def image(self, path_or_bytes):
"""针对图片类型的输入的编码"""
# im = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# The OpenCV cannot handle gif format images, it will return None.
# if im is None:
path_or_stream = io.BytesIO(path_or_bytes) if isinstance(path_or_bytes, bytes) else path_or_bytes
if not path_or_stream:
return "Picture is corrupted: {}".format(path_or_bytes)
try:
pil_image = PIL.Image.open(path_or_stream)
except OSError as e:
return "{} - {}".format(e, path_or_bytes)
use_compress = False
gif_handle = self.model_conf.pre_concat_frames != -1 or self.model_conf.pre_blend_frames != -1
if pil_image.mode == 'P' and not gif_handle:
pil_image = pil_image.convert('RGB')
rgb = pil_image.split()
# if self.mode == RunMode.Trains and use_compress:
# img_compress = io.BytesIO()
#
# pil_image.convert('RGB').save(img_compress, format='JPEG', quality=random.randint(75, 100))
# img_compress_bytes = img_compress.getvalue()
# img_compress.close()
# path_or_stream = io.BytesIO(img_compress_bytes)
# pil_image = PIL.Image.open(path_or_stream)
if len(rgb) == 1 and self.model_conf.image_channel == 3:
return "The number of image channels {} is inconsistent with the number of configured channels {}.".format(
len(rgb), self.model_conf.image_channel
)
size = pil_image.size
# if self.mode == RunMode.Trains and len(rgb) == 3 and use_compress:
# new_size = [size[0] + random.randint(5, 10), size[1] + random.randint(5, 10)]
# background = PIL.Image.new(
# 'RGB', new_size, (255, 255, 255)
# )
# random_offset_w = random.randint(0, 5)
# random_offset_h = random.randint(0, 5)
# background.paste(
# pil_image,
# (
# random_offset_w,
# random_offset_h,
# size[0] + random_offset_w,
# size[1] + random_offset_h
# ),
# None
# )
# background.convert('RGB')
# pil_image = background
if len(rgb) > 3 and self.model_conf.pre_replace_transparent and not gif_handle and not use_compress:
background = PIL.Image.new('RGBA', pil_image.size, (255, 255, 255))
try:
background.paste(pil_image, (0, 0, size[0], size[1]), pil_image)
background.convert('RGB')
pil_image = background
except:
pil_image = pil_image.convert('RGB')
if len(pil_image.split()) > 3 and self.model_conf.image_channel == 3:
pil_image = pil_image.convert('RGB')
if self.model_conf.pre_concat_frames != -1:
im = concat_frames(pil_image, need_frame=self.model_conf.pre_concat_frames)
elif self.model_conf.pre_blend_frames != -1:
im = blend_frame(pil_image, need_frame=self.model_conf.pre_blend_frames)
else:
im = np.array(pil_image)
if isinstance(im, list):
return None
im = preprocessing_by_func(
exec_map=self.model_conf.pre_exec_map,
src_arr=im
)
if self.model_conf.image_channel == 1 and len(im.shape) == 3:
if self.mode == RunMode.Trains:
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY if bool(random.getrandbits(1)) else cv2.COLOR_BGR2GRAY)
else:
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
im = preprocessing(
image=im,
binaryzation=self.model_conf.pre_binaryzation,
)
if self.model_conf.pre_horizontal_stitching:
up_slice = im[0: int(size[1] / 2), 0: size[0]]
down_slice = im[int(size[1] / 2): size[1], 0: size[0]]
im = np.concatenate((up_slice, down_slice), axis=1)
if self.mode == RunMode.Trains and bool(random.getrandbits(1)):
im = preprocessing(
image=im,
binaryzation=self.model_conf.da_binaryzation,
median_blur=self.model_conf.da_median_blur,
gaussian_blur=self.model_conf.da_gaussian_blur,
equalize_hist=self.model_conf.da_equalize_hist,
laplacian=self.model_conf.da_laplace,
rotate=self.model_conf.da_rotate,
warp_perspective=self.model_conf.da_warp_perspective,
sp_noise=self.model_conf.da_sp_noise,
random_brightness=self.model_conf.da_brightness,
random_saturation=self.model_conf.da_saturation,
random_hue=self.model_conf.da_hue,
random_gamma=self.model_conf.da_gamma,
random_channel_swap=self.model_conf.da_channel_swap,
random_blank=self.model_conf.da_random_blank,
random_transition=self.model_conf.da_random_transition,
).astype(np.float32)
else:
im = im.astype(np.float32)
if self.model_conf.resize[0] == -1:
# random_ratio = random.choice([2.5, 3, 3.5, 3.2, 2.7, 2.75])
ratio = self.model_conf.resize[1] / size[1]
# random_width = int(random_ratio * RESIZE[1])
resize_width = int(ratio * size[0])
# resize_width = random_width if is_random else resize_width
im = cv2.resize(im, (resize_width, self.model_conf.resize[1]))
else:
im = cv2.resize(im, (self.model_conf.resize[0], self.model_conf.resize[1]))
im = im.swapaxes(0, 1)
if self.model_conf.image_channel == 1:
return np.array((im[:, :, np.newaxis]) / 255.)
