def main(filter, kernel_size, image, sigma):
"""
main function
reads the image coming from the image path cli arg
handles the switch of argument/ perams for the PA1 program.
accepts two arguments, filter and kernel_size
:argument filter[dict], kernel_size[integer value]
:returns success or failure value from dict filter
"""
image = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
# my_dict = {
# 'box': filters.box(kernel_size, image),
# 'median': filters.median(kernel_size, image),
# 'guassian': filters.gaussian(kernel_size, image),
# 'gradient': filters.gradient(kernel_size, image),
# 'sobel': filters.sobel(image),
# 'fast_gaussian': filters.fast_gaussian(kernel_size, image),
# 'histogram': filters.histogram(kernel_size, image),
# 'thresholding': filters.thesholding(kernel_size, image)
# }
# return my_dict[filter](kernel_size, image)
if filter == 'box':
return filters.box(kernel_size, image)
elif filter == 'median':
return filters.median(image)
elif filter == 'gaussian':
return filters.gaussian(kernel_size, sigma, image)
elif filter == 'gradient':
return filters.gradient(image)
elif filter == 'sobel':
return filters.sobel(image)
elif filter == 'fast_gaussian':
return filters.fast_gaussian(kernel_size, image, sigma)
elif filter == 'histogram':
return filters.histogram(kernel_size, image)
elif filter == 'thresholding':
return filters.thresholding(kernel_size, image)
else:
print("function not recognized")
return 0
def preprocessing(pixels): print "-------------Preprocessing phase--------------" #Gaussian filter print "Passing gaussian filter." pixels = filters.gaussian(pixels) #define threshold to binarization of image k = util.otsu_thresholding(pixels) print "Threshold used in the binarization: %d" % (k) #binarize image using the threshold k print "Getting binarized image." pixels = util.binarization(pixels, k) print "Closing operator." pixels = morphology.binary_closing(pixels) print "Opening operator." pixels = morphology.binary_opening(pixels) return pixels
def all_filters(frame,scale_percent = 20, threshVal1 = 120, threshVal2 = 255,thresholdVal1_inv = 70,thresholdVal2_inv = 255,
thresholdVal3 = 0, thresholdVal4 = 100,median_filter_size = 5,blackign =3):
# ret, frame = cap.read()
# print (frame.shape)
width = int(frame.shape[1] * scale_percent / 100)
height = int(frame.shape[0] * scale_percent / 100)
dsize = (width, height)
frame = cv2.resize(frame, dsize)
H = cv2.cvtColor(frame,cv2.COLOR_RGB2HSV_FULL)
# H = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
# print (frame.shape)
frame2 = threshold(H[:,:,1].copy(),threshVal1,threshVal2)
frame3 = threshold_inv(frame[:,:,1].copy(),thresholdVal1_inv,thresholdVal2_inv)
frame4 = threshold(frame[:,:,0].copy(),thresholdVal3,thresholdVal4)
frame2 = np.array(frame2,np.uint8)
frame3 = np.array(frame3,np.uint8)
frame4 = np.array(frame4,np.uint8)
# print (frame4.shape)
frame2 = median_filter(frame2,filter_size = median_filter_size,plot=False)
frame3 = median_filter(frame3,filter_size = median_filter_size,plot=False)
frame4 = median_filter(frame4,filter_size = median_filter_size,plot=False)
# print (frame4.shape)
frame3 = blacker(frame3,blackign)
# frame3,a = sobel(frame3,True)
frame3 = gaussian(frame3.copy(),9,plot=False)
# frame4 = laplacian(frame2)
a = pixel_count(frame3)
b = pixel_count(frame4)
if b !=0:
ratio_full = (a/b+1)*100
# print ('ammatasiri methnata wenakan enawa')
return frame2, frame3, frame4, a,b, ratio_full
# H = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
frame2 = threshold(H[:, :, 1].copy(), 120, 255)
frame3 = threshold_inv(frame[:, :, 1].copy(), 70, 255)
frame4 = threshold(frame[:, :, 0].copy(), 0, 100)
frame2 = np.array(frame2, np.uint8)
frame3 = np.array(frame3, np.uint8)
frame4 = np.array(frame4, np.uint8)
frame2 = median_filter(frame2, filter_size=3, plot=False)
frame3 = median_filter(frame3, filter_size=3, plot=False)
frame4 = median_filter(frame4, filter_size=5, plot=False)
frame3 = blacker(frame3, 300)
# frame3,a = sobel(frame3,True)
frame3 = gaussian(frame3.copy(), 9, plot=False)
# frame4 = laplacian(frame2)
a = pixel_count(frame3)
b = pixel_count(frame4)
# print('a',a,'\nb',b)
ratio_full = (a / b) * 100
if ratio_full >= 80:
print('ratio_full', ratio_full, '%')
drawer(frame4)
# frame3 = np.array(frame3,np.uint8)
cv2.imshow('frame2', frame2)
cv2.imshow('frame3', frame3)
# cv2.imshow('HSV',H[:,:,0])