develop segmentation preview in streamlit, fix dependency on system to get font

com.unity.perception/Editor/Pyrception/PyrceptionInstaller.cs

         string command = "";
 
 #if UNITY_EDITOR_WIN
-        command = $"cd \"{path}\\DataInsightsEnv\\Scripts\\\" && activate && cd \"{pathToData}\\..\"  && \"{path}\\DataInsightsEnv\\Scripts\\pyrception-utils.exe\" preview --data=\".\"";
+        command = $"cd \"{path}\\DataInsightsEnv\\Scripts\\\" && activate && cd \"{pathToData}\\..\" && \"{path}\\DataInsightsEnv\\Scripts\\pyrception-utils.exe\" preview --data=\".\"";
 #elif (UNITY_EDITOR_OSX || UNITY_EDITOR_LINUX)
         command = $"cd \"{path}/DataInsightsEnv/bin\"; activate; cd \"{pathToData}/..\" ; \"{path}/DataInsightsEnv/bin/pyrception-utils\" preview --data=\".\"";
 #endif

com.unity.perception/Editor/Pyrception/pyrception-utils/pyrception_utils/preview.py

 import argparse
 import os
+import pathlib
 from typing import Dict, List, Tuple
 
 import numpy as np
     :param description: Image description
     :type str:
     """
+    image = image.copy()
-    font = ImageFont.truetype("C:\\Windows\\Fonts\\Arial.ttf", 15)
-    st.text("There are " + str(len(classes)) + " classes")
+    path_to_font = pathlib.Path(__file__).parent.absolute()
+    font = ImageFont.truetype(f"{path_to_font}/NairiNormal-m509.ttf", 15)
+
     for label, box in zip(labels, boxes):
         label = label - 1
         class_name = classes[label]
     st.image(image, use_column_width=True)
 
 
+def draw_image_with_semantic_segmentation(
+    image: Image,
+    height: int,
+    width: int,
+    segmentation: Image,
+    header: str,
+    description: str,
+):
+    """
+    Draws an image in streamlit with labels and bounding boxes.
+
+    :param image: the PIL image
+    :type PIL:
+    :param height: height of the image
+    :type int:
+    :param width: width of the image
+    :type int:
+    :param segmentation: Segmentation Image
+    :type PIL:
+    :param header: Image header
+    :type str:
+    :param description: Image description
+    :type str:
+    """
+    # image_draw = ImageDraw(segmentation)
+    image = image.copy()
+    (seg_r, seg_g, seg_b) = segmentation.getpixel((0, 0))
+
+    st.sidebar.markdown('# Segmentation color intensity')
+    color_intensity = st.sidebar.slider('color intensity (%)', 0, 100, 65);
+    alpha = color_intensity / 100;
+
+    for x in range(0, width - 1):
+        for y in range(0, height - 1):
+            (seg_r, seg_g, seg_b) = segmentation.getpixel((x, y))
+            (r, g, b) = image.getpixel((x, y))
+            # if it isn't a black pixel in the segmentation image then highlight it with the segmentation color
+            if seg_r != 0 or seg_g != 0 or seg_b != 0:
+                image.putpixel((x, y),
+                               (int((1 - alpha) * r + alpha * seg_r),
+                                int((1 - alpha) * g + alpha * seg_g),
+                                int((1 - alpha) * b + alpha * seg_b)))
+
+    st.subheader(header)
+    st.markdown(description)
+    st.image(image, use_column_width=True)
+
+def display_count(
+    header: str,
+    description: str,
+):
+    """
+    :param header: Image header
+    :type str:
+    :param description: Image description
+    :type str:
+    """
+    return
 @st.cache(show_spinner=True, allow_output_mutation=True)
 def load_perception_dataset(path: str) -> Tuple:
     """
             "hello", classes
         )
         image_index = frame_selector_ui(dataset)
-        image, target = dataset[image_index]
+        image, segmentation, target = dataset[image_index]
-            image, classes, labels, boxes, colors, "Synthetic Image Preview", ""
+            image, classes, labels, boxes, colors, "Bounding Boxes Preview", ""
+        )
+        draw_image_with_semantic_segmentation(
+            image, dataset.metadata.image_size[0], dataset.metadata.image_size[1], segmentation, "Semantic Segmentation Preview", ""
         )
 
 
     parser = argparse.ArgumentParser()
     parser.add_argument("data", type=str)
     args = parser.parse_args()
-    print("\n")
-    print("The path is: " + args.data)
     preview_app(args)

com.unity.perception/Editor/Pyrception/pyrception-utils/pyrception_utils/pyrception.py

             image = Image.open(
                 os.path.join(self.metadata.data_dir, self.data[sub_index]["filename"])
             ).convert("RGB")
+            segmentation = Image.open(
+                os.path.join(self.metadata.data_dir, self.data[sub_index]["annotations"][1]["filename"])
+            ).convert("RGB")
         except IndexError:
             print(self.data)
             raise IndexError(f"Index is :{index} Subindex is:{sub_index}")
         # RGB<uuid>/rgb_<image_id>.png
         image_id = self.data[sub_index]["filename"][44:-4]
         target = {"image_id": image_id, "labels": labels, "boxes": boxes}
-        return image, target
+        return image, segmentation, target
 
     def __len__(self) -> int:
         """

com.unity.perception/Editor/Pyrception/pyrception-utils/pyrception_utils/NairiNormal-m509.ttf.meta

+fileFormatVersion: 2
+guid: 34a93e4e97aad7245b71f90115917a11
+TrueTypeFontImporter:
+  externalObjects: {}
+  serializedVersion: 4
+  fontSize: 16
+  forceTextureCase: -2
+  characterSpacing: 0
+  characterPadding: 1
+  includeFontData: 1
+  fontNames:
+  - Nairi
+  fallbackFontReferences: []
+  customCharacters: 
+  fontRenderingMode: 0
+  ascentCalculationMode: 1
+  useLegacyBoundsCalculation: 0
+  shouldRoundAdvanceValue: 1
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: