def augment_image(image_num_pair, args):
filepath, n = image_num_pair
img = cv2.imread(filepath)
filename = filepath.split(os.sep)[-1]
dot_pos = filename.rfind('.')
imgname = filename[:dot_pos]
ext = filename[dot_pos:]
print('Augmenting {} ...'.format(filename))
for i in range(n):
img_varied = img.copy()
varied_imgname = '{}_{:0>3d}_'.format(imgname, i)
if random.random() < args.p_mirror:
img_varied = cv2.flip(img_varied, 1)
varied_imgname += 'm'
if random.random() < args.p_crop:
img_varied = ia.random_crop(img_varied, args.crop_size,
args.crop_hw_vari)
varied_imgname += 'c'
if random.random() < args.p_rotate:
img_varied = ia.random_rotate(img_varied, args.rotate_angle_vari,
args.p_rotate_crop)
varied_imgname += 'r'
if random.random() < args.p_hsv:
img_varied = ia.random_hsv_transform(img_varied, args.hue_vari,
args.sat_vari, args.val_vari)
varied_imgname += 'h'
if random.random() < args.p_gamma:
img_varied = ia.random_gamma_transform(img_varied, args.gamma_vari)
varied_imgname += 'g'
output_filepath = os.sep.join(
[args.output_dir, '{}{}'.format(varied_imgname, ext)])
cv2.imwrite(output_filepath, img_varied)
def augment_images(filelist, args):
# 遍历所有列表内的文件
for filepath, n in filelist:
img = cv2.imread(filepath)
filename = filepath.split(os.sep)[-1]
dot_pos = filename.rfind('.')
# 获取文件名和后缀名
imgname = filename[:dot_pos]
ext = filename[dot_pos:]
print('Augmenting {} ...'.format(filename))
for i in range(n):
img_varied = img.copy()
# 扰动后文件名的前缀
varied_imgname = '{}_{:0>3d}_'.format(imgname, i)
# 按照比例随机对图像进行镜像
if random.random() < args.p_mirror:
# 利用numpy.fliplr(img_varied)也能实现
img_varied = cv2.flip(img_varied, 1)
varied_imgname += 'm'
# 按照比例随机对图像进行裁剪
if random.random() < args.p_crop:
img_varied = ia.random_crop(img_varied, args.crop_size,
args.crop_hw_vari)
varied_imgname += 'c'
# 按照比例随机对图像进行旋转
if random.random() < args.p_rotate:
img_varied = ia.random_rotate(img_varied,
args.rotate_angle_vari,
args.p_rotate_crop)
varied_imgname += 'r'
# 按照比例随机对图像进行HSV扰动
if random.random() < args.p_hsv:
img_varied = ia.random_hsv_transform(img_varied, args.hue_vari,
args.sat_vari,
args.val_vari)
varied_imgname += 'h'
# 按照比例随机对图像进行Gamma扰动
if random.random() < args.p_gamma:
img_varied = ia.random_gamma_transform(img_varied,
args.gamma_vari)
varied_imgname += 'g'
# 生成扰动后的文件名并保存在指定的路径
output_filepath = os.sep.join(
[args.output_dir, '{}{}'.format(varied_imgname, ext)])
cv2.imwrite(output_filepath, img_varied)
def random_augmentation(image):
if random.random() < 0.0:
return image
# 按照比例随机对图像进行镜像
img_varied = random_flip(image)
# 随机亮度
if random.random() < 0.5:
t = random.randint(-100, 100)
blank = np.zeros(image.shape, image.dtype)
img_varied = cv2.addWeighted(img_varied, 1, blank, 1, t)
# 随机对比度
if random.random() < 0.5:
t = random.randint(-100, 100)
blank = np.zeros(image.shape, image.dtype)
img_varied = cv2.addWeighted(img_varied, 1 + t / 100.0, blank,
1 - t / 100.0, 0)
# 按照比例随机对图像进行HSV扰动
if random.random() < 1:
img_varied = ia.random_hsv_transform(img_varied, 10, 0.1, 0.1)
# 按照比例随机对图像进行Gamma扰动
if random.random() < 0.5:
img_varied = ia.random_gamma_transform(img_varied, 2.0)
return img_varied
# files = glob.glob("./data/image/mouth/pos/*.png")
# random.shuffle(files)
# c_image = cv2.imread(files[0])
# cv2.imshow("o",c_image)
# imgae = random_augmentation(c_image)
# cv2.imshow("a",imgae)
# cv2.waitKey(0)
# # 按照比例随机对图像进行裁剪
# if random.random() < 1:
# img_varied = ia.random_crop(
# img_varied,
# 0.1,
# 0.8)
# # 按照比例随机对图像进行旋转
# if random.random() < 1.0:
# img_varied = ia.random_rotate(
# img_varied,
# 1.0,
# 10.0)
def augment_image(image_num_pair, args):
filepath, n = image_num_pair
img = cv2.imread(filepath)
filename = filepath.split(os.sep)[-1]
dot_pos = filename.rfind('.')
