-
Notifications
You must be signed in to change notification settings - Fork 3k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Initial version of FBRS interactive segmentation #2094
Merged
Merged
Changes from all commits
Commits
Show all changes
14 commits
Select commit
Hold shift + click to select a range
25df34b
Initial version of FBRS interactive segmentation
dc29228
Add min_pos_points for dextr
ddc5e39
Fix fbrs serverless function.
490413a
Fix codacy issues.
ab575de
Merge remote-tracking branch 'origin/develop' into nm/fbrs_interactiv…
abe049f
Merge remote-tracking branch 'origin/develop' into nm/fbrs_interactiv…
6e95d08
Minor changes
1144af6
Merge remote-tracking branch 'origin/develop' into nm/fbrs_interactiv…
8202c8e
Merge remote-tracking branch 'origin/develop' into nm/fbrs_interactiv…
ef59eda
Fix codacy issues.
e8b6e99
Fix typo
7acd970
Update CHANGELOG
a668db5
Add license header
4b9ba4d
Fix comments in yaml
File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| metadata: | ||
| name: pth.saic-vul.fbrs | ||
| namespace: cvat | ||
| annotations: | ||
| name: f-BRS | ||
| type: interactor | ||
| spec: | ||
| framework: pytorch | ||
| min_pos_points: 1 | ||
|
|
||
| spec: | ||
| description: f-BRS interactive segmentation | ||
| runtime: "python:3.6" | ||
| handler: main:handler | ||
| eventTimeout: 30s | ||
| env: | ||
| - name: PYTHONPATH | ||
| value: /opt/nuclio/fbrs | ||
|
|
||
| build: | ||
| image: cvat/pth.saic-vul.fbrs | ||
| baseImage: python:3.6.11 | ||
|
|
||
| directives: | ||
| preCopy: | ||
| - kind: WORKDIR | ||
| value: /opt/nuclio | ||
| - kind: RUN | ||
| value: git clone https://github.com/saic-vul/fbrs_interactive_segmentation.git fbrs | ||
| - kind: WORKDIR | ||
| value: /opt/nuclio/fbrs | ||
| - kind: ENV | ||
| value: fileid=1Z9dQtpWVTobEdmUBntpUU0pJl-pEXUwR | ||
| - kind: ENV | ||
| value: filename=resnet101_dh256_sbd.pth | ||
| - kind: RUN | ||
| value: curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" | ||
| - kind: RUN | ||
| value: curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename} | ||
| - kind: RUN | ||
| value: apt update && apt install -y libgl1-mesa-glx | ||
| - kind: RUN | ||
| value: pip3 install -r requirements.txt | ||
| - kind: WORKDIR | ||
| value: /opt/nuclio | ||
|
|
||
| triggers: | ||
| myHttpTrigger: | ||
| maxWorkers: 2 | ||
| kind: "http" | ||
| workerAvailabilityTimeoutMilliseconds: 10000 | ||
| attributes: | ||
| maxRequestBodySize: 33554432 # 32MB | ||
|
|
||
| platform: | ||
| attributes: | ||
| restartPolicy: | ||
| name: always | ||
| maximumRetryCount: 3 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,33 @@ | ||
| # Copyright (C) 2020 Intel Corporation | ||
| # | ||
| # SPDX-License-Identifier: MIT | ||
|
|
||
| import json | ||
| import base64 | ||
| from PIL import Image | ||
| import io | ||
| from model_handler import ModelHandler | ||
|
|
||
| def init_context(context): | ||
| context.logger.info("Init context... 0%") | ||
|
|
||
| model = ModelHandler() | ||
| setattr(context.user_data, 'model', model) | ||
|
|
||
| context.logger.info("Init context...100%") | ||
|
|
||
| def handler(context, event): | ||
| context.logger.info("call handler") | ||
| data = event.body | ||
| pos_points = data["points"] | ||
| neg_points = [] | ||
| threshold = data.get("threshold", 0.5) | ||
| buf = io.BytesIO(base64.b64decode(data["image"].encode('utf-8'))) | ||
| image = Image.open(buf) | ||
|
|
||
| polygon = context.user_data.model.handle(image, pos_points, | ||
| neg_points, threshold) | ||
