add clip

app.py

@@ -9,15 +9,15 @@
 # ------------------------------------------------------------------------------
 
 import os
-# token = os.environ["GITHUB_TOKEN"]
-# os.system(f"pip install git+https://xvjiarui:{token}@github.com/xvjiarui/ODISE_NV.git")
-os.system("pip install git+https://github.com/NVlabs/ODISE.git")
-os.system("pip freeze")
+# os.system("pip install git+https://github.com/NVlabs/ODISE.git")
+# os.system("pip freeze")
 
 import itertools
 import json
 from contextlib import ExitStack
 import gradio as gr
+import numpy as np
+import matplotlib.colors as mplc
 import torch
 from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES
 from PIL import Image
@@ -31,20 +31,13 @@ from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
 from detectron2.evaluation import inference_context
 from detectron2.utils.env import seed_all_rng
 from detectron2.utils.logger import setup_logger
-from detectron2.utils.visualizer import ColorMode, Visualizer, random_color
+from detectron2.utils.visualizer import ColorMode, Visualizer as _Visualizer, random_color
 
 from odise import model_zoo
 from odise.checkpoint import ODISECheckpointer
 from odise.config import instantiate_odise
 from odise.data import get_openseg_labels
 from odise.modeling.wrapper import OpenPanopticInference
-from odise.utils.file_io import ODISEHandler, PathManager
-from odise.model_zoo.model_zoo import _ModelZooUrls
-
-for k in ODISEHandler.URLS:
-    ODISEHandler.URLS[k] = ODISEHandler.URLS[k].replace("https://github.com/NVlabs/ODISE/releases/download/v1.0.0/", "https://huggingface.co/xvjiarui/download_cache/resolve/main/torch/odise/")
-PathManager.register_handler(ODISEHandler())
-_ModelZooUrls.PREFIX = _ModelZooUrls.PREFIX.replace("https://github.com/NVlabs/ODISE/releases/download/v1.0.0/", "https://huggingface.co/xvjiarui/download_cache/resolve/main/torch/odise/")
 
 setup_logger()
 logger = setup_logger(name="odise")
@@ -81,6 +74,55 @@ LVIS_COLORS = list(
     itertools.islice(itertools.cycle([c["color"] for c in COCO_CATEGORIES]), len(LVIS_CLASSES))
 )
 
+class Visualizer(_Visualizer):
+
+    def draw_text(
+        self,
+        text,
+        position,
+        *,
+        font_size=None,
+        color="g",
+        horizontal_alignment="center",
+        rotation=0,
+    ):
+        """
+        Args:
+            text (str): class label
+            position (tuple): a tuple of the x and y coordinates to place text on image.
+            font_size (int, optional): font of the text. If not provided, a font size
+                proportional to the image width is calculated and used.
+            color: color of the text. Refer to `matplotlib.colors` for full list
+                of formats that are accepted.
+            horizontal_alignment (str): see `matplotlib.text.Text`
+            rotation: rotation angle in degrees CCW
+
+        Returns:
+            output (VisImage): image object with text drawn.
+        """
+        if not font_size:
+            font_size = self._default_font_size
+
+        # since the text background is dark, we don't want the text to be dark
+        color = np.clip(color, 0, 1).tolist()
+        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
+        color[np.argmax(color)] = max(0.8, np.max(color))
+
+        x, y = position
+        self.output.ax.text(
+            x,
+            y,
+            text,
+            size=font_size * self.output.scale,
+            family="sans-serif",
+            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
+            verticalalignment="top",
+            horizontalalignment=horizontal_alignment,
+            color=color,
+            zorder=10,
+            rotation=rotation,
+        )
+        return self.output
 
 class VisualizationDemo(object):
     def __init__(self, model, metadata, aug, instance_mode=ColorMode.IMAGE):