import streamlit as st import cv2 import numpy as np from sklearn.cluster import KMeans from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input from tensorflow.keras.preprocessing import image import tempfile import os # Function to extract VGG16 features from a frame def extract_vgg_features(frame): frame = cv2.resize(frame, (224, 224)) # Resize frame to 224x224 (required by VGG16) img_array = image.img_to_array(frame) # Convert frame to a NumPy array img_array = np.expand_dims(img_array, axis=0) # Add batch dimension img_array = preprocess_input(img_array) # Preprocess input for VGG16 features = VGG16(weights="imagenet", include_top=False, pooling="avg").predict(img_array) # Extract features return features.flatten() # Flatten features to 1D array # Function to compute histogram difference def histogram_difference(frame1, frame2): hist1 = cv2.calcHist([frame1], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) # Compute histogram for frame1 hist2 = cv2.calcHist([frame2], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) # Compute histogram for frame2 hist1 = cv2.normalize(hist1, hist1).flatten() # Normalize and flatten histogram hist2 = cv2.normalize(hist2, hist2).flatten() # Normalize and flatten histogram return cv2.compareHist(hist1, hist2, cv2.HISTCMP_BHATTACHARYYA) # Compare histograms # Function to detect scene changes using histogram comparison def detect_scene_changes(video_path, threshold=0.2): cap = cv2.VideoCapture(video_path) # Open the video file prev_frame = None scene_change_frames = [] while True: ret, frame = cap.read() # Read the next frame if not ret: break if prev_frame is not None: diff = histogram_difference(prev_frame, frame) # Compute histogram difference if diff > threshold: # If difference exceeds threshold, consider it a scene change scene_change_frames.append(frame) prev_frame = frame # Update previous frame cap.release() # Release the video capture object return scene_change_frames[:5] # Limit to 5 frames # Function to select frames based on motion def motion_based_selection(video_path, num_frames=5): cap = cv2.VideoCapture(video_path) # Open the video file prev_frame = None motion_scores = [] while True: ret, frame = cap.read() # Read the next frame if not ret: break if prev_frame is not None: diff = cv2.absdiff(prev_frame, frame) # Compute absolute difference between frames motion_score = np.mean(diff) # Compute mean difference as motion score motion_scores.append((frame, motion_score)) # Save frame and motion score prev_frame = frame # Update previous frame cap.release() # Release the video capture object # Sort frames by motion score and select top frames motion_scores.sort(key=lambda x: x[1], reverse=True) selected_frames = [x[0] for x in motion_scores[:num_frames]] return selected_frames # Function to cluster frames using VGG16 features def cluster_frames(video_path, num_clusters=5): cap = cv2.VideoCapture(video_path) # Open the video file frames = [] features = [] while True: ret, frame = cap.read() # Read the next frame if not ret: break frames.append(frame) # Save the frame feature = extract_vgg_features(frame) # Extract features using VGG16 features.append(feature) # Save the features cap.release() # Release the video capture object # Perform K-Means clustering kmeans = KMeans(n_clusters=num_clusters, random_state=42) clusters = kmeans.fit_predict(features) # Cluster the frames # Select one frame from each cluster selected_frames = [] for cluster_id in range(num_clusters): cluster_indices = np.where(clusters == cluster_id)[0] # Find frames in the cluster centroid_index = cluster_indices[0] # Select the first frame in the cluster selected_frames.append(frames[centroid_index]) # Save the frame return selected_frames # Function to convert video to 15 FPS def convert_to_15fps(video_path, output_path): cap = cv2.VideoCapture(video_path) # Open the video file fps = int(cap.get(cv2.CAP_PROP_FPS)) # Get the original FPS width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # Get the frame width height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # Get the frame height # Define the codec and create VideoWriter object fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Use MP4 codec out = cv2.VideoWriter(output_path, fourcc, 15, (width, height)) # Set output FPS to 15 while True: ret, frame = cap.read() # Read the next frame if not ret: break # Write the frame to the output video out.write(frame) # Skip frames to achieve 15 FPS for _ in range(int(fps / 15) - 1): cap.read() cap.release() # Release the video capture object out.release() # Release the video writer object # Streamlit app def main(): st.title("Video Frame Selection App") st.write("Upload a 60-second video to extract the best 5 frames using three methods.") # Upload video uploaded_file = st.file_uploader("Upload a 60-second video", type=["mp4", "avi", "mov"]) if uploaded_file is not None: # Save the uploaded video to a temporary file with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file: temp_file.write(uploaded_file.getbuffer()) temp_video_path = temp_file.name # Convert the video to 15 FPS output_video_path = "temp_15fps_video.mp4" convert_to_15fps(temp_video_path, output_video_path) # Motion-based selection st.header("Motion-Based Frames") motion_frames = motion_based_selection(output_video_path, num_frames=5) for i, frame in enumerate(motion_frames): st.image(frame, caption=f"Motion Frame {i + 1}", use_column_width=True) # Scene change detection st.header("Scene Change-Based Frames") scene_change_frames = detect_scene_changes(output_video_path, threshold=0.2) for i, frame in enumerate(scene_change_frames): st.image(frame, caption=f"Scene Change Frame {i + 1}", use_column_width=True) # Clustering-based selection st.header("Clustering-Based Frames") clustered_frames = cluster_frames(output_video_path, num_clusters=5) for i, frame in enumerate(clustered_frames): st.image(frame, caption=f"Clustered Frame {i + 1}", use_column_width=True) # Clean up temporary files os.unlink(temp_video_path) os.unlink(output_video_path) # Run the app if __name__ == "__main__": main()