to approximate
* epsilon - OPTIONAL- percentage of arc length allowed as max distance from contour to approx
Returns:
* approximated contour
- copy(img)
- Copy an image, removes all refrences to original image
Params:
* img- image to be copied
Returns:
* copied img
- crop(img, x0, y0, x1, y1)
- Image Crop: Returns a Crop Img
Can be Used for ROI
Params:
* img - image
* x0 - X Position (start)
* y0 - Y Position (start)
* x1 - X End Position
* y1 - Y End Position
Returns:
* imgROI - from [y:y+h, x:x+w]
- cropCnt(img, cnt)
- Crops A Contour to a ROI by using extreme points to map values
Params:
* img - image
* cnt - contour
Returns:
* Crop ROI with Extrema as base points
- cropPt(img, start, end)
- Image Cropping/ROI for PTS or Tuples as coordinates
Params:
* img - image
* start - (x, y) tuple for start pt
* end - (x, y) tuple for end pt
Returns:
* Cropped Image ROI in rectangle bounded by start:end
- cvtColor(img, flag)
- Wrapper Function for cv2.cvtColor()
Used to change Color-space of Image
WARNING: No error checking is performed
Params:
* img - image
* flag - color flag, use colorFlags() to see list of flags available, cv2.FLAG
Returns:
* Image in new colorspace
- denoise(img, h=10, hForColor=None, templateWindowSize=7, searchWindowSize=21)
- Image De-noising - Both Colored and Grayscale imgs
Params:
* img - image
* h - OPTIONAL - filter strength; def: 10
* hForColor - OPTIONAL - used if img is color; same as h; def: h
* templateWindowSize - OPTIONAL - odd num; def: 7
* searchWindowSize - OPTIONAL - odd num; def: 21
Returns:
* De-noised img
- depthImg(imgL, imgR, ndisparities=16, blockSize=16)
- EXPERIMENTAL
Returns a depth map of an img
Params:
* imgL - first image in pair
* imgR - second image in pair
* ndisparities - OPTIONAL - def: 16
* blockSize - OPTIONAL - def:16
Returns:
* Depth map of an image
- dictToLst(dictionary)
- Returns a dictionary into two lists of [[key][values]]
Params:
* dictionary - Dictionary to be split
Returns:
* [keys, values]
- dilate(img, kernel=(5, 5), iterations=1)
- Dilation - Increases white region, size of foreground increases
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
* iterations - OPTIONAL - number of iterations for dilate, def is 1
Returns:
* dilated img
- display(img, name='IMAGE', wait=0)
- Displays an Image onto the screen and waits for user to close
Params:
* img - image to display
* name - OPTIONAL - string name of window, def is IMAGE
* wait - OPTIONAL - time in ms for screen to wait, def:0 - INDEFINITE
- displayImgs(imgs, titles=None, wait=0)
- Displays a list of images
Parms:
* imgs - list of images
* titles - OPTIONAL - list of titles of images
* wait - OPTIONAL - wait time in ms ffor screen; def: 0 - INDEFINITE
- distance(pt1, pt2)
- Distance Between Two Points
Params:
* pt1 - first point, (x,y)
* pt2 - second point, (x,y)
Returns:
* Distance Between Two Points
- drawBFMatch(img1, img2, numMatches=None)
- Brute-Force Matcher - Draw Img
Params:
* img1 - Query Image
* img2 - Train Image
* numMatches - OPTIONAL - Number of Matches to Display; def: All
Returns:
* Img with matches drawn
- drawCircle(img, center, radius=3, color=(0, 0, 255), fill=-1)
- Draws a circle at a point with given radius onto img.
Params:
* img - image
* center - (x,y) center of circle
* radius - OPTIONAL - radius of circle, default is 3
* color - OPTIONAL - default is (0, 0, 255) - RED
* fill - OPTIONAL - default is -1, change for outline thickness
- drawContour(img, cnt, color=(0, 255, 0), thickness=2)
- Draws a contour onto an image
Params:
* img - image to draw on
* cnt - contour to draw
* color - OPTIONAL - color of cnt, default is (0, 255, 0)
* thickness - OPTIONAL - thickness of cnt line, default is 2
- drawContours(img, cnt, color=(0, 255, 0), thickness=2)
- Draws all contours from a list
Params:
* img - image to draw on
* cnt - contours to draw
* color - OPTIONAL - color of cnt, default is (0, 255, 0)
* thickness - OPTIONAL - thickness of cnt line, default is 2
- drawEllipse(img, center, axes, angle, startAngle=0, endAngle=360, color=(0, 0, 255), fill=-1)
- Draws an ellipse onto img.
Params:
* img - image
* center - center of ellipse (x,y)
* (Major Axis Len, Minor Axis Len)
* Angle - angle of rotation
* startAngle - OPTIONAL - def is 0
* endAngle - OPTIONAL - def is 360
* color - OPTIONAL - default is (0, 0, 255) - RED
* fill - OPTIONAL - default is -1, change for thickness
- drawFast(img, nonmaxSuppression=True, color=(255, 0, 0))
- FAST Algorithm for Corner Detection - Returns Key Points
Params:
* img - image
* nonmaxSuppression - OPTIONAL - Suppresses number of points if True, def is True
* color - OPTIONAL - def: (255,0,0)
Returns:
* Key Points drawn onto img
- drawFitEllipse(img, cnt)
- Draws a nearest fitting ellipse to a contour
Params:
* img - image to draw on
* cnt - contour
- drawHarrisSubPixel(img, blockSize=2, ksize=3, k=0.04, color1=(0, 0, 255), color2=(0, 255, 0))
- EXPERIMENTAL
Harris Corner Detection with SubPixel Accuracy
Params:
* img - image
* blockSize - OPTIONAL - size of neighborhood considered for corner detection
* ksize - OPTIONAL - Aperture parameter of Sobel derivative used
* k - OPTIONAL - Harris detector free parameter in equation
* color1 - OPTIONAL - def is (0,0,255)
* color1 - OPTIONAL - def is (0,255,0)
Returns:
* Image with corners marked.
- drawHoughCircles(img, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0, colorCircle=(0, 255, 0), colorCenter=(0, 0, 255), centerRadius=2, thickness=2)
- Hough Circle Transform - Finds Circles in an image and draws it
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* minDist - OPTIONAL - Minimum Distance btw centers of detected circles, def is 20
* param1 - OPTIONAL - Higher Threshold of two sent to Canny edge detector, def is 50
* param2 - OPTIONAL - Accumulator Threshold for the circle centers at detection stage, def is 30
* minRadius - OPTIONAL - Minimum Circle Radius, def is 0
* maxRadius - OPTIONAL - Maximum Circle Radius, def is 0
* colorCircle - OPTIONAL - color of circle, def is (0,255,0)
* colorCenter - OPTIONAL - color of circle's center, def is (0,0,255)
* centerRadius - OPTIONAL - radius of center
* thickness - OPTIONAL - thickness of circles
Returns:
* Image with circles and their centers drawn
- drawHoughLines(img, rho=1, theta=0.017453292519943295, color=(0, 0, 255), threshold=200, thickness=2)
- Draws the Found Lines onto the Image
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* rho- OPTIONAL - radius, measured in pixels, def is 1
* theta - OPTIONAL - angle, measured in radians, def is np.pi/180
* color - OPTIONAL - def (0,0,255)
* threshold - OPTIONAL - Minimum Length of Line, def 200
* thickness - OPTIONAL - def 2
Returns:
* Image with lines plotted
- drawHoughLinesProb(img, minLineLength=100, maxLineGap=10, color=(0, 255, 0), thickness=2)
- Draws the result of Probabilistic Hough Line Transform onto img
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* minLineLength - OPTIONAL - Min length of line, any lines shorter rejected, def is 100
* maxLineGap - OPTIONAL - Max allowed gap btw line segments to treat as single line, def is 10
* color - OPTIONAL - color of line, def is (0,255,0)
* thickness - OPTIONAL - def is 2
Returns:
* img with lines draw onto it
- drawKeyPoints(img, kp, color=(255, 0, 0))
- Draw Key Points
Params:
* img - image
* kp - Key Pints List
* color - OPTIONAL - def: (255,0,0)
Returns:
* img with keypoints
- drawLine(img, start, end, color=(0, 0, 255), thickness=3)
- Draws a line btw 2 points onto img.
Params:
* img - image
* start - start point
* end - end point
* color - OPTIONAL - default is (0, 0, 255) - RED
* thickness - OPTIONAL - default is 3, change for thickness
- drawMatch(img, template, color=(255, 255, 0), thickness=2)
- Draws a rectangle over a matching pattern in img from template
Params:
* img - image
* template - pattern to search for
* color - OPTIONAL - color; def: (255,255,0)
* thickness - OPTIONAL - thickness of rectangle; def: 2
Returns:
* img with rectangle over object
- drawMatchMulti(img, template, color=(0, 0, 255), thickness=2)
- Draws a rectangle over each instance of an object it finds
Params:
* img - image
* template - template to search for
* color - OPTIONAL - def (0,0,255)
* thickness - OPTIONAL - def 2
- drawOrb(img, color=(0, 255, 0))
- ORB Corner Detection
Params:
* img - image
Returns:
* img with key points drawn on
* color- OPTIONAL - color, def is (0,255,0)
- drawRectangle(img, top_left, bottom_right, color=(0, 0, 255), thickness=3)
- Draws a rectangle btw 2 points (Top Left and Bottom Right) onto img.
Params:
* img - image
* top_left - top left point of rectangle
* bottom_right - bottom right point of rectangle
* color - OPTIONAL - default is (0, 0, 255) - RED
* thickness - OPTIONAL - default is 3, change for thickness
- edgeDetect(img, minVal=100, maxVal=200)
- Canny Edge Detection - Returns an img with only edges
Params:
* img - image
* minVal - OPTIONAL - Minimum Threshold
* maxVal - OPTIONAL - Maximum Threshold
Returns:
* Image with only Edges
- enhance(img, window=30)
- EXPERMIENTAL
Enhances an Img
Params:
* img - Image
* window - OPTIONAL - Window Size used for High Pass Filter
Returns:
* Enhanced Img
- eqHist(img)
- Histogram Equalization - Improves the contrast of an image via global contrast
Params:
* img - image
Returns:
* Equalized Image
- equivalentDiameter(cnt)
- Returns the Equivalent Diameter (Circle whose area is same as cnt area)
Params:
* cnt = contour
Returns:
* Equivalent Diameter
- erode(img, kernel=(5, 5), iterations=1)
- Erosion - Discard boundary pixels depending on kernel size
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
* iterations - OPTIONAL - number of iterations for erosion, def is 1
Returns:
* eroded img
- errorCheckContour(contours)
- Error checks the contours in images and stops program if something is wrong
Params:
* contours - list of contours to error check
- eventList(filterStr='')
- Returns a List of all Events handled by OpenCV
Params:
* filter - OPTIONAL - filter list results
Returns:
* list of events
- extent(cnt)
- Returns the Extent (Ratio of contour area to bounding rectangle area)
Params:
* cnt - contour
Returns:
* Extent of cnt
- extremePoints(cnt)
- Extreme Points Dictionary
Params:
* cnt - contour
Returns:
* Dictionary of extrema, such that L = Left, R = Right, T = Top, B = Bottom
- extremePointsTup(cnt)
- Returns the Extreme Points of a contour
Params:
* cnt - contour
Returns:
* (LeftMost, RightMost, TopMost, BottomMost)
- fast(img, nonmaxSuppression=True)
- FAST Algorithm for Corner Detection - Returns Key Points
Params:
* img - image
* nonmaxSuppression - OPTIONAL - Suppresses number of points if True, def is True
Returns:
* Key Points
- fetchImg(SYS, DEV)
- EXPERIMENTAL
Loads an image from an EPICS PV Value
Params:
* SYS - System String
* DEV - Device String
Returns:
* Loaded Image
- fetchImgEXP(SYS, DEV)
- EXPERIMENTAL FETCH IMG METHOD
- fillContour(img, cnt, color=(255, 255, 0))
- Fill a Contour - Fills a contour with color onto img
Params:
* img - image
* cnt - contour
* color - OPTIONAL - color of fill, def: (255,255,0)
- filter2D(img, kernel=(5, 5))
- 2D Convolution - Image Filtering
Params:
* img - image to be filtered
* kernel - OPTIONAL - size of average filtering kernel, def is (5,5)
Returns:
* Filtered Image
- filterCnts(cnts, threshold=5)
- Removes contours that contain less points than threshold, EXCLUSIVE
Params:
* cnts - list of contours
* threshold - OPTIONAL - Removes all contours less than it,
i.e only allows for contours greater than it;
def: 5
Returns:
* new list of contours filtered
- findCntHierarchy(img, thresh=127, val=255)
- Returns the contours to a given image, sorted by size of contour
Params:
* img - Image to find contours in
* thresh - OPTIONAL - threhsold value used
* val - OPTIONAL - value to set items in threshold
Returns:
* Hierarchy of Contours found in img
- findContours(img, thresh=127, val=255)
- Returns the contours to a given image, sorted by size of contour
Params:
* img - Image to find contours in
* thresh - OPTIONAL - threhsold value used
* val - OPTIONAL - value to set items in threshold
Returns:
* Sorted List of Contours found in img
- findMax(img)
- Returns the Max value and Location
Params:
* img - image
Returns:
* (MaxVal, MaxLoc) in dictionary format
- findMin(img)
- Returns the Min value and Location
Params:
* img - image
Returns:
* (MinVal, MinLoc) in dictionary format
- fitEllipse(cnt)
- Finds the nearest fitting ellippse to a contour
Params:
* cnt - contour
Returns:
* (center, Major, Minor, angle) - in dicitionary format
- flip(img, code=0)
- Flips a 2D array around vertical, horizontal, or both axes
Params:
* img - image
* code - OPTIONAL - flip code
flip code == 0 -> Vertical
flip code > 0 -> Horizontal
flip code < 0 -> Diagonal
Returns:
* flipped image
- floodFill(img, seedPoint, val=(255, 255, 255), lo=25, hi=25, fixedRng=False, connectivity=8)
- Flood Fill Algorithm
Params:
* img - image
* seedPoint - startPoint
* val -OPTIONAL - New Value; 255,255,255
* lo - OPTIONAL - Max lower birghtness/color diff; def: 20
* hi - OPTIONAL - Max upper birghtness/color diff; def: 20
* fixedRng - OPTIONAL - TRUE=FIXED diff btw curr and see; FALSE=MASK only fills mask def:False
* connectivity - OPTIONAL - 4 or 8 bit neightborrhood, def:8
Returns:
* Flood Filles Img
- fourier(img)
- Performs a Fourier Transform using CV
This is a wrapper fourier function for generic fourier.
More specific fncs are provided as well.
Params:
* img - Image
Returns:
* Magnitude Spectrum of Image
- fourierCV(img)
- Performs a Fourier Transform using OpenCV Methods
Params:
* img - Image
Returns:
* Magnitude Spectrum of Image
- fourierNP(img)
- Performs a Fourier Transform using Numpy Methods
Params:
* img - Image
Returns:
* Magnitude Spectrum of Image
- gaborFilter(img, ksize=31)
- Gabor Filter
Uses the Gabor Filter Convolutions to get Fractalius-like image effect
Params:
* img - image
Returns:
* Gabor Filtered Image Effect
- gaussianBlur(img, kernel=(5, 5))
- Gaussian Filtering/Blur
Params:
* img - image
* kernel - OPTIONAL - kernel size, def is (5,5)
Returns:
* Gaussian Blurred Image
- getBorderFlags()
- Returns border flags and int values
Returns:
* Border values
- getColorFlag(color)
- Retrieves the color flags associated with img with Epics
Params:
* color - COLOR CODE
Returns:
* CV Color Code
- getColorMapFlags()
- Returns the flags and corresponding int values
Returns:
* dicitonary of values to int codes
- getCrossKernel(size=(5, 5))
- Get a cross-shaped kernel
Params:
* size - tuple of size of requested kernel, def is (5,5)
Returns:
* desired kernel
- getEllipticalKernel(size=(5, 5))
- Get a elliptical kernel
Params:
* size - tuple of size of requested kernel, def is (5,5)
Returns:
* desired kernel
- getRectangularKernel(size=(5, 5))
- Get a rectangular kernel
Params:
* size - tuple of size of requested kernel, def is (5,5)
Returns:
* desired kernel
- getSupportedFileFormats()
- Returns Supported File Formats for Reading Images
Returns:
* Dictionary of Supported File Formats and Extensions
- goodFeaturesToTrack(img, numCorners=25, quality=0.01, minDist=10, radius=3, color=(0, 0, 255))
- Good Features to Track Corner Detection Shi Tomasi
Params:
* img - image
* numCorners - OPTIONAL - Number of Corners, def is 25
* quality - OPTIONAL - min quality of corner, btw 0-1, def is 0.01
* minDist - OPTIONAL - Min Euclidean Dist btw Corners Detected