else:
return np.array(im[:, :]) / 255.
def image(self, path_or_bytes):
"""针对图片类型的输入的编码"""
# im = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# The OpenCV cannot handle gif format images, it will return None.
# if im is None:
path_or_stream = io.BytesIO(path_or_bytes) if isinstance(path_or_bytes, bytes) else path_or_bytes
if not path_or_stream:
return "Picture is corrupted: {}".format(path_or_bytes)
try:
pil_image = PIL.Image.open(path_or_stream)
except OSError as e:
return "{} - {}".format(e, path_or_bytes)
if pil_image.mode == 'P':
pil_image = pil_image.convert('RGB')
rgb = pil_image.split()
if len(rgb) == 1 and self.model_conf.image_channel == 3:
return "The number of image channels {} is inconsistent with the number of configured channels {}.".format(
len(rgb), self.model_conf.image_channel
)
size = pil_image.size
gif_handle = self.model_conf.pre_concat_frames != -1 or self.model_conf.pre_blend_frames != -1
if len(rgb) > 3 and self.model_conf.pre_replace_transparent and not gif_handle:
background = PIL.Image.new('RGBA', pil_image.size, (255, 255, 255))
background.paste(pil_image, (0, 0, size[0], size[1]), pil_image)
background.convert('RGB')
pil_image = background
if self.model_conf.pre_concat_frames != -1:
im = concat_frames(pil_image, need_frame=self.model_conf.pre_concat_frames)
elif self.model_conf.pre_blend_frames != -1:
im = blend_frame(pil_image, need_frame=self.model_conf.pre_blend_frames)
else:
im = np.array(pil_image)
if isinstance(im, list):
return None
if self.model_conf.image_channel == 1 and len(im.shape) == 3:
if self.mode == RunMode.Trains:
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY if bool(random.getrandbits(1)) else cv2.COLOR_BGR2GRAY)
else:
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
im = preprocessing(
image=im,
binaryzation=self.model_conf.pre_binaryzation,
)
if self.mode == RunMode.Trains and bool(random.getrandbits(1)):
im = preprocessing(
image=im,
binaryzation=self.model_conf.da_binaryzation,
median_blur=self.model_conf.da_median_blur,
gaussian_blur=self.model_conf.da_gaussian_blur,
equalize_hist=self.model_conf.da_equalize_hist,
laplacian=self.model_conf.da_laplace,
rotate=self.model_conf.da_rotate,
warp_perspective=self.model_conf.da_warp_perspective,
sp_noise=self.model_conf.da_sp_noise,
random_brightness=self.model_conf.da_brightness,
random_saturation=self.model_conf.da_saturation,
random_hue=self.model_conf.da_hue,
random_gamma=self.model_conf.da_gamma,
random_channel_swap=self.model_conf.da_channel_swap,
random_blank=self.model_conf.da_random_blank,
random_transition=self.model_conf.da_random_transition,
).astype(np.float32)
else:
im = im.astype(np.float32)
if self.model_conf.resize[0] == -1:
# random_ratio = random.choice([2.5, 3, 3.5, 3.2, 2.7, 2.75])
ratio = self.model_conf.resize[1] / size[1]
# random_width = int(random_ratio * RESIZE[1])
resize_width = int(ratio * size[0])
# resize_width = random_width if is_random else resize_width
im = cv2.resize(im, (resize_width, self.model_conf.resize[1]))
else:
im = cv2.resize(im, (self.model_conf.resize[0], self.model_conf.resize[1]))
im = im.swapaxes(0, 1)
if self.model_conf.image_channel == 1:
return np.array((im[:, :, np.newaxis]) / 255.)
else:
return np.array(im[:, :]) / 255.