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preprocess.py
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import numpy as np
from PIL import Image, ImageDraw
import os
import random
from tqdm import tqdm
import cfg
from label import shrink
def batch_reorder_vertexes(xy_list_array):
reorder_xy_list_array = np.zeros_like(xy_list_array)
for xy_list, i in zip(xy_list_array, range(len(xy_list_array))):
reorder_xy_list_array[i] = reorder_vertexes(xy_list)
return reorder_xy_list_array
def reorder_vertexes(xy_list):
reorder_xy_list = np.zeros_like(xy_list)
# determine the first point with the smallest x,
# if two has same x, choose that with smallest y,
ordered = np.argsort(xy_list, axis=0)
xmin1_index = ordered[0, 0]
xmin2_index = ordered[1, 0]
if xy_list[xmin1_index, 0] == xy_list[xmin2_index, 0]:
if xy_list[xmin1_index, 1] <= xy_list[xmin2_index, 1]:
reorder_xy_list[0] = xy_list[xmin1_index]
first_v = xmin1_index
else:
reorder_xy_list[0] = xy_list[xmin2_index]
first_v = xmin2_index
else:
reorder_xy_list[0] = xy_list[xmin1_index]
first_v = xmin1_index
# connect the first point to others, the third point on the other side of
# the line with the middle slope
# 计算第一个点与其他三个点连线的斜率，取斜率居中的点作为第三个点
others = list(range(4))
others.remove(first_v)
k = np.zeros((len(others),))
for index, i in zip(others, range(len(others))):
k[i] = (xy_list[index, 1] - xy_list[first_v, 1]) \
/ (xy_list[index, 0] - xy_list[first_v, 0] + cfg.epsilon)
k_mid = np.argsort(k)[1]
third_v = others[k_mid]
reorder_xy_list[2] = xy_list[third_v]
# determine the second point which on the bigger side of the middle line
others.remove(third_v)
b_mid = xy_list[first_v, 1] - k[k_mid] * xy_list[first_v, 0] # 得到中间那条线的截距b
second_v, fourth_v = 0, 0
for index, i in zip(others, range(len(others))):
# delta = y - (k * x + b)
delta_y = xy_list[index, 1] - (k[k_mid] * xy_list[index, 0] + b_mid)
if delta_y > 0: # y点在中间那条线之上就设置为第二个点，否则为第四个点
second_v = index
else:
fourth_v = index
reorder_xy_list[1] = xy_list[second_v]
reorder_xy_list[3] = xy_list[fourth_v]
# compare slope of 13 and 24, determine the final order
# 经过上面的步骤k13有两种情况，要么大于0.要么小于0，k24的斜率正好相反。
k13 = k[k_mid]
k24 = (xy_list[second_v, 1] - xy_list[fourth_v, 1]) / (
xy_list[second_v, 0] - xy_list[fourth_v, 0] + cfg.epsilon)
if k13 < k24: # 当k13小于k24的时候，点4变点3，3->2,2->1,1->4
tmp_x, tmp_y = reorder_xy_list[3, 0], reorder_xy_list[3, 1]
for i in range(2, -1, -1):
reorder_xy_list[i + 1] = reorder_xy_list[i]
reorder_xy_list[0, 0], reorder_xy_list[0, 1] = tmp_x, tmp_y
return reorder_xy_list
def resize_image(im, max_img_size=cfg.max_train_img_size): # 把原图长宽根据max_train_img_size变成32的整数倍
im_width = np.minimum(im.width, max_img_size)
if im_width == max_img_size < im.width:
im_height = int((im_width / im.width) * im.height)
else:
im_height = im.height
o_height = np.minimum(im_height, max_img_size)
if o_height == max_img_size < im_height:
o_width = int((o_height / im_height) * im_width)
else:
o_width = im_width
d_wight = o_width - (o_width % 32)
d_height = o_height - (o_height % 32)
return d_wight, d_height
def preprocess():
data_dir = cfg.data_dir
origin_image_dir = os.path.join(data_dir, cfg.origin_image_dir_name) # 'image_10000/'
origin_txt_dir = os.path.join(data_dir, cfg.origin_txt_dir_name) # 'txt_10000/'
train_image_dir = os.path.join(data_dir, cfg.train_image_dir_name) # 'images_%s/' % train_task_id
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name) # 'labels_%s/' % train_task_id