* radius - OPTIONAL - radius, def is 3
* color - OPTIONAL - def is (0,0,255)
Returns:
* img with corners marked
- goodFeaturesToTrackPts(img, numCorners=25, quality=0.01, minDist=10, radius=3, color=(0, 0, 255))
- Good Features to Track Corner Detection Shi Tomasi
Params:
* img - image
* numCorners - OPTIONAL - Number of Corners, def is 25
* quality - OPTIONAL - min quality of corner, btw 0-1, def is 0.01
* minDist - OPTIONAL - Min Euclidean Dist btw Corners Detected
* radius - OPTIONAL - radius, def is 3
* color - OPTIONAL - def is (0,0,255)
Returns:
* corners detected
- gradient(img, kernel=(5, 5))
- Morphological Gradient - Difference btw Dilation and Erosion of img.
Usually results in outline of object
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
Returns:
* Gradient Img
- grayscale(img)
- Returns a grayscale version of img
Params:
* img - image
Returns:
* Grayscale image
- harrisCorner(img, blockSize=2, ksize=3, k=0.04, color=(0, 0, 255))
- Harris Corner Detection
Params:
* img - image
* blockSize - OPTIONAL - size of neighborhood considered for corner detection
* ksize - OPTIONAL - Aperture parameter of Sobel derivative used
* k - OPTIONAL - Harris detector free parameter in equation
* color - OPTIONAL - def is (0,0,255)
Returns:
* Image with corners marked.
- harrisSubPixel(img, blockSize=2, ksize=3, k=0.04)
- Harris Corner Detection with SubPixel Accuracy
Params:
* img - image
* blockSize - OPTIONAL - size of neighborhood considered for corner detection
* ksize - OPTIONAL - Aperture parameter of Sobel derivative used
* k - OPTIONAL - Harris detector free parameter in equation
Returns:
* Corners.
- highPassFilter(img, window=30)
- Performs a High Pass Filter Operation on the Image
Params:
* img - image
* window - OPTIONAL - window size used for masking in spectrum; def: 30 - results in 60x60 window
Returns:
* High Pass Filtered Image
- hitAndMiss(img)
- Hit And Miss Morphological Transform
Params:
* img - Image
Returns:
* HitAndMiss Img
- houghCircles(img, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0)
- Hough Circle Transform - Finds Circles in an image and draws it
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* minDist - OPTIONAL - Minimum Distance btw centers of detected circles, def is 20
* param1 - OPTIONAL - Higher Threshold of two sent to Canny edge detector, def is 50
* param2 - OPTIONAL - Accumulator Threshold for the circle centers at detection stage, def is 30
* minRadius - OPTIONAL - Minimum Circle Radius, def is 0
* maxRadius - OPTIONAL - Maximum Circle Radius, def is 0
Returns:
* List of circles in (x, y, radius) format
- houghLines(img, rho=1, theta=0.017453292519943295, threshold=200)
- Hough Lines Detection Method
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* rho- OPTIONAL - radius, measured in pixels, def is 1
* theta - OPTIONAL - angle, measured in radians, def is np.pi/180
* threshold - OPTIONAL - Minimum Length of Line, def 200
Returns:
* List of points in [(start, end)] format
- houghLinesProb(img, minLineLength=100, maxLineGap=10)
- Returns the result of Probabilistic Hough Line Transform from img
NOTE: Play around with params to get what you need, each img has different req params
Params:
* img - image
* minLineLength - OPTIONAL - Min length of line, any lines shorter rejected, def is 100
* maxLineGap - OPTIONAL - Max allowed gap btw line segments to treat as single line, def is 10
Returns:
* List of lines in [(start, end)] format
- hsv(img)
- Returns a hsv version of img
Params:
* img - image
Returns:
* HSV image if orginial is RBG
- huMoments(cnt)
- Returns the Hu Moments from moments
Params:
* cnt - Contour
Returns:
* Hu Moments
- imfill(img, threshVal=220)
- Imfill - Creates a mask for an image by removing holes, Isolates shape of object
Params:
* img - image
* threshVal - OPTIONAL - Threshold Value; def: 220
Returns:
* Image with Holes Filled
- imgProp(img)
- Returns properties of an image in a dictionary format
Params:
* img - image
Returns:
* dictionary of properties "shape, rows, columns, channels, size, dtype"
- inRangeThresh(img, lower, upper)
- Thresholds an image between two color ranges, or scalars for grayscale
Params:
* img - Image to threshold
* lower - lower bound, either [X, Y, Z] for color or scalar for gray
* upper - upper bound, either [X, Y, Z] for color or scalar for gray
Retuns:
* In Range Thresholded Img
- integral(img, sqSum=False, tilted=False)
- Integral of an Img
Params:
* img - image
* sqSum - OPTIONAL - if True, returns integral for squared pixel values
* tilted - OPTIONAL - if True, returns integral for img rotated by 45 deg
Returns:
* integral
* sq pixel integral
* tilted integral
- laplacian(img)
- Laplacian Image Gradient
Params:
* img -image
Returns:
* Laplacian Image
- load(filename, flag=None)
- Loads an image from a file name and returns it in color mode (default).
Safety check if img fails to load.
Params:
* filename - filename of image
* flag - OPTIONAL - open mode: 1 = COLOR, 0 = GRAYSCALE, -1 = UNCHANGED
Returns:
* Loaded Img if sucessful
- lowPassFilter(img, window=30)
- Performs a Low Pass Filter Operation on the Image
Params:
* img - image
* window - OPTIONAL - window size used for masking in spectrum; def: 30 - results in 60x60 window
Returns:
* Low Pass Filtered Image
- lstToDict(key, value)
- Turns two lists into a dictionary
Params:
* key - List of Keys
* value - List of Values
Returns:
* Dictionary of Key/Values
- makeGraph(xval, yval, title='GRAPH', xlabel='X AXIS', ylabel='Y AXIS', axisRng=None, style='bo', clear=False)
- Makes and Displays Mat Plot Lib Graph from data
Params:
* xval - X Values
* yval - Y Values
* title - OPTIONAL - Graph Title
* xlabel - OPTIONAL - X Axis Title
* ylabel - OPTIONAL - Y Axis Title
* axisRng - OPTIONAL - Axis Range
* style - OPTIONAL - graph plotting style
* clear - OPTIONAL - if True clears graph; def: False
- mask(img, invert=False)
- Finds the mask for an image
Params:
* img - image
* invert - OPTIONAL - def False; if def, returns mask, if True, returns bitwise not of mask
Returns:
* mask
- matchShapes(cnt1, cnt2)
- Takes two contours and compares them.
Params:
* cnt1 - first contour
* cnt2 - second contour
Returns:
* metric showing similarity, lower the result, better the match
- matchShapesImages(img1, img2)
- Takes two images and compares them.
Params:
* img1 - first image
* img2 - second image
Returns:
* metric showing similarity, lower the result, better the match
- matplotlibDisplay(img, title='Image', colorFlag='gray')
- Displays an image using MatPlotLib
Useful for displaying all images and Magnitude Spectrums
Params:
* img - image
* title - OPTIONAL - name of Image
* colorFlag - OPTIONAL - color flag for imshow; def: 'gray'
- matplotlibDisplayMulti(imgs, titles=None, colorFlag='gray')
- Displays multiple images in a matplotlib window
Params:
* img - image
* title - OPTIONAL - name of Image
* colorFlag - OPTIONAL - color flag for imshow; def: 'gray'
- meanVal(img)
- Returns the Mean Color (Regular Img) / Mean Intensity (Grayscale)
Params:
* img - image
Returns:
* Mean Color / Mean Image
- medianBlur(img)
- Median Filtering
Params:
* img - Image
Returns:
* Median Filtered Img
- minEncloseCircle(cnt)
- Minimum Enclosing Circle
Params:
* cnt - Contour
Returns:
* Min Enclose Circle info
- minMaxLoc(img)
- Returns the Max Val, Min Val, and Locations
Params:
* img - image
Returns:
* (minVal, maxVal, minLoc, maxLoc) in dicitonary format
- moments(cnt)
- Returns the moments of a contour
Params:
* cnt - contour
Returns:
* dictionary of moments
- objectTrackVid(lower, upper)
- Object Tracking Video
Params:
* lower - Lower Bound
* upper - Upper Bound
- opening(img, kernel=(5, 5))
- Opening - Erosion followed by dilation. Useful for removing noise
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
Returns:
* opened img
- orb(img)
- ORB Corner Detection - Detects Corners
Params:
* img - image
Returns:
* key points
- orientation(cnt)
- Returns the Orientation of the Object
Params:
* cnt - Contour
Returns:
* Angle at which object is directed
- otsu(img, invert=False)
- Returns an img after Otsu Binarization
Params:
* img - image
* invert - OPTIONAL - uses inverse binary if True, def: False
Returns:
* An Otsu Binarization of the img
- perimeter(cnt)
- Returns the Contour Perimeter
Params:
* cnt - contour
Returns:
* Perimeter of cnt
- perspectiveTransform(img, pts, newPts, size=None)
- Perspective Transformation - Translates pts to newPts while keeping all lines straight
Params:
* img - image
* pts - 4 points on input image
* newPts - 4 points to be shifted to
* size - OPTIONAL - new size of output image (x,y), def is max newPts
Returns:
Shifted img under Perspective Transformation
- pixelPoints(img, cnt)
- Finds all the points that comprises an object (Pixel Points)
Params:
* img - image
* cnt - contour
Returns:
* pixelpoints
- plotCentroid(img, cnt, radius=3, color=(255, 255, 0))
- Plots a centroid onto an image from a given contour
Params:
* cnt - contour
* img - image to modify
* color - OPTIONAL - specify color of centroid cross
Returns:
* Centroid Pixel Coord
- plotPOI(img, cnt, radius=3, color=(100, 100, 255))
- Plots the points of interest of a cnt
Params:
* img - image
* cnt - contour
* radius - OPTIONAL - radius of point; def: 3
* color - OPTIONAL - color of pt; def: (100, 100, 255)
- plotPoint(img, point, radius=3, color=(0, 0, 255))
- Plots a single point onto an image
Params:
* img - image
* point - pixel tuple to plot at (x, y)
* radius - OPTIONAL - radius of point to be plotted
* color - OPTIONAL - color of pixel to be plotted
- plotPoints(img, points, radius=3, color=(0, 0, 255))
- Plots a list of points onto an img
Params:
* img - image
* points - list of points to plot
* radius - OPTIONAL - radius of points to be plotted
* color - OPTIONAL - color of points to plot, default is (0, 0, 255)
- poi(img, cnt)
- Returns only points of interest for a contour
Params:
* img - image
* cnt- contour
Returns:
* points of interest (max/min, extremas, center)
- pointPolygonTest(cnt, point, distance=True)
- Point Polygon Test - Finds the Shortest Distance between a point in the image and a contour
Params:
* cnt - contour
* point - point coordinates
* distance - OPTIONAL - True if signed distance, False for Inside/Outside/On cnt
Returns:
* Relation of point to cnt
- printCntInfo(img, cnt)
- Prints the contour information line by line via key: value format
Params:
* image - image
* cnt - contour
Returns:
* dicitonary of Contour Info
- printDic(m)
- Prints a Dictionary in Key: Value format
Params:
* m - Dictionary to print
- resize(img, width, height)
- Resize an Image
Params:
* img - image
* width - new width
* height - new height
Returns:
* resized image
- roi(img, x, y, w, h)
- Image ROI: Returns a Region of Interest
Can be Used for Cropping
Params:
* img - image
* x - X Position (start)
* y - Y Position(start)
* w - Width - How much region spans after X
* h - Height - How much region spans after Y
Returns:
* imgROI - from [x:x+w, y:y+h]
- rotate(img, deg, center=None)
- Rotates the image by a given degree
Params:
* img - image
* deg - degrees to rotate
* center - OPTIONAL - coordinate to rotate around, def is center of img
Returns:
* rotated img
- save(img, filename=None)
- Saves an image to a file
Params:
* img - image to save
* filename - OPTIONAL - file to save to, def is TYEARMONTHDAY.jpg
- saveGraph(xval, yval, title='GRAPH', xlabel='X AXIS', ylabel='Y AXIS', axisRng=None, style='bo', filename='graph.png', clear=False)
- Makes and Saves Mat Plot Lib Graph from data
Params:
* xval - X Values
* yval - Y Values
* title - OPTIONAL - Graph Title
* xlabel - OPTIONAL - X Axis Title
* ylabel - OPTIONAL - Y Axis Title
* axisRng - OPTIONAL - Axis Range
* style - OPTIONAL - graph plotting style
* filename - OPTIONAL - name of file to save to
* clear - OPTIONAL - If True Clears the graph
- saveImgs(img, filename=None)
- Saves a list of images to a file
Params:
* img - images to save
* filename - OPTIONAL - file names to save to, def is TYEARMONTHDAYITEM#.jpg
- scalarInfo(img, cnt)
- Returns only scalar information for a contour
Params:
* img- image
* cnt- contour
Returns:
* scalar contour information
- shape(img)
- Image Dimensions
Params:
* img - Image
Returns:
* (x, y, c) - x pixels, y pixels, channels
- sharpen(img, ker=(9, 9), sigX=10.0)
- EXPERIMENTAL
Returns a sharpebded image using the Unsharp Img Algorithm
Params:
* img - image
* ker - OPTIONAL - kernel size tuple; def:(9,9)
* sigX - OPTIONAL - Gaussian kernel standard deviation in X dir; def:10.0
Returns:
* sharpened img
- size(img)
- Returns the Number of pixels in an image
Params:
* img - image
Returns:
* size of image in pixels
- slope(start, end)
- Slope Calculator from two points
Params:
* start - start point (x,y)
* end - end point (x,y)
Returns:
* slope of line connecting two pts, None if undefined
- sobelx(img, ksize=5)
- Sobel X Image Gradient
Params:
* img -image
* ksize - OPTIONAL - Kernel Size, def is 5
Returns:
* Sobel X Image
- sobelxy(img, ksize=5)
- Sobel XY Image Gradient
Params:
* img -image
* ksize - OPTIONAL - Kernel Size, def is 5
Returns:
* Sobel XY Image
- sobely(img, ksize=5)
- Sobel Y Image Gradient
Params:
* img -image
* ksize - OPTIONAL - Kernel Size, def is 5
Returns:
* Sobel Y Image
- solidity(cnt)
- Returns the Solidity (Ratio of area to convex hull area)
Params:
* cnt - contour
Returns:
* Solidity of cnt
- sort(contours)
- Sorts a list of contours by number of points, in descending order
Params:
* contours - list of contours to sort
Returns:
* Sorted list of contour objects such that the largest contour is first
- sortList(lst, reverse=False, key=None)
- Sorts a given list
Params:
* lst - List to be sorted
* reverse - OPTIONAL - TRUE=List Reversed, FALSE = List Normal; def: False
* key - OPTIONAL - function used as comparision; def: None
Returns:
* Sorted List
- sumPixel(img)
- Sums all of the pixels in an image
Best for Grayscale
Params:
* image
Returns:
* sum of pixel values
- templateMatchMulti(img, template)
- Template Matching - Multiple Objects
Params:
* img - image
* template - image to search for
Returns:
* lst of points in [(tl, br), (tl+w, br+h)] format for rectangles
- templateMatchSingle(img, template)
- Template Matching - Single Object, will find the points to draw a rectangle over given template
Params:
* img - image
* template - template image to match
Returns:
* (top left, bottom right) - points of a rectangle
- tophat(img, kernel=(5, 5))
- Top Hat - Difference btw input img and Opening of img
Params:
* img - image
* kernel - OPTIONAL - kernel size in tuple, def is (5,5)
Returns:
* TopHat Img
- trackObject(img, lower, upper)
- Isolates a Color within an Image for tracking
Useful for tracking certain colors
Use within while loop for video
Params:
* img - Color Image
* lower - Lower Threshold of Color, [X, Y, Z] format
* upper - Upper Threshold of Color, [X, Y, Z] format
Returns:
* Isolated img
- translate(img, shift)
- Translates a image by shift (x, y)
Params:
* img - image
* shift - (x, y) tuple that has the shift direction in the x and y directions
Returns:
* Translated image
- transpose(matrix)
- Transposes a matrix
Params:
* matrix - matrix
Returns:
* transpose of matrix
- version()
- Prints the version codes for cvlib, OpenCV, and Numpy. For Refrence
- watershed(img)
- EXPERIMENTAL
Image Segmenting - Watershed Algorithm
Params:
* img - image
Returns:
* Segmented img
|


