def crop_for_train(self, over_samples=None):
"""训练集裁剪
Args:
over_samples (dict): {类别: 重采样次数， ...}
"""
for i in range(len(self.dataset)):
data = self.dataset[i]
# 索引或迭代dataset必须提供包含image字段和anns字段信息
anns = data['anns']
if len(anns) == 0:
print('{} has no label'.format(data['image']))
img = cvtools.imread(data['image'])
self.crop_method.crop(img, anns)
# croped可为空，即没有任何裁剪，同时原始图亦不保留
cropped = self.crop_method.match_anns(anns)
# 过采样扩展，对少样本类别过采样
if over_samples is not None:
add_croped = self.over_sample(img, anns, over_samples)
cropped.update(add_croped)
self.crops.append(cropped)
print('crop image %d of %d: %s' %
(i, len(self.dataset), osp.basename(data['image'])))
# 打印和清空统计信息
print(self.crop_method.get_stats())
self.crop_method.reset_stats()
if hasattr(self.crop_method, 'stats_crop'):
print(self.crop_method.stats_crop)
self.crop_method.stats_crop = {}
def vis_box(self, save_root, has_box=True, has_segm=True, has_crop=True):
for i, image_name in enumerate(self.imgs):
print('Visualize image %d of %d: %s' %
(i, len(self.imgs), image_name))
# read image
image_file = osp.join(self.img_prefix, image_name)
img = cvtools.imread(image_file)
if img is None:
print('{} is None.'.format(image_file))
continue
imgToObjs = self.img_to_objs[i]
for crop_i, img_coor in enumerate(imgToObjs):
if has_crop:
img = cvtools.draw_boxes_texts(
img, img_coor, colors='random', line_width=2)
objs = imgToObjs[img_coor]
if has_box:
boxes = [obj['bbox'] for obj in objs]
img = cvtools.draw_boxes_texts(
img, boxes, colors=[(255, 0, 0)]*len(boxes), box_format='x1y1wh')
if has_segm:
segms = [obj['segmentation'][0] for obj in objs]
img = cvtools.draw_boxes_texts(
img, segms, box_format='polygen')
to_file = osp.join(save_root, 'images', image_name)
cvtools.imwrite(img, to_file)
def augment_one_image(line):
nonlocal annts_lines
line = line.strip().split()
file_path = line[0]
boxes = []
classes = []
for label_str in line[1:]:
bbox_cls_str = label_str.split(',')
boxes.append([float(i) for i in bbox_cls_str][0:4])
classes.append(int(bbox_cls_str[4]))
boxes = np.array(boxes)
classes = np.array(classes)
new_name = osp.splitext(file_path.split(os.sep)[-1])[0]
new = save_path + new_name.replace('.jpg', '') + '_{index}.jpg'
for im_index in range(1, 5): # 每张图片增强出4张
new_image_name = new.format(index=im_index)
if not os.path.isfile(new_image_name):
im = cvtools.imread(file_path)
img, boxes_trans, classes_trans = transfer(im, boxes,
classes)
boxes_trans = boxes_trans.astype(np.int32)
classes_trans = classes_trans.astype(np.int32)
# print('save %s...' % new_image_name)
cv2.imwrite(new_image_name, img)
annts_lines += new_image_name + ' '
for box, cls in zip(boxes_trans, classes_trans):
annts_lines += ','.join(map(str, box)) + \
',' + str(cls) + ' '
annts_lines += '\n'
def crop_with_label(self, save_root):
image_ids = self.COCO.getImgIds()
image_ids.sort()
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))
if cvtools._DEBUG:
roidb = roidb[:10]
for i, entry in enumerate(roidb):
image_name = entry['file_name']
image_file = osp.join(self.img_prefix, image_name)
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
ann = self.COCO.loadAnns(ann_ids)
if len(ann) == 0:
print('{} ann is None.'.format(image_name))
continue
# read image
img = cvtools.imread(image_file)
if img is None:
print('{} is None.'.format(image_file))
continue
print('crop image %d of %d: %s' %
(i, len(roidb), image_name))
# crop image
crop_imgs, starts = self.crop(img, ann)
# handling the box at the edge of the cropped image
self.imgs.append(image_name)
self.match_img_objs(crop_imgs, starts, ann)
# save crop results
self.save_crop_labeltxt(save_root)
def crop_with_label(self, save_root='./', iof_th=0.5):
image_ids = self.COCO.getImgIds()
image_ids.sort()
if cvtools._DEBUG:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))[:10]
else:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))
print('{} images.'.format(len(roidb)))
cvtools.makedirs(save_root + '/images')
cvtools.makedirs(save_root + '/labelTxt+crop')
stats = defaultdict(crop_objs=0,
total_objs=0,
