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from __future__ import absolute_import, division, print_function, unicode_literals
import math
import sys
import cv2
import numpy as np
import six
class DecodeImage(object):
"""decode image"""
def __init__(
self, img_mode="RGB", channel_first=False, ignore_orientation=False, **kwargs
):
self.img_mode = img_mode
self.channel_first = channel_first
self.ignore_orientation = ignore_orientation
def __call__(self, data):
img = data["image"]
if six.PY2:
assert (
type(img) is str and len(img) > 0
), "invalid input 'img' in DecodeImage"
else:
assert (
type(img) is bytes and len(img) > 0
), "invalid input 'img' in DecodeImage"
img = np.frombuffer(img, dtype="uint8")
if self.ignore_orientation:
img = cv2.imdecode(img, cv2.IMREAD_IGNORE_ORIENTATION | cv2.IMREAD_COLOR)
else:
img = cv2.imdecode(img, 1)
if img is None:
return None
if self.img_mode == "GRAY":
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif self.img_mode == "RGB":
assert img.shape[2] == 3, "invalid shape of image[%s]" % (img.shape)
img = img[:, :, ::-1]
if self.channel_first:
img = img.transpose((2, 0, 1))
data["image"] = img
return data
class NRTRDecodeImage(object):
"""decode image"""
def __init__(self, img_mode="RGB", channel_first=False, **kwargs):
self.img_mode = img_mode
self.channel_first = channel_first
def __call__(self, data):
img = data["image"]
if six.PY2:
assert (
type(img) is str and len(img) > 0
), "invalid input 'img' in DecodeImage"
else:
assert (
type(img) is bytes and len(img) > 0
), "invalid input 'img' in DecodeImage"
img = np.frombuffer(img, dtype="uint8")
img = cv2.imdecode(img, 1)
if img is None:
return None
if self.img_mode == "GRAY":
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif self.img_mode == "RGB":
assert img.shape[2] == 3, "invalid shape of image[%s]" % (img.shape)
img = img[:, :, ::-1]
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if self.channel_first:
img = img.transpose((2, 0, 1))
data["image"] = img
return data
class NormalizeImage(object):
"""normalize image such as substract mean, divide std"""
def __init__(self, scale=None, mean=None, std=None, order="chw", **kwargs):
if isinstance(scale, str):
scale = eval(scale)
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
mean = mean if mean is not None else [0.485, 0.456, 0.406]
std = std if std is not None else [0.229, 0.224, 0.225]
shape = (3, 1, 1) if order == "chw" else (1, 1, 3)
self.mean = np.array(mean).reshape(shape).astype("float32")
self.std = np.array(std).reshape(shape).astype("float32")
def __call__(self, data):
img = data["image"]
from PIL import Image
if isinstance(img, Image.Image):
img = np.array(img)
assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage"
data["image"] = (img.astype("float32") * self.scale - self.mean) / self.std
return data
class ToCHWImage(object):
"""convert hwc image to chw image"""
def __init__(self, **kwargs):
pass
def __call__(self, data):
img = data["image"]
from PIL import Image
if isinstance(img, Image.Image):
img = np.array(img)
data["image"] = img.transpose((2, 0, 1))
return data
class Fasttext(object):
def __init__(self, path="None", **kwargs):
import fasttext
self.fast_model = fasttext.load_model(path)
def __call__(self, data):
label = data["label"]
fast_label = self.fast_model[label]
data["fast_label"] = fast_label
return data
class KeepKeys(object):
def __init__(self, keep_keys, **kwargs):
self.keep_keys = keep_keys
def __call__(self, data):
data_list = []
for key in self.keep_keys:
data_list.append(data[key])
return data_list
class Pad(object):
def __init__(self, size=None, size_div=32, **kwargs):
if size is not None and not isinstance(size, (int, list, tuple)):
raise TypeError(
"Type of target_size is invalid. Now is {}".format(type(size))
)
if isinstance(size, int):
size = [size, size]
self.size = size
self.size_div = size_div
def __call__(self, data):
img = data["image"]
img_h, img_w = img.shape[0], img.shape[1]
if self.size:
resize_h2, resize_w2 = self.size
assert (
img_h < resize_h2 and img_w < resize_w2
), "(h, w) of target size should be greater than (img_h, img_w)"
else:
resize_h2 = max(
int(math.ceil(img.shape[0] / self.size_div) * self.size_div),
self.size_div,
)
resize_w2 = max(
int(math.ceil(img.shape[1] / self.size_div) * self.size_div),
self.size_div,
)
img = cv2.copyMakeBorder(
img,
0,
resize_h2 - img_h,
0,
resize_w2 - img_w,
cv2.BORDER_CONSTANT,
value=0,
)
data["image"] = img
return data
class Resize(object):
def __init__(self, size=(640, 640), **kwargs):
self.size = size
def resize_image(self, img):
resize_h, resize_w = self.size
ori_h, ori_w = img.shape[:2] # (h, w, c)
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
img = cv2.resize(img, (int(resize_w), int(resize_h)))
return img, [ratio_h, ratio_w]
def __call__(self, data):
img = data["image"]
if "polys" in data:
text_polys = data["polys"]
img_resize, [ratio_h, ratio_w] = self.resize_image(img)
if "polys" in data:
new_boxes = []
for box in text_polys:
new_box = []
for cord in box:
new_box.append([cord[0] * ratio_w, cord[1] * ratio_h])
new_boxes.append(new_box)
data["polys"] = np.array(new_boxes, dtype=np.float32)
data["image"] = img_resize
return data
class DetResizeForTest(object):
def __init__(self, **kwargs):
super(DetResizeForTest, self).__init__()
self.resize_type = 0