{% extends "reimagine_base.html" %} {% load staticfiles %} {% block content %}
REimagine Competition 2015 – Terms and Conditions
The REimagine competition encourages the creative reimagining of an image or images in The Illustration Archive
Participants are invited to create a work of art or craft inspired by illustration(s) from The Illustration Archive and to submit their entries online by 14th December 2015.
Entries will be judged by a selected panel of experts from the creative industries and the work of nominated winners will be exhibited in Cardiff University and in an online exhibition.
Prizes will be awarded to winners and runners up in two categories: over and under 16s.
Entry/Registration
The closing date for entries is 14th December 2015.
Only entrants who have logged into The Illustration Archive and completed the online entry form are eligible for a prize.
You may enter as often as you like.
There is no entry fee for participants.
Entrants can submit any form of art, text, craft, multimedia or digital work, video or animation.
Entries can reimagine an illustration by reproducing it in a different media or creating an entirely original piece of work inspired by an illustration from the archive.
Please submit entries via the digital form on the Illustration Archive competition website. Do not send physical copies: your design should be submitted as a digital document. Entrants must provide details of the illustration(s) that inspired their entry.
Judging
The panel of judges will review all entries received.
Entries will be assessed using the following criteria:
Evidence of inspiration from the Illustration Archive collection
Creativity and innovation
Evidence of a changed understanding of historic illustrations
Prizes and Exhibition
Prizes of signed Quentin Blake books and Amazon vouchers will be awarded to the winners in each category.
Runners up in each category will receive Illustration Archive goody bags.
Nominated submissions will be exhibited on site at Cardiff University and in a digital exhibition.
Consent and Copyright
All entries submitted must be entirely the entrant’s own work. Entrants must have obtained prior permission from any person(s) and or object(s) that require permission to be featured within the entries before entering into the competition.
Entries must not contain any content that could be construed as offensive, unlawful, derogatory or defamatory.
It is the responsibility of the entrant to ensure they are compliant with ownership and copyright laws. If the Illustration Archive team finds reason to believe entries submitted do not meet these requirements, these entries will not be considered.
In return for entering the competition, entrants grant the Illustration Archive the right to use the submitted entries in digital form without compensation in the context of promotions, resources, marketing, social media, advertising, events and exhibitions.
Submitted entries may also be used in various hard copy and electronic publications and social media to promote the competition, Cardiff University and the Illustration Archive. We will endeavour to state the name of the designer/artist wherever possible.
The entries are exclusive property of the entrant and the Illustration Archive will not assert any claim to the ownership of them.
If you have any problems uploading your images or completing the online entry form, please email the Illustration Archive team at illustrationarchive@cardiff.ac.uk


