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import numpy as np
import cv2
from .data_process import DataProcess
from concern.config import Configurable, State
# random crop algorithm similar to https://github.com/argman/EAST
class RandomCropData(DataProcess):
size = State(default=(512, 512))
max_tries = State(default=50)
min_crop_side_ratio = State(default=0.1)
require_original_image = State(default=False)
def __init__(self, **kwargs):
self.load_all(**kwargs)
def process(self, data):
img = data['image']
ori_img = img
ori_lines = data['polys']
all_care_polys = [line['points']
for line in data['polys'] if not line['ignore']]
crop_x, crop_y, crop_w, crop_h = self.crop_area(img, all_care_polys)
scale_w = self.size[0] / crop_w
scale_h = self.size[1] / crop_h
scale = min(scale_w, scale_h)
h = int(crop_h * scale)
w = int(crop_w * scale)
padimg = np.zeros(
(self.size[1], self.size[0], img.shape[2]), img.dtype)
padimg[:h, :w] = cv2.resize(
img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
img = padimg
lines = []
for line in data['polys']:
poly = ((np.array(line['points']) -
(crop_x, crop_y)) * scale).tolist()
if not self.is_poly_outside_rect(poly, 0, 0, w, h):
lines.append({**line, 'points': poly})
data['polys'] = lines
if self.require_original_image:
data['image'] = ori_img
else:
data['image'] = img
data['lines'] = ori_lines
data['scale_w'] = scale
data['scale_h'] = scale
return data
def is_poly_in_rect(self, poly, x, y, w, h):
poly = np.array(poly)
if poly[:, 0].min() < x or poly[:, 0].max() > x + w:
return False
if poly[:, 1].min() < y or poly[:, 1].max() > y + h:
return False
return True
def is_poly_outside_rect(self, poly, x, y, w, h):
poly = np.array(poly)
if poly[:, 0].max() < x or poly[:, 0].min() > x + w:
return True
if poly[:, 1].max() < y or poly[:, 1].min() > y + h:
return True
return False
def split_regions(self, axis):
regions = []
min_axis = 0
for i in range(1, axis.shape[0]):
if axis[i] != axis[i-1] + 1:
region = axis[min_axis:i]
min_axis = i
regions.append(region)
return regions
def random_select(self, axis, max_size):
xx = np.random.choice(axis, size=2)
xmin = np.min(xx)
xmax = np.max(xx)
xmin = np.clip(xmin, 0, max_size - 1)
xmax = np.clip(xmax, 0, max_size - 1)
return xmin, xmax
def region_wise_random_select(self, regions, max_size):
selected_index = list(np.random.choice(len(regions), 2))
selected_values = []
for index in selected_index:
axis = regions[index]
xx = int(np.random.choice(axis, size=1))
selected_values.append(xx)
xmin = min(selected_values)
xmax = max(selected_values)
return xmin, xmax
def crop_area(self, img, polys):
h, w, _ = img.shape
h_array = np.zeros(h, dtype=np.int32)
w_array = np.zeros(w, dtype=np.int32)
for points in polys:
points = np.round(points, decimals=0).astype(np.int32)
minx = np.min(points[:, 0])
maxx = np.max(points[:, 0])
w_array[minx:maxx] = 1
miny = np.min(points[:, 1])
maxy = np.max(points[:, 1])
h_array[miny:maxy] = 1
# ensure the cropped area not across a text
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:
return 0, 0, w, h
h_regions = self.split_regions(h_axis)
w_regions = self.split_regions(w_axis)
for i in range(self.max_tries):
if len(w_regions) > 1:
xmin, xmax = self.region_wise_random_select(w_regions, w)
else:
xmin, xmax = self.random_select(w_axis, w)
if len(h_regions) > 1:
ymin, ymax = self.region_wise_random_select(h_regions, h)
else:
ymin, ymax = self.random_select(h_axis, h)
if xmax - xmin < self.min_crop_side_ratio * w or ymax - ymin < self.min_crop_side_ratio * h:
# area too small
continue
num_poly_in_rect = 0
for poly in polys:
if not self.is_poly_outside_rect(poly, xmin, ymin, xmax - xmin, ymax - ymin):
num_poly_in_rect += 1
break
if num_poly_in_rect > 0:
return xmin, ymin, xmax - xmin, ymax - ymin
return 0, 0, w, h
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