Upload gen_ai_project1.py

gen_ai_project1.py

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+# -*- coding: utf-8 -*-
+"""Gen AI Project1
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Q27-bhi-hIw4U_QKiDXy3bwfLjPOI02o
+"""
+
+#AI Powered Video Editing
+
+#YOLOv8 (Ultralytics YOLOv8n)
+
+!pip install datasets
+
+!pip install ultralytics
+
+from datasets import load_dataset
+from moviepy.editor import ImageSequenceClip
+from ultralytics import YOLO
+import os
+import cv2
+from PIL import Image
+
+# Load dataset
+dataset = load_dataset("VarunB31990/Video-Editing-Dataset")
+
+# Load YOLO model
+model = YOLO("yolov8n.pt")
+
+# Directory for images
+image_dir = "images/"
+os.makedirs(image_dir, exist_ok=True)
+processed_dir = "processed_frames/"
+os.makedirs(processed_dir, exist_ok=True)
+# Process images and run YOLO
+processed_paths = []
+for i, item in enumerate(dataset["train"]):
+    if "original_image" in item:
+                image = item["original_image"]
+                image_path = os.path.join(image_dir, f"frame_{i}.jpg")
+                image.save(image_path)
+                # Run YOLO on the image
+                results = model(image_path)
+                for result in results:
+                    im_array = result.plot()  # Get YOLO detections
+                    im = Image.fromarray(im_array)
+                    detected_path = os.path.join(processed_dir, f"detected_{i}.jpg")
+                    im.save(detected_path)
+                    processed_paths.append(detected_path)
+# Create video from processed images
+if len(processed_paths)>1:
+   clip = ImageSequenceClip(processed_paths, fps=10)
+   clip.write_videofile("yolo_detection_video.mp4", codec="libx264", fps=10)
+   print("🎥 Video created: yolo_detection_video.mp4")
+else:
+   print("⚠️ Not enough images to create a video.")
+
+from IPython.display import display, Video
+display(Video("yolo_detection_video.mp4", embed=True))