def test_resize_image():
'''
Call resize_image on the test image then pad to 256 x 256 and save the result to file
'''
test_img = cv2.imread('test_image.jpeg')
resized_image = pb.resize_image(test_img, 256)
cv2.imwrite('resized_image' + '.jpg', resized_image)
resized_image = pb.pad_image(resized_image, 256, 256)
cv2.imwrite('resized_padded_image' + '.jpg', resized_image)
def test_gauss_noise():
'''
Call add_gauss_noise on the test image with a large amount of noise and save the result, then do the same with a small amount of noise.
'''
test_img = cv2.imread('test_image.jpeg')
test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
if test_img is None:
print('Image is None')
high_noise_img = pb.add_gauss_noise(test_img,10)
cv2.imwrite('test_high_gauss_noise.jpg',high_noise_img)
low_noise_img = pb.add_gauss_noise(test_img, 1)
cv2.imwrite('test_low_gauss_noise.jpg',low_noise_img)
def test_mirror_image():
'''
Call mirror_image on the test image and save the result to file
'''
test_img = cv2.imread('test_image.jpeg')
mirrored_image = pb.mirror_image(test_img)
cv2.imwrite('mirrored_image' + '.jpg', mirrored_image)
def test_pad_image():
'''
Call pad_image on the test image to pad an image and save the results.
'''
test_img = cv2.imread('test_image.jpeg')
test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
if test_img is None:
print('Image is None')
test_img = pb.pad_image(test_img, 50, 50)
cv2.imwrite('padded_img.jpeg', test_img)
def test_adjust_brightness():
'''
Call adjust_brightness on the test image and save the result to file
'''
test_img = cv2.imread('test_image.jpeg')
if test_img is None:
print('Image is None')
img_file_index = 0
for i in range(-10, 10, 3):
brightned_img = pb.adjust_brightness(test_img, i)
img_file_index = img_file_index + 1
cv2.imwrite('test_brightned_img' + str(img_file_index) + '.jpg', brightned_img)
def test_rotate_image():
'''
Call rotate_image on the test image and rotate the image 5 times and save the results.
'''
test_img = cv2.imread('test_image.jpeg')
test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
if test_img is None:
print('Image is None')
degrees = [np.random.uniform(-5, 5) for i in range(5)]
for i in degrees:
test_img = pb.rotate_image(test_img, i)
cv2.imwrite('rotated_img_' + str(i) +'.jpeg', test_img)
def augment_data_set(input_dir, output_dir):
'''
Augments the data set using the perturbations in pdetect_perturbations.
Parameters
----------
input_dir: string
The input directory containing only image files to be perturbed
output_dir: string
The output directory
Returns
-------
No return value
'''
# initialize constants
max_dim = 256
# list the perturbations you would like to apply
perturbations = ['resize', 'noise', 'resize', 'brightness', 'flip']
file_num = 0 # initialize current file number
file_count = len([
name for name in os.listdir(input_dir)
if (os.path.isfile(input_dir + '/' +
name) and ('.jpeg' in name or '.jpg' in name))
])
for file in os.listdir(input_dir):
if (('.jpeg' not in file) or ('.jpg' not in file) and ('._' in file)):
continue
current_percent = (file_num / file_count) * 100
# load the file
image = cv2.imread(input_dir + '/' + file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# noise - must go first because it will have a much larger
# impact after resizing
if 'noise' in perturbations:
image = pb.add_sp_noise(image, 0.50, 0.00001)
image = pb.add_gauss_noise(image, 0.5)
# apply perturbations
if 'resize' in perturbations:
image = pb.resize_image(image, 256)
image = pb.pad_image(image, 256, 256)
cv2.imwrite(output_dir + '/' + 'resized_' + file, image)
# apply brightness perturbation
if 'brightness' in perturbations:
bness = np.random.uniform(-10, 10)
bright_image = pb.adjust_brightness(image, 10)
cv2.imwrite(output_dir + '/' + 'bright_image_' + file,
bright_image)
# save the non-flipped image and then save the flipped copy
# if enabled
cv2.imwrite(file + '.jpg', image)
if 'flip' in perturbations:
image = pb.mirror_image(image)
cv2.imwrite(output_dir + '/' + 'flipped_' + file, image)
# save the non-rotated image and then save the rotated copy
# if enabled
cv2.imwrite(file, image)
if 'rotate' in perturbations:
angle = np.random.uniform(-5, 5)
image = pb.rotate_image(image, angle)
cv2.imwrite(output_dir + '/' + 'rotated_' + file, image)
image = None # clear out the image
print('Finished file, currently ' + str(current_percent) + ' done.')
file_num += 1