def _resize_bboxes(self, results):
img_shape = results['img_shape']
for key in results.get('bbox_fields', []):
if results[key].shape[-1] == 5:
polys = rotated_box_to_poly_np(results[key]) # to 8 points
elif results[key].shape[-1] == 8:
polys = results[key] # it's already 8 points
else:
polys = rotated_box_to_poly_np(results[key]) # to 8 points
polys = polys * results['scale_factor']
if polys.shape[0] != 0:
polys[:, 0::2] = np.clip(polys[:, 0::2], 0, img_shape[1] - 1)
polys[:, 1::2] = np.clip(polys[:, 1::2], 0, img_shape[0] - 1)
rboxes = poly_to_rotated_box_np(polys) # to x,y,w,h,angle
results[key] = rboxes
def get_visualization(self, input_img, classes, test_cfg):
stitched = "nonew3"
batch_size = input_img.shape[0]
img = tensor2imgs(input_img, **self.last_vals['img_metas'][0]
['img_norm_cfg'])[0] #get input image
from PIL import Image
#Image.fromarray(img).show()
from mmdet.core import rotated_box_to_poly_np
gt = rotated_box_to_poly_np(
self.last_vals['gt_bboxes'][0].cpu().numpy())
#TODO classes are wrong for deepscores
img_gt = imshow_det_bboxes(
img.copy(),
gt,
self.last_vals['gt_labels'][0].cpu().numpy(),
class_names=classes,
show=False,
show_label=True,
rotated=True)
det_boxes_labels = self.get_bboxes(
fam_cls_scores=self.last_vals['fam_cls_scores'],
fam_bbox_preds=self.last_vals['fam_bbox_preds'],
refine_anchors=self.last_vals['refine_anchors'],
odm_cls_scores=self.last_vals['odm_cls_scores'],
odm_bbox_preds=self.last_vals['odm_bbox_preds'],
img_metas=self.last_vals['img_metas'],
cfg=test_cfg)[0]
det_boxes = rotated_box_to_poly_np(det_boxes_labels[0].cpu().numpy())
det_labels = det_boxes_labels[1].cpu().numpy()
if len(det_boxes) > 0:
img_det = imshow_det_bboxes(img.copy(),
det_boxes,
det_labels.astype(np.int) + 1,
class_names=classes,
show=False,
show_label=True,
rotated=True)
else:
img_det = img.copy()
stitched = vt.stitch_big_image([[img_gt], [img_det]])
return [{"name": "stitched_img", "image": stitched}]
def __call__(self, results):
# return the results directly if not rotate
if not self.is_rotate:
results['rotate'] = False
return results
h, w, c = results['img_shape']
img = results['img']
# angle for rotate
angle = self.rand_angle
results['rotate'] = True
results['rotate_angle'] = angle
image_center = np.array((w / 2, h / 2))
abs_cos, abs_sin = abs(np.cos(angle)), abs(np.sin(angle))
if self.auto_bound:
# find the new width and height bounds
bound_w, bound_h = np.rint(
[h * abs_sin + w * abs_cos,
h * abs_cos + w * abs_sin]).astype(int)
else:
bound_w, bound_h = w, h
self.rm_coords = self.create_rotation_matrix(image_center, angle,
bound_h, bound_w)
# Needed because of this problem https://github.com/opencv/opencv/issues/11784
self.rm_image = self.create_rotation_matrix(image_center,
angle,
bound_h,
bound_w,
offset=-0.5)
# rotate img
img = self.apply_image(img, bound_h, bound_w)
results['img'] = img
results['img_shape'] = (bound_h, bound_w, c)
# rotate bboxes
gt_bboxes = results.get('gt_bboxes', [])
labels = results.get('gt_labels', [])
polys = rotated_box_to_poly_np(gt_bboxes).reshape(-1, 2)
polys = self.apply_coords(polys).reshape(-1, 8)
gt_bboxes = poly_to_rotated_box_np(polys)
keep_inds = self.filter_border(gt_bboxes, bound_h, bound_w)
gt_bboxes = gt_bboxes[keep_inds, :]
labels = labels[keep_inds]
if len(gt_bboxes) == 0:
return None
results['gt_bboxes'] = gt_bboxes
results['gt_labels'] = labels
return results
images_folder = "/home/tugg/Documents/RealScores/Realworld_Test"
resize = 1.0
model = init_detector(config_file, checkpoint_file, device='cuda:0')
images = os.listdir(images_folder)
os.makedirs(os.path.join(images_folder, model_name), exist_ok=True)
for img in images:
if os.path.isdir(os.path.join(images_folder, img)):
continue
img_loaded = cv2.imread(os.path.join(images_folder, img))
img_loaded = cv2.resize(
img_loaded,
(int(img_loaded.shape[1] * resize), int(img_loaded.shape[0] * resize)),
interpolation=cv2.INTER_AREA)
result = inference_detector(model, img_loaded)
det_boxes = rotated_box_to_poly_np(result[1][0][0].cpu().numpy())
det_labels = result[1][0][1].cpu().numpy()
img_det = imshow_det_bboxes(img_loaded,
det_boxes,
det_labels.astype(int) + 1,
class_names=CLASSES,
show=False,
show_label=False,
rotated=True)
cv2.imwrite(os.path.join(images_folder, model_name, img), img_det)