add parse mask to fastlabel point

Author: takahiro-tamenishi

README.md

@@ -33,6 +33,7 @@
   - [YOLO To FastLabel](#yolo-to-fastlabel)
   - [Pascal VOC To FastLabel](#pascal-voc-to-fastlabel)
   - [labelme To FastLabel](#labelme-to-fastlabel)
+  - [Mask To FastLabel Segmentation Points](#mask-to-fastlabel-segmentation-points)
 - [Model](#model)
 - [API Docs](#api-docs)
 
@@ -589,7 +590,6 @@ task = client.create_multi_image_classification_task(
 
 ##### Limitation
 
-
 - You can upload up to a size of 20 MB.
 - You can upload up to a total size of 2 GB.
 - You can upload up to 6 files in total.
@@ -2667,7 +2667,7 @@ dataset_object = client.create_dataset_object(
     tags=["dog"], # max 5 tags per dataset object.
     licenses=["MIT", "my-license"],  # max 10 licenses per dataset object
     annotations=[
-        { 
+        {
             "type": "classification",
             "value": "classification",
             "points": [],
@@ -2824,6 +2824,7 @@ client.download_dataset_objects(
 ```
 
 ### Update Dataset Object
+
 ```python
 dataset_object = client.update_dataset_object(
     dataset_id="YOUR_DATASET_ID",
@@ -3210,6 +3211,16 @@ for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"),
 
 > Please check const.COLOR_PALLETE for index colors.
 
+### Mask To FastLabel Segmentation Points
+
+Convert mask image to FastLabel's segmentation coordinate format.
+
+```python
+points = client.mask_to_fastlabel_segmentation_points(
+    mask_image = binary_image_path (or binary_image_array)
+)
+```
+
 ## Model
 
 ### Get training jobs

fastlabel/__init__.py

@@ -70,14 +70,14 @@ def find_image_classification_task(self, task_id: str) -> dict:
         """
         endpoint = "tasks/image/classification/" + task_id
         return self.api.get_request(endpoint)
-    
+
     def find_multi_image_classification_task(self, task_id: str) -> dict:
         """
         Find a single multi image classification task.
         """
         endpoint = "tasks/multi-image/classification/" + task_id
         return self.api.get_request(endpoint)
-    
+
     def find_multi_image_classification_task_by_name(
         self, project: str, task_name: str
     ) -> dict:
@@ -432,9 +432,9 @@ def get_image_classification_tasks(
         if limit:
             params["limit"] = limit
         return self.api.get_request(endpoint, params=params)
-    
+
     def get_multi_image_classification_tasks(
-            self,
+        self,
         project: str,
         status: str = None,
         external_status: str = None,
@@ -1197,7 +1197,7 @@ def create_integrated_image_classification_task(
         self.__fill_assign_users(payload, **kwargs)
 
         return self.api.post_request(endpoint, payload=payload)
-    
+
     def create_multi_image_classification_task(
         self,
         project: str,
@@ -2135,7 +2135,7 @@ def update_image_classification_task(
         self.__fill_assign_users(payload, **kwargs)
 
         return self.api.put_request(endpoint, payload=payload)
-    
+
     def update_multi_image_classification_task(
         self,
         task_id: str,
@@ -4300,7 +4300,7 @@ def download_dataset_objects(
                         "annotations": obj["annotations"],
                         "customMetadata": obj["customMetadata"],
                         "tags": obj["tags"],
-                        "object_type": obj["type"]
+                        "object_type": obj["type"],
                     }
                     for obj in objects
                 ]
@@ -4718,6 +4718,14 @@ def get_histories(
 
         return self.api.get_request(endpoint, params=params)
 
+    def mask_to_fastlabel_segmentation_points(
+        self, mask_image: Union[str, np.ndarray]
+    ) -> List[List[List[int]]]:
+        return [
+            [converters.get_pixel_coordinates(p) for p in point]
+            for point in utils.mask_to_segmentation(mask_image)
+        ]
+
 
 def delete_extra_annotations_parameter(annotations: list) -> list:
     for annotation in annotations:

fastlabel/api.py

@@ -7,7 +7,9 @@
 
 
 class Api:
-    base_url = "https://api.fastlabel.ai/v1/"
+    # base_url = "https://api.fastlabel.ai/v1/"
+    base_url = "http://localhost:4000/v1/"
+
     access_token = None
 
     def __init__(self):

fastlabel/converters.py

@@ -8,7 +8,7 @@
 from operator import itemgetter
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import List, Dict, Optional
+from typing import Dict, List, Optional
 
 import cv2
 import geojson
@@ -239,7 +239,7 @@ def __to_coco_annotation(data: dict) -> dict:
         image_id,
         annotation_type,
         annotation_attributes,
-        rotation
+        rotation,
     )
 
