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| import os | |
| #os.system("pip freeze") | |
| import cv2 | |
| from PIL import Image | |
| import clip | |
| import torch | |
| import math | |
| import numpy as np | |
| import torch | |
| import datetime | |
| import gradio as gr | |
| # Load the open CLIP model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model, preprocess = clip.load("ViT-B/32", device=device) | |
| def inference(video, text): | |
| # The frame images will be stored in video_frames | |
| video_frames = [] | |
| # Open the video file | |
| capture = cv2.VideoCapture(video) | |
| fps = capture.get(cv2.CAP_PROP_FPS) | |
| current_frame = 0 | |
| # Read the current frame | |
| ret, frame = capture.read() | |
| while capture.isOpened() and ret: | |
| ret,frame = capture.read() | |
| print('Read a new frame: ', ret) | |
| current_frame += 1 | |
| if ret: | |
| video_frames.append(Image.fromarray(frame[:, :, ::-1])) | |
| # Print some statistics | |
| print(f"Frames extracted: {len(video_frames)}") | |
| # You can try tuning the batch size for very large videos, but it should usually be OK | |
| batch_size = 256 | |
| batches = math.ceil(len(video_frames) / batch_size) | |
| # The encoded features will bs stored in video_features | |
| video_features = torch.empty([0, 512], dtype=torch.float16).to(device) | |
| # Process each batch | |
| for i in range(batches): | |
| print(f"Processing batch {i+1}/{batches}") | |
| # Get the relevant frames | |
| batch_frames = video_frames[i*batch_size : (i+1)*batch_size] | |
| # Preprocess the images for the batch | |
| batch_preprocessed = torch.stack([preprocess(frame) for frame in batch_frames]).to(device) | |
| # Encode with CLIP and normalize | |
| with torch.no_grad(): | |
| batch_features = model.encode_image(batch_preprocessed) | |
| batch_features /= batch_features.norm(dim=-1, keepdim=True) | |
| # Append the batch to the list containing all features | |
| video_features = torch.cat((video_features, batch_features)) | |
| # Print some stats | |
| print(f"Features: {video_features.shape}") | |
| search_query=text | |
| display_heatmap=False | |
| display_results_count=1 | |
| # Encode and normalize the search query using CLIP | |
| with torch.no_grad(): | |
| text_features = model.encode_text(clip.tokenize(search_query).to(device)) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| # Compute the similarity between the search query and each frame using the Cosine similarity | |
| similarities = (100.0 * video_features @ text_features.T) | |
| values, best_photo_idx = similarities.topk(display_results_count, dim=0) | |
| for frame_id in best_photo_idx: | |
| frame = video_frames[frame_id] | |
| # Find the timestamp in the video and display it | |
| seconds = round(frame_id.cpu().numpy()[0]/fps) | |
| return frame,f"Found at {str(datetime.timedelta(seconds=seconds))}" | |
| title = "Video Search" | |
| description = "Gradio demo for using OpenAI's CLIP to search inside videos. To use it, simply upload your video and add your text. Tororo's demo for Video Analytics." | |
| #examples=[['test.mp4',"gas station"]] | |
| gr.Interface( | |
| inference, | |
| ["video","text"], | |
| [gr.outputs.Image(type="pil", label="Output"),"text"], | |
| title=title, | |
| description=description | |
| ).launch(debug=True,enable_queue=True) |