Week 6 – Lab Work

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# 1. Open image Googly.jpg;
 
# 2. Use thresholding to separate Googly and his friend from the background;
 
# 3. Use statistical analysis to choose a threshold for better results;
 
# 4. Try multiple thresholds on different channels.
 
import numpy as np
import cv2
from matplotlib import pyplot as plt
from matplotlib import image as image
import easygui
 
I = cv2.imread("googly.jpg", cv2.IMREAD_GRAYSCALE)
 
# I = cv2.imread("image.jpg")
 
# RGB = cv2.cvtColor(I, cv2.COLOR_BGR2RGB)
 
T = 100
T, B = cv2.threshold(I, thresh = T, maxval = 255,
type = cv2.THRESH_BINARY)
 
cv2.imshow("Googly", B)
key = cv2.waitKey(0)

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# 1. Open image sudoku.jpg
 
# 2. Use adaptive thresholding to create a black & white image;
 
# 3. Modify the region size and constant for better results.
 
import numpy as np
import cv2
from matplotlib import pyplot as plt
from matplotlib import image as image
import easygui
 
I = cv2.imread("sudoku.jpg", cv2.IMREAD_GRAYSCALE)
 
J = cv2.adaptiveThreshold(I, maxValue = 255,
  adaptiveMethod = cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
  thresholdType = cv2.THRESH_BINARY,
  blockSize = 5, C = 15)
  
cv2.imshow("Sudoku", J)
key = cv2.waitKey(0)

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# 1. Open image Orange.png;
 
# 2. Using thresholding, create a mask with the orange as ROI;
 
# 3. Use this mask to extract the orange from the image;
 
# 4. Open Water.jpg;
 
# 5. Use the inverse of the orange mask to cut an orange-shaped hole in
# the water picture;
 
# 6. Combine the orange and water masked images to create a
# composite image.
 
import numpy as np
import cv2
from matplotlib import pyplot as plt
from matplotlib import image as image
import easygui
 
I = cv2.imread("orange.png")
Z = cv2.imread("water.jpg")
G = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)
 
J = cv2.bitwise_not(G)
 
T = 20
T, B = cv2.threshold(J, thresh = T, maxval = 255,
type = cv2.THRESH_BINARY)
 
K = cv2.bitwise_not(B)
 
ROI = cv2.bitwise_and(I,I,mask=B)
 
ROI2 = cv2.bitwise_and(Z,Z,mask=K)
 
ROI3 = cv2.bitwise_or(ROI,ROI2)
 
cv2.imshow("Composite", ROI3)
key = cv2.waitKey(0)

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# 1. Open Trump.jpg;
 
# 2. Use an appropriate colour space and range to segment out areas of
# flesh (you will need to research this);
 
# 3. Use morphological processes to clean up the ROI.
 
import numpy as np
import cv2
from matplotlib import pyplot as plt
from matplotlib import image as image
import easygui
 
I = cv2.imread("trumpcopy.jpg")
 
HSV = cv2.cvtColor(I, cv2.COLOR_BGR2HSV)
 
# A Numpy array is created of upper and lower fleshtone values.
lowerFleshValue = np.array([3, 48, 80])
upperFleshValue = np.array([20, 255, 255])
# Mask is created of all fleshtone values.
fleshMask = cv2.inRange(HSV, lowerFleshValue, upperFleshValue)
 
# Structuring element
shape = cv2.getStructuringElement(cv2.MORPH_RECT,(6,6))
 
# Closing
# newMask = cv2.dilate(fleshMask,shape)
# newerMask = cv2.erode(newMask,shape)
 
# Opening
# newestMask = cv2.erode(newerMask,shape)
# mostNewestMask = cv2.dilate(newestMask,shape)
 
newMask = cv2.morphologyEx(fleshMask,cv2.MORPH_CLOSE,shape)
newerMask = cv2.morphologyEx(newMask,cv2.MORPH_OPEN,shape)
boundary = cv2.morphologyEx(newerMask,cv2.MORPH_GRADIENT,shape)
 
C = cv2.bitwise_and(I,I,mask=newerMask)
 
cv2.imshow("Original", I)
cv2.imshow("HSV", HSV)
cv2.imshow("Mask", fleshMask)
cv2.imshow("Closing", newMask)
cv2.imshow("Opening", newerMask)
cv2.imshow("Boundary", boundary)
cv2.imshow("Combined", C)
key = cv2.waitKey(0)