Little Mix to play huge outdoor concert at Ayr Racecourse as part of summer 2020 tour
The pop super-group have announced a gig in Ayrshire next summer as part of their 2020 tour.
by Stuart Wilson, https://www.dailyrecord.co.uk/authors/stuart-wilson/Pop superstars Little Mix are to play a huge outdoor gig at Ayr Racecourse.
The world's biggest girl band will visit Ayrshire as part of their 21-date summer tour which hits the road in 2020.
Ayr has been chosen as one of the venues across Scotland, England and Wales to host the show on Saturday, July 25.
Tickets go on sale at 9am this Thursday, November 2019 from www.ticketmaster.co.uk and www.lhgtickets.com
The tour will see Little Mix perform some of their greatest hits including ‘Woman Like Me’, ‘Touch’, ‘Shout Out to My Ex’, ‘Black Magic’ and ‘Wings'.
Venues include London's Hyde Park and the Cardiff City Stadium.
The girls will play only two other dates in Scotland, at the Falkirk Stadium and Aberdeen's Hazlehead Park.
The group said: "Performing live is our favourite thing to do as a band, we love it.
"Our last Summer tour in 2018 was one of our favourites ever, so we can’t wait for some more brilliant outdoor shows next year.
"We want everyone to come party with us in the sunshine!"
Ayr Racecourse managing director, David Brown, added: "Everyone at Ayr is delighted and excited that we have been chosen as a venue for Little Mix’s summer tour.
"We are looking forward to what should be a memorable day next year."
Tickets are expected to be in hot demand for the show, which will be by far the biggest the racecourse has ever staged.
Liz Doogan-Hobbs MBE, CEO of LHG Events, promoters for the Racecourse concert, said: “We're thrilled to announce Little Mix at Ayr Racecourse.
"This is a wonderful opportunity for Mixers across Ayrshire to see the world’s biggest girl band first hand in what will be an amazing show!”
The girls have firmly established their reputation as one of the most successful groups around.
Alongside massive tours of Europe, Australia, Japan and the US, the group have just completed their mammoth LM5 Arena Tour which took in cities across Europe, the UK and Ireland including an amazing five sold out dates at London’s O2 Arena.
They have sold more than 50 million records worldwide, collected more than 17 million Spotify listeners, received more than three billion Youtube views, and collated 12 billion streams worldwide.
Record sales have seen them notch up a total of four UK No.1 singles, four platinum-selling albums and nine platinum selling singles, surpassing a record previously held by the Spice Girls.
Their 2016 album 'Glory Days' was the biggest selling album by a female group this millennium in the UK, alongside being named the longest-reigning Top 40 album for a girl group ever.
Tickets go on sale at 9am Thursday November 2019 from www.ticketmaster.co.uk and www.lhgtickets.com


