Fix create a bounding box label creationg and test it in data generation

environment.yml

@@ -4,4 +4,5 @@ channels:
   - conda-forge
 
 dependencies:
-  - python=3.7
+  - python=3.7
+  - opencv-python=4.3

examples/data_generator.py

@@ -138,12 +138,11 @@ def create_element(objects_paths, backgrounds_paths):
             ),
             flip.transformers.data_augmentation.Flip('x'),
             # ts.ObjectsGetBGColor(), TODO: Modify to work with Apply to Objects
-            # lr.CreateTags(),
             flip.transformers.domain_randomization.Draw(),
-            # lr.CreateTags(),
+            flip.transformers.labeler.CreateBoundingBoxes(),
             flip.transformers.io.SaveImage(OUT_DIR, name),
-            # sr.csv.CreateCSV(OUT_DIR, name)
-            # sr.json.CreateJson(OUT_DIR, name),
+            # flip.transformers.io.CreateCSV(OUT_DIR, name)
+            flip.transformers.io.CreateJson(OUT_DIR, name),
         ]
     )
 

flip/transformers/__init__.py

@@ -1,4 +1,4 @@
-from . import data_augmentation, domain_randomization, io
+from . import data_augmentation, domain_randomization, io, labeler
 
 from .transformer import Transformer, Compose
 from .element import Element

flip/transformers/io/__init__.py

@@ -1,3 +1,3 @@
 from .save_image import SaveImage
-from . import json
-from . import csv
+from .json import CreateJson
+from .csv import CreateCSV

flip/transformers/labeler/__init__.py

@@ -1 +1 @@
-from .core import *
+from .create_bounding_boxes import CreateBoundingBoxes

flip/transformers/labeler/core.py → flip/transformers/labeler/create_bounding_boxes.py

@@ -1,11 +1,10 @@
-import json
-from ..transforms import Element
-from ..common import Transformer
 import cv2
-import matplotlib.pyplot as plt
 
+from flip.transformers.element import Element
+from flip.transformers.transformer import Transformer
 
-class CreateTags(Transformer):
+
+class CreateBoundingBoxes(Transformer):
     def map(self, element: Element) -> Element:
         assert element, "element cannot be None"
 
@@ -16,29 +15,28 @@ def map(self, element: Element) -> Element:
     def create(self, element):
         array = []
         for obj in element.objects:
-            new_x, new_y, new_w, new_h = self.boundingBox(obj.image)
-            data = {}
-            data["name"] = obj.name
-            data["pos"] = {}
-            data["pos"]["x"] = obj.x + new_x
-            data["pos"]["y"] = obj.y + new_y
+            new_x, new_y, new_w, new_h = self.bounding_box(obj.image)
+            data = {"name": obj.name, "pos": {}}
+            data["pos"]["x"] = (obj.x or 0) + new_x
+            data["pos"]["y"] = (obj.y or 0) + new_y
             data["pos"]["w"] = new_w
             data["pos"]["h"] = new_h
 
             array.append(data)
 
         return array
 
-    def boundingBox(self, img):
+    def bounding_box(self, image):
+        """
+        Args:
+          image: image to process its width and height
+        Description:
+          This function
+        Returns:
         """
-    Args:
-      image: image to process its width and height
-    Description:
-      This function
-    Returns:
-    """
+
         # Canny edge detection - edge gradient
-        edged = cv2.Canny(img, 10, 250)
+        edged = cv2.Canny(image, 10, 250)
 
         # Morphological Transformations
         # applying closing function
@@ -61,6 +59,6 @@ def boundingBox(self, img):
             x, y, w, h = cv2.boundingRect(c)
 
             if w > 50 and h > 50:
-                new_img = img[y : y + h, x : x + w]
+                new_img = image[y: y + h, x: x + w]
 
         return x, y, new_img.shape[1], new_img.shape[0]

pyproject.toml

@@ -9,7 +9,7 @@ author-email = "diego@linked.ai"
 classifiers = ["License :: OSI Approved :: MIT License"]
 requires-python=">=3.7"
 requires = [
-    "opencv-python>=3.4,<4",
+    "opencv-python>=4.3",
     "pyyaml",
     "jinja2"
 ]