victormurcia

#Determining most frequently occurring color pixel by pixel
def most_common_used_color(img):
    # Get width and height of Image
    height, width, depth = img.shape
    # Initialize Variable
    r_total = 0
    g_total = 0
    b_total = 0
    count = 0
    # Iterate through each pixel

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#Get color from single pixel in image
#Make list of pixel coordinates based on image shape
y = range(0, height, 25)
x = range(0, width,  25)
#Combine lists above into a list of tuples
merged_list = tuple(zip(x, y)) 

#Initialize the plot
fig,axs = plt.subplots(1, len(y), figsize=(30,30))
i = 0

victormurcia

#Select ROI from image
imagedraw = cv2.selectROI('select',rgb_img)
cv2.waitKey(0)
cv2.destroyWindow('select')
#cropping the area of the image within the bounding box using imCrop() function
roi_image = rgb_img[int(imagedraw[1]):int(imagedraw[1]+imagedraw[3]), 
                    int(imagedraw[0]):int(imagedraw[0]+imagedraw[2])] 

fig,axs = plt.subplots(1,1, figsize=(5,5))
axs.imshow(roi_image)

victormurcia

#Remove green background/field from image prior to clustering 
green = np.array([60,255,255]) #This is green in HSV
loGreen = np.array([30,25,25]) #low green threshold
hiGreen = np.array([90,255,255]) #Upper green threshold

loBlue = np.array([0,25,25]) #low red threshold
hiBlue = np.array([30,255,255]) #Upper red threshold

loRed = np.array([120,25,25]) #low blue threshold
hiRed = np.array([180,255,255]) #Upper blue threshold

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#cv2.Canny(image, minVal, maxVal)
img_gray = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2GRAY)
thresholds = [(5,150), (100,150), (200,225)]
fig,axs = plt.subplots(1,4, figsize=(30,15))
i = 0
axs[i].imshow(rgb_img)
for (minVal, maxVal) in thresholds:
    edge_img = cv2.Canny(img_gray, minVal, maxVal, apertureSize = 3, L2gradient = False)
    axs[i+1].imshow(edge_img)
    i += 1

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params = [(3, 20, 5, 5), (9, 20, 40, 20), (15, 20, 160, 60)]
fig,axs = plt.subplots(4, 3, figsize=(30,30))
i = 0
for (k, diameter, sigmaColor, sigmaSpace) in params:
    simpleblur_image = cv2.blur(rgb_img, (k,k))
    gaussblur_image = cv2.GaussianBlur(rgb_img, (k,k), 0)
    medianblur_image = cv2.medianBlur(rgb_img, k)
    bilateralblur_image = cv2.bilateralFilter(rgb_img, diameter, sigmaColor, sigmaSpace)

    axs[0,i].imshow(simpleblur_image)

victormurcia

gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2GRAY)
bgr_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2BGR)
hsv_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2HSV)

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#cv2.addWeighted(source_img1, alpha, source_img2, beta, gamma)
alpha = [0.75, 0.5, 0.25]
beta =  [0, 1 , 10]
gamma = [0, 10 ,100]

#Vary alpha
alpha_img1 = cv2.addWeighted(rgb_img, alpha[0], rgb_img, beta[0], gamma[0])
alpha_img2 = cv2.addWeighted(rgb_img, alpha[1], rgb_img, beta[0], gamma[0])
alpha_img3 = cv2.addWeighted(rgb_img, alpha[2], rgb_img, beta[0], gamma[0])

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#Resizing an image
#cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]])
xscale = [0.75,0.5,0.25]
yscale = [0.75,0.5,0.25]

rimg1 = cv2.resize(rgb_img, (0,0), fx=xscale[0], fy=yscale[0])
rimg2 = cv2.resize(rgb_img, (0,0), fx=xscale[1], fy=yscale[1])
rimg3 = cv2.resize(rgb_img, (0,0), fx=xscale[2], fy=yscale[2])

fig,axs = plt.subplots(1,3, figsize=(30,15))

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#Need to find the starting/ending column and row index first for the desired cropping
cropIni = [0.15,0.3,0.45]

#Crop width and height of image by 15% each
startRow1 = int(height*cropIni[0])       ;startCol1 = int(width*cropIni[0])
endRow1   = int(height*(1-cropIni[0]))   ;endCol1   = int(width*(1-cropIni[0]))

#Crop width and height of image by 30% each
startRow2= int(height*cropIni[1])        ;startCol2 = int(width*cropIni[1])
endRow2   = int(height*(1-cropIni[1]))   ;endCol2   = int(width*(1-cropIni[1]))