{% extends "layout.html" %} {% block page_title %} Question page {% endblock %} {% block content %} Give us your details We'll use these to provide updates etc. How do we sell collecting this? First name: Last name: Email: Contact phone number: {% endblock %}
We'll use these to provide updates etc. How do we sell collecting this?


Memphis Depay was too young at Man United, claims Holland boss
Memphis Depay was "too young" during his time at Manchester United, according to Ronald Koeman
by The Sport Review staffMemphis Depay was “too young” during his time at Manchester United but he could now be ready for a return to the Premier League, according to Netherlands head coach Ronald Koeman.
The Netherlands international spent two years with Manchester United between 2015 and 2017 but only notched up 33 appearances for the Red Devils as he struggled to find consistent form.
He moved to French side Lyon on the January transfer window in 2017 and he has scored an impressive 41 goals in 99 games for the Ligue 1 outfit.
According to Sky Sports, Depay is now being linked with a potential return to Old Trafford or a move to Premier League rivals Tottenham Hotspur.

Speaking in an interview quoted by Sky Sports, Koeman said: “I think he’s a top player and maybe during his time at Manchester United he was a little bit too young.
“He’s now more experienced and what he’s showed and what he has done for the national team is great and I’m really glad that we have that kind of player in our national team.”
Manchester United are currently in seventh place in the Premier League table as they bid to try and break back into the top four this season.
The Red Devils – who finished sixth and without a trophy last term – will return to Premier League action after the international break on Sunday when they travel to Sheffield United.


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PPP head Goh Meng Seng weighs in on e-scooter ban
- Advertisement -
Singapore— As the country deals with the new ban on e-scooters on public footpaths, which was announced in Parliament on November 4, others have begun to weigh in on the issue, including opposition leader Goh Meng Seng, who is the head of the People’s Power Party (PPP).
Mr Goh has weighed in on the S$7 million grant from the government for PMD riders to switch to e-bikes, an issue that he has talked about for several years now, according to a report from mothership.
He is quoted in the report as saying that Lam Pin Min, Singapore’s Senior Minister of State for Transport “says that he use S$7 million for people to change to e-bike. You change to e-bike, you got to go by the road and it’s more dangerous for them. And yet he says he is more concerned about safety. To me that’s rubbish.”
The opposition leader attended the Meet-the-People Session (MPS) of Dr Lam, who is the Member of Parliament for Sengkang West, on Tuesday, November 12.
Around 280 people had come out to meet Dr Lam, in the biggest MPS among several that have occurred since the announcement of the e-scooter ban, including meetings with MP for Nee Soon GRC K Shanmugam, the country’s Home Affairs and Law Minister, as well as with MP for Jurong GRC Desmond Lee, who is also the Minister for Social and Family Development. Under a hundred people had attended these sessions.
- Advertisement -
The meeting with Dr Lam, which was held at Anchorvale Community Club to accommodate the size of the crowd, was attended by many food delivery riders from GrabFood and Foodpanda, with mothership reporting that some even brought their families and children to the MPS.
Mr Goh, apparently, spoke up at the meeting with Dr Lam, and while what he said exactly has gone unreported, he was cheered several times by the MPS attendees.
The Senior Minister of State for Transport is said to have told Mr Goh that they could talk at a later time so as to give the PMD riders a chance to ask their questions and to air their thoughts.
But this was not the only MPS that the PPP head attended that night. He also went to the one held by Chee Hong Tat, MP for Bishan-Toa Payoh, and Senior Minister of State.
Mr Goh shared a video of him at Mr Chee’s MPS on his Facebook page.
The PPP leader’s main concern seems to be road safety for delivery riders when the switch to e-bikes have been made, especially if Singapore’s roads are suddenly inundated with new riders.
Mr Goh asked on his Facebook account on November 13, “Will he (referring to Lam Pin Min) ban Ebikes when more fatal accidents occur after he gave grants for delivery riders to change to it?”
When a netizen asked, “What exactly is your solution?” Dr Goh replied that the PPP has a policy position.
“We have given a comprehensive long term solution.. To create the whole Ecosystem for PMD, not just piece meal bits and pieces solution.” -/TISG
Read related: Netizens increasingly unhappy with PMD ban
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THE WATERBOY
Reviewed by Harvey Karten, Ph.D. Touchstone Pictures Director: Frank Coraci Writer: Tim Herlihy, Adam Sandler Cast: Adam Sandler, Fairuza Balk, Kathy Bates, Henry Winkler, Blake Clark, Allen Covert, Peter Dante, Jonathan Loughran, Dan Patrick, Jerry Reed
Those who can, play. Those who can't, manage. Those who can't manage, become assistant managers. In high schools around the country, kids who want to be part of their sports teams but just can't cut it become managers, often the butt of jokes by members of the team and by fellow students alike. They're called everything from waterboy to stuff that's unprintable in a family review. But vengeance is theirs in Frank Coraci's film, "The Waterboy," about a nerdy guy who proves that he can hew wood, draw water, and throw a mean tackle all at the same time. The difference here is that the waterboy is not a high-school kid but a 31-year-old who appears mentally challenged but who, in reality, has been kept out of school and away from girls by a backwater mama who is afraid of being deserted by her little boy as she was by her husband some time back.
How good can a movie be if it centers on a waterboy?  No problem. This one really IS moronic enough to be entertaining and could inspire the fabulously successfully IDT publications to put out a "Dummies' Guide to Football." Nope, it probably would better meet the standard for "The Complete Idiot's Guide to Football." While lots of films have been dumbed down so that the lowest common denominator among moviegoers can enjoy (witness, for example, "A Night at the Roxbury"), the folks who put this one out could not possibly have THAT low an estimate of the audience, so we've got to assume--in the words of its star, Adam Sandler, that "We're just having fun and I hope everyone has fun right along with us." As one critic has said about the movie, "If you like the Chris Farley stuff, and if you're one of Adam Sandler's regular fan club, you'll have a rollicking good time. If not, stay away."
While the talented Mr. Sandler had a more intelligent role in "The Wedding Singer," this time he goes all out to be Thoreauvian, paring away all the complexities of life and becoming as simple as a human body can get. As the eponymous waterboy, Bobby Boucher, he speaks with a stutter and is the perfect student of his backwater mother's (Kathy Bates) homilies. He has never been with another woman, has no friends because (his mom insists) "he doesn't have social graces" and gets his fulfillment by taking the job of waterboy seriously enough to sterilize the H20 and to check it repeatedly for Ph content. His concern is not appreciated by the jocks he serves on a college football team. They spit in the water at best, and at worst take turns tackling him amid a flurry of insults, turning over the table on which he so carefully tends to his product.
One day, infuriated by yet another affront to his dignity cast at him by a member of the team, he dashes into the field tackling the offender so viciously that Coach Klein (Henry Winkler) puts him into a game, counsels him to visualize people who have humiliated him in the past, and turns him loose against some of the most brawny contenders south of the Mason-Dixon line. It's the sort of feel-good movie in which games are won in the final seconds and women swoon over their sportsman-heroes--in this case, the sexy, criminally inclined Vicki Vallencourt (Fairuza Balk) pushes her man to victory with a promise to introduce the 31-year-old to the mysterious of love.
Directed conventionally by Frank Coraci, the screenplay by Tim Herlihy and Adam Sandler has fun at the expense of the deep south, portraying the Louisiana Bayou country as a hillbilly heaven of snake-eaters and of folks with accents that not even a southerner could understand. Great fun is had at the expense of Farmer Fran (Blake Clark), who cannot utter a single understandable word and Mama Boucher (Kathy Bates) as a grasping, lonely woman who covers up her insecurities behind falsely fundamentalist religious convictions. Insisting that football (which she pronounces "foosball") is just plain silly and out of bounds for her boy and that Vicki Vallencourt is the Devil (she's on safer grounds there), she keeps Bobby locked into arrested development for her own selfish ends.
Henry Winkler turns in the best performance as the coach who is afraid of the other team's trainer until Bobby imparts to him the safe advice he received: to visualize the man you fear as though he were a baby, a cocker spaniel, or a guy who has mortified you in the past. It's a toned-down discharge of a role that the Fonz would have played as low comedy back in the seventies. Ms. Balk plays to type as the low-life girl friend much as she was in the quite different film, "American History X" in a movie whose quality is, well, as we said, dependent on what you think of the Adam Sandler genre.
Rated PG-13. Running Time: 86 minutes. (C) 1998 Harvey Karten
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The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) is commonly used to compare the performance of mixed integer optimization solvers. This package provides functions to access 'MIPLIB' from the 'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB' can be found in the paper by Koch et al. available at <http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>. The 'README.md' file illustrates how to use this package.
| Version: | 0.0-1 |
| Depends: | R (≥ 2.10) |
| Imports: | R.utils, Rglpk, ROI (≥ 0.2-0) |
| Published: | 2017-05-23 |
| Author: | Florian Schwendinger [aut, cre], Stefan Theussl [aut] |
| Maintainer: | Florian Schwendinger <FlorianSchwendinger at gmx.at> |
| License: | GPL-3 |
| URL: | http://R-Forge.R-project.org/projects/roi |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | ROI.models.miplib results |
| Reference manual: | ROI.models.miplib.pdf |
| Package source: | ROI.models.miplib_0.0-1.tar.gz |
| Windows binaries: | r-devel: ROI.models.miplib_0.0-1.zip, r-release: ROI.models.miplib_0.0-1.zip, r-oldrel: ROI.models.miplib_0.0-1.zip |
| OS X binaries: | r-release: ROI.models.miplib_0.0-1.tgz, r-oldrel: ROI.models.miplib_0.0-1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=ROI.models.miplib to link to this page.


