[WIP] Autopropagate annotations

app/__init__.py

@@ -9,6 +9,7 @@
 from .models import *
 from .authentication import login_manager
 from .util import query_util, color_util
+from .util.autoannotator import Autoannotator
 
 import threading
 import requests
@@ -63,6 +64,15 @@ def create_app():
 if Config.LOAD_IMAGES_ON_START:
     ImageModel.load_images(Config.DATASET_DIRECTORY)
 
+if Config.AUTOANNOTATOR_ENABLED:
+    Autoannotator.start(
+        max_workers=Config.AUTOANNOTATOR_MAX_WORKERS,
+        max_queue_size=Config.AUTOANNOTATOR_QUEUE_SIZE,
+        max_mismatched=Config.AUTOANNOTATOR_MAX_MISMATCHED,
+        diff_threshold=Config.AUTOANNOTATOR_DIFF_THRESHOLD,
+        verbose=Config.AUTOANNOTATOR_VERBOSE,
+        logger=app.logger)
+
 
 @app.route('/', defaults={'path': ''})
 @app.route('/<path:path>')

app/api/annotator.py

@@ -4,6 +4,8 @@
 
 from ..util import query_util
 from ..util import coco_util
+from ..util.autoannotator import Autoannotator
+from ..util import annotation_util
 from ..models import *
 
 
@@ -37,6 +39,10 @@ def post(self):
         current_user.update(preferences=data.get('user', {}))
 
         annotated = False
+
+        if Autoannotator.enabled:
+            autoannotator_ids = list()
+
         # Iterate every category passed in the data
         for category in data.get('categories', []):
             category_id = category.get('id')
@@ -79,6 +85,11 @@ def post(self):
                     segmentation, area, bbox = coco_util.\
                         paperjs_to_coco(width, height, paperjs_object)
 
+                    if Autoannotator.enabled:
+                        if not annotation_util.segmentation_equal(
+                                segmentation, db_annotation.segmentation):
+                            autoannotator_ids.append(annotation_id)
+
                     db_annotation.update(
                         set__segmentation=segmentation,
                         set__area=area,
@@ -89,6 +100,10 @@ def post(self):
                     if area > 0:
                         annotated = True
 
+        if autoannotator_ids:
+            Autoannotator.submit(image_id, autoannotator_ids,
+                                 wait_for_next=True, wait_for_prev=True)
+
         image_model.update(
             set__metadata=image.get('metadata', {}),
             set__annotated=annotated,

app/api/datasets.py

@@ -9,7 +9,7 @@
 from ..util.pagination_util import Pagination
 from ..util import query_util, coco_util
 from ..models import *
-
+import imantics as im
 
 import datetime
 import json
@@ -312,7 +312,7 @@ def post(self, dataset_id):
                 errors.append({'category': category_name,
                                'message': 'Creating category ' + category_name + '.'})
 
-                new_category = CategoryModel(name=category_name, color=color_util.random_color_hex())
+                new_category = CategoryModel(name=category_name, color=im.Color.random().hex)
                 new_category.save()
                 categories_id[category_id] = new_category.id
                 print("Category not found! (Creating new one)", flush=True)
@@ -384,7 +384,7 @@ def post(self, dataset_id):
                 annotation.category_id = category_model_id
                 # annotation.iscrowd = is_crowd
                 annotation.segmentation = segmentation
-                annotation.color = color_util.random_color_hex()
+                annotation.color = im.Color.random().hex
                 annotation.save()
 
                 image_model.update(set__annotated=True)

app/config.py

@@ -21,6 +21,18 @@ class Config:
     INITIALIZE_FROM_FILE = os.getenv("INITIALIZE_FROM_FILE")
     LOAD_IMAGES_ON_START = os.getenv("LOAD_IMAGES_ON_START", False)
 
+    # Autoannotator options
+    AUTOANNOTATOR_ENABLED = os.getenv("AUTOANNOTATOR_ENABLED", False)
+    AUTOANNOTATOR_VERBOSE = os.getenv("AUTOANNOTATOR_VERBOSE", False)
+    AUTOANNOTATOR_MAX_WORKERS = int(
+        os.getenv("AUTOANNOTATOR_MAX_WORKERS", 4))
+    AUTOANNOTATOR_QUEUE_SIZE = int(
+        os.getenv("AUTOANNOTATOR_QUEUE_SIZE", 32))
+    AUTOANNOTATOR_MAX_MISMATCHED = int(
+        os.getenv("AUTOANNOTATOR_MAX_MISMATCHED", 5))
+    AUTOANNOTATOR_DIFF_THRESHOLD = float(
+        os.getenv("AUTOANNOTATOR_DIFF_THRESHOLD", 0.0035))
+
     # User Options
     LOGIN_DISABLED = os.getenv('LOGIN_DISABLED', False)
     ALLOW_REGISTRATION = True

app/models.py

@@ -151,6 +151,9 @@ def copy_annotations(self, annotations):
 
             clone.save(copy=True)
 
+        if not self.annotated:
+            self.update(set__annotated=True)
+
         return annotations.count()
 
     def __call__(self):

app/util/annotation_util.py

@@ -0,0 +1,63 @@
+import cv2
+import numpy as np
+
+
+def extract_cropped_patch(img, mask, bbox):
+    """
+    Creates a copy of img whereever the pixels of mask are non-zero,
+    then crops the copy by the provided bbox
+    """
+    patch = cv2.bitwise_and(img, img, mask=mask)
+    x, y, w, h = bbox
+    return patch[y:y + h, x:x + w]
+
+
+def rect_union(a, b):
+    x = min(a[0], b[0])
+    y = min(a[1], b[1])
+    w = max(a[0] + a[2], b[0] + b[2]) - x
+    h = max(a[1] + a[3], b[1] + b[3]) - y
+    return (x, y, w, h)
+
+
+def bbox_for_contours(contours):
+    bbox = (0, 0, 0, 0)
+    for cnt in contours:
+        bbox = rect_union(bbox, cv2.boundingRect(cnt))
+    return bbox
+
+
+def segmentation_to_contours(segmentation):
+    contours = list()
+    for poly in segmentation:
+        if len(poly) % 2 != 0:
+            raise ValueError(
+                "Each polygon should have even number of elements")
+        polygon = []
+        for i in range(0, len(poly), 2):
+            polygon.append([poly[i], poly[i + 1]])
+        contours.append(np.array(polygon, dtype=np.int32))
+    return contours
+
+
+def segmentation_equal(seg_a, seg_b):
+    """
+    Compares 2 segmentations for equality
+    """
+    if len(seg_a) != len(seg_b):
+        return False
+
+    for i, _ in enumerate(seg_a):
+        # remove excess points used for closing the loop
+        if len(seg_a[i]) >= 4:
+            if seg_a[i][0] == seg_a[i][-2] and seg_a[i][1] == seg_a[i][-1]:
+                seg_a[i] = seg_a[i][:-2]
+        if len(seg_b[i]) >= 4:
+            if seg_b[i][0] == seg_b[i][-2] and seg_b[i][1] == seg_b[i][-1]:
+                seg_b[i] = seg_b[i][:-2]
+        if len(seg_a[i]) != len(seg_b[i]):
+            return False
+        for j in range(len(seg_a[i])):
+            if seg_a[i][j] != seg_b[i][j]:
+                return False
+    return True