def val_dataset(record):
image_id = record['ID']
gtboxes = record['gtboxes']
image_path = os.path.join(config.image_folder, image_id + '.jpg')
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox', config.class_names)
gt_boxes[:, 2:4] += gt_boxes[:, :2]
return dict(data=img, boxes = gt_boxes, ID = image_id)
def load_record(self, record):
if self.training:
if_flap = np.random.randint(2) == 1
else:
if_flap = False
# image
# image_path = os.path.join(self.config.image_folder, record['ID']+'.png')
image_path = osp.join(self.config.image_folder, record['ID'] + '.png')
image = load_img(image_path)
# image_h = image.shape[0]
# image_w = image.shape[1]
image_h, image_w = image.shape[:2]
if if_flap:
image = cv2.flip(image, 1)
if self.training:
# ground_truth
gtboxes = load_gt(record, 'gtboxes', 'fbox', self.config.class_names)
keep = (gtboxes[:, 2]>=0) * (gtboxes[:, 3]>=0)
gtboxes=gtboxes[keep, :]
gtboxes[:, 2:4] += gtboxes[:, :2]
if if_flap:
gtboxes = flip_boxes(gtboxes, image_w)
# im_info
nr_gtboxes = gtboxes.shape[0]
im_info = np.array([0, 0, 1, image_h, image_w, nr_gtboxes])
return image, gtboxes, im_info
else:
# image
t_height, t_width, scale = target_size(
image_h, image_w, self.short_size, self.max_size)
# INTER_CUBIC, INTER_LINEAR, INTER_NEAREST, INTER_AREA, INTER_LANCZOS4
resized_image = cv2.resize(image, (t_width, t_height), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.transpose(2, 0, 1)
image = image.astype(np.float32)
gtboxes = load_gt(record, 'gtboxes', 'fbox', self.config.class_names)
gtboxes[:, 2:4] += gtboxes[:, :2]
gtboxes = gtboxes.astype(np.float32)
# im_info
nr_gtboxes = gtboxes.shape[0]
im_info = np.array([t_height, t_width, scale, image_h, image_w, nr_gtboxes]).astype(np.float32)
return image, gtboxes, im_info, record['ID']
def val_dataset(record):
image_id = record['ID']
gtboxes = record['gtboxes']
image_path = osp.join(config.image_folder, image_id + '.png')
assert osp.exists(image_path)
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox', config.class_names)
gt_boxes[:, 2:4] += gt_boxes[:, :2]
# batch_gtboxes = np.ones([config.max_boxes_of_image, 5])
# n = gt_boxes.shape[0]
# batch_gtboxes[:n] = gt_boxes
return dict(data=img, boxes = gt_boxes, ID = image_id)
def val_dataset(record):
image_id = record['ID']
gtboxes = record['gtboxes']
image_path = os.path.join(config.image_folder, image_id + '.jpg')
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
if img is None:
image_path = os.path.join(config.image_folder, image_id + '.png')
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
if img is None:
raise '[!]Img not loaded correctly!' + image_path + '/jpg'
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox',
config.class_names)
gt_boxes[:, 2:4] += gt_boxes[:, :2]
return dict(data=img, boxes=gt_boxes, ID=image_id)
def train_dataset(seed=config.seed_dataprovider):
root = config.image_folder
source = config.train_source
batch_per_gpu = config.batch_per_gpu
short_size = config.train_image_short_size
max_size = config.train_image_max_size
records = misc_utils.load_json_lines(source)
nr_files = len(records)
print('training image number: {}'.format(nr_files))
np.random.seed(seed)
np.random.shuffle(records)
file_idx = 0
while file_idx < nr_files:
batch_records = []
batch_images_list = []
hw_stat = np.zeros((batch_per_gpu, 2), np.int32)
for i in range(batch_per_gpu):
record = records[file_idx]
