def main(_):
args = default_argument_parser().parse_args()
test_dir = args.test_data_dir
save_dir = args.save_data_dir
wmlu.create_empty_dir(save_dir, remove_if_exists=True)
files = glob.glob(os.path.join(test_dir, "*.jpg"))
m = predm.PredictModel()
m.restoreVariables()
m.remove_batch()
def id_to_text(id):
return m.trainer.category_index[id]
for file in files:
img = wmli.imread(file)
img = np.expand_dims(img, axis=0)
m.predictImages(img)
save_path = os.path.join(save_dir, os.path.basename(file))
xml_path = wmlu.change_suffix(save_path, "xml")
shutil.copy(file, save_path)
labels = [id_to_text(id) for id in m.res_data[RD_LABELS]]
pvt.writeVOCXml(xml_path, m.res_data[RD_BOXES], labels)
if RD_FULL_SIZE_MASKS in m.res_data:
annotations = lmt.trans_odresult_to_annotations_list(m.res_data)
json_path = wmlu.change_suffix(save_path, "json")
lmt.save_labelme_datav1(json_path,
file,
img,
annotations,
label_to_text=id_to_text)
img_save_path = wmlu.change_suffix(xml_path, "jpg")
wmli.imwrite(img_save_path, m.res_data[RD_RESULT_IMAGE])
def trans_one_file(xml_file,save_dir,labels,img_suffix):
img_file = wmlu.change_suffix(xml_file,img_suffix)
if not osp.exists(img_file):
print(f"Find {img_file} faild.")
return
shape, bboxes, labels_names, difficult, truncated,probs = read_voc_xml(xml_file,absolute_coord=True)
_bboxes = []
_labels_name = []
remove_nr = 0
remove_labels = []
bboxes2remove = []
for i,l in enumerate(labels_names):
if l in labels:
remove_nr += 1
bboxes2remove.append(bboxes[i])
remove_labels.append(l)
continue
_bboxes.append(bboxes[i])
_labels_name.append(l)
if remove_nr==0:
wmlu.try_link(img_file, save_dir)
shutil.copy(xml_file,save_dir)
else:
print(f"{wmlu.base_name(xml_file)} remove {remove_nr} labels, labels is {remove_labels}")
img_save_path = osp.join(save_dir,osp.basename(img_file))
xml_save_path = osp.join(save_dir,osp.basename(xml_file))
img = wmli.imread(img_file)
img = wmli.remove_boxes_of_img(img,np.array(bboxes2remove).astype(np.int32))
wmli.imwrite(img_save_path,img)
write_voc_xml(xml_save_path,img_save_path,shape,_bboxes,_labels_name,is_relative_coordinate=False)
def writeVOCXmlByImg(img,
img_save_path,
bboxes,
labels,
difficult=None,
truncated=None,
probs=None,
is_relative_coordinate=True):
if isinstance(bboxes, np.ndarray):
bboxes = bboxes.tolist()
if isinstance(labels, np.ndarray):
labels = labels.tolist()
if isinstance(difficult, np.ndarray):
difficult = difficult.tolist()
if isinstance(truncated, np.ndarray):
truncated = truncated.tolist()
img_shape = img.shape
dir_path = os.path.dirname(img_save_path)
base_name = os.path.basename(img_save_path)
base_name = base_name[:-4] + ".xml"
save_path = os.path.join(dir_path, base_name)
wmli.imwrite(img_save_path, img)
write_voc_xml(save_path,
img_save_path,
img_shape,
bboxes,
labels,
difficult,
truncated,
probs=probs,
is_relative_coordinate=is_relative_coordinate)
def trans_data(data_dir,save_dir):
global name_to_id_dict
wmlu.show_dict(name_to_id_dict)
wmlu.create_empty_dir(save_dir,remove_if_exists=False)
def name_to_id(x):
return name_to_id_dict[x]
ignored_labels = ["construction--barrier--ambiguous","construction--barrier--separator"]
data = LabelMeData(label_text2id=name_to_id, shuffle=False)
data.read_data(data_dir)
for i,x in enumerate(data.get_items()):
full_path, img_info, category_ids, category_names, boxes, binary_mask, area, is_crowd, num_annotations_skipped = x
if len(category_ids) == 0:
print(f"Skip {full_path}")
continue
new_mask = odm.dense_mask_to_sparse_mask(binary_mask,category_ids,default_label=255)
#r_base_name = wmlu.base_name(full_path)
r_base_name = f"IMG_{i+1:05d}"
base_name = r_base_name+".png"
save_path = os.path.join(save_dir,base_name)
if resize_size is not None:
new_mask = wmli.resize_img(new_mask,resize_size,keep_aspect_ratio=True,interpolation=cv2.INTER_NEAREST)
img = wmli.imread(full_path)
img = wmli.resize_img(img,resize_size,keep_aspect_ratio=True)
img_save_path = os.path.join(save_dir,r_base_name+".jpg")
wmli.imwrite(img_save_path,img)
new_mask = new_mask.astype(np.uint8)
if os.path.exists(save_path):
print(f"WARNING: File {save_path} exists.")