 
@@ -254,7 +254,11 @@ def __get_coco_category_by_name(categories: list, name: str) -> Optional[dict]:
 
 def __get_coco_annotation_keypoints(keypoints: list, category_keypoints: list) -> list:
     coco_annotation_keypoints = []
-    keypoint_values = {keypoint["key"]: keypoint["value"] for keypoint in keypoints if keypoint["value"]}
+    keypoint_values = {
+        keypoint["key"]: keypoint["value"]
+        for keypoint in keypoints
+        if keypoint["value"]
+    }
     for category_key in category_keypoints:
         value = keypoint_values.get(category_key, [0, 0, 0])
         # Adjust fastlabel data definition to coco format
@@ -271,20 +275,22 @@ def __get_coco_annotation(
     image_id: str,
     annotation_type: str,
     annotation_attributes: Dict[str, AttributeValue],
-    rotation: int
+    rotation: int,
 ) -> dict:
     annotation = {}
     annotation["num_keypoints"] = len(keypoints) if keypoints else 0
     annotation["keypoints"] = (
-        __get_coco_annotation_keypoints(keypoints, category["keypoints"]) if keypoints else []
+        __get_coco_annotation_keypoints(keypoints, category["keypoints"])
+        if keypoints
+        else []
     )
     annotation["segmentation"] = __to_coco_segmentation(annotation_type, points)
     annotation["iscrowd"] = 0
     annotation["area"] = __to_area(annotation_type, points)
     annotation["image_id"] = image_id
     annotation["bbox"] = (
-        __get_coco_bbox(points, rotation) 
-        if annotation_type == AnnotationType.bbox 
+        __get_coco_bbox(points, rotation)
+        if annotation_type == AnnotationType.bbox
         else __to_bbox(annotation_type, points)
     )
     annotation["rotation"] = rotation
@@ -329,6 +335,7 @@ def __get_rotated_rectangle_coordinates(
 
     return rotated_corners
 
+
 def __get_coco_bbox(
     points: list,
     rotation: int,
@@ -788,12 +795,12 @@ def to_pixel_coordinates(tasks: list) -> list:
                 for region in annotation["points"]:
                     new_region = []
                     for points in region:
-                        new_points = __get_pixel_coordinates(points)
+                        new_points = get_pixel_coordinates(points)
                         new_region.append(new_points)
                     new_regions.append(new_region)
                 annotation["points"] = new_regions
             elif annotation["type"] == AnnotationType.polygon.value:
-                new_points = __get_pixel_coordinates(annotation["points"])
+                new_points = get_pixel_coordinates(annotation["points"])
                 annotation["points"] = new_points
             elif annotation["type"] == AnnotationType.bbox.value:
                 points = annotation["points"]
@@ -871,14 +878,15 @@ def __remove_duplicated_coordinates(points: List[int]) -> List[int]:
     return new_points
 
 
-def __get_pixel_coordinates(points: List[int or float]) -> List[int]:
+def get_pixel_coordinates(points: List[int | float]) -> List[int]:
     """
     Remove diagonal coordinates and return pixel outline coordinates.
     """
     if len(points) == 0:
         return []
 
     new_points = []
+    print(points)
     new_points.append(int(points[0]))
     new_points.append(int(points[1]))
     for i in range(int(len(points) / 2)):
@@ -1209,4 +1217,3 @@ def _get_coco_annotation_attributes(annotation: dict) -> Dict[str, AttributeValu
     for attribute in attributes:
         coco_attributes[attribute["key"]] = attribute["value"]
     return coco_attributes
-

fastlabel/utils.py renamed to fastlabel/utils/__init__.py

@@ -10,6 +10,8 @@
 
 from fastlabel import const
 
+from .mask_image_util import mask_to_segmentation  # noqa: F401
+
 
 def base64_encode(file_path: str) -> str:
     with open(file_path, "rb") as f: