def get_val_dicts():
data = json.load(open("stars_carla_val.json"))
for im in data:
anno = im['annotations']
for an in anno:
an['bbox_mode'] = BoxMode(an['bbox_mode'])
return data
def get_data_dicts(self, json_file):
data = json.load(open(json_file))
for im in data:
anno = im["annotations"]
for an in anno:
an["bbox_mode"] = BoxMode(an["bbox_mode"])
return data
def load_mosaic(self, image, data_dict):
"""
from https://github.com/ultralytics/yolov3
"""
# loads images in a mosaic
data_dicts = [data_dict]
data_dicts.extend([
copy.deepcopy(self._dataset[random.randint(0,
len(self._dataset) -
1)]) for _ in range(3)
])
images = [image]
labels = []
w, h = 1024, 1024
for i, data_dict in enumerate(data_dicts):
if i > 0:
# load image and boxes
image = self.load_image(data_dict)
# if random.random() < self.mixup_prob:
# image, data_dict = self.load_mixup(image, data_dict)
# data_dicts[i] = data_dict
images.append(image)
if "annotations" in data_dict:
labels.append(
np.array([[obj['category_id']] + obj['bbox']
for obj in data_dict["annotations"]],
dtype=np.float32))
else:
labels.append(np.array([]))
w, h = min(w, data_dict["width"]), min(h, data_dict["height"])
xc, yc = int(random.uniform(w * 0.25, w * 0.75)), int(
random.uniform(h * 0.25, h * 0.75)) # mosaic center x, y
if self.img_format == "BGR":
fill_val = (103, 116, 123)
else: # rgb
fill_val = (123, 116, 103)
mosaic_img = np.full(
(h, w, 3), fill_val,
dtype=np.uint8) # base image with 4 tiles, BGR order
mosaic_labels = []
for i, (image, data_dict) in enumerate(zip(images, data_dicts)):
wi, hi = data_dict['width'], data_dict['height']
# place img in mosaic_img
if i == 0: # top left
x1a, y1a, x2a, y2a = max(xc - wi, 0), max(
yc - hi, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
x1b, y1b, x2b, y2b = wi - (x2a - x1a), hi - (
y2a - y1a), wi, hi # xmin, ymin, xmax, ymax (small image)
elif i == 1: # top right
x1a, y1a, x2a, y2a = xc, max(yc - hi, 0), min(xc + wi, w), yc
x1b, y1b, x2b, y2b = 0, hi - (y2a - y1a), min(wi,
x2a - x1a), hi
elif i == 2: # bottom left
x1a, y1a, x2a, y2a = max(xc - wi, 0), yc, xc, min(h, yc + hi)
x1b, y1b, x2b, y2b = wi - (x2a - x1a), 0, max(xc, wi), min(
y2a - y1a, hi)
elif i == 3: # bottom right
x1a, y1a, x2a, y2a = xc, yc, min(xc + wi, w), min(h, yc + hi)
x1b, y1b, x2b, y2b = 0, 0, min(wi,
x2a - x1a), min(y2a - y1a, hi)
mosaic_img[y1a:y2a,
x1a:x2a] = image[y1b:y2b,
x1b:x2b] # img4[ymin:ymax, xmin:xmax]
padw = x1a - x1b
padh = y1a - y1b
# Labels
if len(labels[i]) > 0:
labels_i = labels[i].copy()
# xywh to xyxy
labels_i[:, 1] += padw
labels_i[:, 2] += padh
labels_i[:, 3] = labels_i[:, 1] + labels_i[:, 3]
labels_i[:, 4] = labels_i[:, 2] + labels_i[:, 4]
mosaic_labels.append(labels_i)
# Concat/clip labels
if len(mosaic_labels):
mosaic_labels = np.concatenate(mosaic_labels, 0)
np.clip(mosaic_labels[:, 1], 0, w, out=mosaic_labels[:, 1])
np.clip(mosaic_labels[:, 3], 0, w, out=mosaic_labels[:, 3])
np.clip(mosaic_labels[:, 2], 0, h, out=mosaic_labels[:, 2])
np.clip(mosaic_labels[:, 4], 0, h, out=mosaic_labels[:, 4])
mosaic_labels.astype(np.int32)
mosaic_labels = mosaic_labels[np.where(
(mosaic_labels[:, 3] - mosaic_labels[:, 1]) *
(mosaic_labels[:, 4] - mosaic_labels[:, 2]) > 0)]
# Augment
# mosaic_img, mosaic_labels = random_affine(mosaic_img,
# mosaic_labels,
# degrees=0.0,
# translate=0.0,
# scale=0.5,
# shear=0.0,
# border=-w // 2) # border to remove
# translate back to detectron2 format
annos = []
for i in range(mosaic_labels.shape[0]):
bbox = [
mosaic_labels[i, 1], mosaic_labels[i, 2],
max(mosaic_labels[i, 3] - mosaic_labels[i, 1], 0),
max(mosaic_labels[i, 4] - mosaic_labels[i, 2], 0)
]
anno = {
'iscrowd': 0,
# xyxy to xywh
'bbox': bbox,
'category_id': int(mosaic_labels[i, 0]),
'bbox_mode': BoxMode(1), # xywh
'area': bbox[2] * bbox[3],
}
annos.append(anno)
data_dict = {
"file_name": data_dicts[0]['file_name'],
"height": h,
"width": w,
"image_id": data_dicts[0]['image_id'],
"annotations": annos
}
return mosaic_img, data_dict