imgname = filename[:dot_pos]
ext = filename[dot_pos:]
print('Augmenting {} ...'.format(filename))
for i in range(n):
img_varied = img.copy()
varied_imgname = '{}_{:0>3d}_'.format(imgname, i)
if random.random() < args.p_mirror:
img_varied = cv2.flip(img_varied, 1)
varied_imgname += 'm'
if random.random() < args.p_crop:
img_varied = ia.random_crop(
img_varied,
args.crop_size,
args.crop_hw_vari)
varied_imgname += 'c'
if random.random() < args.p_rotate:
img_varied = ia.random_rotate(
img_varied,
args.rotate_angle_vari,
args.p_rotate_crop)
varied_imgname += 'r'
if random.random() < args.p_hsv:
img_varied = ia.random_hsv_transform(
img_varied,
args.hue_vari,
args.sat_vari,
args.val_vari)
varied_imgname += 'h'
if random.random() < args.p_gamma:
img_varied = ia.random_gamma_transform(
img_varied,
args.gamma_vari)
varied_imgname += 'g'
output_filepath = os.sep.join([
args.output_dir,
'{}{}'.format(varied_imgname, ext)])
cv2.imwrite(output_filepath, img_varied)
def augment_images(filelist, args):
for filepath, n in filelist:
filepath = filepath.replace('\\', '/')
img = cv_imread(filepath)
#print(filepath)
#print(img)
filename = filepath.split('/')[-1]
dot_pos = filename.rfind('.')
imgname = filename[:dot_pos]
ext = filename[dot_pos:]
#print(ext, imgname,filename)
print('Augmenting {} ...'.format(filename))
for i in range(n):
img_varied = img.copy()
varied_imgname = '{}_{:0>3d}_'.format(imgname, i)
if random.random() < args.p_mirror:
img_varied = cv.flip(img_varied, 1)
varied_imgname += 'm'
if random.random() < args.p_crop:
img_varied = ia.random_crop(img_varied, args.crop_size,
args.crop_hw_vari)
varied_imgname += 'c'
if random.random() < args.p_rotate:
img_varied = ia.random_rotate(img_varied,
args.rotate_angle_vari,
args.p_rotate_crop)
varied_imgname += 'r'
if random.random() < args.p_hsv:
img_varied = ia.random_hsv_transform(img_varied, args.hue_vari,
args.sat_vari,
args.val_vari)
varied_imgname += 'h'
if random.random() < args.p_gamma:
img_varied = ia.random_gamma_transform(img_varied,
args.gamma_vari)
varied_imgname += 'g'
output_filepath = os.sep.join(
[args.output_dir, '{}{}'.format(varied_imgname,
ext)]).replace('\\', '/')
#cv.imwrite(output_filepath, img_varied)
cv.imencode('.png', img_varied)[1].tofile(output_filepath)