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# Copyright (c) OpenMMLab. All rights reserved.
import io
import json
import logging
import os
from urllib.parse import urlparse
import boto3
from botocore.exceptions import ClientError
from label_studio_ml.model import LabelStudioMLBase
from label_studio_ml.utils import (DATA_UNDEFINED_NAME, get_image_size,
get_single_tag_keys)
from label_studio_tools.core.utils.io import get_data_dir
from mmdet.apis import inference_detector, init_detector
logger = logging.getLogger(__name__)
class MMDetection(LabelStudioMLBase):
"""Object detector based on https://github.com/open-mmlab/mmdetection."""
def __init__(self,
config_file=None,
checkpoint_file=None,
image_dir=None,
labels_file=None,
score_threshold=0.5,
device='cpu',
**kwargs):
super(MMDetection, self).__init__(**kwargs)
config_file = config_file or os.environ['config_file']
checkpoint_file = checkpoint_file or os.environ['checkpoint_file']
self.config_file = config_file
self.checkpoint_file = checkpoint_file
self.labels_file = labels_file
# default Label Studio image upload folder
upload_dir = os.path.join(get_data_dir(), 'media', 'upload')
self.image_dir = image_dir or upload_dir
logger.debug(
f'{self.__class__.__name__} reads images from {self.image_dir}')
if self.labels_file and os.path.exists(self.labels_file):
self.label_map = json_load(self.labels_file)
else:
self.label_map = {}
self.from_name, self.to_name, self.value, self.labels_in_config = get_single_tag_keys( # noqa E501
self.parsed_label_config, 'RectangleLabels', 'Image')
schema = list(self.parsed_label_config.values())[0]
self.labels_in_config = set(self.labels_in_config)
# Collect label maps from `predicted_values="airplane,car"` attribute in <Label> tag # noqa E501
self.labels_attrs = schema.get('labels_attrs')
if self.labels_attrs:
for label_name, label_attrs in self.labels_attrs.items():
for predicted_value in label_attrs.get('predicted_values',
'').split(','):
self.label_map[predicted_value] = label_name
print('Load new model from: ', config_file, checkpoint_file)
self.model = init_detector(config_file, checkpoint_file, device=device)
self.score_thresh = score_threshold
def _get_image_url(self, task):
image_url = task['data'].get(
self.value) or task['data'].get(DATA_UNDEFINED_NAME)
if image_url.startswith('s3://'):
# presign s3 url
r = urlparse(image_url, allow_fragments=False)
bucket_name = r.netloc
key = r.path.lstrip('/')
client = boto3.client('s3')
try:
image_url = client.generate_presigned_url(
ClientMethod='get_object',
Params={
'Bucket': bucket_name,
'Key': key
})
except ClientError as exc:
logger.warning(
f'Can\'t generate presigned URL for {image_url}. Reason: {exc}' # noqa E501
)
return image_url
def predict(self, tasks, **kwargs):
assert len(tasks) == 1
task = tasks[0]
image_url = self._get_image_url(task)
image_path = self.get_local_path(image_url)
model_results = inference_detector(self.model,
image_path).pred_instances
results = []
all_scores = []
img_width, img_height = get_image_size(image_path)
print(f'>>> model_results: {model_results}')
print(f'>>> label_map {self.label_map}')
print(f'>>> self.model.dataset_meta: {self.model.dataset_meta}')
classes = self.model.dataset_meta.get('classes')
print(f'Classes >>> {classes}')
for item in model_results:
print(f'item >>>>> {item}')
bboxes, label, scores = item['bboxes'], item['labels'], item[
'scores']
score = float(scores[-1])
if score < self.score_thresh:
continue
print(f'bboxes >>>>> {bboxes}')
print(f'label >>>>> {label}')
output_label = classes[list(self.label_map.get(label, label))[0]]
print(f'>>> output_label: {output_label}')
if output_label not in self.labels_in_config:
print(output_label + ' label not found in project config.')
continue
for bbox in bboxes:
bbox = list(bbox)
if not bbox:
continue
x, y, xmax, ymax = bbox[:4]
results.append({
'from_name': self.from_name,
'to_name': self.to_name,
'type': 'rectanglelabels',
'value': {
'rectanglelabels': [output_label],
'x': float(x) / img_width * 100,
'y': float(y) / img_height * 100,
'width': (float(xmax) - float(x)) / img_width * 100,
'height': (float(ymax) - float(y)) / img_height * 100
},
'score': score
})
all_scores.append(score)
avg_score = sum(all_scores) / max(len(all_scores), 1)
print(f'>>> RESULTS: {results}')
return [{'result': results, 'score': avg_score}]
def json_load(file, int_keys=False):
with io.open(file, encoding='utf8') as f:
data = json.load(f)
if int_keys:
return {int(k): v for k, v in data.items()}
else:
return data
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