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from collections import OrderedDict
import torch
import numpy as np
from concern.config import Configurable, State
from .data_process import DataProcess
import cv2
class MakeICDARData(DataProcess):
shrink_ratio = State(default=0.4)
def __init__(self, debug=False, cmd={}, **kwargs):
self.load_all(**kwargs)
self.debug = debug
if 'debug' in cmd:
self.debug = cmd['debug']
def process(self, data):
polygons = []
ignore_tags = []
annotations = data['polys']
for annotation in annotations:
polygons.append(np.array(annotation['points']))
# polygons.append(annotation['points'])
ignore_tags.append(annotation['ignore'])
ignore_tags = np.array(ignore_tags, dtype=np.uint8)
filename = data.get('filename', data['data_id'])
if self.debug:
self.draw_polygons(data['image'], polygons, ignore_tags)
shape = np.array(data['shape'])
return OrderedDict(image=data['image'],
polygons=polygons,
ignore_tags=ignore_tags,
shape=shape,
filename=filename,
is_training=data['is_training'])
def draw_polygons(self, image, polygons, ignore_tags):
for i in range(len(polygons)):
polygon = polygons[i].reshape(-1, 2).astype(np.int32)
ignore = ignore_tags[i]
if ignore:
color = (255, 0, 0) # depict ignorable polygons in blue
else:
color = (0, 0, 255) # depict polygons in red
cv2.polylines(image, [polygon], True, color, 1)
polylines = staticmethod(draw_polygons)
class ICDARCollectFN(Configurable):
def __init__(self, *args, **kwargs):
pass
def __call__(self, batch):
data_dict = OrderedDict()
for sample in batch:
for k, v in sample.items():
if k not in data_dict:
data_dict[k] = []
if isinstance(v, np.ndarray):
v = torch.from_numpy(v)
data_dict[k].append(v)
data_dict['image'] = torch.stack(data_dict['image'], 0)
return data_dict
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