cv2.imwrite(save_path,new_mask)
sys.stdout.write(f"\r{i}")
def main(_):
is_training = False
args = default_argument_parser().parse_args()
cfg = setup(args)
data_loader = DataLoader(cfg=cfg, is_training=is_training)
data_args = DATASETS_REGISTRY[cfg.DATASETS.TEST]
data, num_classes = data_loader.load_data(*data_args, batch_size=1, is_training=False)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
cfg.MODEL.SSD.NUM_CLASSES = num_classes
cfg.MODEL.RETINANET.NUM_CLASSES = num_classes
cfg.MODEL.CENTERNET.NUM_CLASSES = num_classes
cfg.MODEL.YOLACT.NUM_CLASSES = num_classes
cfg.MODEL.FCOS.NUM_CLASSES = num_classes
cfg.MODEL.NUM_CLASSES = num_classes
cfg.DATASETS.NUM_CLASSES = num_classes
cfg.freeze()
config.set_global_cfg(cfg)
model = PredictModel(cfg=cfg,is_remove_batch=True)
model.restoreVariables()
data_path = args.test_data_dir
if os.path.isdir(data_path):
files = wmlu.recurse_get_filepath_in_dir(data_path,suffix=".jpg")
else:
files = [data_path]
save_path = args.save_data_dir
wmlu.create_empty_dir(save_path,remove_if_exists=True)
for file in files:
img = wmli.imread(file)
imgs = np.expand_dims(img,axis=0)
res = model.predictImages(imgs)
if RD_MASKS in res:
r_img = odv.draw_bboxes_and_mask(img,res[RD_LABELS],res[RD_PROBABILITY],res[RD_BOXES],
res[RD_MASKS],
show_text=True)
else:
r_img = odv.bboxes_draw_on_imgv2(img,res[RD_LABELS],res[RD_PROBABILITY],res[RD_BOXES],
text_fn=text_fn,
show_text=True)
name = wmlu.base_name(file)
img_save_path = os.path.join(save_path,name+".png")
wmli.imwrite(img_save_path,r_img)
if res[l, 0] < res[r, 0] or res[l, 2] < 0.1 or res[r, 2] < 0.1:
is_good = False
break
head_bbox = self.get_head_pos(mpii_kps)
if is_good and head_bbox is not None and self.kps_in_bbox(
coco_kps[self.coco_idxs], head_bbox):
res[self.coco_idxs] = coco_kps[self.coco_idxs]
return res
if __name__ == "__main__":
file_path = '/home/wj/ai/mldata1/crowd_pose/CrowdPose/crowdpose_train.json'
images_dir = '/home/wj/ai/mldata1/crowd_pose/images'
save_dir = '/home/wj/ai/mldata1/crowd_pose/tmp/vis'
wmlu.create_empty_dir(save_dir, remove_if_exists=False)
datas = read_crowd_pose(file_path)
do_vis = True
for data in datas:
image_name, kps, bbox = data
image = osp.join(images_dir, image_name)
img = wmli.imread(image)
img = odv.draw_keypoints(img, kps, no_line=True)
t_bboxes = np.array([bbox])
t_bboxes = odb.npchangexyorder(t_bboxes)
img = odv.draw_bboxes(img,
bboxes=t_bboxes,
is_relative_coordinate=False)
save_path = osp.join(save_dir, image_name)
wmli.imwrite(save_path, img)
def view_data(name, save_dir, nr=20):
print(f"View {name}")
raw_name = name
names = name.split("--")
if names[0] == "void" or "ambiguous" in raw_name:
return
if "road" not in raw_name:
return
for x in names:
save_dir = os.path.join(save_dir, x)
wmlu.create_empty_dir(save_dir, remove_if_exists=False)
allowed_names = [raw_name]
NAME2ID = {}
ID2NAME = {}
def name_to_id(x):
global lid
if x in NAME2ID:
return NAME2ID[x]
else:
NAME2ID[x] = lid
ID2NAME[lid] = x
lid += 1
return NAME2ID[x]
data = MapillaryVistasData(label_text2id=name_to_id,
shuffle=False,
ignored_labels=None,
label_map=None,
allowed_labels_fn=allowed_names)
data.read_data(wmlu.home_dir("ai/mldata/mapillary_vistas"))
i = 0
for x in data.get_items():
full_path, img_info, category_ids, category_names, boxes, binary_mask, area, is_crowd, num_annotations_skipped = x
img = wmli.imread(full_path)