Projects
Brain Hardware: Step away, Neuralink. Currently in incubation :)
Nearer: Nearby Object Detector for Bicycles using Ultrasonic sensors
LabelLearn: This project aims to enable a labeller to be consistent with both their fellow labelers and their past self while seeing the live class distribution of the dataset.
Sundae: A text based journaling chatbot that uses NLP and sentimental analysis to cheer you up!
Messiah: Messiah is an oracle management interface that developers can interact with to get the most reliable data from blockchains.
MyoVision: A workout assister that uses Myo and statistics to improve your posture while also serving as a tracker of your activity.


Based off the classic game Bomberman, my team of 8 people tackle how agents acomplish tasks with infinitely meaningful variations through competitive multi-agent learning. Our team was split betwen two teams; Pytorch and TensorForce, with the goal of seeing if different frameworks lead to different results but trying the same algorithms being PPO and A2C.
We leverage pre-existing architecture and code from Pommerman Competition as cited below.
Table of Contents
Introduction
Pommerman is a game environment which copies the famous game from Nintendo, Bomberman, for reinforcement learning purposes. The environment is a symmetric 11x11 grid where there are four agents in each corner. In the game environment, there are wooden and rigid walls. Both wooden and rigid walls are impassable, however, a wooden wall can be destroyed. When a wooden wall is destroyed, it can become a passage or power-up. Power-ups can only be obtained by blowing up a wooden wall.
The agent can move up, down, left, or right and can lay a bomb. Agents start with a bomb and can place bombs whenever they want. After nine time steps, bombs explode and any wooden walls, agents, power-ups, and other bombs in its range are destroyed. The goal of each agent is to be the final surviving player.
There are also power-ups players can obtain. Powerups have a chance to spawn when a wooden box is exploded. These powerups are: get an extra bomb, get increased bomb explosion range, or get the ability to kick a bomb. An extra bomb allows the agent to drop one more bomb. The increased range increases the agent’s bomb blast strength by one. The can kick power-up allows the agents to kick bombs by running into them. When kicked, the bombs continue to travel in the direction the agent was moving until they encounter a bomb, a player, or a wall.
The initial rewards function only rewards an agent with +1/-1 reward at the end of each game, and each game has a large variance in timesteps, depending on how long it takes for all other players to die. This creates a problem with delayed, sparse rewards for an AI agent, which will require some custom rewards shaping to help reinforcement learning algorithms to learn effectively.
To tackle this project, we had to research and understand different reinforcement learning algorithms as well as the game’s environment. We decided to try two different reinforcement learning algorithms, PPO and A2C. This was because a few papers had results where vanilla DQN does not work well with the Pommerman environment. Testing and creating rewards shaping functions was another essential part of agent learning that heavily impacts training time and efficiency.
Related Work
Pommerman has had many previous competitions to see which research teams could create the best AI agent. The most prominent agents to compete in competitions have all used PPO or A2C algorithms as the learning algorithm. However, these learning algorithms are not enough for the agent to learn to play Pommerman effectively due to the sparse rewards issue and the difficulty of laying bombs without suicide.
One previous competition submission received strong competition results by using PPO algorithm alongside a custom action pruning function that would not allow an agent to move into positions that would lead to suicide. This helped prevent the agent from committing suicide during training so it could steadily learn to use bombs effectively. Also included a FIFO queue to keep track of previously visited coordinates, and gives rewards for traversing unvisited coordinates to encourage exploration.
The winner of NeuroIPS Pommerman competition in 2018 used A2C algorithm alongside an implementation inspired by population-based training, where multiple A2C agents are created with different hyperparameters (specifically the discount factor), and each A2C agent is ranked based on match results.
Methodology
PyTorch vs TensorFlow
Our approach to this project is to try running the agent with two frameworks, PyTorch and TensorFlow, alongside our different learning algorithms. We used a TensorFlow implementation of PPO and PyTorch implementation of A2C. PyTorch and TensorFlow don’t have built-in implementations of PPO and A2C, but there are frameworks built on top of PyTorch and TensorFlow which implement these algorithms. We used Tensorforce for TensorFlow and Ilya Kostrikov’s implementation for PyTorch. The Pommerman competition Playground package is implemented on top of OpenAI Gym. This package gives us direct access to game state and game environment for training and testing. During training, we log the model’s layer weights, hyper parameters, and other observable features using Tensorboard. Tensorboard is not only compatible with both Pytorch and Tensorforce, but also allows us to easily monitor the agent during training.
Reward Functions
One obstacle we want to tackle is how the agent overcomes the local minima. This specific obstacle is tackled by reward shaping which helps the agent lay a bomb. As it currently is, our agent rarely places a bomb and instead runs away from other agents.
Since the original rewards function only rewarded an agent at the end of each game depending on whether the agent was dead or alive, there is a sparse rewards issue. To fix this, all of our implemented reward functions will give the agent a reward at each timestep instead. We also tried to make our rewards as “hands off” as possible to avoid making something similar to a rule-based agent. The goal of our rewards functions was to help coax the agent to learn a more optimal policy.
The rewards functions we implemented included a bomb dodging function. This function gave the agent a negative reward if that agent was in a bomb blast zone, while it rewarded the agent positively if the agent was in a safe location. The goal of this function was to help the agent learn to avoid bombs, so that it would not committ suicide. Another goal for rewards shaping was to give the agent an incentive to explore the grid. Without help, the agent would just learn to hide in its spawn corner and get reward for surviving as long as possible. This is suboptimal as we want our agent to pick up powerups and gain an advantage on its opponents. We introduced some rewards rules to help the agent explore and pick up power ups, such as a visited memory, where agents are rewarded for visiting coordinates not yet in the visited memory. We also incentivised blowing up wooden boxes, agents and also picking up power through specific reward functions.
To test our dodging reward function, we gave another reward function that automatically awarded the agent for laying a bomb. This is so we can instantly have the agent drop a bomb and begin learning to avoid it. The introduction of this reward function is only temporary and for research and demonstration purposes. A long term goal is to not require an explicit reward for the agent to use bombs, but for the agent to learn its usefulness on its own.
Environment
The Pommerman environment we train in has three structures; no walls, some walls, and default. The default environment has 32 wooden walls and 32 rigid walls with 20 power ups. Although the wall placement is randomly generated, there is a small section for each agent that is 4 walls that guarantees that the agent has a path to traverse. In regarding some walls, we use a combination of wooden-rigid; 16-16, 24-24, and 8-8 with the power ups being two less than the number of wooden walls.
The last type of environment we use is one with no rigid or wooden walls. This is to see if and how much the agent’s behavior deviates from an environment with wooden walls.
Algorithms
A2C
A2C stands for Advantage Actor Critic and is a reinforcement learning algorithm that incorporates two models, and actor and a critic. The actor model consists of a model for the agent policy, and given observations, will determine the best course of action. The critic model learns to evaluate the agent and its game state, and generates a “value score” for the actor’s action given the state. The actor critic model will iteratively improve the actor policy weights that would result in the best increase in value score calculated by the critic.
PPO
PPO stands for Proximal Policy Optimization and is less computationally expensive than other policy gradient methods such as TRPO (Trust Region Policy Optimization). Policy Gradient methods use gradient ascent to update policy in order to maximize expected rewards. However, the update to policy could be too large, and the drastic policy change could lead to a suboptimal policy that could take a long time to recover from. PPO and TRPO incorporates a constraint to limit the size of each policy update to prevent this from happening, while PPO is computationally inexpensive compared to TRPO.
Findings
The Tensorforce PPO agent underperformed compared to the PyTorch implementation of A2C agent. We are not yet sure if this underperformance is due to the Tensorforce implementation of the PPO algorithm or if it is due to the PPO algorithm itself. A2C agent results displayed more exploration during training while Tensorforce PPO agent would consistently get stuck in a suboptimal policy routine.
For rewards shaping, we were able to implement three rewards shaping functions that helped the agents better perform certain tasks in the game. We implemented a rewards function to incentify staying out of the bomb blast radius, which helped the agent learn to better dodge bombs. We also created a rewards function that incentivized the agents to explore unexplored tiles in the game grid. Lastly, we incentivized the agent to pick up power ups. We were not yet able to find some combination of rewards functions that would lead to the agent using bombs correctly. Implementing these different rewards functions by themselves led to improved results for the respective actions they were intended for, but more rewards value tuning is needed in order for these functions to work well together.
Challenges
There were multiple issues with finding appropriate frameworks and open-source code that produced consistent and reliable results. The Pommerman codebase was outdated and required updating to run with the current version of Tensorforce. Tensorforce implementation of PPO had a variety of issues. Tensorforce lacked a lot of documentation and features, such as multiprocessing for training. Tensorforce PPO agent also was learning a lot less efficiently than PyTorch A2C agent, and ultimately the results from PyTorch implementation of A2C far surpassed the results from Tensorforce implementation of PPO.
Another challenge was the limited training resources. We were limited by what we could train from the GCP credits and also by the Cinema computer resources being that we could not train on those computers for long periods of time. Rewards shaping was our most challenging task. We had to use creativity and trial and error to find useful reward functions to help coax our policy agent to a more optimal policy. We used ideas from other papers to help build our own rewards shaping functions, such as for exploration and bomb survival. This was a very time consuming task as we had to train a model to test each reward function. This does not include training to test for optimal reward values. Even after testing a single new rewards function and getting favorable results, combining multiple rewards functions together required additional testing to balance the reward values of the different reward functions in relation to each other.
A huge challenge while designing rewards functions was to make sure the functions were not too specific that the agent would essentially be rule-based. It was also difficult to determine the effectiveness of a rewards function because training time was also a hyperparameter. Even the lack of rewards functions could also lead to results if trained long enough.
Future Scope
Continue Rewards shaping experimentation to find the most effective and minimal combination of rewards functions. Tune rewards function values. Train PPO agent in PyTorch and compare it to our A2C agent and Tensorforce PPO agent.
Conclusion
Currently as it stands, the PPO agents perform a better than the A2C agents by 5 games (out of 100). However what is to be noted is that the PPO agents comparison between the two frame works Pytorch and TensorFlow, the Pytorch agent does a lot better (by 10 games) but this maybe because of the flaw in setting up the TensorFlow agent. Though this project is now concluded, I would like to take another look and see what the specific problem with the TensorFlow agent was. Additionally training these models was a really big hassle, we ran out of GCP credits and had to train on our own machines which took a substancial amount of time and resources.
On the brightside, another team did take over this project and did their own investigation. Take a look here at this link. https://github.com/csci-599-applied-ml-for-games/PommermanS2020/blob/master/materials/CS599%20EDD.pdf I’m currently looking for a better link that has more information.
References
- Pommerman Competitions
- Cinjon Resnick etc. PlayGround: AI Research into Multi-Agent Learning
- Ross Wightman, PyTorch Reinforcement Learning for Pommerman
- Tensorforce: a TensorFlow library for applied reinforcement learning
- Jiang Guo, Human-level control through deep reinforcement learning
- TensorBoard
- The Pommerman team competition or: How we learned to stop worrying and love the battle.
- Ilya Kostrikov’s PyTorch Implementation for A2C, PPO, and ACKTR
- Multi-Agent Strategies for Pommerman
- On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
- Continual Match Based Training in Pommerman: Technical Report
- Proximal Policy Optimization Algorithms
- Asynchronous Methods for Deep Reinforcement Learning
Goals
Status
This project has mostly been completed as it was a semester long class project. However I’m doing some additional work being implementing PPO on Pytorch and trying out more reward shaping. However this is no longer a working project as I’m focusing on other work. Check out the video!


