def save_sliding_windows(in_dir, out_dir, stepSize, windowSize):
img_cnt = 0
ff.check_and_mkdir(out_dir)
for subdir, dirs, files in os.walk(in_dir):
for f in files:
if ff.is_image(f):
file_path = os.path.join(subdir, f)
image = cv2.imread(file_path)
windowSize = random.randint(80, 100)
#print(image.shape)
#image = generate_padding_image(image, stepSize, windowSize)
#print(image.shape)
for x in range(0, image.shape[0], stepSize):
for y in range(0, image.shape[0], stepSize):
if (x + windowSize <= image.shape[0]
and y + windowSize <= image.shape[0]):
save_img = image[x:x + windowSize,
y:y + windowSize, :]
if (save_img.shape[0] == save_img.shape[1]):
img_cnt += 1
save_dir = os.path.join(
out_dir, "Crop_%d.png" % img_cnt)
print("Saving %s ..." % save_dir)
cv2.imwrite(save_dir, save_img)
def change_img_dir(in_dir, out_dir):
img_cnt = 0
ff.check_and_mkdir(out_dir)
for subdir, dirs, files in os.walk(in_dir):
for f in files:
if ff.is_image(f):
file_path = os.path.join(subdir, f)
img = cv2.imread(file_path)
img_cnt += 1
out_path = os.path.join(out_dir, "RBC_%d.png" % img_cnt)
print("Saving %s ..." % out_path)
cv2.imwrite(out_path, img)
def pick_random_slides(in_dir, out_dir):
ff.check_and_mkdir(out_dir)
path, dirs, files = os.walk(in_dir).next()
file_count = len(files)
lst = random.sample(range(0, file_count), (1000 - 377 + 25))
for file_num in lst:
file_path = os.path.join(in_dir, "Crop_%d.png" % file_num)
img = cv2.imread(file_path)
save_path = os.path.join(out_dir, "Crop_%d.png" % file_num)
print(save_path)
cv2.imwrite(save_path, img)
def save_random_crop(in_dir, out_dir):
windowSize = random.randint(80, 100)
img_cnt = 0
subdir_cnt = 0
ff.check_and_mkdir(out_dir)
for subdir, dirs, files in os.walk(in_dir):
for f in files:
if ff.is_image(f):
file_path = os.path.join(subdir, f)
image = cv2.imread(file_path)
print("Saving from %s ... " % f)
subdir_cnt += 1
for i in range(5):
crop = random_crop(image, (windowSize, windowSize, 3))
img_cnt += 1
save_dir = os.path.join(out_dir, "Crop_%d.png" % img_cnt)
print("\tSaving %s ..." % save_dir)
cv2.imwrite(save_dir, crop)
print(subdir_cnt)
else:
path = raw_input('Enter [original/resized] : ')
if (not path in ['original', 'resized']):
print(
"[Error] : Please define the mode between [original/resized]."
)
else:
if (path == 'original'):
ff.read_all_imgs(cf.data_base)
elif (path == 'resized'):
ff.read_all_imgs(cf.resize_dir)
#############################################
# @ Module 3 : Resize and check images
elif (mode == 'resize' or mode == '3'):
ff.check_and_mkdir(cf.resize_base)
if (sys.version_info > (3, 0)):
target_size = int(input('Enter size : '))
else:
target_size = int(raw_input('Enter size : '))
ff.resize_images(cf.data_base, cf.resize_dir, target_size)
# ff.resize_and_contrast(cf.data_base, cf.resize_dir, target_size)
#############################################
# @ Module 4 : Train-Validation split
elif (mode == 'split' or mode == '4'):
ff.check_and_mkdir(cf.split_base)
split_dir = ff.create_train_val_split(cf.resize_dir, cf.split_dir)
print("Train-Validation split directory = " + cf.split_dir)
############################################