first commit

app.py

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+import gradio as gr
+import cv2
+import supervision as sv
+from ultralytics import YOLOv10
+# https://www.youtube.com/watch?v=h53XYgKzYYE&t=1534s
+# https://github.com/Codewello/Yolo-v8-and-hugginface/blob/main/hugginface/app.py
+# https://www.youtube.com/watch?v=29tnSxhB3CY&t=564s
+# https://colab.research.google.com/drive/1Sv3_0S4zhZT763bBMjYxf0HhTit4Bvh6?usp=sharing#scrollTo=kAi4PvrItTCf
+# https://github.com/roboflow/supervision/blob/develop/demo.ipynb
+def yoloV10_func(image: gr.Image() = None,
+                image_size: gr.Slider() = 640,
+                conf_threshold: gr.Slider() = 0.25,
+                iou_threshold: gr.Slider() = 0.45):
+    """This function performs YOLOv10 object detection on the given image.
+
+    Args:
+        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
+        image_size (gr.Slider, optional): Desired image size for the model. Defaults to 640.
+        conf_threshold (gr.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
+        iou_threshold (gr.Slider, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
+    """
+    # Load the YOLOv10 model from the 'best.pt' checkpoint
+    model_path = './drug_yolov10.pt'
+    model = YOLOv10(model_path)
+
+    # Perform object detection on the input image using the YOLOv10 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    print("Object type:", box.cls)
+    print("Coordinates:", box.xyxy)
+    print("Probability:", box.conf)
+
+    # Render the output image with bounding boxes around detected objects
+    # render = render_result(model=model, image=image, result=results[0])
+    # results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results[0])
+    box_annotator = sv.BoxAnnotator()
+    labels = [
+        f"{model.model.names[class_id]} {confidence:.2f}"
+        for class_id, confidence in zip(detections.class_id, detections.confidence)
+    ]
+    image = cv2.imread(image)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    annotated_image = box_annotator.annotate(
+        image.copy(), detections=detections, labels=labels
+    )
+    return annotated_image
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Slider(minimum=120, maximum=1280, value=640,
+                     step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.25,
+                     step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45,
+                     step=0.05, label="IOU Threshold"),
+]
+outputs = gr.Image(type="filepath", label="Output Image")
+
+title = "YOLOv10 101: Custom Object Detection on Pill Types"
+
+
+examples = [['RXNAV-600_13668-0095-90_RXNAVIMAGE10_D145E8EF.jpg', 640, 0.2, 0.7],
+            ['RXBASE-600_00071-1014-68_NLMIMAGE10_5715ABFD.jpg', 280, 0.2, 0.6],
+            ['RXBASE-600_00074-7126-13_NLMIMAGE10_C003606B.jpg', 640, 0.2, 0.8]]
+yolo_app = gr.Interface(
+    fn=yoloV10_func,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
+# Launch the Gradio interface in debug mode with queue enabled
+yolo_app.launch(debug=True)

drug_yolov10.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d953e3db8e56519197a3e6d74d2b078226f7f1cf1de07064631003acb4a493f3
+size 33211887

requirements.txt

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+gradio==4.32.1
+opencv_python==4.8.1.78
+opencv_python_headless==4.8.0.74
+supervision==0.20.0
+git+https://github.com/THU-MIG/yolov10.git