| return context.Response(body=json.dumps(polygon), | ||
| headers={}, | ||
| content_type='application/json', | ||
| status_code=200) | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| # Copyright (C) 2020 Intel Corporation | ||
| # | ||
| # SPDX-License-Identifier: MIT | ||
|
|
||
| import torch | ||
| import numpy as np | ||
| from torchvision import transforms | ||
| import cv2 | ||
| import os | ||
|
|
||
| from isegm.inference.predictors import get_predictor | ||
| from isegm.inference.utils import load_deeplab_is_model, load_hrnet_is_model | ||
| from isegm.inference.clicker import Clicker, Click | ||
|
|
||
| def convert_mask_to_polygon(mask): | ||
| mask = np.array(mask, dtype=np.uint8) | ||
| cv2.normalize(mask, mask, 0, 255, cv2.NORM_MINMAX) | ||
| contours = None | ||
| if int(cv2.__version__.split('.')[0]) > 3: | ||
| contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[0] | ||
| else: | ||
| contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[1] | ||
|
|
||
| contours = max(contours, key=lambda arr: arr.size) | ||
| if contours.shape.count(1): | ||
| contours = np.squeeze(contours) | ||
| if contours.size < 3 * 2: | ||
| raise Exception('Less then three point have been detected. Can not build a polygon.') | ||
|
|
||
| polygon = [] | ||
| for point in contours: | ||
| polygon.append([int(point[0]), int(point[1])]) | ||
|
|
||
| return polygon | ||
|
|
||
| class ModelHandler: | ||
| def __init__(self): | ||
| torch.backends.cudnn.deterministic = True | ||
| base_dir = os.environ.get("MODEL_PATH", "/opt/nuclio/fbrs") | ||
| model_path = os.path.join(base_dir, "resnet101_dh256_sbd.pth") | ||
| state_dict = torch.load(model_path, map_location='cpu') | ||
|
|
||
| self.net = None | ||
| backbone = 'auto' | ||
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' | ||
| for k in state_dict.keys(): | ||
| if 'feature_extractor.stage2.0.branches' in k: | ||
| self.net = load_hrnet_is_model(state_dict, self.device, backbone) | ||
| break | ||
|
|
||
| if self.net is None: | ||
| self.net = load_deeplab_is_model(state_dict, self.device, backbone) | ||
| self.net.to(self.device) | ||
|
|
||
| def handle(self, image, pos_points, neg_points, threshold): | ||
| input_transform = transforms.Compose([ | ||
| transforms.ToTensor(), | ||
| transforms.Normalize([.485, .456, .406], [.229, .224, .225]) | ||
| ]) | ||
|
|
||
| image_nd = input_transform(image).to(self.device) | ||
|
|
||
| clicker = Clicker() | ||
| for x, y in pos_points: | ||
| click = Click(is_positive=True, coords=(y, x)) | ||
| clicker.add_click(click) | ||
|
|
||
| for x, y in neg_points: | ||
| click = Click(is_positive=False, coords=(y, x)) | ||
| clicker.add_click(click) | ||
|
|
||
| predictor_params = { | ||
| 'brs_mode': 'f-BRS-B', | ||
| 'brs_opt_func_params': {'min_iou_diff': 0.001}, | ||
| 'lbfgs_params': {'maxfun': 20}, | ||
| 'predictor_params': {'max_size': 800, 'net_clicks_limit': 8}, | ||
| 'prob_thresh': threshold, | ||
| 'zoom_in_params': {'expansion_ratio': 1.4, 'skip_clicks': 1, 'target_size': 480}} | ||
| predictor = get_predictor(self.net, device=self.device, | ||
| **predictor_params) | ||
| predictor.set_input_image(image_nd) | ||
|
|
||
| object_prob = predictor.get_prediction(clicker) | ||
| if self.device == 'cuda': | ||
| torch.cuda.empty_cache() | ||
| object_mask = object_prob > threshold | ||
| polygon = convert_mask_to_polygon(object_mask) | ||
|
|
||
| return polygon | ||
|
|
||
|
|
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
will the
neg_pointsin the next implementation?