yolov8 accident detector

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+best.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import torch
+from ultralyticsplus import YOLO, render_result
+
+def yolov8_func(image):
+                #image_size: gr.inputs.Slider = 640,
+                #conf_threshold: gr.inputs.Slider = 0.4,
+                #iou_threshold: gr.inputs.Slider = 0.50):
+    
+    model_path = "best.pt"
+    model = YOLO(model_path)
+
+    results = model.predict(image,
+                            conf = 0.4,
+                            iou = 0.6,
+                            imgsz = 640)
+    
+    box = results[0].boxes
+
+    print("Object type: ", box.cls)
+    print("Coordinates: ", box.xyxy)
+    print("Probability: ", box.conf)
+
+    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")
+# ]
+
+# gr.HTML
+# outputs = gr.outputs.Image(type="filepath", label="Output Image")
+
+# yolo_app = gr.Interface(
+#     fn=yolov8_func,
+#     inputs=inputs,
+#     outputs=outputs,
+#     title="Accident detector",
+# )
+
+# yolo_app.launch(debug=True, enable_queue=True)
+
+with gr.Blocks(title="YOLOS Object Detection - ClassCat", css=".gradio-container {background:lightyellow;}") as demo:
+    gr.HTML('<h1>Yolo Object Detection</h1>')
+    #gr.HTML("<h4>supported objects are [aeroplane,bicycle,bird,boat,bottle,bus,car,cat,chair,cow,diningtable,dog,horse,motorbike,person,pottedplant,sheep,sofa,train,tvmonitor]</h4>")
+    gr.HTML("<br>")
+    with gr.Row():
+        input_image = gr.Image(label="Input image", type="pil")
+        output_image = gr.Image(label="Output image", type="pil")
+    gr.HTML("<br>")
+    #gr.HTML("<h4>object centre detection threshold means the object centre will be considered a new object if it's value is above threshold</h4>")
+    #gr.HTML("<p>less means more objects</p>")
+    #gr.HTML("<h4>bounding box threshold is IOU value threshold. If intersection/union area of two bounding boxes are greater than threshold value the one box will be suppressed</h4>")
+    #gr.HTML("<p>more means more bounding boxes<p>")
+    #gr.HTML("<br>")
+
+    #obj_threshold = gr.Slider(0, 1.0, value=0.2, label=' object centre detection threshold')
+    #gr.HTML("<br>")
+    #bb_threshold = gr.Slider(0, 1.0, value=0.3, label=' bounding box draw threshold')
+    #gr.HTML("<br>")
+
+    send_btn = gr.Button("Detect")
+    gr.HTML("<br>")
+    #gr.Examples(['./samples/out_1.jpg'], inputs=input_image)
+
+    send_btn.click(fn=yolov8_func, inputs=[input_image], outputs=[output_image])
+
+
+demo.launch(debug=True)

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fa294fcc7b5de8d86155a0370f2336cee784b459552104ca25904aee740ded7
+size 6233753

requirements.txt

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+gradio==4.16.0
+torch==2.1.2
+ultralyticsplus==0.0.29