def text_fn(classes, scores):
return f"{ID2NAME[classes]}"
if len(category_ids) == 0:
continue
wmlu.show_dict(NAME2ID)
odv.draw_bboxes_and_maskv2(img=img,
classes=category_ids,
scores=None,
bboxes=boxes,
masks=binary_mask,
color_fn=None,
text_fn=text_fn,
thickness=4,
show_text=True,
fontScale=0.8)
base_name = os.path.basename(full_path)
save_path = os.path.join(save_dir, base_name)
wmli.imwrite(save_path, img)
i += 1
if i >= nr:
break
def main(_):
is_training = False
args = default_argument_parser().parse_args()
cfg = setup(args)
data_loader = DataLoader(cfg=cfg, is_training=is_training)
data_args = DATASETS_REGISTRY[cfg.DATASETS.TEST]
data, num_classes = data_loader.load_data(*data_args,
batch_size=1,
is_training=False)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
cfg.MODEL.SSD.NUM_CLASSES = num_classes
cfg.MODEL.RETINANET.NUM_CLASSES = num_classes
cfg.MODEL.CENTERNET.NUM_CLASSES = num_classes
cfg.MODEL.YOLACT.NUM_CLASSES = num_classes
cfg.MODEL.FCOS.NUM_CLASSES = num_classes
cfg.DATASETS.NUM_CLASSES = num_classes
cfg.freeze()
config.set_global_cfg(cfg)
model = PredictModel(cfg=cfg, is_remove_batch=True)
model.restoreVariables()
save_path = args.save_data_dir
wmlu.create_empty_dir(save_path, remove_if_exists=True)
metrics = COCOEvaluation(num_classes=90)
items = eval_dataset()
for data in items:
full_path, shape, gt_labels, category_names, gt_boxes, binary_masks, area, is_crowd, num_annotations_skipped = data
img = wmli.imread(full_path)
imgs = np.expand_dims(img, axis=0)
res = model.predictImages(imgs)
if RD_MASKS in res:
r_img = odv.draw_bboxes_and_mask(img,
res[RD_LABELS],
res[RD_PROBABILITY],
res[RD_BOXES],
res[RD_MASKS],
show_text=True)
else:
r_img = odv.bboxes_draw_on_imgv2(img,
res[RD_LABELS],
res[RD_PROBABILITY],
res[RD_BOXES],
text_fn=text_fn,
show_text=True)
kwargs = {}
kwargs['gtboxes'] = gt_boxes
kwargs['gtlabels'] = gt_labels
kwargs['boxes'] = res[RD_BOXES]
kwargs['labels'] = res[RD_LABELS]
kwargs['probability'] = res[RD_PROBABILITY]
kwargs['img_size'] = shape
metrics(**kwargs)
if model.step % 100 == 0:
metrics.show()
name = wmlu.base_name(full_path)
img_save_path = os.path.join(save_path, name + ".png")
wmli.imwrite(img_save_path, r_img)
metrics.show()
dataset.read_data("/home/wj/ai/mldata/boesemantic")
save_dir = wmlu.home_dir("ai/tmp/boe_images2")
wmlu.create_empty_dir(save_dir,remove_if_exists=False)
color_map = fill_colormap_and_names("/home/wj/ai/mldata/mapillary_vistas/config_v2.0.json")
def text_fn(l):
if l in ID_TO_READABLE_NAME:
return ID_TO_READABLE_NAME[l]
else:
return "NA"
def color_fn(l):
return color_map[l*3:l*3+3]
legend_img = draw_legend(list(ID_TO_NAME.keys()),text_fn,img_size=(2448,300),color_fn=color_fn)
for ifn,img,mask in dataset.get_items():
base_name = wmlu.base_name(ifn)
wmlu.safe_copy(ifn,save_dir)
rgb_mask = convert_semantic_to_rgb(mask,color_map,True)
if rgb_mask.shape[0] != legend_img.shape[0]:
legend_img = wmli.resize_img(legend_img,(legend_img.shape[1],rgb_mask.shape[0]))
rgb_mask = np.concatenate([rgb_mask,legend_img],axis=1)
mask_path = os.path.join(save_dir,base_name+".png")
wmli.imwrite(mask_path,rgb_mask)
mask_image = draw_semantic_on_image(img,mask,color_map,ignored_label=255)
mask_image = np.concatenate([mask_image,legend_img],axis=1)
mask_image_path = os.path.join(save_dir,base_name+"1.png")
wmli.imwrite(mask_image_path,mask_image)