THE MUMMY RETURNS A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): **
THE MUMMY RETURNS, again directed by Stephen Sommers, is a film with a complex. It's a relentless movie that seems in constant fear that if it ever slows down for a minute the entire audience will walk out. Rarely pausing to smell the flowers or to provide the requisite motivation and background, the confusing story scurries around like the thousands of scorpions that take the place of Indiana Jones's snakes.
And speaking of Indiana Jones, the movie wants badly to morph into an Indiana Jones-type franchise, a movie it mimics constantly. It also borrows liberally from the fight sequences of STAR WARS EPISODE I - THE PHANTOM MENACE and the creatures from GREMLINS. Gone is the campy charm from the previous MUMMY. Instead, we have lots of nifty computer generated effects and warriors and non-stop action. This is the perfect movie for 12-year-old boys, since most have grown too sophisticated for Saturday morning cartoon mayhem.
The stars from last time are back. Rick (Brendan Fraser) and Evie (Rachel Weisz) are now married and have an 8-year-old son, Alex (Freddie Boath). Alex is a chip off the old block, curious and adventuresome like his parents. This time Weisz gets many opportunities to flex her muscles, and she turns out to be a surprisingly credible action star.
Arnold Vosloo again plays the mummy Im-Ho-Tep. Not content with a single villain, the sequel adds WWF's The Rock playing The Scorpion King, Im-Ho-Tep's competition. Rick will have to take them both out or the world will end. What seems to never end is the movie itself. I lost count of the number of "endings" that were merely preludes to other fight sequences.
As a display of computer generated images, the movie does dazzle and impress. It can also be imaginative with a Jules Verne inspired boat/dirigible being the most memorable.
"You know," Rick confesses, "a couple of years ago, this would have seemed really strange to me." That's the problem with the sequel. We've seen it all before. The only idea that the director had for the sequel was "more." More special effects and lots more fighting. Its target audience will undoubtedly love it.
THE MUMMY RETURNS runs a long 2:01. It is rated PG-13 for adventure action and violence and would be acceptable for kids around 11 and up. Younger ones could be severely frightened.
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{{< layout-referee}} {{$pageTitle}}Track a passport application{{/pageTitle}} {{$main-content}}
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{% extends "layout.html" %} {% block content %}
The RU-nstylo web application is a Django/Python based web interface to run R-stylo using a local version of 'rserve'.
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THE PAPER A film review by Jon A. Webb Copyright 1994 Jon A. Webb
In THE PAPER Michael Keaton is the assistant managing editor of a tabloid newspaper in New York City. He relentlessly pursues truth in the face of opposition from his wife (Marisa Tomei), who is 8-1/2 months pregnant and doesn't want him to work so hard, and the managing editor (Glenn Close) who wants to maintain control over the paper and doesn't like being shown up. Naturally, all this leads to a huge dust-up over Truth. Does Keaton win? You'll have to see the film (or practically any other film based on the legend of the Fisher King) to find out.
The depiction of life in a newspaper room is very realistic and compelling. It has just about the right amount of tension and disorder that you'd expect, with conflicting personalities playing a major role. This is the most entertaining part of the film.
On the downside, there is an artificial juxtaposition of three or four subplots leading up to the big tension-filled climax of the film. It's just not credible that so many things would happen all at once. Since the film aims for accuracy, this silly device diminishes the whole thing.
Michael Keaton is very good, if familiar. All this Type A frenetic wise cracking is beginning to wear a little thin. I would like to see him stretch himself a little (though I suppose BATMAN qualifies.) Marisa Tomei plays her role successfully, but she should be trying something more demanding. After her Oscar last year she should be taking the lead in a film that does not require her to act like a up-from-lower class New Yorker, or she is going to get type cast.
I found Glenn Close's performance to be the most satisfying of the major characters. She is right on the edge, heading over the hill, threatened by Keaton, a cold and distant woman who has no one to fall back on when things start to go wrong. The part is a little too small to earn her an Oscar nomination, but she is clearly an actress at the peak of her abilities.
Robert Duvall is very nice as the editor of the paper. I really enjoy watching him play these gentle fatherly roles. I don't think the part is well-written--the interaction with his daughter seems very cliche--but he's still fun to watch.
Randy Quaid has a nice small role, and Spalding Gray has fun as the guy at the New York Times who tries to hire Keaton away from his tabloid. It's always nice to see Mr. Gray in a movie, and to think about his working this experience into one of his monologues.
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This webpage was created by Kevin Rivera, 5/1/2019, for Sheaff's ECE 331 Project 2 [Spring 2019].
In order to complete this project, the following steps were taken:
1) Create a SQL database which will contain the sensor values from the RPiHat.
2) Create a perl script that will obtain the sensor values from the RPiHat and insert the sensor values and the date and time of the reading into the database.
3) Setup a crontab job which runs the perl script every minute.
4) Setup a web server (apache2) in order to use ChartJS to create charts for each sensor's values over time.
5) Create a php script to access the database and convert the recorded data into JSON format.
6) Create a javascript script in ChartJS that graphs the JSON formatted data for the last 24 hours worth of data.
7) Once the webpage(s) are displaying what is required, setup port forwarding on the network's router so the webpage(s) are accessible from the public internet.
8) Have fun staring at charts!
Click one of the buttons below to go to a sensor's chart!


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In 2011, shortly after I learned that I was going to be a grandmother for the first time I also learned that I had breast cancer.
I decided it was time for a career change and began looking for some way to use my creativity, something that would still involve helping others and if at all possible, something a bit less stressful dare I hope, even fun?
In the summer of 2012 I learned that one of my favorite stores in town was for sale. Panara Imports has always offered high quality merchandise from around the world. I was one of the best customers but I never considered that I would own it. With the encouragement of friends and family I decided to pursue this new venture.
Carrying on with the traditional Panara Fare I used the first two years to learn the ropes, to become acquainted with the vendors, and to put my stamp on the look and feel of the store. Like most new business people I made some mistakes that I won't make again. I learned a great deal, but most importantly I learned that if this store was going to reflect me and the things that I am personally passionate about I would need to do more.
I was committed to supporting local artists and established a consignment program as a way for them to showcase their work. I became very interested in the Principles of Fair Trade and to finding products that support families throughout the world by paying fair wages, and not employing children.
I've made some small steps in my goal to develop a store that reflects my values. The outcome should be a store that not only provides high quality merchandise at reasonable prices, but one that also supports and educates about the power we all have to make choices that ultimately make a difference in someone's life.


Laundromat.io
- Originality:
This game is about collecting more clothes then the other team! It implements multiplayer with client-server model.
- Technical Merit:
This was way more technical than I first thought it was going to be.
Client-Server model game using express.js and socket.io to send realtime data back and forth.
Server hosted on AWS EC2 instance so anyone can connect over the internet.
- Prototype Postmortem:
I think I bit off a bit more than I could chew. The tutorial I followed to set it up was easy and informative. However getting my server onto
aws was a bit of a challenge. Luckily I have work experience with all of these frameworks so it made the process much smoother.
- Prototype Assets:
Did you make your prototype assets from scratch? Did you borrow them? Cite your sources here.
- Shirt: self-made
- Washing Machine: self-made
- Music: Cool video game arcade music.
- Prototype Closest Other Game:
Which other game most closely resembles your game? If you are borrowing code from a Phaser Example, you must say so here. If you borrow code from elsewhere, you must say so here.
- High Concept:
Wash more clothes than the enemy team.
- Theme:
Laundromat battle!
- Mandated Variety:
A freakin multiplayer game man.
- Prototype Goal:
Create a basic multiplayer prototype that you could connect to from anywhere.
- Player Experience Goals:
I want a shared experience game where players can interact with eacher other slightly competitively.
- Gameplay:
The core mechanic is collecting clothes to wash as others try and out wash you.
Free for all!
- Strategies:
Not sure what the strategies are would need to playtest more.
- Story/Setting/Premise:
It is all out chaos at the laundromat and only the best launderer can make it out alive.
- Target Audience:
Anyone, the game has a low barrier of entry.
- Play Time:
Endless??