if "image_shape" in kwargs:
self.image_shape = kwargs["image_shape"]
self.resize_type = 1
elif "limit_side_len" in kwargs:
self.limit_side_len = kwargs["limit_side_len"]
self.limit_type = kwargs.get("limit_type", "min")
elif "resize_long" in kwargs:
self.resize_type = 2
self.resize_long = kwargs.get("resize_long", 960)
else:
self.limit_side_len = 736
self.limit_type = "min"
def __call__(self, data):
img = data["image"]
src_h, src_w, _ = img.shape
if self.resize_type == 0:
# img, shape = self.resize_image_type0(img)
img, [ratio_h, ratio_w] = self.resize_image_type0(img)
elif self.resize_type == 2:
img, [ratio_h, ratio_w] = self.resize_image_type2(img)
else:
# img, shape = self.resize_image_type1(img)
img, [ratio_h, ratio_w] = self.resize_image_type1(img)
data["image"] = img
data["shape"] = np.array([src_h, src_w, ratio_h, ratio_w])
return data
def resize_image_type1(self, img):
resize_h, resize_w = self.image_shape
ori_h, ori_w = img.shape[:2] # (h, w, c)
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
img = cv2.resize(img, (int(resize_w), int(resize_h)))
# return img, np.array([ori_h, ori_w])
return img, [ratio_h, ratio_w]
def resize_image_type0(self, img):
"""
resize image to a size multiple of 32 which is required by the network
args:
img(array): array with shape [h, w, c]
return(tuple):
img, (ratio_h, ratio_w)
"""
limit_side_len = self.limit_side_len
h, w, c = img.shape
# limit the max side
if self.limit_type == "max":
if max(h, w) > limit_side_len:
if h > w:
ratio = float(limit_side_len) / h
else:
ratio = float(limit_side_len) / w
else:
ratio = 1.0
elif self.limit_type == "min":
if min(h, w) < limit_side_len:
if h < w:
ratio = float(limit_side_len) / h
else:
ratio = float(limit_side_len) / w
else:
ratio = 1.0
elif self.limit_type == "resize_long":
ratio = float(limit_side_len) / max(h, w)
else:
raise Exception("not support limit type, image ")
resize_h = int(h * ratio)
resize_w = int(w * ratio)
resize_h = max(int(round(resize_h / 32) * 32), 32)
resize_w = max(int(round(resize_w / 32) * 32), 32)
try:
if int(resize_w) <= 0 or int(resize_h) <= 0:
return None, (None, None)
img = cv2.resize(img, (int(resize_w), int(resize_h)))
except:
print(img.shape, resize_w, resize_h)
sys.exit(0)
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return img, [ratio_h, ratio_w]
def resize_image_type2(self, img):
h, w, _ = img.shape
resize_w = w
resize_h = h
if resize_h > resize_w:
ratio = float(self.resize_long) / resize_h
else:
ratio = float(self.resize_long) / resize_w
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
img = cv2.resize(img, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return img, [ratio_h, ratio_w]
class E2EResizeForTest(object):
def __init__(self, **kwargs):
super(E2EResizeForTest, self).__init__()
self.max_side_len = kwargs["max_side_len"]
self.valid_set = kwargs["valid_set"]
def __call__(self, data):
img = data["image"]
src_h, src_w, _ = img.shape
if self.valid_set == "totaltext":
im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
img, max_side_len=self.max_side_len
)
else:
im_resized, (ratio_h, ratio_w) = self.resize_image(
img, max_side_len=self.max_side_len
)
data["image"] = im_resized
data["shape"] = np.array([src_h, src_w, ratio_h, ratio_w])
return data
def resize_image_for_totaltext(self, im, max_side_len=512):
h, w, _ = im.shape
resize_w = w
resize_h = h
ratio = 1.25
if h * ratio > max_side_len:
ratio = float(max_side_len) / resize_h
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def resize_image(self, im, max_side_len=512):
"""
resize image to a size multiple of max_stride which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
"""
h, w, _ = im.shape
resize_w = w
resize_h = h
# Fix the longer side
if resize_h > resize_w:
ratio = float(max_side_len) / resize_h
else:
ratio = float(max_side_len) / resize_w
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
class KieResize(object):
def __init__(self, **kwargs):
super(KieResize, self).__init__()
self.max_side, self.min_side = kwargs["img_scale"][0], kwargs["img_scale"][1]
def __call__(self, data):
img = data["image"]
points = data["points"]
src_h, src_w, _ = img.shape
(
im_resized,
scale_factor,
[ratio_h, ratio_w],
[new_h, new_w],
) = self.resize_image(img)
resize_points = self.resize_boxes(img, points, scale_factor)
data["ori_image"] = img
data["ori_boxes"] = points
data["points"] = resize_points
data["image"] = im_resized
data["shape"] = np.array([new_h, new_w])
return data
def resize_image(self, img):
norm_img = np.zeros([1024, 1024, 3], dtype="float32")
scale = [512, 1024]
h, w = img.shape[:2]
max_long_edge = max(scale)
max_short_edge = min(scale)
scale_factor = min(max_long_edge / max(h, w), max_short_edge / min(h, w))
resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(
h * float(scale_factor) + 0.5
)
max_stride = 32
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(img, (resize_w, resize_h))
new_h, new_w = im.shape[:2]
w_scale = new_w / w
h_scale = new_h / h
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
norm_img[:new_h, :new_w, :] = im
return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
def resize_boxes(self, im, points, scale_factor):
points = points * scale_factor
img_shape = im.shape[:2]
points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
return points
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