Upload 2 files

app (1).py

@@ -0,0 +1,268 @@
+import spaces
+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+import cv2
+import tempfile
+import numpy as np
+
+def download_model(model_filename):
+    """
+    Downloads a YOLO model from the Hugging Face Hub.
+
+    This function fetches a specified YOLO model file from the
+    'atalaydenknalbant/Yolov13' repository on the Hugging Face Hub.
+
+    Args:
+        model_filename (str): The name of the model file to download
+                              (e.g., 'yolov13n.pt').
+
+    Returns:
+        str: The local path to the downloaded model file.
+    """
+    return hf_hub_download(repo_id="atalaydenknalbant/Yolov13", filename=model_filename)
+
+@spaces.GPU
+def yolo_inference(input_type, image, video, model_id, conf_threshold, iou_threshold, max_detection):
+    """
+    Performs object detection inference using a YOLOv13 model on either an image or a video.
+
+    This function downloads the specified YOLO model, then applies it to the
+    provided input. For images, it returns an annotated image. For videos, it
+    processes each frame and returns an annotated video. Error handling for
+    missing inputs is included, returning blank outputs with messages.
+
+    Args:
+        input_type (str): Specifies the input type, either "Image" or "Video".
+        image (PIL.Image.Image or None): The input image if `input_type` is "Image".
+                                         None otherwise.
+        video (str or None): The path to the input video file if `input_type` is "Video".
+                             None otherwise.
+        model_id (str): The identifier of the YOLO model to use (e.g., 'yolov13n.pt').
+        conf_threshold (float): The confidence threshold for object detection.
+                                Detections with lower confidence are discarded.
+        iou_threshold (float): The Intersection over Union (IoU) threshold for
+                               Non-Maximum Suppression (NMS).
+        max_detection (int): The maximum number of detections to return per image or frame.
+
+    Returns:
+        tuple: A tuple containing two elements:
+            - PIL.Image.Image or None: The annotated image if `input_type` was "Image",
+                                     otherwise None.
+            - str or None: The path to the annotated video file if `input_type` was "Video",
+                           otherwise None.
+    """
+    model_path = download_model(model_id)
+
+    if input_type == "Image":
+        if image is None:
+            width, height = 640, 480
+            blank_image = Image.new("RGB", (width, height), color="white")
+            draw = ImageDraw.Draw(blank_image)
+            message = "No image provided"
+            font = ImageFont.load_default(size=40)
+            bbox = draw.textbbox((0, 0), message, font=font)
+            text_width = bbox[2] - bbox[0]
+            text_height = bbox[3] - bbox[1]
+            text_x = (width - text_width) / 2
+            text_y = (height - text_height) / 2
+            draw.text((text_x, text_y), message, fill="black", font=font)
+            return blank_image, None
+
+        model = YOLO(model_path)
+        results = model.predict(
+            source=image,
+            conf=conf_threshold,
+            iou=iou_threshold,
+            imgsz=640,
+            max_det=max_detection,
+            show_labels=True,
+            show_conf=True,
+        )
+        for r in results:
+            image_array = r.plot()
+            annotated_image = Image.fromarray(image_array[..., ::-1])
+        return annotated_image, None
+
+    elif input_type == "Video":
+        if video is None:
+            width, height = 640, 480
+            blank_image = Image.new("RGB", (width, height), color="white")
+            draw = ImageDraw.Draw(blank_image)
+            message = "No video provided"
+            font = ImageFont.load_default(size=40)
+            bbox = draw.textbbox((0, 0), message, font=font)
+            text_width = bbox[2] - bbox[0]
+            text_height = bbox[3] - bbox[1]
+            text_x = (width - text_width) / 2
+            text_y = (height - text_height) / 2
+            draw.text((text_x, text_y), message, fill="black", font=font)
+            temp_video_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            out = cv2.VideoWriter(temp_video_file, fourcc, 1, (width, height))
+            frame = cv2.cvtColor(np.array(blank_image), cv2.COLOR_RGB2BGR)
+            out.write(frame)
+            out.release()
+            return None, temp_video_file
+
+        model = YOLO(model_path)
+        cap = cv2.VideoCapture(video)
+        fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 25
+        frames = []
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            pil_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+            results = model.predict(
+                source=pil_frame,
+                conf=conf_threshold,
+                iou=iou_threshold,
+                imgsz=640,
+                max_det=max_detection,
+                show_labels=True,
+                show_conf=True,
+            )
+            for r in results:
+                annotated_frame_array = r.plot()
+                annotated_frame = cv2.cvtColor(annotated_frame_array, cv2.COLOR_BGR2RGB)
+            frames.append(annotated_frame)
+        cap.release()
+        if not frames:
+            return None, None
+
+        height_out, width_out, _ = frames[0].shape
+        temp_video_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        out = cv2.VideoWriter(temp_video_file, fourcc, fps, (width_out, height_out))
+        for f in frames:
+            f_bgr = cv2.cvtColor(f, cv2.COLOR_RGB2BGR)
+            out.write(f_bgr)
+        out.release()
+        return None, temp_video_file
+
+    return None, None
+
+def update_visibility(input_type):
+    """
+    Adjusts the visibility of Gradio components based on the selected input type.
+
+    This function dynamically shows or hides the image and video input/output
+    components in the Gradio interface to ensure only relevant fields are visible.
+
+    Args:
+        input_type (str): The selected input type, either "Image" or "Video".
+
+    Returns:
+        tuple: A tuple of `gr.update` objects for the visibility of:
+               (image input, video input, image output, video output).
+    """
+    if input_type == "Image":
+        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)
+
+def yolo_inference_for_examples(image, model_id, conf_threshold, iou_threshold, max_detection):
+    """
+    Wrapper function for `yolo_inference` specifically for Gradio examples that use images.
+
+    This function simplifies the `yolo_inference` call for the `gr.Examples` component,
+    ensuring only image-based inference is performed for predefined examples.
+
+    Args:
+        image (PIL.Image.Image): The input image for the example.
+        model_id (str): The identifier of the YOLO model to use.
+        conf_threshold (float): The confidence threshold.
+        iou_threshold (float): The IoU threshold.
+        max_detection (int): The maximum number of detections.
+
+    Returns:
+        PIL.Image.Image or None: The annotated image. Returns None if no image is processed.
+    """
+    annotated_image, _ = yolo_inference(
+        input_type="Image",
+        image=image,
+        video=None,
+        model_id=model_id,
+        conf_threshold=conf_threshold,
+        iou_threshold=iou_threshold,
+        max_detection=max_detection
+    )
+    return annotated_image
+
+theme = gr.themes.Ocean(primary_hue="blue", secondary_hue="pink")
+
+with gr.Blocks(theme=theme) as app:
+    gr.Markdown("# Yolov13: Object Detection")
+    gr.Markdown("Upload an image or video for inference using the latest YOLOv13 models.")
+    gr.Markdown("📝 **Note:** Better-trained models will be deployed as they become available.")
+    with gr.Accordion("Paper and Citation", open=False):
+        gr.Markdown("""
+        This application is based on the research from the paper: **YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception**.
+
+        - **Authors:** Mengqi Lei, Siqi Li, Yihong Wu, et al.
+        - **Preprint Link:** [https://arxiv.org/abs/2506.17733](https://arxiv.org/abs/2506.17733)
+
+        **BibTeX:**
+        ```
+        @article{yolov13,
+          title={YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception},
+          author={Lei, Mengqi and Li, Siqi and Wu, Yihong and et al.},
+          journal={arXiv preprint arXiv:2506.17733},
+          year={2025}
+        }
+        ```
+        """)
+
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(type="pil", label="Image", visible=True)
+            video = gr.Video(label="Video", visible=False)
+            input_type = gr.Radio(
+                choices=["Image", "Video"],
+                value="Image",
+                label="Input Type",
+            )
+            model_id = gr.Dropdown(
+                label="Model Name",
+                choices=[
+                    'yolov13n.pt', 'yolov13s.pt', 'yolov13l.pt', 'yolov13x.pt',
+                ],
+                value="yolov13n.pt",
+            )
+            conf_threshold = gr.Slider(minimum=0, maximum=1, value=0.35, label="Confidence Threshold")
+            iou_threshold = gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU Threshold")
+            max_detection = gr.Slider(minimum=1, maximum=300, step=1, value=300, label="Max Detection")
+            infer_button = gr.Button("Detect Objects", variant="primary")
+        with gr.Column():
+            output_image = gr.Image(type="pil", show_label=False, show_share_button=False, visible=True)
+            output_video = gr.Video(show_label=False, show_share_button=False, visible=False)
+            gr.DeepLinkButton(variant="primary")
+
+    input_type.change(
+        fn=update_visibility,
+        inputs=input_type,
+        outputs=[image, video, output_image, output_video],
+    )
+
+    infer_button.click(
+        fn=yolo_inference,
+        inputs=[input_type, image, video, model_id, conf_threshold, iou_threshold, max_detection],
+        outputs=[output_image, output_video],
+    )
+
+    gr.Examples(
+        examples=[
+            ["zidane.jpg", "yolov13s.pt", 0.35, 0.45, 300],
+            ["bus.jpg", "yolov13l.pt", 0.35, 0.45, 300],
+            ["yolo_vision.jpg", "yolov13x.pt", 0.35, 0.45, 300],
+        ],
+        fn=yolo_inference_for_examples,
+        inputs=[image, model_id, conf_threshold, iou_threshold, max_detection],
+        outputs=[output_image],
+        label="Examples (Images)",
+    )
+
+if __name__ == '__main__':
+    app.launch(mcp_server=True)

requirements (1).txt

@@ -0,0 +1,4 @@
+git+https://github.com/iMoonLab/yolov13
+spaces
+Pillow
+huggingface_hub