Just because something doesn’t do what you planned it to do doesn’t mean it’s useless.
“MindHack delivered on what was committed and within on aggressive schedule.MindHack provided all work and included instructions. Adjustments and modifications, within reason, were made after seeing results and They incorporated those modifications without issue. I am happy with the effort and work produced and will consider MindHack on future projects”
Android,
D3.js,
JQuery,
JavaScript
Django, Flask, ML/DL,AWS,Python
Django, Angular, Algorithms, ML/DL
Android,Python,PHP,
Web
Photoshop, Illustrator, Angular, Type Script, HTML5, CSS3, JS
Django,ML,Android,Python,
Vuejs


@* Indexpage in plain Html and CSS *@
On this webpage you can play the classic strategy game Stratego. This game is made by students at the University of Applied Sciences in Konstanz (HTWG) and is basically written in Java.
Stratego is a game in which you need to capture the flag of your opponent while defending your own flag. To capture the flag you use your army of 40 pieces. Pieces have a rank and represent individual officers and soldiers in an army. In addition to those ranked pieces you can use bombs to protect your flag.
Pieces move 1 square per turn, horizontally or vertically. Only the scout can move over multiple empty squares per turn. Pieces cannot jump over another piece.
If a piece is moved onto a square occupied by an opposing piece, their identities are revealed. The weaker piece is removed from the board, and the stronger piece is moved into the place formerly occupied by the weaker piece. If the engaging pieces are of equal rank, they are both removed. Pieces may not move onto a square already occupied by another piece without attacking. Exception to the rule of the higher rank winning is the spy. When the spy attacks the marshal, the spy defeats the higher ranked marshal. However, when the marshal attacks the spy, the spy loses. Bombs lose when they are defused by a miner.
The bombs and the flag cannot be moved. A bomb defeats every piece that tries to attack it, except the miner. The flag loses from every other piece. When you capture the flag of your opponent you win the game.
The Stratego board consists of 10 x 10 squares. Within the board there are two obstacles of 2 x 2 squares each. Pieces are not allowed to move there.
The following pieces are used in Stratego (ranked from low to high):


--- layout: post title: Loose ink line date: '2014-10-21T07:30:21+02:00' categories: - Klare Lijn — Ligne Claire - Architecture picture_url: https://film-Storyboards.tumblr.com/post/100580351562/loose-ink-line-digital ---
loose ink line (digital)


Multipage Walkthrough: 1
This walkthrough spans two pages. Local storage is used to remember continue the walkthrough between different page loads
linkThis walkthrough spans two pages. Local storage is used to remember continue the walkthrough between different page loads
link


{% extends "layout.html" %} {% block content %}
Hello everyone, my name is Michael Drozdowski. I am a senior here at NJIT studying information technology. I am currently enrolled in NJIT's BS/MS program, which allows me to take masters courses, like this one, as an undergraduate student. I am taking this course to further my skills in web development and become better at coding overall. Finally, I hope to continue learning website development throughout the semester.
View LinkedinHi everyone, my name is Stanley Ordonez. I am a first year Masters student at NJIT majoring in IS. I graduated with a BS in Computer Technology in Fall 2018. I am a part time student working full time as a Technology Consultant at Cognizant Technology Solutions. I'm happy to be back at NJIT so I can pursue my education and obtain a Master's degree so I can further develop my career path in the field of Technology.
View Linkedin


Minus.css
Voltar
Typography
Grids
Buttons
Forms
Lists
Navbars
List elements
Lists
Features
Small
Works
Responsive
Cross browser compatible
CDN and NPM (comming soon)
Modules
Typography
apple-system
Segoe UI
Roboto
Ubuntu
Cantarell
...and more
Grid
Buttons
Forms
...and more


Package SampleUdpDevice
package SampleUdpDevice
-
ClassDescriptionJposDevice based implementation of some JavaPOS device service implementations for the sample device implemented in SampleUdpDevice.tcl.Factory class for Belt device class of sample UDP service implementationFactory class for BillAcceptor device class of sample UDP service implementation.Factory class for BillDispenser device class of sample UDP service implementation.JposDevice based implementation of a JavaPOS Biometrics device service implementation for the sample device implemented in SampleUdpDevice.tcl.Factory class for Biometrics device class of sample UDP service implementation.Factory class for CashChanger device class of sample UDP service implementation.Factory class for CashDrawer device class of sample UDP service implementation.Factory class for CoinAcceptor device class of sample UDP service implementation.Factory class for CoinDispenser device class of sample UDP service implementation.JposDevice based implementation base for JavaPOS device service implementations for the sample device implemented in SampleUdpDevice.tcl.Class implementing the BillDispenser Interface for the sample udp device.Class implementing the CashChanger Interface for the sample udp device.


Zappa, C.J., M.L. Banner, R.P. Morison and S.E. Brumer (2016). On the variation of the effective breaking strength in oceanic sea states. JPO, 46(7), 2049-2061, doi:10.1175/JPO-D-15-0227.1. [PDF]
Brumer S.E., C.J. Zappa, S.P. Anderson and J.P. Dugan (2016). Riverine Skin Temperature Response to Subsurface Processes in Low Wind Speeds, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC010746. [PDF]
Brumer, S.E. et al. On the dependence of whitecap coverage on environmental parameters observed during HiWinGS and SO GasEx. JPO.
Brumer, S.E. et al., Wave-related Reynolds number parameterizations of CO2 and DMS transfer velocities. GRL.
Brumer, S.E. et al. Wave breaking and sea state dependence of gas transfer of varying solubility.
Brooks I.M., S.E. Brumer et al. A comparison of modelled and observed wave spectra during the High Wind Gas Exchange Study.
Blomquist B., C.Fairall, B.Huebert, S.E. Brumer et al. Air-sea transfer of CO2 and DMS under high wind speed conditions: results from the High Wind speed Gas exchange Study


/* * Copyright (c) 1999 Apple Computer, Inc. All rights reserved. * * @APPLE_LICENSE_HEADER_START@ * * Portions Copyright (c) 1999 Apple Computer, Inc. All Rights * Reserved. This file contains Original Code and/or Modifications of * Original Code as defined in and that are subject to the Apple Public * Source License Version 1.1 (the "License"). You may not use this file * except in compliance with the License. Please obtain a copy of the * License at http://www.apple.com/publicsource and read it before using * this file.  Please see the * License for the specific language governing rights and limitations * under the License. * * @APPLE_LICENSE_HEADER_END@ */ /* * Copyright 1988-1996 NeXT Software, Inc. * * objc-msg.s * * 31-Dec-96 Umesh Vaishampayan<EMAIL_ADDRESS>* Added support for ppc. cleaned m98k and m88k stuff. */ #import "../objc-config.h" #if defined (m68k) #if defined(OBJC_COLLECTING_CACHE) #include "objc-msg-m68k-nolock.s" #else #include "objc-msg-m68k-lock.s" #endif #elif defined (WIN32) #include "objc-msg-i386-nextpdo-winnt3.5.s" #elif defined (__i386__) || defined (i386) #include "objc-msg-i386.s" #elif defined (hppa) #if defined(NeXT_PDO) #if defined(NSBUILDINGHPUXSHLIB) #include "objc-msg-hppa-pdo-pic.s" #else #include "objc-msg-hppa-pdo.s" #endif #elif defined(OBJC_COLLECTING_CACHE) #include "objc-msg-hppa-nolock.s" #else #include "objc-msg-hppa-lock.s" #endif #elif defined (sparc) #if defined(NeXT_PDO) #include "objc-msg-sparc-pdo.s" #else #include "objc-msg-sparc.s" #endif #elif defined (__ppc__) || defined(ppc) #include "objc-msg-ppc.s" #else #error Architecture not supported #endif


Vegan Mac & Cheese
I first discovered this recipe in the New Farm Vegetarian Cookbook, and you will not find a better recipe for Vegan Mac & Cheeze. Vegans and non-vegans alike will enjoy this hot dish, but don't expect it to taste exactly like Mac & Cheese. Instead, just think of it as another delicious, savory casserole that you put in your mouth. Yum.
Don't be afraid to use whole wheat noodles and whole wheat flour. It still tastes amazing, is way better for you, and it's got that nice, hearty texture. Also, I prefer the flavor of Bragg's Liquid Aminos, but Soy Sauce works just fine.
Everything fits snuggly in a 9" x 13" pan.
Dry Ingredients
- 7 Cups of Uncooked Macaroni
- 1 Cup of Flour
- 3 Tsp of Salt
- 3 Tsp of Garlic Powder
- A Pinch of Tumeric
- 2 Cups of Nutritional Yeast
- Cayenne Pepper (optional)
Wet Ingredients
- 1 Cup of Olive Oil
- 7 Cups of Boiling Water
- 4 Tbsp of Braggs or Soy Sauce
Instructions
- Cook the macaroni, then strain, rinse, and set aside. If you've got a couple big pots you can do things simultaneously, but that might be a bit much on your first go 'round.
- In a large pot, heat the olive oil on medium low and then slowly add the flour, mixing continuously with a whisk to keep it nice and smooth.
- Pour in the boiling water very slowly, stirring continuously. You will start to see the contents expand and thicken! You are making a roux (pronounced roo) and they are like magic.
- After you've added all of the boiling water and everything is nice and smooth, add the salt, Braggs, garlic powder, and tumeric. Give it a couple good stirs.
- Add the nutritional yeast slowly, stirring continuously. The sauce will thicken up again.
- Add the cooked noodles to the sauce and stir, and then pour the entire thing into a 9" x 13" pan.
- At this point I like to sprinkle the top of the casserole with cayenne pepper. It gives it some nice color and adds a subtle little spice to the whole thing. I usually give it a good covering.
- Bake at 350 degrees for about 30 minutes, then broil for about 10 minutes to make the top all nice and crispy.


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