Examples panel added

app.py

@@ -1,5 +1,6 @@
 from typing import List
 
+import cv2
 import torch
 import gradio as gr
 import numpy as np
@@ -16,6 +17,10 @@ This is a demo of zero-shot instance segmentation using [YOLO-World](https://git
 Powered by Roboflow [Inference](https://github.com/roboflow/inference) and [Supervision](https://github.com/roboflow/supervision).
 """
 
+EXAMPLES = [
+    ['https://media.roboflow.com/dog.jpeg', 'dog, eye, nose, tongue, car', 0.005, 0.1, True, False, False],
+]
+
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 EFFICIENT_SAM_MODEL = load(device=DEVICE)
 YOLO_WORLD_MODEL = YOLOWorld(model_id="yolo_world/l")
@@ -32,15 +37,17 @@ def process_categories(categories: str) -> List[str]:
 def process_image(
         input_image: np.ndarray,
         categories: str,
-        confidence_threshold: float = 0.003,
+        confidence_threshold: float = 0.005,
         iou_threshold: float = 0.5,
         with_segmentation: bool = True,
-        with_confidence: bool = True
+        with_confidence: bool = False,
+        with_class_agnostic_nms: bool = False,
 ) -> np.ndarray:
     categories = process_categories(categories)
     YOLO_WORLD_MODEL.set_classes(categories)
     results = YOLO_WORLD_MODEL.infer(input_image, confidence=confidence_threshold)
-    detections = sv.Detections.from_inference(results).with_nms(iou_threshold)
+    detections = sv.Detections.from_inference(results)
+    detections = detections.with_nms(class_agnostic=with_class_agnostic_nms, threshold=iou_threshold)
     if with_segmentation:
         masks = []
         for [x_min, y_min, x_max, y_max] in detections.xyxy:
@@ -55,9 +62,11 @@ def process_image(
         zip(detections.class_id, detections.confidence)
     ]
     output_image = input_image.copy()
+    output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
     output_image = MASK_ANNOTATOR.annotate(output_image, detections)
     output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
     output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
+    output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
     return output_image
 
 
@@ -79,6 +88,12 @@ with gr.Blocks() as demo:
             scale=5
         )
         submit_button_component = gr.Button('Submit', scale=1)
+    gr.Examples(
+        fn=process_image,
+        examples=EXAMPLES,
+        inputs=[input_image_component, categories_text_component],
+        outputs=output_image_component
+    )
 
     submit_button_component.click(
         fn=process_image,

requirements.txt

@@ -1,3 +1,3 @@
-inference-gpu[yolo-world]==0.9.12
+inference-gpu[yolo-world]==0.9.13
 supervision==0.19.0rc3
 gradio==4.19.0