Upload app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+
+def check_acc(box):
+    res_index_list = box.cls.tolist()
+    result = ""
+
+    for index in res_index_list:
+        if index == 1:
+            result = "Bike Bike Accident Detected"
+            break
+        elif index == 2:
+            result = "Bike Object Accident Detected"
+            break
+        elif index == 3:
+            result = "Bike Person Accident Detected"
+            break
+        elif index == 5:
+            result = "Car Bike Accident Detected"
+            break
+        elif index == 6:
+            result = "Car Car Accident Detected"
+            break
+        elif index == 7:
+            result = "Car Object Accident Detected"
+            break
+        elif index == 8:
+            result = "Car Person Accident Detected"
+            break
+    
+    return result
+
+def image_predict(image):
+    res = ""
+    model_path = "best.pt"
+    model = YOLO(model_path)
+    results = model.predict(image,conf = 0.6,iou = 0.3,imgsz = 512)
+    box = results[0].boxes
+    res = check_acc(box)
+    annotated_frame = results[0].plot()
+    if len(res) >0:
+        return (res, annotated_frame)
+
+    return ("No Class Detected", None)
+
+def extract_frames(video):
+    vidcap = cv2.VideoCapture(video)
+    vidcap = cv2.VideoCapture(video)
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    nof = 4
+    frame_no = 0
+    while vidcap.isOpened():
+        res = ""
+        render = None
+        success, image = vidcap.read()
+        
+        if success ==False:
+            break
+        
+        # Check if it's time to process the frame based on the desired interval
+        if (frame_no % (int(fps / nof))) == 0:
+            model_path = "best.pt"
+            model = YOLO(model_path)
+            results = model.predict(image,conf = 0.6,iou = 0.3,imgsz = 512)  
+            box = results[0].boxes
+            res = check_acc(box) 
+            annotated_frame = results[0].plot()
+
+            if len(res) >0:
+                return (res, annotated_frame)
+            
+        frame_no += 1  # Increment frame number
+        
+    return ("No Class Detected", None)
+
+
+def take_input(image, video):
+    if(video != None):
+        res = extract_frames(video)
+    else:
+        res = image_predict(image)
+    return res
+
+
+with gr.Blocks(title="YOLOS Object Detection", css=".gradio-container {background:lightyellow;}") as demo:
+    gr.HTML('<h1>Accident Detection Using Yolov8</h1>')
+    gr.HTML("<br>")
+    with gr.Row():
+        input_image = gr.Image(label="Input image")
+        input_video = gr.Video(label="Input video")
+        output_label = gr.Text(label="output label")
+        output_image = gr.Image(label="Output image")
+    gr.HTML("<br>")
+    send_btn = gr.Button("Detect")
+    gr.HTML("<br>")
+
+    send_btn.click(fn=take_input, inputs=[input_image, input_video], outputs=[output_label, output_image])
+
+demo.launch(debug=True)