batch_records.append(record)
image_path = os.path.join(config.image_folder,
record['ID'] + '.png')
img = misc_utils.load_img(image_path)
batch_images_list.append(img.copy())
hw_stat[i, :] = img.shape[:2]
file_idx += 1
if file_idx >= nr_files:
file_idx = 0
np.random.shuffle(records)
batch_image_height = np.max(hw_stat[:, 0])
batch_image_width = np.max(hw_stat[:, 1])
is_batch_ok = True
batch_resized_height, batch_resized_width = get_hw_by_short_size(
batch_image_height, batch_image_width, short_size, max_size)
batch_images = np.zeros((batch_per_gpu, 3, max_size, max_size),
dtype=np.float32)
batch_gts = np.zeros(
(batch_per_gpu, config.max_boxes_of_image, config.nr_box_dim),
dtype=np.float32)
batch_info = np.zeros((batch_per_gpu, 6), dtype=np.float32)
for i in range(batch_per_gpu):
record = batch_records[i]
img = batch_images_list[i]
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox',
config.class_names)
keep = (gt_boxes[:, 2] >= 0) * (gt_boxes[:, 3] >= 0)
gt_boxes = gt_boxes[keep, :]
nr_gtboxes = gt_boxes.shape[0]
if nr_gtboxes == 0:
is_batch_ok = False
break
gt_boxes[:, 2:4] += gt_boxes[:, :2]
padded_image = pad_image(img, batch_image_height,
batch_image_width, config.image_mean)
original_height, original_width, channels = padded_image.shape
resized_image, scale = resize_img_by_short_and_max_size(
padded_image, short_size, max_size)
gt_boxes[:, 0:4] *= scale
resized_gt = gt_boxes
if np.random.randint(2) == 1:
resized_image, resized_gt = flip_image_and_boxes(
resized_image, resized_gt)
resized_image = resized_image.transpose(2, 0, 1).astype(np.float32)
batch_images[i, :, :int(resized_image.shape[1]), :int(
resized_image.shape[2])] = resized_image
batch_gts[i, :nr_gtboxes] = resized_gt
batch_info[i, :] = (resized_image.shape[1], resized_image.shape[2],
scale, original_height, original_width,
nr_gtboxes)
if not is_batch_ok:
continue
yield dict(data=batch_images, boxes=batch_gts, im_info=batch_info)
def eval_dataset(args):
source = config.eval_source
batch_per_gpu = config.train_batch_per_gpu
short_size = config.train_image_short_size
max_size = config.train_image_max_size
#Custom
records = load_coco_json_lines(source)
nr_files = len(records)
print('[!] Eval image number: {}'.format(nr_files))
yield nr_files
file_idx = 0
while file_idx < nr_files:
batch_records = []
batch_images_list = []
hw_stat = np.zeros((batch_per_gpu, 2), np.int32)
done = False
for i in range(batch_per_gpu):
record = records[file_idx]
batch_records.append(record)
image_path = os.path.join(config.image_folder,
record['ID'] + '.jpg')
if not os.path.exists(image_path):
image_path = os.path.join(config.image_folder,
record['ID'] + '.png')
img = misc_utils.load_img(image_path)
batch_images_list.append(img.copy())
hw_stat[i, :] = img.shape[:2]
if file_idx == 0:
done = True
file_idx += 1
if file_idx >= nr_files:
file_idx = 0
batch_image_height = np.max(hw_stat[:, 0])
batch_image_width = np.max(hw_stat[:, 1])
is_batch_ok = True
batch_resized_height, batch_resized_width = get_hw_by_short_size(
batch_image_height, batch_image_width, short_size, max_size)
batch_images = np.zeros((batch_per_gpu, 3, max_size, max_size),
dtype=np.float32)
batch_gts = np.zeros(
(batch_per_gpu, config.max_boxes_of_image, config.nr_box_dim),
dtype=np.float32)
batch_info = np.zeros((batch_per_gpu, 6), dtype=np.float32)
#[torch.Size([8, 256, 208, 304]), torch.Size([8, 256, 104, 152]), torch.Size([8, 256, 52, 76]), torch.Size([8, 256, 26, 38])]