missing_objs=0,
total_croped_images=0)
for entry in tqdm(roidb):
if cvtools._DEBUG:
print('crop {}'.format(entry['file_name']))
# read image
image_name = entry['file_name']
image_file = osp.join(self.img_prefix, image_name)
img = cvtools.imread(image_file)
if img is None:
print('{} is None.'.format(image_file))
continue
# crop image
crop_imgs, starts = self.crop(img)
# handling the box at the edge of the cropped image
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
img_to_objs, obj_to_num = self.deal_edged_boxes(ann_ids,
crop_imgs,
starts,
iof_th=iof_th)
if img_to_objs is None:
continue
# stats
for _, num in obj_to_num.items():
stats['crop_objs'] += num
stats['total_objs'] += len(ann_ids)
stats['missing_objs'] += len(set(ann_ids) - set(obj_to_num.keys()))
for obj in img_to_objs.values():
if len(obj) > 0:
stats['total_croped_images'] += 1
# save results
# self.save_crop_labeltxt(image_name, img_to_objs, save_root)
# save stats values
total_images = len(roidb)
stats['total_images'] = len(roidb)
stats['objs_per_croped_image'] = stats['total_croped_images'] / float(
total_images)
stats['objs_per_image'] = stats['total_objs'] / float(total_images)
cvtools.save_json(stats, to_file='stats.json')
def read_img_or_crop(self, entry):
image_name = entry['file_name']
image_file = osp.join(self.img_prefix, image_name)
try:
img = cvtools.imread(image_file)
except FileNotFoundError:
print('image {} is not found!'.format(image_file))
img = None
image_name = osp.splitext(image_name)[0]
if 'crop' in entry:
img = img[entry['crop'][1]:entry['crop'][3] + 1,
entry['crop'][0]:entry['crop'][2] + 1]
image_name = '_'.join([image_name] + list(map(str, entry['crop'])))
return img, image_name
def vis_instances(self,
save_root,
vis='bbox',
box_format='x1y1wh',
by_cat=False):
if by_cat:
self._vis_instances_by_cat(save_root, vis, box_format)
image_ids = self.COCO.getImgIds()
image_ids.sort()
if cvtools._DEBUG:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))[:10]
else:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))
print('{} images.'.format(len(roidb)))
cvtools.makedirs(save_root)
for i, entry in enumerate(roidb):
print('Visualize image %d of %d: %s' %
(i, len(roidb), entry['file_name']))
image_name = entry['file_name']
image_file = osp.join(self.img_prefix, image_name)
img = cvtools.imread(image_file)
image_name = osp.splitext(image_name)[0]
if 'crop' in entry:
img = img[entry['crop'][1]:entry['crop'][3],
entry['crop'][0]:entry['crop'][2]]
image_name = '_'.join([image_name] +
list(map(str, entry['crop'])))
if img is None:
print('{} is None.'.format(image_file))
continue
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
objs = self.COCO.loadAnns(ann_ids)
if len(objs) == 0:
continue
# Sanitize bboxes -- some are invalid
for obj in objs:
vis_obj = []
if 'ignore' in obj and obj['ignore'] == 1:
continue
if vis in obj:
vis_obj = obj[vis]
class_name = self.COCO.cats[
obj['category_id']]['name'] if 'category_id' in obj else ''
img = cvtools.draw_boxes_texts(img,
vis_obj,
class_name,
box_format=box_format)
# save in jpg format for saving storage
cvtools.imwrite(img, osp.join(save_root, image_name + '.jpg'))
def vis_dets(self, img_name, result):
dets = result['results']
img = cvtools.imread(img_name)
for cls_id, det_cls in dets.items():
det_cls = det_cls[det_cls[:, -1] > 0.5]
if len(det_cls) == 0: continue
ids = poly_nms.poly_gpu_nms(det_cls, 0.15)
det_cls = det_cls[ids]
if cls_id == 15:
img = cvtools.draw_boxes_texts(img, det_cls[:, :-1],
line_width=2,
box_format="polygon")
img_name = osp.basename(img_name)
to_file = osp.join('/code/CenterNet/exp/ctdet/vis', img_name)
cvtools.imwrite(img, to_file)
def _vis_instances_by_cat(self,
save_root,
vis_cats=None,
vis='bbox',
box_format='x1y1wh'):
catImgs = copy.deepcopy(self.COCO.catToImgs)
catImgs = {cat: set(catImgs[cat]) for cat in catImgs}
for cat_id, image_ids in catImgs.items():
cat_name = self.COCO.cats[cat_id]['name']
if vis_cats is not None and cat_name not in vis_cats:
continue
print('Visualize %s' % cat_name)
