Upload 4 files

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2e68e96e61feb870a2a776c0e8be952e293e3fe214a379add20d9f59ccb6adc
+size 136719657

requirements.txt

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+Pillow
+opencv-python
+streamlit
+torch
+matplotlib
+numpy
+requests
+gradio

test.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import os
+import numpy as np
+import sys
+import asyncio
+
+if sys.platform == 'win32':
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+# Load your trained model
+model = YOLO(r"C:\Users\Falcon\Downloads\Projects\Falcon Projects\Drone-Detection-System\drone_detection_system\best.pt")
+
+def predict_image(image):
+    # Perform detection
+    detections = model.track(source=image, conf=0.3, iou=0.5, show=False)
+    
+    # Render the detected image
+    detected_image = np.squeeze(detections[0].plot())
+    
+    # Check if any drones were detected
+    num_drones = len(detections[0])  # Assuming detection results are stored in xywh format
+    message = "Drone detected!" if num_drones > 0 else "No drones detected."
+
+    return detected_image, message  # Return the detected image and the message
+
+
+
+def predict_video(video_path):
+    # Load the video
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        return "Error: Could not open video.", ""
+
+    drone_detected = False
+    # Prepare to write the output video
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    out_fps = cap.get(cv2.CAP_PROP_FPS)
+    output_path = 'output_video.mp4'
+    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), out_fps, (frame_width, frame_height))
+
+    # Process each frame
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Perform detection on the frame
+        results = model.track(frame, imgsz=640, conf=0.3, iou=0.5)
+        
+        # Check if any drones were detected in this frame
+        if len(results[0]) > 0:
+            drone_detected = True
+
+        # Draw boxes on the frame
+        annotated_frame = np.squeeze(results[0].plot())
+
+        # Write the frame to the output video
+        out.write(annotated_frame)
+
+    # Release everything when done
+    cap.release()
+    out.release()
+
+    message = "Drone detected in video!" if drone_detected else "No drones detected in video."
+    print("Video processing complete. Saved to:", output_path)
+    return output_path, message  # Return the path to the output video and the message
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("### Drone Detection System")
+    with gr.Tab("Introduction"):
+        gr.Markdown("**This Application helps in detection of DRONES in an IMAGE, VIDEO or from your WEBCAM depending on your App mode.**")
+        gr.Markdown("You Don't Necessarily need a Drone to run this app; you can use an image from google.\n\n**SAMPLE OUTPUT:**")
+        gr.Video("C:\\Users\\Falcon\\Downloads\\Projects\\Falcon Projects\\Drone-Detection-System\\Drone_Detection_Using_YOLOv5\\Drone Detection.mp4", width=800, height=600)
+    with gr.Tab("Upload Image"):
+        image_input = gr.Image()
+        image_output = gr.Image()
+        alert = gr.Label()
+        image_input.change(fn=predict_image, inputs=image_input, outputs=[image_output, alert])
+    with gr.Tab("Upload Video"):
+        video_input = gr.Video(sources="upload")
+        video_output = gr.Video(render=True)
+        alert_video = gr.Label()
+        video_input.change(fn=predict_video, inputs=video_input, outputs=[video_output, alert_video])
+    with gr.Tab("Live"):
+        gr.Markdown("Live detection will be implemented soon.")
+
+demo.launch()