for i in range(batch_per_gpu):
record = batch_records[i]
img = batch_images_list[i]
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox',
config.class_names)
keep = (gt_boxes[:, 2] >= 0) * (gt_boxes[:, 3] >= 0)
gt_boxes = gt_boxes[keep, :]
nr_gtboxes = gt_boxes.shape[0]
if nr_gtboxes < 2:
is_batch_ok = False
break
gt_boxes[:, 2:4] += gt_boxes[:, :2]
padded_image = pad_image(img, batch_image_height,
batch_image_width, config.image_mean)
original_height, original_width, channels = padded_image.shape
resized_image, scale = resize_img_by_short_and_max_size(
padded_image, short_size, max_size)
gt_boxes[:, 0:4] *= scale
resized_gt = gt_boxes
resized_image = resized_image.transpose(2, 0, 1).astype(np.float32)
batch_images[i, :, :int(resized_image.shape[1]), :int(
resized_image.shape[2])] = resized_image
batch_gts[i, :nr_gtboxes] = resized_gt
batch_info[i, :] = (resized_image.shape[1], resized_image.shape[2],
scale, original_height, original_width,
nr_gtboxes)
if not is_batch_ok:
continue
results = dict(data=batch_images,
boxes=batch_gts,
im_info=batch_info,
done=done)
yield results
def train_dataset_debug(args, seed=config.seed_dataprovider):
source = config.train_source
batch_per_gpu = config.train_batch_per_gpu
short_size = config.train_image_short_size
max_size = config.train_image_max_size
#Custom
records = load_coco_json_lines(source)
nr_files = len(records)
print(records[0])
print('[!] Training image number: {}'.format(nr_files))
yield nr_files
np.random.seed(seed)
np.random.shuffle(records)
file_idx = 0
while file_idx < nr_files:
batch_records = []
batch_images_list = []
hw_stat = np.zeros((batch_per_gpu, 2), np.int32)
done = False
for i in range(batch_per_gpu):
record = records[file_idx]
batch_records.append(record)
image_path = os.path.join(config.image_folder,
record['ID'] + '.jpg')
if not os.path.exists(image_path):
image_path = os.path.join(config.image_folder,
record['ID'] + '.png')
img = misc_utils.load_img(image_path)
batch_images_list.append(img.copy())
hw_stat[i, :] = img.shape[:2]
if file_idx == 0:
done = True
file_idx += 1
if file_idx >= nr_files:
file_idx = 0
np.random.shuffle(records)
batch_image_height = np.max(hw_stat[:, 0])
batch_image_width = np.max(hw_stat[:, 1])
is_batch_ok = True
batch_resized_height, batch_resized_width = get_hw_by_short_size(
batch_image_height, batch_image_width, short_size, max_size)
batch_images = np.zeros((batch_per_gpu, 3, max_size, max_size),
dtype=np.float32)
batch_gts = np.zeros(
(batch_per_gpu, config.max_boxes_of_image, config.nr_box_dim),
dtype=np.float32)
batch_info = np.zeros((batch_per_gpu, 6), dtype=np.float32)
for i in range(batch_per_gpu):
record = batch_records[i]
img = batch_images_list[i]
img_ = np.uint8(img) #
debugdir = '/home/felix_wu/debugarg/' #
cv2.imwrite(debugdir + '/test{}.jpg'.format('A'), img_) #
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox',
config.class_names)
keep = (gt_boxes[:, 2] >= 0) * (gt_boxes[:, 3] >= 0)
gt_boxes = gt_boxes[keep, :]
nr_gtboxes = gt_boxes.shape[0]
if nr_gtboxes < 2:
is_batch_ok = False
break
gt_boxes[:, 2:4] += gt_boxes[:, :2]
padded_image = pad_image(img, batch_image_height,
batch_image_width, config.image_mean)
img_ = np.uint8(padded_image) #
debugdir = '/home/felix_wu/debugarg/' #
cv2.imwrite(debugdir + '/test{}.jpg'.format('B'), img_) #
original_height, original_width, channels = padded_image.shape
