Create app.py

app.py

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+from typing import Tuple
+
+import gradio as gr
+import numpy as np
+import supervision as sv
+from inference import get_model
+
+MARKDOWN = """
+<h1 style='text-align: center'>Evolving-YOLO-V8-V9-V10</h1>
+Welcome to Evolving-YOLO-V8-V9-V10! This demo showcases the performance of various YOLO models 
+pre-trained on the COCO dataset.
+- **YOLOv8**
+  <div style="display: flex; align-items: center;">
+    <a href="https://github.com/ultralytics/ultralytics" style="margin-right: 10px;">
+      <img src="https://badges.aleen42.com/src/github.svg">
+    </a>
+    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
+      <img src="https://colab.research.google.com/assets/colab-badge.svg">
+    </a>
+  </div>
+- **YOLOv9**
+  <div style="display: flex; align-items: center;">
+    <a href="https://github.com/WongKinYiu/yolov9" style="margin-right: 10px;">
+      <img src="https://badges.aleen42.com/src/github.svg">
+    </a>
+    <a href="https://arxiv.org/abs/2402.13616" style="margin-right: 10px;">
+      <img src="https://img.shields.io/badge/arXiv-2402.13616-b31b1b.svg">
+    </a>
+    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
+      <img src="https://colab.research.google.com/assets/colab-badge.svg">
+    </a>
+  </div>
+- **YOLOv10**
+  <div style="display: flex; align-items: center;">
+    <a href="https://github.com/THU-MIG/yolov10" style="margin-right: 10px;">
+      <img src="https://badges.aleen42.com/src/github.svg">
+    </a>
+    <a href="https://arxiv.org/abs/2405.14458" style="margin-right: 10px;">
+      <img src="https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg">
+    </a>
+    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
+      <img src="https://colab.research.google.com/assets/colab-badge.svg">
+    </a>
+  </div>
+"""
+
+IMAGE_EXAMPLES = [
+    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.1],
+    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.1],
+    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.1],
+]
+
+YOLO_V8_MODEL = get_model(model_id="coco/8")
+YOLO_V9_MODEL = get_model(model_id="coco/17")
+YOLO_V10_MODEL = get_model(model_id="coco/22")
+
+LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.black())
+BOUNDING_BOX_ANNOTATORS = sv.BoundingBoxAnnotator()
+
+
+def detect_and_annotate(
+    model,
+    input_image: np.ndarray,
+    confidence_threshold: float,
+    iou_threshold: float,
+    class_id_mapping: dict = None
+) -> np.ndarray:
+    result = model.infer(
+        input_image,
+        confidence=confidence_threshold,
+        iou_threshold=iou_threshold
+    )[0]
+    detections = sv.Detections.from_inference(result)
+
+    if class_id_mapping:
+        detections.class_id = np.array([
+            class_id_mapping[class_id]
+            for class_id
+            in detections.class_id
+        ])
+
+    labels = [
+        f"{class_name} ({confidence:.2f})"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    annotated_image = input_image.copy()
+    annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(
+        scene=annotated_image, detections=detections)
+    annotated_image = LABEL_ANNOTATORS.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    return annotated_image
+
+
+def process_image(
+    input_image: np.ndarray,
+    yolo_v8_confidence_threshold: float,
+    yolo_v9_confidence_threshold: float,
+    yolo_v10_confidence_threshold: float,
+    iou_threshold: float
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    yolo_v8_annotated_image = detect_and_annotate(
+        YOLO_V8_MODEL, input_image, yolo_v8_confidence_threshold, iou_threshold)
+    yolo_v9_annotated_image = detect_and_annotate(
+        YOLO_V9_MODEL, input_image, yolo_v9_confidence_threshold, iou_threshold)
+    yolo_10_annotated_image = detect_and_annotate(
+        YOLO_V10_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
+
+    return (
+        yolo_v8_annotated_image,
+        yolo_v9_annotated_image,
+        yolo_10_annotated_image
+    )
+
+
+yolo_v8_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLOv8 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."
+    ))
+
+yolo_v9_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLOv9 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."
+    ))
+
+yolo_v10_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLOv10 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):
+        with gr.Row():
+            yolo_v8_confidence_threshold_component.render()
+            yolo_v9_confidence_threshold_component.render()
+            yolo_v10_confidence_threshold_component.render()
+        iou_threshold_component.render()
+    with gr.Row():
+        input_image_component = gr.Image(
+            type='pil',
+            label='Input'
+        )
+        yolo_v8_output_image_component = gr.Image(
+            type='pil',
+            label='YOLOv8'
+        )
+    with gr.Row():
+        yolo_v9_output_image_component = gr.Image(
+            type='pil',
+            label='YOLOv9'
+        )
+        yolo_v10_output_image_component = gr.Image(
+            type='pil',
+            label='YOLOv10'
+            )
+    submit_button_component = gr.Button(
+        value='Submit',
+        scale=1,
+        variant='primary'
+    )
+    gr.Examples(
+        fn=process_image,
+        examples=IMAGE_EXAMPLES,
+        inputs=[
+            input_image_component,
+            yolo_v8_confidence_threshold_component,
+            yolo_v9_confidence_threshold_component,
+            yolo_v10_confidence_threshold_component,
+            iou_threshold_component
+        ],
+        outputs=[
+            yolo_v8_output_image_component,
+            yolo_v9_output_image_component,
+            yolo_v10_output_image_component
+        ]
+    )
+
+    submit_button_component.click(
+        fn=process_image,
+        inputs=[
+            input_image_component,
+            yolo_v8_confidence_threshold_component,
+            yolo_v9_confidence_threshold_component,
+            yolo_v10_confidence_threshold_component,
+            iou_threshold_component
+        ],
+        outputs=[
+            yolo_v8_output_image_component,
+            yolo_v9_output_image_component,
+            yolo_v10_output_image_component
+        ]
+    )
+
+demo.launch(debug=False, show_error=True, max_threads=1)