if not os.path.exists(train_image_dir):
os.mkdir(train_image_dir)
if not os.path.exists(train_label_dir):
os.mkdir(train_label_dir)
draw_gt_quad = cfg.draw_gt_quad # True
show_gt_image_dir = os.path.join(data_dir, cfg.show_gt_image_dir_name) # 'show_gt_images_%s/' % train_task_id
if not os.path.exists(show_gt_image_dir):
os.mkdir(show_gt_image_dir)
show_act_image_dir = os.path.join(cfg.data_dir, cfg.show_act_image_dir_name) # 'show_act_images_%s/' % train_task_id
if not os.path.exists(show_act_image_dir):
os.mkdir(show_act_image_dir)
o_img_list = os.listdir(origin_image_dir)
print('found %d origin images.' % len(o_img_list))
train_val_set = []
for o_img_fname, _ in zip(o_img_list, tqdm(range(len(o_img_list)))):
with Image.open(os.path.join(origin_image_dir, o_img_fname)) as im: # 打开每张图片
# d_wight, d_height = resize_image(im)
d_wight, d_height = cfg.max_train_img_size, cfg.max_train_img_size
scale_ratio_w = d_wight / im.width
scale_ratio_h = d_height / im.height
im = im.resize((d_wight, d_height), Image.NEAREST).convert('RGB') # 图片缩放
show_gt_im = im.copy()
# draw on the img
draw = ImageDraw.Draw(show_gt_im)
with open(os.path.join(origin_txt_dir, o_img_fname[:-4] + '.txt'), 'r', encoding='UTF-8') as f:
anno_list = f.readlines()
xy_list_array = np.zeros((len(anno_list), 4, 2))
for anno, i in zip(anno_list, range(len(anno_list))):
anno_colums = anno.strip().split(',')
anno_array = np.array(anno_colums)
xy_list = np.reshape(anno_array[:8].astype(float), (4, 2))
xy_list[:, 0] = xy_list[:, 0] * scale_ratio_w # 坐标缩放
xy_list[:, 1] = xy_list[:, 1] * scale_ratio_h
xy_list = reorder_vertexes(xy_list) # 坐标顺序转换为统一格式
xy_list_array[i] = xy_list
# 将groundtruth文本框内缩，论文为0.3，实际中发现太大，改为0.2
_, shrink_xy_list, _ = shrink(xy_list, cfg.shrink_ratio) # shrink_ratio=0.2，返回长短边都缩后的结果
shrink_1, _, long_edge = shrink(xy_list, cfg.shrink_side_ratio) # shrink_side_ratio=0.6，返回仅长边收缩的结果以及长边下标
if draw_gt_quad:
draw.line([tuple(xy_list[0]), tuple(xy_list[1]),
tuple(xy_list[2]), tuple(xy_list[3]),
tuple(xy_list[0])
],
width=2, fill='green')
draw.line([tuple(shrink_xy_list[0]),
tuple(shrink_xy_list[1]),
tuple(shrink_xy_list[2]),
tuple(shrink_xy_list[3]),
tuple(shrink_xy_list[0])
],
width=2, fill='blue')
vs = [[[0, 0, 3, 3, 0], [1, 1, 2, 2, 1]],
[[0, 0, 1, 1, 0], [2, 2, 3, 3, 2]]]
for q_th in range(2): # 框出头跟尾巴的像素
draw.line([tuple(xy_list[vs[long_edge][q_th][0]]),
tuple(shrink_1[vs[long_edge][q_th][1]]),
tuple(shrink_1[vs[long_edge][q_th][2]]),
tuple(xy_list[vs[long_edge][q_th][3]]),
tuple(xy_list[vs[long_edge][q_th][4]])],
width=3, fill='yellow')
if cfg.gen_origin_img:
im.save(os.path.join(train_image_dir, o_img_fname))
np.save(os.path.join(
train_label_dir,
o_img_fname[:-4] + '.npy'),
xy_list_array) # 保存顺序一致处理后的坐标点集
if draw_gt_quad:
show_gt_im.save(os.path.join(show_gt_image_dir, o_img_fname))
train_val_set.append('{},{},{}\n'.format(o_img_fname,
d_wight,
d_height))
train_img_list = os.listdir(train_image_dir)
print('found %d train images.' % len(train_img_list))
train_label_list = os.listdir(train_label_dir)
print('found %d train labels.' % len(train_label_list))
random.shuffle(train_val_set)
val_count = int(cfg.validation_split_ratio * len(train_val_set))
with open(os.path.join(data_dir, cfg.val_fname), 'w') as f_val:
f_val.writelines(train_val_set[:val_count])
with open(os.path.join(data_dir, cfg.train_fname), 'w') as f_train:
f_train.writelines(train_val_set[val_count:])
if __name__ == '__main__':
preprocess()