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util.py
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util.py
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import os
import cv2
import numpy as np
from os.path import join, split
import random
def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = (box[0] + box[1]) / 2.0 - 1
y = (box[2] + box[3]) / 2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
def issmallobject(bbox, thresh):
if bbox[0] * bbox[1] <= thresh:
return True
else:
return False
def read_label_txt(label_dir):
labels = []
with open(label_dir) as fp:
for f in fp.readlines():
labels.append(f.strip().split(' '))
return labels
def load_txt_label(label_dir):
return np.loadtxt(label_dir, dtype=str)
def load_txt_labels(label_dir):
labels = []
for l in label_dir:
la = load_txt_label(l)
labels.append(la)
return labels
def check_dir(dir):
if not os.path.exists(dir):
os.makedirs(dir)
def rescale_yolo_labels(labels, img_shape):
height, width, nchannel = img_shape
rescale_boxes = []
for box in list(labels):
x_c = float(box[1]) * width
y_c = float(box[2]) * height
w = float(box[3]) * width
h = float(box[4]) * height
x_left = x_c - w * .5
y_left = y_c - h * .5
x_right = x_c + w * .5
y_right = y_c + h * .5
rescale_boxes.append([box[0], int(x_left), int(y_left), int(x_right), int(y_right)])
return rescale_boxes
def draw_annotation_to_image(img, annotation, save_img_dir):
for anno in annotation:
cl, x1, y1, x2, y2 = anno
cv2.rectangle(img, pt1=(x1, y1), pt2=(x2, y2), color=(255, 0, 0))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, cl, (int((x1 + x2) / 2), y1 - 5), font, fontScale=0.8, color=(0, 0, 255))
cv2.imwrite(save_img_dir, img)
def bbox_iou(box1, box2):
cl, b1_x1, b1_y1, b1_x2, b1_y2 = box1
cl, b2_x1, b2_y1, b2_x2, b2_y2 = box2
# get the corrdinates of the intersection rectangle
inter_rect_x1 = max(b1_x1, b2_x1)
inter_rect_y1 = max(b1_y1, b2_y1)
inter_rect_x2 = min(b1_x2, b2_x2)
inter_rect_y2 = min(b1_y2, b2_y2)
# Intersection area
inter_width = inter_rect_x2 - inter_rect_x1 + 1
inter_height = inter_rect_y2 - inter_rect_y1 + 1
if inter_width > 0 and inter_height > 0: # strong condition
inter_area = inter_width * inter_height
# Union Area
b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
iou = inter_area / (b1_area + b2_area - inter_area)
else:
iou = 0
return iou
def swap(x1, x2):
if (x1 > x2):
temp = x1
x1 = x2
x2 = temp
return x1, x2
def norm_sampling(search_space):
# 随机生成点
search_x_left, search_y_left, search_x_right, search_y_right = search_space
new_bbox_x_center = random.randint(search_x_left, search_x_right)
new_bbox_y_center = random.randint(search_y_left, search_y_right)
return [new_bbox_x_center, new_bbox_y_center]
def flip_bbox(roi):
roi = roi[:, ::-1, :]
return roi
def sampling_new_bbox_center_point(img_shape, bbox):
#### sampling space ####
height, width, nc = img_shape
cl, x_left, y_left, x_right, y_right = bbox
bbox_w, bbox_h = x_right - x_left, y_right - y_left
### left top ###
if x_left <= width / 2:
search_x_left, search_y_left, search_x_right, search_y_right = width * 0.6, height / 2, width * 0.75, height * 0.75
if x_left > width / 2:
search_x_left, search_y_left, search_x_right, search_y_right = width * 0.25, height / 2, width * 0.5, height * 0.75
return [search_x_left, search_y_left, search_x_right, search_y_right]
def random_add_patches(bbox, rescale_boxes, shape, paste_number, iou_thresh):
temp = []
for rescale_bbox in rescale_boxes:
temp.append(rescale_bbox)
cl, x_left, y_left, x_right, y_right = bbox
bbox_w, bbox_h = x_right - x_left, y_right - y_left
center_search_space = sampling_new_bbox_center_point(shape, bbox)
success_num = 0
new_bboxes = []
while success_num < paste_number:
new_bbox_x_center, new_bbox_y_center = norm_sampling(center_search_space)
print(norm_sampling(center_search_space))
new_bbox_x_left, new_bbox_y_left, new_bbox_x_right, new_bbox_y_right = new_bbox_x_center - 0.5 * bbox_w, \
new_bbox_y_center - 0.5 * bbox_h, \
new_bbox_x_center + 0.5 * bbox_w, \
new_bbox_y_center + 0.5 * bbox_h
new_bbox = [cl, int(new_bbox_x_left), int(new_bbox_y_left), int(new_bbox_x_right), int(new_bbox_y_right)]
ious = [bbox_iou(new_bbox, bbox_t) for bbox_t in rescale_boxes]
if max(ious) <= iou_thresh:
# for bbox_t in rescale_boxes:
# iou = bbox_iou(new_bbox[1:],bbox_t[1:])
# if(iou <= iou_thresh):
success_num += 1
temp.append(new_bbox)
new_bboxes.append(new_bbox)
else:
continue
return new_bboxes
def sampling_new_bbox_center_point2(img_shape, bbox):
#### sampling space ####
height, width, nc = img_shape
bbox_h, bbox_w, bbox_c = bbox
### left top ###
'''
search_x_left, search_y_left, search_x_right, search_y_right = width * 0.55 , height * 0.5 , \
width * 0.9 , height * 0.95
'''
search_x_left, search_y_left, search_x_right, search_y_right = width * 0.35 , height * 0.6 , \
width * 1 , height * 0.95
return [search_x_left, search_y_left, search_x_right, search_y_right]
def random_add_patches2(bbox_img, rescale_boxes, shape, paste_number, iou_thresh):
temp = []
for rescale_bbox in rescale_boxes:
temp.append(rescale_bbox)
bbox_h, bbox_w, bbox_c = bbox_img
img_h,img_w,img_c = shape
center_search_space = sampling_new_bbox_center_point2(shape, bbox_img) # 选取生成随机点区域
success_num = 0
new_bboxes = []
cl = 1
while success_num < paste_number:
new_bbox_x_center, new_bbox_y_center = norm_sampling(center_search_space) # 随机生成点坐标
if new_bbox_x_center-0.5*bbox_w < 0 or new_bbox_x_center+0.5*bbox_w > img_w:
continue
if new_bbox_y_center-0.5*bbox_h < 0 or new_bbox_y_center+0.5*bbox_h > img_h:
continue
new_bbox_x_left, new_bbox_y_left, new_bbox_x_right, new_bbox_y_right = new_bbox_x_center - 0.5 * bbox_w, \
new_bbox_y_center - 0.5 * bbox_h, \
new_bbox_x_center + 0.5 * bbox_w, \
new_bbox_y_center + 0.5 * bbox_h
new_bbox = [cl, int(new_bbox_x_left), int(new_bbox_y_left), int(new_bbox_x_right), int(new_bbox_y_right)]
ious = [bbox_iou(new_bbox, bbox_t) for bbox_t in rescale_boxes]
ious2 = [bbox_iou(new_bbox,bbox_t1) for bbox_t1 in new_bboxes]
if ious2 == []:
ious2.append(0)
if max(ious) <= iou_thresh and max(ious2) <= iou_thresh:
success_num += 1
temp.append(new_bbox)
new_bboxes.append(new_bbox)
else:
continue
return new_bboxes