def load_image(image_bytes):
data_stream = io.BytesIO(image_bytes)
pil_image = PIL_Image.open(data_stream)
if model.pre_concat_frames == -1 and model.pre_concat_frames == -1:
pil_image = pil_image.convert('RGB')
# if pil_image.mode == 'P':
# pil_image = pil_image.convert('RGB')
rgb = pil_image.split()
size = pil_image.size
gif_handle = model.pre_concat_frames != -1 or model.pre_blend_frames != -1
if (len(rgb) > 3
and model.pre_replace_transparent) and not gif_handle:
background = PIL_Image.new('RGB', pil_image.size,
(255, 255, 255))
background.paste(pil_image, (0, 0, size[0], size[1]),
pil_image)
pil_image = background
if model.pre_concat_frames != -1:
im = concat_frames(pil_image, model.pre_concat_frames)
elif model.pre_blend_frames != -1:
im = blend_frame(pil_image, model.pre_blend_frames)
else:
im = np.asarray(pil_image)
if extract_rgb:
im = rgb_filter(im, extract_rgb)
im = preprocessing_by_func(exec_map=model.exec_map,
key=param_key,
src_arr=im)
if model.image_channel == 1 and len(im.shape) == 3:
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
im = preprocessing(
image=im,
binaryzation=model.pre_binaryzation,
)
if model.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)
image = im.astype(np.float32)
if model.resize[0] == -1:
ratio = model.resize[1] / size[1]
resize_width = int(ratio * size[0])
image = cv2.resize(image, (resize_width, model.resize[1]))
else:
image = cv2.resize(image, (model.resize[0], model.resize[1]))
image = image.swapaxes(0, 1)
return (image[:, :, np.newaxis]
if model.image_channel == 1 else image[:, :]) / 255.
def load_image(image_bytes):
data_stream = io.BytesIO(image_bytes)
pil_image = PIL_Image.open(data_stream)
rgb = pil_image.split()
size = pil_image.size
gif_handle = model.pre_concat_frames != -1 or model.pre_blend_frames != -1
if len(rgb) > 3 and model.pre_replace_transparent and gif_handle:
background = PIL_Image.new('RGB', pil_image.size,
(255, 255, 255))
background.paste(pil_image, (0, 0, size[0], size[1]),
pil_image)
pil_image = background
if model.pre_concat_frames != -1:
im = concat_frames(pil_image, model.pre_concat_frames)
elif model.pre_blend_frames != -1:
im = blend_frame(pil_image, model.pre_blend_frames)
else:
im = np.array(pil_image)
if model.image_channel == 1 and len(im.shape) == 3:
im = im.mean(axis=2).astype(np.float32)
im = preprocessing(
image=im,
binaryzation=model.pre_binaryzation,
)
if model.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)
image = im.astype(np.float32)
if model.resize[0] == -1:
ratio = model.resize[1] / size[1]
resize_width = int(ratio * size[0])
image = cv2.resize(image, (resize_width, model.resize[1]))
else:
image = cv2.resize(image, (model.resize[0], model.resize[1]))
image = image.swapaxes(0, 1)
return (image[:, :, np.newaxis]
if model.image_channel == 1 else image[:, :]) / 255.