resized_image, scale = resize_img_by_short_and_max_size(
padded_image, short_size, max_size)
gt_boxes[:, 0:4] *= scale
resized_gt = gt_boxes
if np.random.randint(2) == 1:
resized_image, resized_gt = flip_image_and_boxes(
resized_image, resized_gt)
resized_image = resized_image.transpose(2, 0, 1).astype(np.float32)
batch_images[i, :, :int(resized_image.shape[1]), :int(
resized_image.shape[2])] = resized_image
batch_gts[i, :nr_gtboxes] = resized_gt
batch_info[i, :] = (resized_image.shape[1], resized_image.shape[2],
scale, original_height, original_width,
nr_gtboxes)
if not is_batch_ok:
continue
results = dict(data=batch_images,
boxes=batch_gts,
im_info=batch_info,
done=done)
yield results
def _train_dataset_sample(args,
batch_images_list,
batch_records,
hw_stat,
seed=config.seed_dataprovider):
short_size = config.train_image_short_size
max_size = config.train_image_max_size
batch_per_gpu = config.train_batch_per_gpu
batch_image_height = np.max(hw_stat[:, 0])
batch_image_width = np.max(hw_stat[:, 1])
is_batch_ok = True
batch_resized_height, batch_resized_width = get_hw_by_short_size(
batch_image_height, batch_image_width, short_size, max_size)
batch_images = np.zeros((batch_per_gpu, 3, max_size, max_size),
dtype=np.float32)
if args.flip_aug:
batch_aug_images = np.zeros((batch_per_gpu, 3, max_size, max_size),
dtype=np.float32)
batch_gts = np.zeros(
(batch_per_gpu, config.max_boxes_of_image, config.nr_box_dim),
dtype=np.float32)
batch_info = np.zeros((batch_per_gpu, 6), dtype=np.float32)
for i in range(batch_per_gpu):
record = batch_records[i]
img = batch_images_list[i]
if args.flip_aug:
if args.FA_heavy:
aug_img = heavy_aug_seq(image=img)
else:
aug_img = aug_seq(image=img)
gt_boxes = misc_utils.load_gt(record, 'gtboxes', 'fbox',
config.class_names)
keep = (gt_boxes[:, 2] >= 0) * (gt_boxes[:, 3] >= 0)
gt_boxes = gt_boxes[keep, :]
nr_gtboxes = gt_boxes.shape[0]
if nr_gtboxes < 2:
is_batch_ok = False
break
gt_boxes[:, 2:4] += gt_boxes[:, :2]
padded_image = pad_image(img, batch_image_height, batch_image_width,
config.image_mean)
original_height, original_width, channels = padded_image.shape
resized_image, scale = resize_img_by_short_and_max_size(
padded_image, short_size, max_size)
if args.flip_aug:
padded_aug_img = pad_image(aug_img, batch_image_height,
batch_image_width, config.image_mean)
resized_aug_img, _ = resize_img_by_short_and_max_size(
padded_aug_img, short_size, max_size)
gt_boxes[:, 0:4] *= scale
resized_gt = gt_boxes
if np.random.randint(2) == 1:
resized_image, resized_gt = flip_image_and_boxes(
resized_image, resized_gt)
if args.flip_aug:
resized_aug_img, _ = flip_image_and_boxes(
resized_aug_img, resized_gt.copy())
resized_image = resized_image.transpose(2, 0, 1).astype(np.float32)
batch_images[i, :, :int(resized_image.shape[1]), :int(
resized_image.shape[2])] = resized_image
if args.flip_aug:
resized_aug_img = resized_aug_img.transpose(2, 0,
1).astype(np.float32)
batch_aug_images[i, :, :int(resized_image.shape[1]), :int(
resized_image.shape[2])] = resized_aug_img
batch_gts[i, :nr_gtboxes] = resized_gt
batch_info[i, :] = (resized_image.shape[1], resized_image.shape[2],
scale, original_height, original_width, nr_gtboxes)
results = dict(data=batch_images, boxes=batch_gts, im_info=batch_info)
if args.flip_aug:
results['aug_data'] = batch_aug_images
return is_batch_ok, results if is_batch_ok else None
