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import random
import cv2
import numpy as np
from shapely.geometry import Polygon
from shapely import affinity
from concern.config import Configurable, State
def regular_resize(image, boxes, tags, crop_size):
h, w, c = image.shape
if h < w:
scale_ratio = crop_size * 1.0 / w
new_h = int(round(crop_size * h * 1.0 / w))
if new_h > crop_size:
new_h = crop_size
image = cv2.resize(image, (crop_size, new_h))
new_img = np.zeros((crop_size, crop_size, 3))
new_img[:new_h, :, :] = image
boxes *= scale_ratio
else:
scale_ratio = crop_size * 1.0 / h
new_w = int(round(crop_size * w * 1.0 / h))
if new_w > crop_size:
new_w = crop_size
image = cv2.resize(image, (new_w, crop_size))
new_img = np.zeros((crop_size, crop_size, 3))
new_img[:, :new_w, :] = image
boxes *= scale_ratio
return new_img, boxes, tags
def random_crop(image, boxes, tags, crop_size, max_tries, w_axis, h_axis, min_crop_side_ratio):
h, w, c = image.shape
selected_boxes = []
for i in range(max_tries):
xx = np.random.choice(w_axis, size=2)
xmin = np.min(xx)
xmax = np.max(xx)
xmin = np.clip(xmin, 0, w-1)
xmax = np.clip(xmax, 0, w-1)
yy = np.random.choice(h_axis, size=2)
ymin = np.min(yy)
ymax = np.max(yy)
ymin = np.clip(ymin, 0, h-1)
ymax = np.clip(ymax, 0, h-1)
if xmax - xmin < min_crop_side_ratio*w or ymax - ymin < min_crop_side_ratio*h:
# area too small
continue
if boxes.shape[0] != 0:
box_axis_in_area = (boxes[:, :, 0] >= xmin) & (boxes[:, :, 0] <= xmax) \
& (boxes[:, :, 1] >= ymin) & (boxes[:, :, 1] <= ymax)
selected_boxes = np.where(np.sum(box_axis_in_area, axis=1) == 4)[0]
if len(selected_boxes) > 0:
if (tags[selected_boxes] == False).astype(np.float).sum() > 0:
break
else:
selected_boxes = []
break
if i == max_tries - 1:
return regular_resize(image, boxes, tags, crop_size)
image = image[ymin:ymax+1, xmin:xmax+1, :]
boxes = boxes[selected_boxes]
tags = tags[selected_boxes]
boxes[:, :, 0] -= xmin
boxes[:, :, 1] -= ymin
return regular_resize(image, boxes, tags, crop_size)
def regular_crop(image, boxes, tags, crop_size, max_tries, w_array, h_array, w_axis, h_axis, min_crop_side_ratio):
h, w, c = image.shape
mask_w = np.arange(w - crop_size)
mask_h = np.arange(h - crop_size)
keep_w = np.where(np.logical_and(
w_array[mask_w] == 0, w_array[mask_w + crop_size - 1] == 0))[0]
keep_h = np.where(np.logical_and(
h_array[mask_h] == 0, h_array[mask_h + crop_size - 1] == 0))[0]
if keep_w.size > 0 and keep_h.size > 0:
for i in range(max_tries):
xmin = np.random.choice(keep_w, size=1)[0]
xmax = xmin + crop_size
ymin = np.random.choice(keep_h, size=1)[0]
ymax = ymin + crop_size
if boxes.shape[0] != 0:
box_axis_in_area = (boxes[:, :, 0] >= xmin) & (boxes[:, :, 0] <= xmax) \
& (boxes[:, :, 1] >= ymin) & (boxes[:, :, 1] <= ymax)
selected_boxes = np.where(
np.sum(box_axis_in_area, axis=1) == 4)[0]
if len(selected_boxes) > 0:
if (tags[selected_boxes] == False).astype(np.float).sum() > 0:
break
else:
selected_boxes = []
break
if i == max_tries-1:
return random_crop(image, boxes, tags, crop_size, max_tries, w_axis, h_axis, min_crop_side_ratio)
image = image[ymin:ymax, xmin:xmax, :]
boxes = boxes[selected_boxes]
tags = tags[selected_boxes]
boxes[:, :, 0] -= xmin
boxes[:, :, 1] -= ymin
return image, boxes, tags
else:
return random_crop(image, boxes, tags, crop_size, max_tries, w_axis, h_axis, min_crop_side_ratio)
class RandomCrop(object):
def __init__(self, crop_size=640, max_tries=50, min_crop_side_ratio=0.1):
self.crop_size = crop_size
self.max_tries = max_tries
self.min_crop_side_ratio = min_crop_side_ratio
def __call__(self, image, boxes, tags):
h, w, _ = image.shape
h_array = np.zeros((h), dtype=np.int32)
w_array = np.zeros((w), dtype=np.int32)
for box in boxes:
box = np.round(box, decimals=0).astype(np.int32)
minx = np.min(box[:, 0])
maxx = np.max(box[:, 0])
w_array[minx:maxx] = 1
miny = np.min(box[:, 1])
maxy = np.max(box[:, 1])
h_array[miny:maxy] = 1
h_axis = np.where(h_array == 0)[0]
w_axis = np.where(w_array == 0)[0]
if len(h_axis) == 0 or len(w_axis) == 0:
# resize image
return regular_resize(image, boxes, tags, self.crop_size)
if h <= self.crop_size + 1 or w <= self.crop_size + 1:
return random_crop(image, boxes, tags, self.crop_size, self.max_tries, w_axis, h_axis, self.min_crop_side_ratio)
else:
return regular_crop(image, boxes, tags, self.crop_size, self.max_tries, w_array, h_array, w_axis, h_axis, self.min_crop_side_ratio)
class RandomCropAug(Configurable):
size = State(default=640)
def __init__(self, size=640, *args, **kwargs):
self.size = size or self.size
self.augment = RandomCrop(size)
def __call__(self, data):
'''
This augmenter is supposed to following the process of `MakeICDARData`,
in which labels are mapped to this specific format:
(image, polygons: (n, 4, 2), tags: [Boolean], ...)
'''
image, boxes, ignore_tags = data[:3]
image, boxes, ignore_tags = self.augment(image, boxes, ignore_tags)
return (image, boxes, ignore_tags, *data[3:])
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