initial commit

.gitignore

@@ -0,0 +1,2 @@
+venv/
+.idea/

README.md

@@ -1,6 +1,6 @@
 ---
 title: YOLO ARENA
-emoji: 🏃
+emoji: 🏟️
 colorFrom: pink
 colorTo: green
 sdk: gradio

app.py

@@ -0,0 +1,177 @@
+from typing import Tuple
+
+import gradio as gr
+import numpy as np
+import supervision as sv
+from inference import get_model
+
+MARKDOWN = """
+# YOLO-ARENA 🏟️
+
+Powered by Roboflow [Inference](https://github.com/roboflow/inference) and 
+[Supervision](https://github.com/roboflow/supervision).
+"""
+
+IMAGE_EXAMPLES = [
+    ['https://media.roboflow.com/dog.jpeg', 0.3]
+]
+
+YOLO_V8_MODEL = get_model(model_id="yolov8s-640")
+YOLO_NAS_MODEL = get_model(model_id="coco/14")
+YOLO_V9_MODEL = get_model(model_id="coco/17")
+
+LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.black())
+BOUNDING_BOX_ANNOTATORS = sv.BoundingBoxAnnotator()
+
+
+def process_image(
+    input_image: np.ndarray,
+    confidence_threshold: float,
+    iou_threshold: float
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    yolo_v8_result = YOLO_V8_MODEL.infer(
+        input_image,
+        confidence=confidence_threshold,
+        iou_threshold=iou_threshold
+    )[0]
+    yolo_v8_detections = sv.Detections.from_inference(yolo_v8_result)
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(yolo_v8_detections["class_name"], yolo_v8_detections.confidence)
+    ]
+
+    yolo_v8_annotated_image = input_image.copy()
+    yolo_v8_annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(
+        scene=yolo_v8_annotated_image, detections=yolo_v8_detections)
+    yolo_v8_annotated_image = LABEL_ANNOTATORS.annotate(
+        scene=yolo_v8_annotated_image, detections=yolo_v8_detections, labels=labels)
+
+    yolo_nas_result = YOLO_NAS_MODEL.infer(
+        input_image,
+        confidence=confidence_threshold,
+        iou_threshold=iou_threshold
+    )[0]
+    yolo_nas_detections = sv.Detections.from_inference(yolo_nas_result)
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(yolo_nas_detections["class_name"], yolo_nas_detections.confidence)
+    ]
+
+    yolo_nas_annotated_image = input_image.copy()
+    yolo_nas_annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(
+        scene=yolo_nas_annotated_image, detections=yolo_nas_detections)
+    yolo_nas_annotated_image = LABEL_ANNOTATORS.annotate(
+        scene=yolo_nas_annotated_image, detections=yolo_nas_detections, labels=labels)
+
+    yolo_v9_result = YOLO_V9_MODEL.infer(
+        input_image,
+        confidence=confidence_threshold,
+        iou_threshold=iou_threshold
+    )[0]
+    yolo_v9_detections = sv.Detections.from_inference(yolo_v9_result)
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(yolo_v9_detections["class_name"], yolo_v9_detections.confidence)
+    ]
+
+    yolo_v9_annotated_image = input_image.copy()
+    yolo_v9_annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(
+        scene=yolo_v9_annotated_image, detections=yolo_v9_detections)
+    yolo_v9_annotated_image = LABEL_ANNOTATORS.annotate(
+        scene=yolo_v9_annotated_image, detections=yolo_v9_detections, labels=labels)
+
+    return yolo_v8_annotated_image, yolo_nas_annotated_image, yolo_v9_annotated_image
+
+
+confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="Confidence Threshold",
+    info=(
+        "The confidence threshold for the YOLO model. Lower the threshold to "
+        "reduce false negatives, enhancing the model's sensitivity to detect "
+        "sought-after objects. Conversely, increase the threshold to minimize false "
+        "positives, preventing the model from identifying objects it shouldn't."
+    ))
+
+iou_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.5,
+    step=0.01,
+    label="IoU Threshold",
+    info=(
+        "The Intersection over Union (IoU) threshold for non-maximum suppression. "
+        "Decrease the value to lessen the occurrence of overlapping bounding boxes, "
+        "making the detection process stricter. On the other hand, increase the value "
+        "to allow more overlapping bounding boxes, accommodating a broader range of "
+        "detections."
+    ))
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN)
+    with gr.Accordion("Configuration", open=False):
+        confidence_threshold_component.render()
+        iou_threshold_component.render()
+    with gr.Row():
+        input_image_component = gr.Image(
+            type='numpy',
+            label='Input Image'
+        )
+        yolo_v8_output_image_component = gr.Image(
+            type='numpy',
+            label='YOLOv8 Output'
+        )
+    with gr.Row():
+        yolo_nas_output_image_component = gr.Image(
+            type='numpy',
+            label='YOLO-NAS Output'
+        )
+        yolo_v9_output_image_component = gr.Image(
+            type='numpy',
+            label='YOLOv9 Output'
+        )
+    submit_button_component = gr.Button(
+        value='Submit',
+        scale=1,
+        variant='primary'
+    )
+    gr.Examples(
+        fn=process_image,
+        examples=IMAGE_EXAMPLES,
+        inputs=[
+            input_image_component,
+            confidence_threshold_component,
+            iou_threshold_component
+        ],
+        outputs=[
+            yolo_v8_output_image_component,
+            yolo_nas_output_image_component,
+            yolo_v9_output_image_component
+        ]
+    )
+
+    submit_button_component.click(
+        fn=process_image,
+        inputs=[
+            input_image_component,
+            confidence_threshold_component,
+            iou_threshold_component
+        ],
+        outputs=[
+            yolo_v8_output_image_component,
+            yolo_nas_output_image_component,
+            yolo_v9_output_image_component
+        ]
+    )
+
+demo.launch(debug=False, show_error=True, max_threads=1)

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.19.2
+inference==0.9.15
+supervision==0.18.0