if cvtools._DEBUG:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))[:10]
else:
roidb = copy.deepcopy(self.COCO.loadImgs(image_ids))
for i, entry in enumerate(roidb):
print('Visualize image %d of %d: %s' %
(i, len(roidb), entry['file_name']))
image_name = entry['file_name']
image_file = osp.join(self.img_prefix, image_name)
img = cvtools.imread(image_file) # support chinese
image_name = osp.splitext(image_name)[0]
if 'crop' in entry:
img = img[entry['crop'][1]:entry['crop'][3],
entry['crop'][0]:entry['crop'][2]]
image_name = '_'.join([image_name] +
list(map(str, entry['crop'])))
if img is None:
print('{} is None.'.format(image_file))
continue
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
objs = self.COCO.loadAnns(ann_ids)
for obj in objs:
if obj['category_id'] != cat_id:
continue
if 'ignore' in obj and obj['ignore'] == 1:
continue
vis_obj = []
if vis in obj:
vis_obj = obj[vis]
class_name = [cat_name if 'category_id' in obj else '']
img = cvtools.draw_boxes_texts(img,
vis_obj,
class_name,
box_format=box_format)
# save in jpg format for saving storage
cvtools.imwrite(img, osp.join(save_root, cat_name, image_name + '.jpg'))
def crop_for_test(self, save):
from collections import defaultdict
imgs = cvtools.get_images_list(self.img_prefix)
self.test_dataset = defaultdict(list)
for image_file in tqdm(imgs):
if cvtools._DEBUG:
print('crop {}'.format(image_file))
image_name = osp.basename(image_file)
img = cvtools.imread(image_file) # support chinese
if img is None:
print('{} is None.'.format(image_file))
continue
crop_imgs, starts = self.crop(img)
for crop_img, start in zip(crop_imgs, starts):
crop_rect = start[0], start[1], start[0] + crop_img.shape[
1], start[1] + crop_img.shape[0]
self.test_dataset[image_name].append(crop_rect)
cvtools.save_json(self.test_dataset, save)
def crop_in_order_for_test(self, save, w=1920, h=1080, overlap=0.):
assert 1920 >= w >= 800 and 1080 >= h >= 800 and 0.5 >= overlap >= 0.
from collections import defaultdict
imgs = cvtools.get_images_list(self.img_prefix)
crop = CropInOder(width_size=w, height_size=h, overlap=overlap)
self.test_dataset = defaultdict(list)
for image_file in tqdm(imgs):
if cvtools._DEBUG:
print('crop {}'.format(image_file))
image_name = osp.basename(image_file)
img = cvtools.imread(image_file) # support chinese
if img is None:
print('{} is None.'.format(image_file))
continue
crop_imgs, starts, _ = crop(img)
for crop_img, start in zip(crop_imgs, starts):
crop_rect = start[0], start[1], start[0] + crop_img.shape[
1], start[1] + crop_img.shape[0]
self.test_dataset[image_name].append(crop_rect)
cvtools.save_json(self.test_dataset, save)
def check_image(self, img_path):
file = osp.join(self.root, img_path)
if not cvtools.isfile_casesensitive(file):
image_types = ['.jpg', '.png', '.jpeg', '.JPG', '.PNG', '.JPEG']
for suffix in image_types:
img_path = osp.splitext(img_path)[0] + suffix
file = osp.join(self.root, img_path)
if cvtools.isfile_casesensitive(file):
break
if not cvtools.isfile_casesensitive(file):
print("No images found in {}".format(osp.basename(img_path)))
return None
else:
if self.read_test:
try:
img = cvtools.imread(osp.join(self.root, img_path))
except Exception as e:
print(e, 'filter images {}'.format(img_path))
return None
if img is None:
print('image {} is None'.format(img_path))
return None
return img_path
def crop_for_test(self, w, h, save=None):
imgs = cvtools.get_images_list(self.img_prefix)
if cvtools._DEBUG:
imgs = imgs[:10]
self.test_dataset = defaultdict(list)
for i, image_file in enumerate(imgs):
image_name = osp.basename(image_file)
img = cvtools.imread(image_file) # support chinese
if img is None:
print('{} is None.'.format(image_file))
continue
print('crop image %d of %d: %s' %
(i, len(imgs), image_name))
crop_imgs, starts = sliding_crop(img, w, h)
for crop_img, start in zip(crop_imgs, starts):
crop_rect = start[0], start[1], \
start[0]+crop_img.shape[1], start[1]+crop_img.shape[0]
self.test_dataset[image_name].append(crop_rect)
if save is not None:
cvtools.save_json(self.test_dataset, save)
return self.test_dataset
