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Use MONAI Label Server directly for cvat annotation plugin (#1776)
* Use MONAI Label Server directly for cvat annotation plugin Signed-off-by: Sachidanand Alle <[email protected]> * Update Readme Signed-off-by: Sachidanand Alle <[email protected]> --------- Signed-off-by: Sachidanand Alle <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,144 @@ | ||
| # Copyright (c) MONAI Consortium | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| import base64 | ||
| import io | ||
| import json | ||
| import logging | ||
| import os | ||
| import tempfile | ||
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| import numpy as np | ||
| from PIL import Image | ||
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| from monailabel.client import MONAILabelClient | ||
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| logging.basicConfig( | ||
| level=logging.INFO, | ||
| format="[%(asctime)s] [%(process)s] [%(threadName)s] [%(levelname)s] (%(name)s:%(lineno)d) - %(message)s", | ||
| datefmt="%Y-%m-%d %H:%M:%S", | ||
| ) | ||
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| def init_context(context): | ||
| context.logger.info("Init context... 0%") | ||
| server = os.environ.get("MONAI_LABEL_SERVER", "http://0.0.0.0:8000") | ||
| model = os.environ.get("MONAI_LABEL_MODEL", "tooltracking") | ||
| client = MONAILabelClient(server) | ||
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| info = client.info() | ||
| model_info = info["models"][model] if info and info["models"] else None | ||
| context.logger.info(f"Monai Label Info: {model_info}") | ||
| assert model_info | ||
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| context.user_data.model = model | ||
| context.user_data.model_handler = client | ||
| context.logger.info("Init context...100%") | ||
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| def handler(context, event): | ||
| model: str = context.user_data.model | ||
| client: MONAILabelClient = context.user_data.model_handler | ||
| context.logger.info(f"Run model: {model}") | ||
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| data = event.body | ||
| image = Image.open(io.BytesIO(base64.b64decode(data["image"]))) | ||
| context.logger.info(f"Image: {image.size}") | ||
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| image_file = tempfile.NamedTemporaryFile(suffix=".jpg").name | ||
| image.save(image_file) | ||
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| params = {"output": "json"} | ||
| _, output_json = client.infer(model=model, image_id="", file=image_file, params=params) | ||
| if isinstance(output_json, str) or isinstance(output_json, bytes): | ||
| output_json = json.loads(output_json) | ||
|
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| results = [] | ||
| prediction = output_json.get("prediction") | ||
| if prediction: | ||
| context.logger.info(f"(Classification) Prediction: {prediction}") | ||
| # CVAT Limitation:: tag is not yet supported https://github.com/opencv/cvat/issues/4212 | ||
| # CVAT Limitation:: select highest score and create bbox to represent as tag | ||
| e = None | ||
| for element in prediction: | ||
| if element["score"] > 0: | ||
| e = element if e is None or element["score"] > e["score"] else e | ||
| context.logger.info(f"New Max Element: {e}") | ||
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| context.logger.info(f"Final Element with Max Score: {e}") | ||
| if e: | ||
| results.append( | ||
| { | ||
| "label": e["label"], | ||
| "confidence": e["score"], | ||
| "type": "rectangle", | ||
| "points": [0, 0, image.size[0] - 1, image.size[1] - 1], | ||
| } | ||
| ) | ||
| context.logger.info(f"(Classification) Results: {results}") | ||
| else: | ||
| annotations = output_json.get("annotations") | ||
| for a in annotations: | ||
| annotation = a.get("annotation", {}) | ||
| if not annotation: | ||
| continue | ||
|
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| elements = annotation.get("elements", []) | ||
| for element in elements: | ||
| label = element["label"] | ||
| contours = element["contours"] | ||
| for contour in contours: | ||
| points = np.array(contour, int) | ||
| results.append( | ||
| { | ||
| "label": label, | ||
| "points": points.flatten().tolist(), | ||
| "type": "polygon", | ||
| } | ||
| ) | ||
|
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| return context.Response( | ||
| body=json.dumps(results), | ||
| headers={}, | ||
| content_type="application/json", | ||
| status_code=200, | ||
| ) | ||
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| if __name__ == "__main__": | ||
| import logging | ||
| from argparse import Namespace | ||
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| logging.basicConfig( | ||
| level=logging.INFO, | ||
| format="[%(asctime)s] [%(process)s] [%(threadName)s] [%(levelname)s] (%(name)s:%(lineno)d) - %(message)s", | ||
| datefmt="%Y-%m-%d %H:%M:%S", | ||
| ) | ||
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| def print_all(*args, **kwargs): | ||
| return {"args": args, **kwargs} | ||
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| with open("/home/sachi/Datasets/endo/frame001.jpg", "rb") as fp: | ||
| image = base64.b64encode(fp.read()) | ||
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| event = {"body": {"image": image}} | ||
| event = Namespace(**event) | ||
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| context = Namespace( | ||
| **{ | ||
| "logger": logging.getLogger(__name__), | ||
| "user_data": Namespace(**{"model": None, "model_handler": None}), | ||
| "Response": print_all, | ||
| } | ||
| ) | ||
| init_context(context) | ||
| response = handler(context, event) | ||
| logging.info(response) |
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