def thresholding(image, threshold, flag=1):
image = rgb2gray(image)
height, width = image.shape
output = np.zeros_like(image, dtype=np.uint8)
# 0 or 255
if flag == 1:
for row in range(height):
for col in range(width):
if image[row, col] > threshold:
output[row, col] = 255
else:
output[row, col] = 0
# change pixels > threshold
elif flag == 2:
for row in range(height):
for col in range(width):
if image[row, col] > threshold:
output[row, col] = 255
# pixels between 0 and threshold
elif flag == 3:
for row in range(height):
for col in range(width):
if image[row, col] > threshold:
output[row, col] = threshold
return output
def laplacian_filter_test():
i = imageio.imread('images/moon.jpeg')
i = colors.rgb2gray(i)
img = cv2.imread('images/moon.jpeg', 0)
it1 = cv2.Laplacian(img, cv2.CV_64F)
it2 = bk.laplacian_filter(i)
util.subplot_img(it1, it2)
def histogram_test_gray():
i = imageio.imread('images/hamster.jpeg')
i = colors.rgb2gray(i)
img = cv2.imread('images/hamster.jpeg', 0)
hist1 = cv2.calcHist([img], [0], None, [256], [0, 256])
hist2 = bk.histogram(i)
print(hist1.ravel())
print(hist2)
util.subplot_hist(hist1.ravel(), hist2)
def negative_filter_test():
i = imageio.imread('images/breast.jpeg')
i = colors.rgb2gray(i)
it1 = cv2.bitwise_not(i)
it2 = bk.negative_filter(i)
util.subplot_img(it1, it2)
def sobel_filter_test():
i = imageio.imread('images/lens.jpeg')
i = colors.rgb2gray(i)
it = bk.sobel_filter(i)
util.subplot_img(i, it)
def median_filter_test():
i = imageio.imread('images/salt_pepper.jpeg')
i = colors.rgb2gray(i)
it1 = cv2.medianBlur(i, 3)
it2 = bk.median_filter(i, 3)
util.subplot_img(it1, it2)
def mean_filter_test():
i = imageio.imread('images/blurring.jpeg')
i = colors.rgb2gray(i)
it1 = cv2.blur(i, (5, 5))
it2 = bk.mean_filter(i, 5)
util.subplot_img(it1, it2)
def gamma_filter_test():
i = imageio.imread('images/aerial.jpeg')
i = colors.rgb2gray(i)
it = bk.gamma_filter(i, 1, 5)
util.subplot_img(i, it)
def logarithm_filter_test():
i = imageio.imread('images/dft.jpeg')
i = colors.rgb2gray(i)
it = bk.logarithm_filter(i, 1)
util.subplot_img(i, it)
for x in range(width):
for y in range(height):
xp = int((x - center_x) * np.cos(angle) -
(y - center_y) * np.sin(angle) + center_x)
yp = int((x - center_x) * np.sin(angle) +
(y - center_y) * np.cos(angle) + center_y)
if 0 <= xp < width and 0 <= yp < height:
output[x, y] = image[xp, yp]
return output
if __name__ == '__main__':
filename = 'einstein'
path = 'images/' + filename + '.jpeg'
i = imageio.imread(path)
i = rgb2gray(i)
print('Shape: ', i.shape)
#it = bilinearRotate(i, 30)
#it = nnscale(i, i.shape[0]*1.5, i.shape[1]*1.5)
it = nnrotate(i, 30)
print('New shape: ', it.shape)
#imshow(it)
#it2 = image.rotate(30)
#it = resizeBilinearInterpolation(i, i.shape[0]+50, i.shape[1]+50)
#cv2.imshow('ImageWindow', cv2.cvtColor(it, cv2.COLOR_BGR2RGB))
#cv2.waitKey()
#cv2.imshow('ImageWindow', cv2.cvtColor(i, cv2.COLOR_BGR2RGB))
#cv2.waitKey()
subplot_img(i, it)