Upload 3 files

app.py

@@ -0,0 +1,259 @@
+import gradio as gr
+import torch
+from ultralyticsplus import YOLO, render_result
+from PIL import Image
+
+# torch.hub.download_url_to_file(
+#     'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg')
+# torch.hub.download_url_to_file(
+#     'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
+# torch.hub.download_url_to_file(
+#     'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
+
+
+def yoloV8_func(image: gr.inputs.Image = None, image_size = (1024, 768),
+                # conf_threshold: gr.inputs.Slider = 0.4,
+                # iou_threshold: gr.inputs.Slider = 0.50
+                ):
+# def yoloV8_func(image):
+
+    """This function performs YOLOv8 object detection on the given image.
+
+    Args:
+        image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
+        image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
+        conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
+        iou_threshold (gr.inputs.Slider, optional): Intersection ผover Union threshold for object detection. Defaults to 0.50.
+    """
+    # Load the YOLOv8 model from the 'best.pt' checkpoint
+    model_path = ('runs/best.pt')
+    model = YOLO(model_path)
+
+    # model.conf = 0.40  # Confidence threshold
+    # model.iou = 0.45   # IoU threshold
+    # model.agnostic = True  # NMS class-agnostic
+    # model.multi_label = False  # Whether to evaluate as multi-label classification
+    # model.max_det = 100  # Maximum number of detections per image
+
+    #image = Image.fromarray(image).resize(1024, 768)
+    
+    # Perform object detection on the input image using the YOLOv8 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])
+    return render
+
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image", shape=(1024, 768)),
+    #interface
+    # gr.inputs.Slider(minimum=320, maximum=1280, default=640,
+    #                  step=32, label="Image Size"),
+    # gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25,
+    #                  step=0.05, label="Confidence Threshold"),
+    # gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45,
+    #                  step=0.05, label="IOU Threshold"),
+]
+
+outputs = [gr.outputs.Image(type="filepath", label="Output Image")]
+
+title = "YOLOv8 Cocoa Seed Classification"
+
+
+# examples = [['one.jpg',0.5, 0.7],
+#             ['two.jpg', 0.5, 0.6],
+#             ['three.jpg',0.5, 0.8]]
+
+yolo_app = gr.Interface(
+    fn=yoloV8_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, enable_queue=True)
+
+# import gradio as gr
+# import torch
+# from ultralyticsplus import YOLO, render_result
+
+
+# torch.hub.download_url_to_file(
+#     'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg')
+# torch.hub.download_url_to_file(
+#     'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
+# torch.hub.download_url_to_file(
+#     'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
+
+
+# def yoloV8_func(image: gr.inputs.Image = None,
+#                 image_size: gr.inputs.Slider = 640,
+#                 conf_threshold: gr.inputs.Slider = 0.4,
+#                 iou_threshold: gr.inputs.Slider = 0.50):
+#     """This function performs YOLOv8 object detection on the given image.
+
+#     Args:
+#         image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
+#         image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
+#         conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
+#         iou_threshold (gr.inputs.Slider, optional): Intersection ผover Union threshold for object detection. Defaults to 0.50.
+#     """
+#     # Load the YOLOv8 model from the 'best.pt' checkpoint
+#     model_path = ('runs/best.pt')
+#     model = YOLO(model_path)
+
+#     # Perform object detection on the input image using the YOLOv8 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])
+#     return render
+
+
+# inputs = [
+#     gr.inputs.Image(type="filepath", label="Input Image"),
+#     gr.inputs.Slider(minimum=320, maximum=1280, default=640,
+#                      step=32, label="Image Size"),
+#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25,
+#                      step=0.05, label="Confidence Threshold"),
+#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45,
+#                      step=0.05, label="IOU Threshold"),
+# ]
+
+
+# outputs = gr.outputs.Image(type="filepath", label="Output Image")
+
+# title = "YOLOv8 101: Custom Object Detection on Construction Workers"
+
+
+# examples = [['one.jpg', 640, 0.5, 0.7],
+#             ['two.jpg', 800, 0.5, 0.6],
+#             ['three.jpg', 900, 0.5, 0.8]]
+
+# yolo_app = gr.Interface(
+#     fn=yoloV8_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, enable_queue=True)
+
+
+
+
+
+
+
+
+
+
+
+# import gradio as gr
+# import torch
+# from ultralyticsplus import YOLO, render_result
+# from PIL import Image
+
+# # Load your model
+# model_path = ('runs/best.pt')
+# model = YOLO(model_path)
+# # model.conf = 0.40
+# # model.iou = 0.45
+# # model.agnostic = True
+# # model.multi_label = False
+# # model.max_det = 100
+# # model.overrides['conf'] = 0.25  # NMS confidence threshold
+# # model.overrides['iou'] = 0.45  # NMS IoU threshold
+# # model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+# # model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+# #css = ".output_image {height: 40rem !important; width: 100% !important;}"
+
+# def predict(input_image):
+#     try:
+#         # Perform inference
+#         results = model(input_image, size=(1024, 768), augment=True)
+
+#         # Convert result image with bounding boxes to PIL format for Gradio output
+#         result_image = Image.fromarray(results.render()[0])
+
+#         return result_image
+
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
+# # Set up Gradio interface
+# interface = gr.Interface(
+#     fn=predict,
+#     inputs=gr.inputs.Image(type="pil", label="Upload an Image1"),
+#     outputs=gr.outputs.Image(type="pil", label="Result1"),
+#     #css = css,
+#     title="Object Detection using YOLOv5 - NEW MODEL 2024",
+#     description="Upload an image to detect objects using the YOLOv5 model"
+# )
+# interface.launch()
+# import gradio as gr
+# import torch
+# from ultralyticsplus import YOLO, render_result
+# from PIL import Image
+
+
+# torch.hub.download_url_to_file(
+#     'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg')
+# torch.hub.download_url_to_file(
+#     'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
+# torch.hub.download_url_to_file(
+#     'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
+
+# model_path = ('runs/best.pt')
+# model = YOLO(model_path)
+
+# def predict(input_image):
+#     try:
+#         # Perform inference
+#         results = model(input_image, size=(1024, 768), augment=True)
+
+#         # Convert result image with bounding boxes to PIL format for Gradio output
+#         result_image = Image.fromarray(results.render()[0])
+
+#         return result_image
+
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
+# # Set up Gradio interface
+# interface = gr.Interface(
+#     fn=predict,
+#     inputs=gr.inputs.Image(type="pil", label="Upload an Image1"),
+#     outputs=gr.outputs.Image(type="pil", label="Result1"),
+#     #css = css,
+#     title="Object Detection using YOLOv8 - NEW MODEL 2024",
+#     description="Upload an image to detect objects using the YOLOv8 model"
+# )
+# interface.launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c0c102143d76641779e797f2c49b289cec0d534a66ca99d9c101d656770acb5
+size 6280494

runs/best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c0c102143d76641779e797f2c49b289cec0d534a66ca99d9c101d656770acb5
+size 6280494